⚙ E-1.1.1 Looking back - paths by seeing directions
Aside the global explanation of thoughts there is an index contents. 🔰 When the image link fails, click here.
Living with ICT, can feel:
(IV) - beautiful, full of uncertainty
(III) - feel abandoned, lonely
(I) - strictly controlled
(II) - luxurious settled, friendly
The ordering starts at the bottom right corner, than clockwise in the figure.
Numbering: left to right and top down.
It is a convention to use over and over again.
❗ 🤔 Reviewing the problems for systems started with the simple question why the idea of archiving knowledge over systems is:
that hard, that difficult for a unified framework,
that hard, that difficult for a unified support in technology.
The idea is about logical information systems, Information Communication Technology(ICT).
State of art of available technology makes is at that level to create solutions.
The advantages in solving the ICT knowledge gaps is however missed. So we are keeping to do what always wa done.
❓ Why is nobody wanting to be the first one, being the leader in that kind of innovation?
The result is less interesting than the followed path.
❶ Handing over a solution looks simple but is bypassing what problem it solves and how it is solving the problem.
When the problem doesn't cause much personal pain and there easy circumenventions for avoiding personal pain by problems in a system there are no real divers to solve the issye of problems in a system. ❷ Convincing for change changin in improvementn is breaking habits known behaviour know situations that have been felt comfortable.
The question for how to solve a problem changes into how to convince others for the solution of a problem.
Bad experiences in previous proposed solutions that failed, makes this even harder. ❸ Supporting information systems Information Communication Technology (ICT) has a long history of hypes and promises of solutions that are failing due to following only the technology but missing the what and how to solve the problems in systems.
Describing the path, journey for knowledge
Everybody has his own experiences that influence the way of thinking.
My personal experience in ICT is going into the early 80's.
There was a lot going on in that era as everything for ICT was new and a way of working had to be established.
I wouldn't have started this when that idea of helping in what needs to be documented didn't get a nice experience going far into what could be a generic product.
Starting just writing down more on that idea resulted in:
Frustrations: seeing many blocking issues by culture, assumptions that are the way of working.
Problematic: motivation for change when real drivers by decision makers are missing.
Ignoring those issues as situations to improve as a "why" changed my approach into "what" and "how" to improve.
This goes beyond the comfort zone of everybody.
Systems thinking is abstracting situations in reflections, it is evaluating assumption seeking confirmation for what is known.
This field did have a start in the twentieth century but after that evolvements obvious halted.
From what is known and promoted much more is possible to rethink in systems thinking.
In systems thinking there is a statement you must be able to adapt in more variety then the system has in variety you are part of.
At first sight this sounds weird until you see systems were the additional space is needed for stability and avoiding problems.
Contentstructure
The pages has a complex intellectual structure in the same approach of analysing the problems.
There are three chapters, each with six paragraphs. The idea is a two dimensional ordering of perspectives for the "why".
Each chapter works towards a condition, vision, mission in the sixth paragraph.
The sixth paragraphs condition, vision, mission are working from technology, technology and business into optimized business and technology.
Working into the optimized business and technology, there is gap in knowledge assurance and tools helping in that process of knowledge assurance.
Proposals to solve the gaps are made by the indicative name "Jabes".
During the analysing more proposals another idea was coming up: "Jabsa".
Many more popped up when the why was discussed: "Simf".
Extending the processes and analyses of systems far beyond that original idea of "Jabes".
This went into the ideas of modelling in system thinking.
Modelling in systems thinking is focussing on the problems in systems.
The challenge changed into how systems for ICT supporting organisations are working.
⚒ E-1.1.4 Progress
2019 week 18
Starting to made references and cites to management theory.
The Enterprise Architecture, EA, getting some hands on that elephant.
2023 week 52
Adjustment in the setup of the content
Adding new ideas new content
Adding vuca bani, note dragon1
2025 week 08
Closing a loop of what was started end 2023
Started was for a question on why a functional and technical tool is problematic to get into a normal behaviour.
That resulted in more abstracted questions for different areas and topics going into systems thinking.
2025 week 11
The first six chapters 1.1 to 1.6 got a new draft version, week 10.
The content of these are what common standard knowledge was there in the knowledge journey for what is holding off in the goal of Jabes.
❓ What are the theoretical impediments for not getting acceptance?
The second six chapters 2.1 to 2.6 got a new draft version, week 11.
The Jabes framework has resulted in a theoretical universal systems model. This is a very abstracted one but surprisingly a good fit with what has been done in the 1960's.
❓ What is the theoretical theory of 1960's and is there more to tell about, is there an explanations why those didn't proceed?
E-1.2 Brainstorm, Ideate, Thinktank
ICT is like exploring of a big elephant having a lot of details that could be concerns and solutions.
Usually only three topics are mentioned, but is that all?
Information is a result of some measurement (data) used with an interpretation, but is the interpretation a good perspective for the reality?
Communication is the interaction between a sender and receiver, but do the really understand each other from what is in the message?
Technology is about tools that should help in solving the organisational goals, but are the organisational goals visions missions really leading ❓
⚒ E-1.2.1 Categorizing from working experience
Searching Strategic Alignment
Focus on Technology with ICT is not nice.
Aside being a technology component "Communication" is indispensable for human interactions.
I prefer using ICT above IT.
ICT is sometimes used synonymously with IT (for information technology); however, ICT is generally used to represent a broader, more comprehensive list of all components related to computer and digital technologies than IT.
A shortlist for topics at ICT:
IT governance
Organization / Business alignment
Compliancy, regulations, directives
Security, Data governance
Tools, Platforms, Middleware
Process patterns
Change: Life Cycle Management
Change: Innovation
..
😉 ICT role "architect", several agile roles are in the hype: "Scrum master", "product owner" "Chapter lead", enterprise architect data architect ...
In lean the POU"point of use" "water strider " all are about human interaction, communication aside doing a task.
Focused on a single detailed topic easily conflicts with reality.
The following gets easily lost:
Generic security with compliancy. It is part of describing data, went into an organisational accountability where technology offers approaches.
The value stream process for the business. Common issue: it is seen too technical resulting in limited only to ICT activities where it is an organisational accountability.
Process Maturity: ICT as Asset (cmm2) into enabler (cmm4) into holistic optimization (cmm5). Initial ICT is a cost centre (cmm1) where it is in reality an enabler for profits.
Using systems thinking the relationship become more clear. Systems thinking abbrevation VSM ViSM. The new challenge is defining what balances are tot balance.
Documenting working experience knowledge
Activity: Documenting knowledge on this web page. ❶ 🤔 My first attempt did focus on multiple DTAP layers with involved security patterns.
Frustrations by problems to do this correctly conform strategical viewpoints was my motivation.
The result was too technical and not having distance from my personal situation. ❷ 🤔 My second attempt did a clean-up, added experiences for "data driven processes".
It extents Business Intelligence (BI), switching in how business logic is created. It has a match with "Data Mesh" although I didn´t know of that at that moment.
Still trying a bottom-up brain dump approach to get structured.
The result was that explanations and Foreword on top became chaotic. Very hard to understand.
The positive effect was having learned new insights in relations, challenges and options. ❸ 🤔 This is my third attempt, it is switching continuously between a bottom up approach and top down.
The First updates made are on the "index" entry page "metier".
After that got content, starting with the elucidation page, this page.
The first content was on oriented to the framework and technology of "Jabes".
After all other pages were done found it not applicable anymore, should be changed into the evolvement of thinking about the acceptance problem of "Jabes".
The goal is making some sense of what has historical grown and pointing at possible breakthroughs.
⚙ E-1.2.2 ICT Experiences focus to Jabes
Using categories at documenting
In the bottom up approach I used words and abbreviations that are not commonly used in the top down strategic approach.
Translations are needed, adding meaning of those new ones.
Tech
Logic
Context
VSM
6w1H
BPM
Steer
How the organisation is operated, qualitative and quantitative.
Portfolio management, prioritizing activities in available resources are logical task/roles.
Sys1
How
SDLC
Serve
Activities for technology operations & architecting engineering innovations for core business activities.
Fulfilling the mission of the organization.
Sys3
What
BIANL
Shape
Change, innovation, solving operational.
The mediation, external regulations and indispensable facilities are logical task/roles.
Sys4
Who
Data
C6isr
Managing the activities, facilities by culture, mission, vision.
Command & Control: change abandoning classical hierarchical is needed.
Sys5
Which
Meta
Jabes
The goal of Jabes i simplifying communication in the flows of the operational values stream (VSM, VaSM) and the change of the value stream.
Sys2
When
Math
Know
Very theoretical foundation knowledge. It refers where the metadata content of Jabes is related to.
The ix dimensional object idea for organisational systems.
extern
Where
The why is in the content area, when that is to answer rethinking the situation.i
Philosophical using 6w1h
Philosophy delves into the nature of existence, knowledge, and ethics using a variety of questions to guide exploration and understanding.
Logic
Context
What
Define or classify concepts, objects, or phenomena to clarify their nature or essence.
How
Examine mechanisms or processes involved in phenomena, exploring methods, ways of occurrence.
Where
Explore spatial aspects of phenomena, examining locations or settings.
Who
Investigate identity or agents involved in phenomena, focusing on individuals or groups.
When
Examine temporal aspects of phenomena, exploring timing or historical context.
Which
Focus on choices or alternatives, comparing options or possibilities.
Why
Uncover reasons or causes behind phenomena, seeking understanding of purpose and motivation.
There is one related to all others e.g. the why question.
Using categoreis words at documenting
The Iron Triangle refers to the three key constraints that can affect a project.
Central to the concept of an iron triangle is the assumption that bureaucratic agencies, as political entities, seek to create and consolidate their own power base.
The communication with stakeholders is often forgotten or ignored.
Working on questions what kind of information processing is a difficult one without needed insight without communication.
Left is the focus on Technology.
This gives little hope for adding value.
💡 Going for a real lean approach:
- optimizing information processing
- avoiding all evils conforming real lean
- balancing all three topics holistic
⚠ Following hypes will not automagically solve all problems. Leadership is decisive.
⚖ E-1.2.3 Solving frictions, problems
Types of problems
For conflict handling and making decisions there is: Cynefin framework ,
explanation
The Cynefin Framework helps managers to identify how they perceive situations and make sense of their own and other people´s behavior. ➡
The dark confusion domain in the centre represents situations where there is no clarity about which of the other domains apply.
By definition it is hard to see when this domain applies. "Here, multiple perspectives jostle for prominence, factional leaders argue with one another, and cacophony rules", write Snowden and Boone.
"The way out of this realm is to break down the situation into constituent parts and assign each to one of the other four realms.
Leaders can then make decisions and intervene in contextually appropriate ways." ... ➡
As knowledge increases, there is a "clockwise drift" from chaotic through complex and complicated to clear.
Similarly, a "buildup of biases", complacency or lack of maintenance can cause a "catastrophic failure": a clockwise movement from clear to chaotic, represented by the "fold" between those domains.
There can be counter-clockwise movement as people die and knowledge is forgotten, or as new generations question the rules; and a counter-clockwise push from chaotic to clear can occur when a lack of order causes rules to be imposed suddenly.
How to use cynefin framework
Using the Cynefin framework can help executives sense which context they are in so that they can not only make better decisions but also avoid the problems that arise when their preferred management style causes them to make mistakes. ...
Leaders need to avoid micromanaging and stay connected to what is happening in order to spot a change in context.
By and large, line workers in a simple situation are more than capable of independently handling any issues that may arise.
In a complex context, however, right answers can’t be ferreted out. The car is static, and the whole is the sum of its parts. The rainforest, is in constant flux the whole is far more than the sum of its parts.
This is the realm of “unknown unknowns,” and it is the domain to which much of contemporary business has shifted.
Complex: System Development Life Cycle SDLC
⚠ There is a lack of SDLC (Software/System Development Life Cycle) awareness.
Conservative running as-is (vertical)
👉🏾 stable operations
👎🏾 no process changes, no innovation, legacy
Disruptive innovation (horizontal)
👎🏾 unstable operations
👉🏾 process changes, innovations, transitions
Doing two major changes at the same time often results in unwanted surprises. Sensible: scheduling actions in time.
For administrative tasks, all the technology enabling automation just relative recently became available. 😲 The physical ICT technology did show an incredible growth in capacity.
When the generic complaint is that productivity growth is lagging with information processing, obviously something fundamental is still missing.
The logical distinct approaches for layers in different types for Life Cycle management is after all years still problematic.
Conflicts in projects by misunderstandings are common for:
Category
Context
Technology
hardware, network, CPU Memory IO capacity and capability, including platforms.
Operational plane
Information (data) & business rules (code), the "Value Stream".
Analytical plane
Needing real production information from the other two layers.
The easy management bypass is mentioning a versioning tool, "hammer & nail paradigm".
The VUCA world of the 2000/2010s
The term was first coined by the U.S. Army War College to describe the challenges of operating in a post-Cold War world.
From there, the acronym made its way into management and leadership literature and business school lecture halls at the turn of the millennium.
Category
Context
Volatility
Don´t expect standard values being applicable for all situations. Fluctuations Diverse Dynamic.
Uncertainty
Don´t expect situations to be stable and immutable. Instability.
Complexity
Expect dependencies to external parties impacting internal(s). Tied together, non-transparant.
Ambiguity
It depends: Never simply black or white, anything can be viewed and interpreted in myriad ways.
The VUCA concept has long been used to describe the volatility that has become the norm in the business world.
However, the BANI model goes a step further and helps companies consider the chaotic and completely unpredictable impacts that can have a major impact on their operations.
The BANI model of the 2020s
Category
Context
Brittle
it is about a sudden and unforeseen shock to or even the destruction of a seemingly stable system, which may lead to a global ripple effect.
Anxious
feelings of power- and helplessness, turning people rigid with fear. Anxiety can also be triggered by misinformation and fake news.
Non-linear
No more law of cause and effect, these are either dencoupled or disproportionate.
Incomprehensible
Human mind is no longer able to grasp complexity, occurrences in their entirety.
A shift from Internal to External
Using generic commercial software (cots) assumes the "business application" is generic, not a differentiator.
An easy assumption: t cots applications are always cheaper and faster to implement for a business question. 🤔 That assumption is ignoring the question whether the undefined quality "good" is appropriate for what is really needed.
The result of that are very costly failures, long running projects.
Failing fast will decrease damage and financial losses but is no solution for the real problem.
Purchasing external technology
With the shift from only in house build software into preferred everything externally retrieved a lot has changed.
The position of Strategy is aside Tactics Operation also repositioned to be delivered by external parties, the big consultancy "partners". 🤔 That assumption is ignoring the question whether the "Stratagy" is appropriate and what that is.
The result of that are very problematic failures in disfunctional organisations.
Failing fast will decrease short term impact but is no solution for the problem.
E-1.3 Brainstorm for orientation, position
Going for lean, agile, doing more with less is usually about cost saving.
Only focus on costs is not real lean.
The leading example or lean is TPS, toyota car manufacturing (Japan).
That approach embraces avoiding evils, embraces a culture in using the people instead of exploiting them.
The culture question is fundamental: Toyota Production System(TPS) or 'Thinking People System' (TPS).
TPS is a fundamental principle of the Toyota Production System, which emphasizes the active involvement of team members in the decision-making process to ensure the smooth operation of the system.
Similar to the 'law of conservation of energy' there is a 'law of conservation of evil'.
Waste, the only problem ❓
⚖ E-1.3.1 Recognizing cultural challenges, issues
Muda Mura Muri - burn out, bore out
Recognizing the 3M evils.
What problematic is with the three evils: they are complicated and there are three of them.
Muda, Mura, Muri
You will never reach the full potential if you only look at one of the three evils.
⚠
Muda: The most famous of the three evils of manufacturing is waste (muda). This is commonly divided into the famous seven types of waste:
Transportation
Movement
Waiting
Over-Processing
Defects and Rework
Inventory
Overproduction (the worst one)
⚠
Mura: the following is a list of examples where unevenness could happen and cause problems:
Uneven customer demand
Inventory swings ? from too much to too little
Uneven production speed or changing production quantities
Uneven quality of good parts (however, if the part fails or has to be scrapped it is waste)
Irregular or erratic working rhythm
Uneven training of the workers
Uneven distribution of the workload
⚠
Muri: as per translation, muri is overburden, unreasonableness, and things that are too difficult. Naturally, the main focus here is on people.
However, it also can apply to materials, machines, and organizations. Here are a few examples:
Working too long hours (and yes, I am frequently guilty of that myself)
Heavy lifting, Noise, Lack of training
Unsuitable posture or inadequate ergonomics
Too-difficult tasks
Too-easy tasks (which may be boring or mentally tiring)
Anything that leads to burn out, bore out, or repetitive strain injury
Humiliation, but possibly also excessive praise
Dangerous, dirty, and difficult tasks (the 3K in Japanese)
⚠
Similarly, but less common, is the opposite of a burnout, a bore-out. The employee has so little to do or such a tedious, mind-numbing task that he will have a bore-out.
Note that different employees have different tolerances and even preferences on this.
Avoiding the tree evils
🤔 There is no option to focus on only problem, they should be mitigated all missing none: ⚠ when not seen ⚠ when not noticed ⚠ when ignored ⚠ when not in area of influence ⚠ when not having priority
This is about managing by decisive leadership.
⚙ E-1.3.2 Interactions communication at organizations
How organizations should work
Aligning to organsitional processes requires understanding those basics. Management books are a big market, usefull fundamental insights scarce. NDMA Dean Meyer
Your organizational operating model sends signals that guide people day by day. Organizational transformation is a matter of "reprogramming" these signals.
So, where do those signals come from, and what can executives "program" in organizations?
Structure: the organization chart that determines people's specialties and the reporting hierarchy
Internal economy: the resource-governance processes that decide budgets (business planning); align resources with business priorities, approve projects, and manage demand
Culture: the patterns of behavior generally manifested throughout the organization
Processes, methods, and tools: the cross-boundary processes, procedures, methods, skills, and technologies that enhance people's competence
Metrics and rewards: dashboards people use to monitor their work and adjust behaviors accordingly; the performance metrics (KPIs) that their managers use to judge their work
To gauge success, enterprises employing a governance and management framework need to measure the performance of things they do.
Designing the measurement and assessment methodology for maturity prior to implementation of a governance structure can have advantages, it can:
make instrumentation and collection of information more efficient,
better alignment of metrics and key performance indicators (KPIs) with objectives and goals,
lead to areas of opportunity for leveraging one measurement capability across multiple areas.
Connecting Jabes to maturity levels reuse the CMMI concepts. CMM
The term "maturity" relates to the degree of formality and optimization of processes, from ad hoc practices, to formally defined steps, to managed result metrics, to active optimization of the processes.
... Though the model comes from the field of software development, it is also used as a model to aid in business processes generally, and has also been used extensively worldwide in government offices, commerce, and industry.
Evolvment in CMM levels situation
For SEI CMM originally there were five levels defined.
Evaluating enterprises at a high level gives an impression on possible attributes.
Initial situation, best effort, best practice is not more as basic.
The level 3 descriptions is hard to believe for most of us.
level 1:
GOVERNANCE: No formal process,cost center, reactive priorities
PARTNERSHIP: Conflict; IT a cost of doing business
COMMUNICATIONS: Business/IT lack understanding
...
level 3:
GOVERNANCE: Relevant process across the organization
PARTNERSHIP: IT seen as an asset; Process driver;Conflict seen as creative
COMMUNICATIONS: Good understanding; Emerging relaxed
Cybersecurity CMMI - ISACA
Maturity for cybersecurity is getting attention and some traction. CMMI Whitepaper.
When optimizing cybersecurity capability, the scope must be holistic and discretely applied. Cybersecurity needs to operate effectively at a micro and at a macro level.
At a macro level, the entirety of an enterprise processes, risk profles and tolerances, personnel and enterprise culture, and so on must be taken into consideration,
as must external environmental factors, such as the market environment that the enterprise operates within and public perceptions of its actions.
CMMI standard
The original five levels have added a sixth level (0) by
CMMI
Note the difference in capability levels 0-3 (no 4, 5) and the maturity levels (0-5).
Appraisal levels:
Maturity
Context
O:
Incomplete
Ad hoc and unknown. Work may or may not get completed.
1:
Initial
Unpredictable and reactive. Work gets completed but is often delayed and over budget.
2:
Managed
On project level: Projects are planned, performed, measured, and controlled.
3:
Defined
Proactive, rather than reactive. Organization-wide standards provide guidance across projects, programs, and portfolios.
4:
Quantitatively Managed
Measured and controlled. Organization is data-driven with quantitative performance improvement objectives that are predictable and align to meet the needs of internal and external stakeholders.
5:
Optimizing
Stable and flexible. Organization is focused on continuous improvement and is built to pivot and respond to opportunity and change. The organization’s stability provides a platform for agility and innovation.
⚒ E-1.3.4 Searching Strategic ICT Alignment
Dramatic consequences of IT4IT
In the beginning of becoming ICT to commodity in organisations every was looking for how that new thing should become part of the organisation.
The motiviatation to support the organisation was high as there were no barriers in between.
That changed rapidly by advisories of big consultancy firms to isolate ICT in dedicated functional isolated containers. 🤔
The Open Group IT4IT® Standard is an example of rigidity aside others.
In chapter 3. "Digital Management" there are recent improvements.
Hidden is a lean optimization, mentioned is "value".
Three cycles are presented, missing the closing fourth cycle: is how to get innovative ideas, covering also applying corrections improvements.
in a figure:
See right side.
Push: "drive IT value"
Pull: "measure and create insight"
Managing Technology Processes IT4IT
The devops setting (build - deliver- run) is shown with a counter clockwise cycle to improve the portfolio.
The four steps starting from the portfolio is similar to the PDCA cycle. ⚠Missing: tools for portfolio management. ⚠Missing:knowledge reference how to manage a portfolio.
The framework is theoretical only.
The Value Network is further defined through the interaction of the Digital Product with customers, business partners, vendors, as well as government and regulatory entities.
The Digital Product is creating value for its consumers.
The business partners and service providers can both provide resources for delivering Digital Products as well as creating their own value using the Digital Products delivered through the IT4IT Standard. ⚠ The framework is although correct in its limitation, overwhelming and complicated.
These eight functional groups shown in Figure 7 are then populated by 33 functional components that control 44 key data objects.
The 44 data objects are manipulated as the seven value streams are exercised: exploring, integrating, deploying, releasing, consuming, operating, and evaluating the Digital Products. ⚠ What seems obvious more easy approaches solving the problems in hypes are getting preferred (Agile Scrum Safe Less).
The break down of the Agile hype (2024) is changing that for a focus to products, the same issue of a hype lacking the understanding.
Managing Technology Processes IT4IT
In chapter 5. "IT4IT Value Streams":
The value stream "Evaluate" contributes to the business strategy and portfolio planning activities.
It provides a blueprint for optimizing products, services, and investment Portfolio Management.
This value stream is focused on the continuous assessment and evaluation of the entire Digital Product Portfolio to optimize co-creation and alignment of business and technology Strategic Objectives.
Many organizations have portfolio processes and solutions in place but suffer from the following limitations:
Poor data quality and consistency
No holistic portfolio view across the enterprise
Inconsistent Portfolio, Service, and Product Management
Poor tracking and correlation of the product lifecycle
Organizations need accurate and point-in-time information to understand the inter-relationships and inter-dependencies required to truly orchestrate all the moving parts of Digital Products in ways that can help support business objectives and goals.
The mentioned seven processes for value streams is missing another: knowledge assurance, documenting what is known, expected was done.
E-1.4 Going in directions without compass, map
Compared to brick-and-mortar architectures, which are tangible and perennial, enterprise architectures are works in progress to be carried out all along the life cycle of enterprises.
Hence the need of maps to monitor changes in business and technical environments, ensuring the continuity and consistency of representations and the traceability and accountability of decisions-making processes.
From a functional perspective the role of ontologies is to manage knowledge representations (KR).
The challenge in this: The interactions between the ICT provisioning and the organisation.
Both have their strategies, competences and governance.
Both are working on an architecture for processes needing skills.
Are these obvious problems or confused ones ❓
⚖ E-1.4.1 Strategic alignment going to silos
SAM - Henderson Venkatraman (1993 IBM)
It is hard to imagine but the strategic alignment question had the goal of understanding better how to manage, command & control the new capabilities of information processing.
The result is the opposite by the creation of silos, blocking communication lines, blocking autonomy in favour of micromanagement.
The classic one started in 1993.
The Strategic Alignment Model (see Figure) identifies the need to specify two types of integration between business and I/T domains.
The first, termed strategic integration, is the link between business strategy and I/T strategy reflecting the external components.
More specifically, it deals with the capability of I/T functionality to both shape and support business strategy.
This capability is particularly important as I/T has emerged as an important source of strategic advantage to firms.
The second type, termed operational integration, deals with the corresponding internal domains, namely,
the link between organizational infrastructure and processes and infrastructure and processes.
in a figure:
See right side.
The diagonal communication approach is eliminated because of passing two communication levels.
Authority, decisions
Abstraction, knowledge
Reviewing this with the VSM "systems thinking" model for five required functionalities it was fail waiting to happen.
System-2 is missing and only two system-1 and system-3 functions are defined.
SAME AIM - Maes (1999)
Added in between: Information management.
Referencing to: Amsterdam Information Model (AIM).
Interesting is the learning loop from: "Working Paper 99-03 A Generic Framework for Information Management R. Maes, April 1999".
Working for more details later,
AIM 9plane (R.Maes 2007)
For a long period, strategists have distanced themselves from operations on the shop floor: "operational effectiveness is not a strategy" (Michael Porter).
The strategic importance of excellent operations has rather recently been (re)discovered in the context of customer-oriented thinking;
It goes together with a reorientation of core capabilities (Hayes, Pisano and Upton, 1996) and with the breakthrough of ERP software packages.
Reviewing this with the VSM "systems thinking" model for five required functionalities it was fail waiting to happen.
System-2 is added ambigous but still only two system-1 and system-3 functions are defined.
🤔 A mistake , adding the loops was not done fully, there should have been four of them.
The strategic loop should have been above the top and the tactical at what is shown as strategical.
What would got solved?
All VSM systems 1-5 would have been present.
System-2 the communication is the vertical middle named "Information Management"
The dichotmy of static inforamtion and processes is covered by the loops.
Another dicotomous perspective of this is functionality and functioning.
What has happened is that selling these frameworks went along with advices for reorganisations with cost saving arguments.
The real result was and is:
increasing siloed approach lack of collaboration,
decreasing efficiency of the systems as a whole,
increasing distrust & demotivation of the workers.
⚒ E-1.4.2 Controlling Information Technology
it4it without communication
ICT is often referred to as IT without the indication of communication. ⚠ "IT strategy" for the technology scope was never and will never be activity that each organisation will solve on their own.
Generic and commercial available frameworks with solutions will be preferred. They are more robust having more functionality and cost less than being build in house. ⚠ cause of concerns: IT governance not a part of Business strategy.
With the GDPR and more it is made clear that this idea is untenable.
The data controller role is mandatory within domain & scope of a the business.
⚠ Another concern: administrative infrastructure is separated from IT.
These days administration is not possible without IT.
Some organisations their core processes, value streams, are administrative only.
Without communication,isolated managed as component: not part of a system as a whole.
it4it abstraction
Example (opengroup.org it4it) an older infographic:
The it4it Reference Architecture breaks these activities down further to a Service Model and the essential functional components and
data objects that IT produces or consumes in the IT Value Chain in order to advance the service lifecycle.
Boundaryless Information Flow, a shorthand representation of access to integrated information to support business process improvements represents a desired state of an enterprise's infrastructure and is specific to the business needs of the organization.
An infrastructure that provides Boundaryless Information Flow has open standard components that provide services in a customer's extended enterprise that:
Combine multiple sources of information
Securely deliver the information whenever and wherever it is needed, in the right context for the people or systems using that information
in a figure:
See right side.
The framework was promoted by the technology provider.
⚒ E-1.4.3 ICT a component of a system as a whole
ICT building software components
Disappointing: the Agile Manifesto
claims "building software" for core activity. For most organisations building software is not the core process.
The Agile Alliance has officially joined the Project Management Institute (PMI), forming the PMI Agile Alliance as of December 31, 2024.
The partnership aims to enhance global project management by integrating Agile principles with PMI's resources and reach.
Planning designing ICT at a conceptual level has the goal of a structured approach for a realisation in planning.
There is no isolated information technology (IT) domain.
IT governance, command & control and operations is an indispensable part of an organization.
ICT Architecture Definition Model
Nice understandable generic lines: SDLC and IT governance, Compliance, Security, Tools.
The blog of prabasiva.
EIA . (200808)
in a figure:
See left side.
Consistency and repeatability in the architecture definition process is a must have requirement in an organization. ...
Picture speaks more than thousand words.
Horizontal collaboration is assumed.
Explanation: how to do the required communication for a shared goal is missing.
ICT building software components
The system-x hierarchiesare assumed to cooperate well aligned for what is needed from external suppliers for "know how" and tools.
External parties have their own business models.
in a figure:
See right side.
Vertical alignment is assumed.
Explaination: how to do is missing.
Internal conflicts by different interests are common.
Conflicts by different interests internal vs external are usual.
⚒ E-1.4.4 ICT architecturing for the system as a whole
Architecture technology: Togaf
Togaf: understanding requirements is the central point to define what should be done. Togaf (wikipedia)
Togaf (10-th editon)
The Open Group Architecture Framework (TOGAF) is the most used framework for enterprise architecture as of 2020 that provides an approach for
designing, planning, implementing, and governing an enterprise information technology architecture.
TOGAF is a high-level approach to design. ...
It relies heavily on modularization, standardization, and already existing, proven technologies and products.
Throughout the ADM cycle, there needs to be frequent validation of results against the original expectations, both those for the whole ADM cycle, and those for the particular phase of the process.
... The ADM is a generic method for architecture development, which is designed to deal with most system and organizational requirements.
Issue: Togaf is very complicated, difficult to understand, overwhelming, technology.
It is not understood by stakeholders at the organisation.
Cobit Overhauled
The COBIT 5/CMMI
COBIT® 2019 Framework: Governance and Management Objectives describes the expected capability level for each of the 1202 COBIT activities.
From the score obtained for each of those activities, it is possible to determine the maturity level for the 231 practices, the 40 objectives and the five domains constituting the COBIT 2019 framework.
Figure 1 gives a sample of the governance practices, example metrics, activities and expected capability levels.
Issue: Cobit is very complicated, difficult to understand, overwhelming, technology.
It is not understood by stakeholders at the organisation.
E-1.5 Moving around without compass, map
Definitions should never turn into war of words as they should only be judged on their purpose and utility, with such assessment best achieved by comparing and adjusting the meaning of neighboring concepts with regard to tasks at hand.
Whatever the idiosyncrasies and fuzziness of business concerns and contexts, at the end of the day business and functional requirements of supporting systems will have to be coerced into the uncompromising logic of computers.
The challenge in this: It is not about words but misunderstandings, wrong perceptions and territory wars.
Definitions are they obvious problems or confused ones ❓
⚙ E-1.5.1 The model of processes: a confused problem
ALC - responsibility accountability
A common misunderstanding is that when a machine does something there would be no Responsible and no Accountable persons.
The easy popular discredit of: "computer says no". ❶ ⚠ Why is this happeing, what is going on?
There is an interesting story, the efficiency movement starting with C.Bedaux .
Bedaux was one of the leading contributors in the field of work measurement or labor measurement, one aspect of the scientific management movement. In this, he was strongly influenced by F. W. Taylor's book Shop Management.
The distinguishing feature of the Bedaux System was its use of the Bedaux Unit or B, a universal measure for all manual work.
The "B" was defined as fractions of a minute allocated to work or rest.
Productivity goals were set for so many B's per hour, and bonuses were paid for exceeding that goal.
He also mimicked Frank Gilbreth by introducing a motion study Kodascope package which he propagated with an early Bedaux client, Kodak. ❷ It is part of the Efficiency_movement 1890-1930.
It was a major movement in the United States, Britain and other industrial nations in the early 20th century that sought to identify and eliminate waste in all areas of the economy and society, and to develop and implement best practices.
Coming into usage in 1990, the Western term lean manufacturing (lean enterprise, lean production, or simply "lean") refers to a business idea that considered the expenditure of resources for anything other than the creation of value for the end customer to be wasteful, and thus a target for elimination.
Today the Lean concept is broadening to include a greater range of strategic goals, not just cost-cutting and efficiency. ❸ Efficiency was seen as an improvement in quality of life not a threat to the people.
Leading philanthropists such as Andrew Carnegie[9] and John D. Rockefeller actively promoted the efficiency movement. In his many philanthropic pursuits, Rockefeller believed in supporting efficiency. He said:
"To help an inefficient, ill-located, unnecessary school is a waste ...it is highly probable that enough money has been squandered on unwise educational projects to have built up a national system of higher education adequate to our needs, if the money had been properly directed to that end. " ❹ The disjoint was by a split in the way it influenced social status and personal wealth.
Boston lawyer Louis Brandeis argued bigness conflicted with efficiency and added a new political dimension to the Efficiency Movement.
He stated that "big business is not more efficient than little business" and that "it is a mistake to suppose that the department stores can do business cheaper than the little dealer.
As early as 1895 Brandeis had warned of the harm that giant corporations could do to competitors, customers, and their own workers.
The growth of industrialization was creating mammoth companies which he felt threatened the well-being of millions of Americans.
❺ This is a surprising connection to the impact by results. L.Brandeis
defined modern notions of the individual right to privacy in a path-breaking article he published with his partner, Warren, in the Harvard Law Review of December 15, 1890, on "The Right to Privacy."
He was a leading figure in the antitrust movement at the turn of the century, particularly in his resistance to the monopolization.
💣
There are two relevant approache for processes:
ALC-V2 where humans desing and decide for the algorithms
ALC-V3 where information of the past is used to help in those algorithms.
Ignoring the accountability responsibility is bad.
There is lot to have documented for this, this is too often not clear and ignored.
It is bizarre because:
decisions in the ALC-v2 approach have a clear definition in who is Responsible and no Accountable who are Informed and who are Consulted (RACI).
In the ALC-v3 approach responsibility accountability is not different, clear on the who.
📚 E-1.5.2 Technical Data Driven Processes
ALC-V2: Dictated processing by human experiences
With different concerns, different interests, different type of persons conflicts will arise, avoiding is an illusion.
The challenge is how to manage those conflicts in an acceptable way.
The ethical questions are not always felt as conflicts although they should be seen as a conflict.
Characteristic: Business decisions based on human intuition.
ALC-v2 in a figure:
See right side.
The conflict: classic application life cycle management, failure in differentiation:
Business logic
Technology, platforms
Prohibited usage production information for measurements (development).
Focus on code to run (executed)
ALC-V3: Guided processing by informed experiences
Artifical Intelligence (AI) is transforming ICT into using ML (Machine Learning), a subarea of AI.
Processes how to create, implement and monitor are not settled yet.
Characteristic: Business decisions based on controlled feed back loops, monitored by human decision makers.
ALC-v3 in a figure:
See right side.
The conflict: modern application life cycle management, failure in understanding:
Process accountablity
uncertaintities in situations used for inputs.
uncertaintities in situations used for results, impact.
Focus on operational (prodcution) information that is analysed.
Data driven the process cycle
The evolution from solving "data LCM layers" to life cycles is not immediate obvious.
Requirements for LCM approaches for:
Data provision distinct type of layers (green diagonal).
Model processing distinct processing types (orange diagonal).
Model Life Cycle distinct model types (score deployment).
ALC-v3 in an animated visual:
See left side.
A closer look it could be any type of information processing by this visualisation.
The modern devops using machine learning (AI) and old days Cobol rules based processes.
⚖ E-1.5.3 Technology concept: "the data quantum"
Data Mesh
A moment in time for a change in approach for manageing information.
It breaks the classic flow process symbol, see figure "data monolith to mesh".
It´s an accepted convention that the monolithic data platform hosts and owns the data that logically belong to different domains.
Instead of flowing the data from domains into a centrally owned data lake or platform, domains need to host and serve their domain datasets in an easily consumable way. data-mesh principles
It includes code for:
data pipelines responsible for consuming, transforming and serving upstream data - data received from domain's operational system or an upstream data product
APIs that provide access to data, semantic and syntax schema, observability metrics and other metadata
enforcing traits such as access control policies, compliance, provenance, etc.
Data and Metadata:
well that´s what we are all here for, the underlying analytical and historical data in a polyglot form.
Depending on the nature of the domain data and its consumption models, data can be served as events, batch files, relational tables, graphs, etc., while maintaining the same semantic. ....
Infrastructure:
The infrastructure component enables building, deploying and running the data product's code, as well as storage and access to big data and metadata.
Though this model provides some level of scale, by assigning teams to different stages of the pipeline, it has an inherent limitation that slows the delivery of features.
It has high coupling between the stages of the pipeline to deliver an independent feature or value. It's decomposed orthogonally to the axis of change.
The concepts of Data Mesh are giving the focus to the missions and visons of organisations.
⚖ E-1.5.4 ICT product fucntioning and functionality
Budget planning, product planning
Getting financial budgets for purchasing is convincing the business responsible persons.
⚠ sometimes the decisionmakers are making decisions without any alignment, without ideas from their staff, within their organisation.
A futuristic vision is a positive attitude but can easily become negative when too far from reality.
Going to be data driven is combining the ALC-V2, ALC-V3 into a business alignment process cycle.
Process Model monitoring - Closed-loop
The highest maturity level is aligning the vision mission with what is happening. BIDM
BI analytics is integrated or not in the business process can strongly affect the decision making process.
Hence, we consider this category to be a very important one when delimiting a maturity stage
initiation (user driven - activity initiated by the user, process driven - activity initiated by a process)
process integration (data centric - BI analytics is usually supported by a data warehouse, process centric - BI analytics is integrated in the business processes)
processing model (store and analyze; analyze and store)
event stream processing
"closed-loop" environment
In a figure:
See right side.
Although having the mindset set for BI (Business Intelligence) it is very generic.
E-1.6 Starting a journey in understanding
Decision-making is often confused with problem-solving, namely how to pick a solution given a set of resources, typically people, information, financing, materials.
That paradigm ignores the temporal dimension of enterprises decision-making which are made of interdependent commitments meant to be carried out across shifting backgrounds and overlapping timescales.
Decision-making is supposed to be informed, for enterprises best achieved through ontologies.
The challenge in this: It are not the results being important, it is the journey of learning, understanding.
The knowledge of the journey to share ❗
What to ask, how to ask, was a long running challenge for thinking in complex problems.
Reducing the problem into single more easy problems to question is not always possible.
The 6w1h usage for this was seen in the 80's but:
was missing an understandable explanation for how to use that.
not well possible by the limited options in available technology.
Got remembered a hint I got: ➡ start in the middle - logic
Zachman 6W1H: What, Where, When, Who, How, Why, Which
Challenging is: ontology all levels in one pas, a split up:
High level architectural functional design: (1) Logical, Conceptual Contextual.
🤔 Why should I, you work with this?
Zachman mythes
Widely misunderstood and misrepresented, the Zachman Architecture Framework is simply a thinking tool, not a methodology of any kind.
Its being fundamentally neutral with respect to methodology.
Overall acceleration is what you want, and not just for the build activity.
You also want it for the inevitable, myriad changes to business rules you can expect after the business rules are deployed.
Such solutions don't happen by accident, they require deliberate engineering. 🤔 Zachman ordered categories:
It is not mysterious why the people who build any object that is sufficiently complex to warrant Architecture came up with that set of description representations.
They are answering the six primitive interrogatives that constitute the total set of questions that have to be answered to have a complete description of anything.
The 6w1h framework in cybersecurity
Grown from a security perspective, the enterprise level gets involved.
The generic concepts from technology perspective are good.
The problem is that the authority is not the same as accountability.
The Zachman approach is found SABSA
SABSA (Sherwood Applied Business Security Architecture) is a model and methodology for developing a risk-driven enterprise information security architecture and service management,
to support critical business processes.
in a figure:
See right side.
... everything must be derived from an analysis of the business requirements for security, .. .
Service for processes en proces monitoring is a shared mission, should be a shared vision. ❗ There is no technological deepening of the context for security.
Issues without certainties cultural
We are grown up with the education of:
Questions only have one acceptable answer you should know.
The known problem are obvious, understandable with a prescriptive solution.
You should prove your knowledge of the standards by certifications.
The the reality starts with:
Problems and questions that have zero to many solutions.
there is expectations for solving new unknown problems and questions
🤔
This is a mismatch that takes a lot of time to correct by experiences.
Issues without certainties mathematical
😱
Understanding this is required for all but going wrong mostly.
Accuracy and precision are two measures of observational error.
Accuracy is how close a given set of measurements (observations or readings) are to their true value.
Precision is how close the measurements are to each other.
Precision is a description of random errors (a measure of statistical variability)
Accuracy has two different definitions:
More commonly, a description of systematic errors (a measure of statistical bias of a given measure of central tendency, such as the mean).
This concept corresponds to ISO's trueness.
A combination of both precision and trueness, so that high accuracy requires both high precision and high trueness.
This is what is understood in public, confusions and misundertandings are a logical result.
😱
Understanding this is required for all but going wrong mostly.
Trying to correct the precision is not easy.
Moving the target while the accuracy was correct will increase the errors not decreasing. ❶ Applying statical corrections will smoothening decrease the spread increase assumed precision.
The real observations might miss the target in alle cases but the target location gets known.
In statistics and control theory, Kalman filtering
(also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step.
It was during a visit by Kálmán to the NASA Ames Research Center that Schmidt saw the applicability of Kalman's ideas to the nonlinear problem of trajectory estimation for the Apollo program resulting in its incorporation in the Apollo navigation computer. ...
This Kalman filtering was first described and developed partially in technical papers by Swerling (1958), Kalman (1960) and Kalman and Bucy (1961). ❷ There are several types of filters.
In signal processing, the Wiener filter
is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. ...
The filter was proposed by Norbert Wiener during the 1940s and published in 1949. ❸ That is a link to cybernetis by a lesser known person.
Norbert Wiener
is considered the originator of cybernetics, the science of communication as it relates to living things and machines, with implications for engineering, systems control, computer science, biology, neuroscience, philosophy, and the organization of society.
His work heavily influenced computer pioneer John von Neumann, information theorist Claude Shannon, anthropologists Margaret Mead and Gregory Bateson, and others.
It is fundamental part for systems. It failed in the fist hype early in the twentieth century. 😱 Never felt this was in place during all years of working.
In Jabes this is the start to solve for getting viable systems.
Layers - Technology, infrastructure
DTAP approaches maturity for LCM is going into additional distinct layers:
landing zone processes: Operational plane ⚒I
hardware- operating system: Tools middelware ⚒T
landing zone monitoring: Analytical plane ⚒C
The lower area is enabling the upper part in a relationship.
It is the confusing word "application" without understanding the relationships that is causing the trouble.
It must be robust enough for the requirements of the serviced organization.
This is the start to solve knowledge assurance for viable systems. 🤔
This is a mismatch that takes a lot of time to correct by experiences.
In Jabes the approach for knowledge assurance is a split in three:
Strategical purpose goal, high level requirements. Set by the organisation with high level alignment in possibilities.
Tactical decisions, state of art Evel requirements. Set by engineering architecting the solutions with alignment in possibilities.
Operational implementation, detailed level requirements. Set by the engineering in cooperation with operations for choices.
LCM SDLC for a system as a whole
Platforms (tools) & operational, analytical planes.
This is what the serviced organization will use.
A process can be build from scratch, starting with accruing hardware or from change requests driven by optimization analyses.
There are at least three components to be aligned for a system.
Changing one leaves other relationships untouched.
There will be a debt gap ❗ either at the technical or functional connection. 🤔
This is a mismatch that takes a lot of time to correct by experiences.
CMM for a system as a whole
For Jabes portfolio artefacts a CMM attribute is proposed, CMM is not in place. 🤔
This is a mismatch that takes a lot of time to correct by experiences.
Quality an ambiguous attribute
Why Quality in the Iron Triangle is Poor Project Management?
Quality represents only one aspect of a product or service's technical performance. Technical performance indicates how the product or service operates within its technical specifications. If this performance is not evaluated during the development cycle, asserting confidently that the project is on schedule and within budget becomes impossible.
Failing to understand this is a common issue—likely the most common cause—of project failure. Using the term "Iron Triangle" doesn’t help connect the dots between any project's three core variables.
The connections between the top-level variables on a project are Cost, Schedule, and Technical Performance.
What is a Technical Performance Measure?
A plan of the expected technical achievement by which the actual progress is measured or tested
Tools that show how well a system is satisfying its requirements or meeting its goals
Provide assessment of the product and the processes through design, implementation, and testing.
🤔
This is a mismatch that takes a lot of time to correct by experiences.
⚙ E-1.6.3 Being subject to the ICT system culture
Using external knowledge: Itil Togaf
There is a lot of old theory, old practices, old frameworks mentioned. It is avoiding the hype and finding good methodical understandable approach.
Reusing knowledge, learning from experiences, sharing knowledge is what will result into improvements. >
For example:
Is it4it the next threat to the IT Management market?
inform-it.org
Now we have it4it. Initially set up by a number of vendors (Accenture, CapGemini, HP, PwC) and some user organizations (Shell a.o.),
but then transferred to the Open Group, where it was handled by again some of the global leading consulting organizations and a number of user organizations (read Geoff Harmer´s analysis).
The faces of it4it now are Accenture, HP, and of course a few customer organizations to avoid the idea of a commercial interest (Shell, Achmea).
Should you avoid it4it? Definitely not. It encompasses some major improvements compared to the 'old school' frameworks.
Should you adopt it4it as-is? Definitely not, like you should not adopt any of the other frameworks as-is. it4it is not a method.
It provides guidance, but you will only be able to achieve your result effectively and efficiently if you have your own management system firmly in place.
🤔
This is a mismatch that takes a lot of time to correct by experiences.
⚙ E-1.6.4 Defining & using appropiated technology patterns
Extending operational plane, historical data
Most data should be retrieved from operational production systems whne doing analytics.
The result of an analytical process, report scoring any artifact, can be as important as the operational ones.
Archiving results, building up history is a requirement in those cases.
BIDM
.. an enterprise-wide data warehouse could accomplish various useful objectives (Airinei, 2002):
the possibility of accessing historical, summarized and consolidated organizational data
a single version of truth because the data from a data warehouse are consistent as they been previously cleaned, transformed and integrated
combined summarized/detailed access to data – OLAP technology and other reporting tools offer the possibility of visualizing the information at different hierarchical levels through operations like roll-up, drill-down, slice, dice and pivot
separation of the operational and decisional or analytical processing as they have a very different architecture
Although having the mind set for BI (Business Intelligence) it is generic.
E-2 Aligning ICT systems to organisational systems
E-2.1 Alignment goals in perspectives
Plots are the backbone of any story they provide the “why” of what happens.
Narratives tell “how” what happened is being told.
In principle, plots deal with possibilities and narratives with realizations.
In fact plots remain unknown until being narrated; in other words fictions are like Schrödinger's cat: there is no way to set possibilities and realizations apart. 🤔💡🎭 Expectations can be charted in terms of plots and narratives:
Objectives, plots, are meant to apply continuously and consistently to different agents, concerns, contexts.
They are best defined as rules and constraints in declarative schemes.
Stories, narratives, are supposed to translate as soon as possible into transactions.
Defined as sequences of operations governed by choices, procedural schemes.
⚖ E-2.1.1 Knowledge needing defined classifications
Data driven - process driven
There is strong relationship between two approaches that can't exist without the other.
They are complementary properties of an artifact in time.
data-driven
process-driven
❶ How to visualise this ying-yang relationship? There is an information flow for business processes.
fluxicon disco manual (vdaalst)
Data science is the profession of the future. However, it is not sufficient to focus on data storage and data analysis.
The data scientist also needs to relate data to processes.
At the same time, process analysis professionals need to learn how to incorporate data from the IT systems into their work.
In a figure:
See left side
The beginning of classifications
Information classification is the process of organizing information into categories that make it easy to retrieve, sort and store for future use.
A well-planned information classification system makes essential data easy to find and retrieve.
This can be of particular importance for risk management, legal discovery and regulatory compliance. ❷ The triad:
Classification
Description
Confidentiality
A classification system can help safeguard highly sensitive information.
Integrity
A system that focuses on data integrity requires more storage resources and more sophisticated user permissions and access control.
Availability
Information should be consistently and readily accessible for authorized parties.
Situation Input Action Result, SIAR
From the process block of the flow chart only having I (input) and O (output Result) an change improvement.
Adding the Control and measurement (analyse or asses) lines.
This in line with Data-Mesh, that is adding the BI analytics management information in an analytical plan and experience area.
Service, right bottom corner: Requests (R) entering and ending as results (R).
Organise: running Steer (S) change understanding the Situation (S).
Purchase / collect: Run Input (I) Initiation change to build (I).
Produce / deliver: Assemble (A) Asses change using the V-model (A).
❸ The SIAR model (see figure) is highly abstracted generalized.
I started it because the many models that are used seperately are missing to align the concepts and ideas as a whole.
❹ There are many other contexts aside information processing in a similar way.
Only the details for the activities and flows are different.
For example:
Business / Organization:
Financial objectives
Human resources
Alignment to society and ecological environment
Facilities / Industrials:
Buildings - other infrastructure
energy - chemistry
Food - Health
Financials, Governments are heavily relying on information processing.
Facilities, Industrials are having an important administration component and needing information processing for optimizing. ❺ It is a combination of many perspectives:
Classification
Description
The value stream
Premature negotiations (blue double arrows), main flow Left to Right
A cyclic process
Segregation in quadrants (2*2), and 3*3 partitions
Control from strategy, by tactical, to operational
From the eye in the middle to every compass direction. Origin BI management information flow.
Information process duality
Interactions change intermediates in between every compass direction.
Well known frameworks are embedded:
What
Where in the SIAR figure
Pull
IV (R-S) Control a new Request for processing III (S-I) Plan new Request, check for inventory and processing capacity.
Push
I (I-A) Assemble input to a new product II (A-R) Verify quality of the new product and prepare for delivery result
PDCA
Follow: III Plan, I Do, II Check, IV Asses Act
DMAIC
Follow: II Define, I Measure, III Analyze, IV Ideate Improve
❻ The PDCA DMAIC and OODA perspectives are included and as aid for using it in searching the direction.
❼ The change improvement, innovation itself is possible by several maturity stages.
C&C Maturity
Wow Steer / Manage
WoW supply chain
WoW product - assembly
WoW demand / delivery
Reactive for what is going on in the now
Measurabele processes
Practices efficient
Instructions Effective
Service valuable
Pro-active for what is soon expected for the now
Control processes
Practices collect
Instrcutions Execution
Services evaluation
Pro-active for what is seen by a vision for the future
Control governance
Ideation collect
Operations Execution
Validation evaluation
The PDCA and DMAIC are cycles starting at a different point in the cylce.
They are also found in the diagonal contexts.
in a figure:
See right side.
⚖ E-2.1.2 Way of working: flow interactions
Foundation in methodologies
❽ Shu-Ha-Ri
It is a way of thinking about how to learn and master a technique. There are 3 stages to acquiring knowledge:
Shu: learn the basics by following the teaching of one master. Imitating the work of great masters also falls in this stage.
Ha: start experimenting, learn from masters, and integrate the learning into the practice.
Ri: This stage focuses on innovation and adapting the learning to different situations.
“When the student is ready the teacher will appear. When the student is truly ready… The teacher will Disappear.” ― Tao Te Ching
PDCA, DMAIC, OODA informed actions changes
Processing information using ICT is assembling parts of information into new information products.
Aligning this approach to what has become lean processing at industry.
This will break a lot "doing what has always been done" with ICT approaches. ❾ Essential:
PDCA
Plan-Do-Check-Act is one of the key elements in lean manufacturing, or for that matter in any kind of improvement process.
Another: Dmaic a PDCA variant?
PDCA variants
This DMAIC (Define, Measure, Analyze, Improve Control) is a PDCA offshoot in the Six Sigma offshoot of lean manufacturing. While it has more words, the meaning is somewhat similar.
For this I don´t agree. Using PDCA in the flow of the production and DMAIC doing that backwards, determining what issues there are in the operational environment, makes more sense.
Problem solving
in a figure:
See right side.
Push Pull - Value Stream
The (True) Difference Between Push and Pull
But what exactly is the difference between push and pull? Also, what makes pull systems so superior to push systems?
❿ All About Pull Production.
The figure "Elements of the Kanban Formula" is counter clockwise. Flipping top and bottom will give a clockwise order.
Customer demand is a Request & Result.
in a figure:
See right side.
The context of the audience: specialists vor value stream, lean.
A person, individual is limited in what he can achieve.
By collaboration with other much more is possible.
Human society is based on groups of humans.
Their interactions are indispensable for alignment in intentions.
Organisations are using systems alignment
Alignment is realised by using methodologies.
Methodologies are parts of the culture and culture is hard to change.
Evolution in methodologies
Organisations are build with humans as components, we should understand their behaviour with limitations and opportunities when going for the whole.
Anthropology
Systems theory in anthropology is an interdisciplinary, non-representative, non-referential, and non-Cartesian approach that brings together natural and social sciences to understand society in its complexity.
The basic idea of a system theory in social science is to solve the classical problem of duality; mind-body, subject-object, form-content, signifier-signified, and structure-agency.
interactions can adapt to changing conditions but maintain a balance both between the various parts and as a whole; this balance is maintained through homeostasis.
Anthropologist Gregory Bateson is the most influential and earliest propagator of systems theory in social sciences.
The anatomy is a complicated challenge, it can be reduced into more simple ones.
The complexity of an organisation is by what it does and it does that, the goal of the organisation in efficiency end effectivity.
What the real goal of an organisation is, is not what it does.
The SIMF model to model the organisation
The anatomy of the organisation is for clear responsibilities accountabilities.
As a start: it should support the intended flow of a product (good, service) for the organisational and technology aspects.
Both lines of interest have 4 levels (system-5,4,3,1) that should work together in a system as a whole.
A logical layout for this would be by the following figure:
in a figure:
See right side.
The most valuable diagonal is from I to IV delivering value to the external customer.
There is a logical internal conflict at each side of the diagonal
The internal III to II line is the alignment: enabling, monitoring and giving the external service.
There is logic internal conflict over the diagonal.
Evolution in architecting organisations
There is a logical growth for responsibilities accountabilities :
Integral The owners, founders, are also the leaders.
The assumption is all knowledge of "how to do" is at this single point.
This works in a small setting.
When complexity growths it fails by overloading overburdening the leaders.
Functional segmentation in hierarchical levels.
This works when the delegated leaders are able to act for "how to do" for their functional part.
When complexity growths it fails and the responsibility accountability gets lost.
Matrix is the segregation in the knowing "how to do" and the authority with accountability.
This works as long as the knowledge is explainable and usable for decision makers.
When complexity growths it fails with coordination for underpinned decisions for goals.
Divisional segmentation for components reduces the complexity for components in the system.
When complexity growths it fails for alignment for the components for the system as a whole.
Cluster segmentation for goals reduces the complexity for each system that is component of a system.
When complexity growths it fails for the goal for the system as a whole.
E-2.2 Knowledge Assurance: framework & tools
Objective definitions has to find its terms between the Charybdis of abstractions and the Scylla of specific processes.
The first to be avoided because they are by nature detached from reality, the latter because they would be too specific and restrictive. 🤔💡🎭 In-between objectives would be best defined through:
Strategic objectives expressed using symbolic categories applied to environments, products, and resources.
Modal time-frames identified in reference to events and qualified by assumptions with regard to symbolic categories.
Functions to be optimized given a set of constraints.
⚒ E-2.2.1 Goals alignment: Engineering building platforms
Platform engineering, the beginning
We don't start to learn what is going on in a real changing environment.
We start learning in an isolated education environment that is assuming a fixed mindset in predefined fixed requirements, fixed methodologies.
Adapting change and what was learned is an after fact activity. Increasing your Agility: An interview with Dave Thomas (2015)
People go through phases when they learn something.
When they first start out, they have no understanding of the context they find themselves in.
There aren't many decisions they can meaningfully make—all they can really do is follow rules.
But, as you gain experience, you start to understand the bigger picture.
And with this understanding comes a growing intuition about how things work. ❶
At this point, the rules you had originally hold you back.
You can see further and more clearly, and you can see how doing things differently might be better.
So you start to experiment, tweaking things here and there.
If the result is better, you tweak some more.
It it is worse, you roll back and try something new.
What we have here is that during the education time we have no other choice than to follow work what is being told.
After becoming more proficient thing change.
There is another choice, after learning the basics in those fixed methodologies:
Become more proficient in that knowledge to be able to change the things, doing it different in different methodologies.
Become more proficient in that knowledge to get more throughput in efficiency of being more effective in the results.
Switch to a managerial role trying to lead in what is not known and not capable in, or set in serving managerial role in enabling those others.
These schisma's are a source four confusion and conflicts.
As the popularity of the manifesto grew, consultants and companies started to sense an opportunity.
We started seeing people using the tag "Agile" with their existing offerings in the same way detergent manufacturers add the word "Improved" to their product name.
Often they'd ignore the fact that "agile" is an adjective and instead use it as a noun: "learn Agile today!" ❷
Why do they do this?
Because it is easier to sell nouns (things) than adjectives (qualities of things).
You can't buy blue, but you can buy a ball.
If they wanted to sell "agile" they'd need to make it a thing.
The flow of engineering platforms
Development, engineering, a prefered methodology approach in several areas for a v-model and following Lean & AGIL (Adaption, Goal attainment, Integration, Latency). ❸ The four areas:
Front-End: portfolio assets alignment and feedback to external stakeholders.
Setting a goal and identity for the internal organisation.
Mission realisation: portfolio management by the organisation enabling the future.
Budgets generated form the operations in the now. (Portfolio-Plan)
Technology realisation: The development of new and improved solutions with the goal serving the external customer. Aligned with operations in the now (Dev-OPS)
Back-End: supply chain alignment and feedback to internal stakeholders.
Planning for what is realistic possible by available resources.
In a figure:
❹ There is a balancing force in another context:
Portfolio suggestions backlog: the pipeline for the future.
Technology knowledge: Ideas in improvements, innovations for design and products.
Portfolio products specifications: The product knowledge in the now.
Product validations: quality and quantity assurance & monitoring.
Platform engineering, the evolution
Increasing your Agility: An interview with Dave Thomas (2015)
I don't think we can fight this.
But I do think we can bypass it.
I think the original values are still valid, and we can use them to inform the way we work.
I like to express it like this. ❺
Every team that develops with agility follows these steps:
Know where you are
Take a small step towards where you want to be
Evaluate what happened
Repeat
This sounds easy—it isn't. It is hard because it applies to everything, at all levels. It applies to naming your variables, and it applies to defining your architecture.
And implicit in the steps is the often overlooked fact that we are never done.
We don't stop the process when we reach some goal. We stop it when the incremental value we deliver is less that the cost of delivering that value.
The way all of this similar attempts in history has gone wild is the real question to answer and think about.
My suggestion is, the real issue are:
an easy financial business model is seen by setting mandatory methodologies for an idea.
there is an social culture in not accepting the really impactful changes of the idea.
the goal gets lost in mandatory methodologies, the methodologies are loosing their value.
blaming the failures to something that is out of control, enabling to repeat the same.
⚒ E-2.2.2 Way of working: flow interactions communication
The Incose systems thinking (US based)
A change in the context of engineering going out of the box of technology.
The evolution of systems engineering as a transdiscipline (Michael Pennotti, Peter Brook, David Rousseau 2024) ❻
Systems engineering (SE) is a relatively young discipline, but evolving rapidly in the face of increasing recognition of the need for a systems approach to facilitate not only the successful engineering of complex systems but also the creative development of elegant solutions to complex problems.
The need for this systems approach arises inter alia via the:
increasing evidence of the rapidly growing financial and performance risks associated with complex development projects of increasing scale and interdependence;
increasing recognition of the systemic complexity of the urgent problems whose solutions matter most to human and ecological wellbeing.
Widening recognition of the need for synergistic co-operation between multiple disciplines collaborating towards the solution of complex problems, especially those of global scale;
explosion of opportunities for technical capability based on the exponential growthofscientific and technological insight;
increasing recognition of the immaturity of the theoretical underpinnings of SE, especially as regards its ability to understand, and design/engineer for, what works or what matters across disciplinary interests (e.g., general systems theory).
❼
A clear sign of the struggle to cope with this increasing complexity in the light of so much change and risk is the burgeoning efforts to compile lists or catalogues of heuristics, principles, and other guiding assumptions and propositions that can assist SEs in learning from past experience while avoiding the risk of being locked into past views of “best practice” which might be inadequate in different futures.
In a figure:
See left side
In the face of these concerns, some systems engineers began to develop prescriptive processes and standards in an attempt to make the practice more systematic.
In doing so, however, they weakened the link between the SE and the domain within which it was applied.
SE became more abstract and disconnected from the rest of the engineering required to bring systems to life. ... ❽
Griffin suggested four criteria for design elegance:
Does the system work?
Is it robust? If the context changes, does it degrade gracefully or fail cata strophically?
Is it efficient? .. not only in terms of financial resources, but also human resources, energy resources, environmental resources, etc.
Does it minimize unintended actions, side effects, and conse-quences?
... First, Iandoli et al. have shown that elegant designs remove unnecessary complexity and also render designs simpler to implement.
Second, Rousseau Billingham and Calvo-Amodiohave argued that the parameters of Griffin elegance amount to the fac-tors that contribute to assessing a system as “good,” and hence count as“systemic virtues” in the same sense as factors that make a person good (“personal virtues”) and a scientific theory good (“theoretical virtues”).
The SCIO and others systems thinking (UK based)
Holistic Flexibility for Systems Thinking and Practice (Researchgate: Rajneesh Chowdhury 2024)
Complexity theorists talk about underlying structures that govern behaviors of systems that are manifested in patterns and trends over time that we experience in our societies and organizations.
Order emerges out of such underlying structures and it need not be imposed by extraneous forces.
Several factors are inextricably intertwined resulting in complex dynamics in social, economic, regulatory, and technological spheres.
These dynamics are often characterized by local rules, nonlinearity, unpredictability, and volatility. ... ❾
The origins of complexity theory lay in mathematics, and traces its roots to Edward Lorenz’s chaos theory (Gleick 1987; Jackson 2000).
In the 1960s, involving 12 nonlinear differential equations, Lorenz decided to look for complex behaviors and he was led to the phenomenon of rolling fluid convection - this was part of his work on solving the problem of long- range weather forecasting using a simple computer simulation. ...
Organized complexity resides in the parts of a system that are non-random, clearly correlated, and display identifiable interactions between the parts.
Due to correlations, differentiated subsystems can be observed.
Interaction with other systems and subsystems are clear.
In the case of disorganized complexity, interactions within and between the subsystems or systems are random. ...
As Jackson (2019) notes, the primary concern in restricted complexity is with deterministic chaos, but this is not the world that practitioners encounter.
Rather, they confront general complexity, where the crux of complexity is in the human agents who form the bases of interactions in the systems and subsystems.
Human agents carry their own values and motivations and have their own power dynamics at the micro and macro levels. ❿
Therefore, any attempt to simplify disorganized complexity or general complexity using nonlinear dynamical programming is unlikely to offer deeper insights as the outcomes of such efforts rest at the level of computer simulations, rather than empirical observations (Jackson 2019). ...
Bogdanov (1902) developed a concept of “universal organization science”, an original systems theory that sought to find solutions to generalized scientific and philosophical questions for the unification of all biological, physical, and human sciences.
He advocated that this is only possible if sciences are approached as systems of relationships that are driven by fundamental principles of interrelationships and emergent behaviors. ...
⚖ E-2.2.3 The Jabes Framework knowledge cycle
What: Knowledge & Interactions in a cycle
A proposal is the framework for generic interaction and knowledge sharing.
Using this framework a clear structured definition of a portfolio becomes possible.
The technology, tools, context:
in a figure:
See left side.
Context of the audience:
Technology driven structural knowledge assurance.
With a very generic model of information systems there is a possible generic technology approach as product a tool for this.
The more detailed tools for more detailed situations are possible better fits in details but at a generic level it is adding sustainable value.
Recognizing Knowledge & Interactions unique ID
Using an uniform product identification enables trade and exchange while exporting and importing the database containing an information product conforming the Jabes metadata model.
Following a naming convention schema an identification could be like:
PPIC:ITC-00-000-001:ACT:ScoreNewCust-03
Licensing the identification, numbering, for uniqueness is a business model.
⚙ E-2.2.4 The Jabes Product knowledge assurance
Why: Knowledge & Interactions in a cycle
Product Pitching:
Extending the framework with a product supporting the portfolio operational and transformational is unique & distinctive.
There is nothing like this existing in the market.
There is a high demand to be more in control for information processing.
The shown data model is for a transformation building up a platform supporting business administrative processes.
A trial was done using only Word (moc). Real interfaces api's databases, to be done.
Licensing a product or running it as a services (SAAS) is a business model.
How: Knowledge & Interactions in a cycle
The metamodel covers all elemements in three layers, servicing the life cycle stages.
Innovation or solving known issues needs a defined "backlog".
The "backlog" items should be made clear enough and well understood to define requirements.
in a figure:
See left side.
Technology driven structural knowledge assurance.
E-2.3 Engineering collaboration by processes, services
Work units are to be understood in relation to their arrangement into processes and they should be defined accordingly. 🤔💡🎭 Linking processes to narratives, work units have three unities for Aristotles drama:
Unity of action: one main objective, a single authority.
Unity of place: executed at one space, all resources addressed & accessed independently, communications without mediation.
Unity of time: set within fixed and continuous time-frames.
That is a necessity if controlled synchronization of tasks to be managed independently of context contingencies.
The Perilous Afterthought: Why Product Documentation Cannot Be an After-the-Fact Endeavour (Li Shumin Chen 2025)
In the fast-paced world of product development the allure of rapid iteration and quick deployment can be overwhelming.
However, this urgency often leads to a critical oversight: treating product documentation as an afterthought.
This practice, while seemingly efficient in the short term, is a recipe for disaster, a potential malpractice that can have far-reaching consequences. ❶
ASD-STE100, or Simplified Technical English, has a wide range of applications, extending beyond its origins in the aerospace industry:
Manufacturing: Equipment manuals, maintenance procedures, and safety guidelines.
Defence: Military equipment documentation, training materials, and operational procedures.
Information Technology (IT): Software documentation, user guides, and troubleshooting manuals.
Medical Devices: Operating instructions, safety information, and regulatory documents.
Energy Sector: Plant maintenance, safety protocols, and operational procedures.
Regulatory Compliance: Meeting requirements for clear and understandable documentation in regulated industries.
Customer Support Materials: Creating easy to understand help files, and FAQ documentation.
❷
The solution lies in integrating documentation into the product development process from the very beginning.
By treating documentation as a core component of the product, companies can ensure accuracy, completeness, and consistency.
Early Planning and Collaboration: Documentation should be planned and initiated during the early stages of product development, with close collaboration between developers, technical writers, and subject matter experts.
Continuous Updates and Revisions: Documentation should be continuously updated and revised throughout the product lifecycle to reflect any changes or modifications.
Utilising Modern Documentation Tools: Leveraging modern documentation tools and technologies can streamline the documentation process and improve efficiency.
By prioritising product documentation and integrating it into the development process, companies can mitigate risks, enhance safety, and build trust.
The operational flow of a value stream
Operating, executing, has a big similarity with engineering.
The preferred methodology approach is based on lean.
Removing the limited constraint when doing improvements.
For a stable system there should only one controlled constraint.
❸ The four areas:
Alignment in products : the vision in what is the flow in demand and delivery.
Setting a goal and identity for the internal organisation.
Prepare picking: The planning of what is going to be delivered.
Resources for materials aligned for the operations in the now.
Demand at Customers: The product flow demand (sales) approach. Aligned with operations, "prepare picking", in the now.
Collecting packaging: quality and quantity assurance before delivering.
Planning for what is realistic possible by the properties in the deliveries.
In a figure:
❹ There is a balancing force in another context:
Delivery chain: continuity assurance in external dependencies.
Alignment in resources : The vision in flow at demand and delivery. (Portfolio-Plan)
Run Operations: Equipment SMED (Single Minute Exchange of Dies). (Dev-OPS)
Supply chain: continuity assurance in external dependencies.
Value stream engineering, the evolution
The problem in the value stream is understanding of the product (good, service).
When everything was data the meaning and context of data got lost.
When all was about agile the meaning and context of agile got lost.
The agile hype is over, the sizzle into a new word: Product.
Is your Product Owner actually a product OWNER? (W.J.Ageling 2025)
Most Product Owners I know are something else ... ❺
instead they are one of the following:
Feature Owners They are accountable for maximizing the value of a product feature. They work on an element of the product that fulfils user needs but can’t function on its own.
A user may be happy with a product feature, but unhappy with how features work together and therefore unhappy with the product as a whole.
Component Owners They are accountable for a distinguishable part of the product that often can’t function without other parts. Examples are the database, the front end, and reporting.
Team Owners are accountable for the delivery of the product parts by their team.
They are working with a team backlog that is a subset of the program backlog.
Product Owners that are one of the above are Product Owners in name only.
They aren’t solely accountable for the value of the product.
More rigid is: the CPO (Chief Product Officer) is the one with authority and accountability for the product as a whole.
Is there no CPO than there is no lead, no C&C in place.
A chief product officer (CPO), sometimes known as head of product or VP of product, is a corporate title referring to an executive responsible for various product-related activities in an organization.
The CPO is to the business's product what the CTO is to technology.
The role is a complex one of several area's. The social interaction is very important. ❻ Anthropology
The Macy Cybernetics Conferences (1946-1953):
The principal purpose of these series of conferences was to set the foundations for a general science of the workings of the human mind.
These were one of the first organized studies of interdisciplinarity, spawning breakthroughs in systems theory, cybernetics, and what later became known as cognitive science.
M Weber
Weber attempts an interpretive understanding of social action in order to arrive at a "causal explanation of its course and effects.
The social actions have subjective meanings that should be understood in its given context.
Weber's interpretive approach in understanding the meaning of an action in relation to its environment delineated a contextualized social framework for cultural relativism.
Evolution in methodologies at systems
T Parsons
work significantly impacted American sociology by integrating European classical theory, particularly the ideas of Max Weber and Émile Durkheim.
His theories sought to bridge the micro-level interactions of individuals with the macro-level structures of society, providing a holistic view of how societies function and maintain stability.
This integrative approach has made Parsons a central figure in sociological theory, despite the criticisms and debates his work has generated. ❼
A social system, according to Parsons, is a complex set of relationships among individuals and groups that interact within a structured environment.
Parsons defined a social system as a plurality of individual actors interacting with each other in a situation that has at least a physical or environmental aspect.
The interactions within the social system are guided by culturally shared symbols, which provide the normative guidelines for behavior.
These symbols include language, values, and norms that shape the expectations and actions of individuals.
The primary function of the social system is to maintain stability and order by regulating the behavior of its members.
This is achieved through the integration of individual actions into a cohesive whole, ensuring that the system operates harmoniously and efficiently.
The social system is thus seen as a dynamic and adaptive structure that responds to both internal and external challenges.
This system is characterized by the interdependence of its parts, the presence of shared norms and values, and the orientation of actors towards achieving collective goals. ❽
The integration of actors into social structures is crucial for the stability and functioning of the social system.
By internalizing shared norms and values, individuals align their behavior with the expectations of the system, contributing to social cohesion and order.
This process reduces the need for external control and enforcement, as individuals regulate their own behavior in accordance with the system’s norms.
His work on the AGIL schema, for example, provides a framework for analyzing the functional imperatives necessary for the survival of social systems.
In a figure:
Evolution in methodologies
G Bateson
Bateson argues that there are "ecological systems, social systems, and the individual organism plus the environment with which it interacts is itself a system in this technical sense."
By adding environment with systems, Bateson closes the gap between the dualities such as subject and object.
"Playing upon the differences between formalization and process, or crystallization and randomness, Bateson sought to transcend other dualisms mind versus nature, organism versus environment, concept versus context, and subject versus object." ❾
In short, the behaviour of person X affects person Y, and the reaction of person Y to person X's behaviour will then affect person X's behaviour, which in turn will affect person Y, and so on.
He then discerned two models of schismogenesis: symmetrical and complementary.
Symmetrical relationships are those in which the two parties are equals, competitors.
Complementary relationships feature an unequal balance, such as dominance-submission (parent-child), or exhibitionism-spectatorship (performer-audience).
Schismogenesis is a term in anthropology that describes the formation of social divisions and differentiation. ❿
(schizophrenia) Full double bind requires several conditions to be met:
contradictory injunctions or emotional messages on different levels of communication.
No metacommunication is possible – for example, asking which of the two messages is valid or describing the communication as making no sense.
The victim cannot leave the communication field.
Failing to fulfill the contradictory injunctions is punished .
The strange behaviour and speech of schizophrenics were explained by Bateson et al. as an expression of this paradoxical situation, and were seen in fact as an adaptive response, which should be valued as a cathartic and transformative experience.
⚒ E-2.3.3 Way of working: Jabes flow administration
What: Knowledge & Interactions in a cycle
A proposal is the framework for generic interaction and knowledge sharing.
Using this framework a clear structured definition of a portfolio becomes possible.
Logical information context:
in a figure:
See left side.
Context of the audience:
Organisational structural knowledge assurance.
With a very generic model of information systems there is a possible generic technology approach as product a tool for this.
The more detailed tools for more detailed situations are possible better fits in details but at a generic level it is adding sustainable value.
Recognizing Knowledge & Interactions unique ID
Using an uniform product identification enables trade and exchange while exporting and importing the database containing an information product conforming the Jabes metadata model.
Following a naming convention schema an identification could be like:
PPIC:FLI-00-000-001:ACT:ScoreNewCust-03
Licensing the identification, numbering, for uniqueness is a business model.
⚒ E-2.3.4 Way of working: Jabes flow alignment
Why: Knowledge &mp interactions in a cycle
Product pitching:
Using the framework with a product supporting the portfolio operational and transformational is unique & distinctive in the beginning.
There is nothing like this existing in the market.
There is a high demand to be more in control for information processing.
The shown data model is applicable for many business administrative processes but are not in place yet.
The first one will be the first in defining details.
The first one will run and/or enable the approach by a bootstrap approach.
Creating the framework and products, running it as a services are business models.
How: Knowledge &mp interactions in a cycle
A generic fraemwork and a tool is a project to implement and maintain in life cycle stages.
Innovation or solving issues needs a defined "backlog".
The "backlog" impacts all the stakelhoders in the organisation and the organisation by requirements.
in a figure:
See left side.
Technology driven structural knowledge assurance.
E-2.4 Architecting collaboration in unpredictability
At the beginnings there were only objects as given by nature.
That changed by seeing them as artifacts that could be made on design.
🤔💡🎭 As communities set on making more complex products:
they began to think about collaboration,
subsequently about engineering processes,
controlling & managing change as an afterthought,
Unpredictability of contexts and execution making intermediate milestones necessary.
The first two narratives and action clearly depends on the idiosyncrasies in to ICT concerns, the other two in organization models.
⚒ E-2.4.1 Controlling and planning the now
The four types of variability
If flow is the paramount in system variability and system variability is blocking the flow, one should explore the nature of system variability encountered. ❶ Variability can be systematically minimized and managed but not eliminated.
External types:
Demand, Request variability characterized by fluctuations end deviation experienced in request patterns and plans.
Supply, Input variability occurs in the supply network or deviations by dates (seasonally differences) for supply or promised in requests.
Internal types:
Execution, variability is normal random variability by the system in a steady state.
Control, variability is associated with the human element making decision.
Consumable Solutions
Disciplined Agile
It isn't sufficient to simply produce something that is 'potentially shippable,' instead it must also be something that is:
Usable. People should be able to easily understand and work with your solution.
Desirable. Think of it like this, if you ship something that nobody wants to work with, do you consider that a success? Of course not.
Functional. The solution must meet the needs of its stakeholders.
❷
The fundamental observation is we as IT professionals do far more than just develop software.
Yes, software is important, but in addressing the needs of our stakeholders we will:
Develop high-quality software
Provide new or upgraded hardware/platform
Change the business/operational processes which stakeholders follow
Change the organizational structure in which our stakeholders work
Update supporting documentation
Minimally IT professionals should have the skills and desire to produce great software, but what they really need are the skills and desire to provide great solutions.
We need strong technical skills, but we also need strong "soft skills" such as user interface design and process design.
Operational support for the flow in the now
Operating, support executing, is in line with what has been engineered.
The preferred methodology approach is based on lean.
Removing the limited constraint when doing improvements.
For a stable system there should only one controlled constraint.
❸ The four internal areas :
Alignment in visions, missions : the identity of an organisation.
Nurturing the goal and identity at the internal organisation.
Executing capabilities: The standards in the ways of working. (Provision-Buyer)
Nurturing the standards of working in the operations for the now.
Enabling capabilities: assignment processes. (Motive-Assets)
Allowing people, resources to use defined capabilities.
Execution of operations: quality and quantity assurance before delivering.
Allowing the defined functional operations to happen.
In a figure:
❹ There are external balancing forces in another context:
Drivers which: reactive on what is happening for events to adapt.
Drivers when: proactive and reactive adaption of policy processes.
Drivers where: reactive and proactive mitigations of threat events.
Drivers Who: Interacting at opportunities in communications.
A start to build for the needed extension in mindset
❺ External references in the context of ICT:
link , newstopic interest
who, source
date
The
it4it used by companies, industries of all sizes
Underpinning necessity distinct LCM approaches requires salving a misunderstanding. ❻ Doing LCM correctly for each distinct type of artifact seems to be positioned emotionally as only a technical problem.
There is no connection between reality of nature and decision makers.
Ethical scandals and LCM challenges
These are not technical problems, a list of organisational challenges: ❼ Information LCM, data flow, delivery and data quality.
#
date
Event link newstopic
1
1985
⚠ Event: Enron
was cited as the biggest audit failure.
Regulations SOX (Sarbanes_Oxley Act), Basel, Solvency were a result.
2
2020
⚠ Event: Wirecard
a series of corrupt business practices and fraudulent financial reporting.
No guarantee to prevent recurrence.
⚠ Event: Ransomware: Maersk
business interruption to Maersk, the world's largest container ship and supply vessel operator.
Getting attentions with cybersecurity cyberwarfare.
A lot not going well in organisations:
Lack of technical segmentations
Lack of functional segmentations
lack of leadership involvement
2
2024
⚠ Event: Mistake: Crowdstrike
Cybersecurity company CrowdStrike distributed a faulty update to its Falcon Sensor security software that caused widespread problems.
The dogma of getting updates as soon as they are available being too rigid applied.
The used audit checklists did not foresee the alternative impact in failure in an update.
A lot not going well in organisations:
⚠⚖ Event: Space Shuttle Challenger disaster
root cause design: .. The Challenger accident has been used as a case study for subjects such as engineering safety, the ethics of whistleblowing, communications and group decision-making, and the dangers of groupthink.
Failing culture
remarkable, 2003 Columbia disiaster, "the causes of the institutional failure responsible for Challenger have not been fixed."
2
2018
⚠⚖ Event: 737 max
root cause design: .. seems to be caused by neglecting safety in favor of profit and meeting deadlines.
Failing culture
Failing regulatory controls
remarkable the first reactions blaming the computer AI as the cause where the real root cause are the human leadership.
❿ Analytics, operational research, AI ML (artificial intelligence).
#
date
Event link newstopic
1
1960's
👓⚙ Sizzle: Operations research (wikipedia)
was emerging for improving situations beyond assumptions.
It got other names: systems thinking, AI. AI went emotionally incomprehensible with non-linear situations but wheterforecast & smartphones got normal
2
2020's
⚠⚒ Non-Events: futurisme
feeding concerns with anxious and brittleness. Reality: nothing to be afraid of.
💣 Don´t expect when fundaments are not robust, what is build on those, will be robust.
Information security is not possible when there is no data governance.
Anti-Cyber Crimes (Vellore Institute of Technology University, M.K.Jayanthi Kannan 2017)
Data Governance Strategy Development is based on the model proposed by Enterprise Information Management Primer Developing a Roadmap for an Enterprise Information Management Program.
The Data Governance Strategy of an organization should focus on the following aspects ..
Inputs from the following list of stakeholders need to be considered and analyzed for the development of Information Security Strategic Plan. ...
in a figure:
See left side.
There is an explanation for why to use the SABSA framework in this.
For the safety scope there are four pillars shown.
These are a variation of the generic four pillars.
Algorithms and models for interactions
ICT is a binary world the assumption is everything is true/false.
Reality is a complex and complicated one full of uncertainties without that binary assumption. Should We Trust Algorithms? (David Spiegelhalter 2020)
There is increasing use of algorithms in the health care and criminal justice systems, and corresponding increased concern with their ethical use.
But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us.
It is illuminating to distinguish between the trustworthiness of claims made about an algorithm, and those made by an algorithm,
which reveals the potential contribution of statistical science to both evaluation and "intelligent transparency."
In particular, a four-phase evaluation structure is proposed, parallel to that adopted for pharmaceuticals. ...
Finally, whenever I hear claims about any algorithm, my shortlist of questions:
Is it any good when tried in new parts of the real world?
Would something simpler, and more transparent and robust, be just as good?
Could I explain how it works (in general) to anyone who is interested?
Could I explain to an individual how it reached its conclusion in their particular case?
Does it know when it is on shaky ground, and can it acknowledge uncertainty?
Do people use it appropriately, with the right level of skepticism?
Does it actually help in practice?
I feel that question 5 is particularly important.
⚒ E-2.4.4 Realised activities: flow alignment
The metaphor model, ICT state of art
The common used model for information processes is a medieval settlement.
It is an outdated approach in many aspect in the age of the 21e century but explains a lot of the human behaviour.
The ivory tower for the ones with authority.
Cultural segmentation by what is seen of value.
Segmentation in tribes and guilds for actvities.
System viability ambiguity: no external connections, limited new energy, self sustaining goal.
Operational research (OR) is the overarching whole of what is divided into other phase spaces, living worlds.
From the Tightness Stereotypes (SB "Decision and Control" 1966, chapter 4) OR has to deal with:
Scientists: the education gives him a bias. ➡ Use an interdisciplinary team.
Problems: are declared with a bias. ➡ research the problem as a whole.
Science: bias in cope for chance questions. ➡ nature is probabilistic.
Solutions: biased by chosen phase space. ➡ research for a shared phase space.
Pay-off: determined by management bias ➡ what it demands and can assimilate.
Success: condition fulfilments are hard. ➡ ambiguities confusion by half measures.
The metaphor model, ICT adapting change
A change leaving the old metaphor as hard, attempts are made.
The medieval settlement is getting transferred into adapting changes, see figure.
The social society the settlement is part of is placed at the top, VSM system-5.
Missing:
the way of the overarching government control.
Executing operating technology components at the bottom, VSM system-1.
Missing:
Administrative enabling support in the now
Way of working executing capabilities in the now
Alignment in shared mission, culture and identity
There should be many more system-1 tasks.
An Information & application plane for managing the operations, VSM system-3.
Misplaced are:
The operational execution of the organisation: ICT, functional, communications, structure
common ground, dwh, digital twin, archives
The future cloud be "organisation & chained processes" VSM system-4.
Misplaced are:
The operational management for execution of the system (organisation)
social services, safety, living environment
System-2 is not there in using regulators and algadonic channels.
There is work in progress to move to a viable system but without awareness of doing that.
E-2.5 Knowledge Assurance: measure, maturity
Measurements are not facts but observations obtained through conceptual & physical apparatus on purpose. 🤔💡🎭 For software architecting, engineering there are four purposes:
Organisational and customer value of applications.
Functionalities of supporting information systems.
The first two metrics clearly depends on the idiosyncrasies in organization models, the other two to ICT concerns.
📚 E-2.5.1 Controlling and planning the future
Understanding risks for change
Learning from Mistakes is Overrated (LI G.Alleman 2025)
These questions and others need to be asked and answered before we can assess whether learning from our mistakes is a good idea.
The alternative to learning from our mistakes is to do the job right the first time.
Why didn't we know this would be a mistake before we started?
Or could we have known this before we discovered it failed?
Or even better, was the failure we just experienced knowable at all?
This knowability question is the key to all project planning processes.
If something is not knowable, we cannot have known, so we only discover our mistake after the fact.
If it was knowable and we chose not to address it then we'll overrun our plan for no good reason. ❶
A critical concept that must be addressed in any credible management process:
Investigative work - discovery is always needed on any development project. To think otherwise means we have a production process.
Learning what we need to learn requires a budget. This is the basis of the increasing maturity of the Integrated Master Plan paradigm.
Someone has to pay for us to learn what we don't currently know and we need to make the cost of this learning visible as soon as possible.
Hiring smart people is pointless if they aren't allowed to make mistakes.
We must address who, what, when, where, why, and how this discovery process will be paid for FIRST.
Then, we can start failing on purpose to make the follow-on work better.
Variety in change
How to Manage Your Lean Projects – Number of Active Projects
Let's face it – you have more things to do than you can reasonably do in the available time.
A constant stream of tasks or problems are waiting for a lean solution. ... ❷
So how many projects should you have active simultaneously?
It depends. A single person works best with around two to three active projects (including daily chores).
A simple way to limit the number of projects that are active simultaneously is a project management board.
Operational support for the flow in the future
Operating, executing, has a big similarity with engineering.
The preferred methodology approach is based on lean.
Enable the workfoce in seeing: Muri, Mudi, Muda, first are people
Removing the limited constraint when doing improvements.
For a stable system there should only one controlled constraint.
❸ The four areas:
Information knowledge qualities: the understanding what it is all about.
Advisories in goals and identity for the internal organisation.
Innovation, organisation: legal obligations liabilities and visibility for service demand.
Resources for materials aligned for the operations in the future.
Safety - Continuity: all kind of technology related aspects that should get mitigate threats in risk evaluation.
One of the hyping words is cybersecurity.
Organisational stability: Risk management on impact by decisions, mandatory legal obligations.
Doing what is necessary in alls aspects for trustworthiness.
In a figure:
❹ There is a balancing force in another context:
Authorities in decisions : The accountabilities to align with vision.
Execution capabilities: continuity assurance by set policies(Motive-Assets)
Viable Operations: Equipment avoiding the issue in technical debt.
Variety in change
How to Manage Your Lean Projects – Prioritize
The question is now, among the many different projects waiting, which one do we start?
For my everyday practical decisions, I prefer a much cheaper and faster version of the cost benefit analysis: an impact–effort matrix.
There are different versions of this matrix found on the web, often with slightly different names.
In essence, however, one axis shows the effort/cost/difficulty/time that has to be put into a project and the other shows the impact/value/benefit/profit that the project will yield.
What is lean is difficult to define.
There is a long list of similar ones all practices, known patterns to improve effectiveness improve efficiency.
Operations research (OR) is not well understood. ❺❗ Operations research
attempts to provide those who manage organized systems with an objective and quantitative basis for decision; it is normally carried out by teams of scientists and engineers drawn from a variety of disciplines.
Thus, operations research is not a science itself but rather the application of science to the solution of managerial and administrative problems, and it focuses on the performance of organized systems taken as a whole rather than on their parts taken separately.
OR as an science and application of science is an higher abstraction level in science.
Cybernetics is a word for OR in the area of information processing
Seeing all those as scoped biased suffering from tightness in OR is enlightening.
📚 E-2.5.2 Viable systems variety and maturity
Story telling getting it more practical
With OR Markov chains, closing the system using closed loops, System dynamics for chaotic systems modelling there are a lot of scopes.
These are all very abstracted approaches for systems. ❻ Complexity Theory & Political Change: Talcott Parsons Occupies Wall Street (researchgate: Martin Zwick 2012)
This paper revisits an early cybernetic and systems-theoretic model - today it might be called a complex systems model - proposed by the sociologist Talcott Parsons (1966, 1971), and argues that this model can help us understand some of the underlying causes of the major recession afflicting the US economy today.
Indeed, this model was once characterized as being inherently conservative, because it allegedly assumed the stability and functionality of societal structures.
This characterization is incorrect, since Parsons' structural functionalism can actually be used to explain either stability or instability and either functionality or conflict.
Parsons conceived of the AGIL system as having fractal self-similarity, so each component can itself be decomposed into A, G, I, and L parts.
❼
Using ideas from cybernetics and systems theory, Parsons orders the four components of the system hierarchically, indicated by the zigzag sequence of directed links shown in bold in the figure.
The top component (culture) is information-like; the bottom component (economy) is matter-energy-like.
Structural analysis goes beyond considering possible links between components.
Each component has sub-components and thus also an inner structure.
Community, for example, which is the I component of the societal system, has four sub-components; its polity-like subcomponent, i.e., the G in I, is “citizenship,” with its opportunities and obligations.
Parsons' model is not introduced here to discover societal problems or possible solutions to these problems that are now unrecognized.
It is introduced as a framework within which known problems and their possible solutions can be discussed coherently.
Abstract models may be more effective than concrete models for identifying the essence or deep structure of societal problems, for seeing the forest rather than the many trees.
The postion of persons in managing affairs
The problem in systems thinking, operational research having another abstraction level is a recent one.
It started at the beginning of the 20th century. (SB "Decision and Control" 1966, introduction) ❽
In the sixty odd years of the present century, there has been a colossal intellectual revolution in the basic thinking of science.
Basically, certainly chronologically, it began with the overthrow of classical physics.
The universe of space, time and gravitation became a different universe for the scientist after the theory of relativity became known.
The particles with measurable position and momentum which populated that universe took on a different meaning for the scientist after the discoveries of quantum mechanics.
The intellectual revolution of twentieth-century science has been accepted by the scientist, for it is proper to his work to uncover the essential characteristics of things.
From that revolution has stemmed a series of new discoveries, and indeed new sciences.
Atomic physics, astrophysics, a new chemistry, a new genetics, biochemistry and biophysics are all children of a revolutionary regime. ❾
For the man of affairs, however, the position is quite different.
His job is not to seek the truth, but to be the Prince.
He manages companies and industries, civil and military services, parties and policies, administrations and governments.
He manipulates large systems of men, materials, machinery and money.
The intellectual revolution of science has largely passed him by; it does not exist for him.
For the man of affairs to know much about science at all is rather unfashionable.
We have heard company directors boast of their ignorance of science, as if this automatically conferred a certificate of preoccupation with the higher things of life. ❿
The whole idea of using hard science as an intrinsic part of the managerial process is alien to many.
It is a proposal often countered by such remarks as 'management cannot be reduced to a science', or even 'management is an art'.
But neither of these replies is at all relevant to the issue.
The processes of management are complicated. They are complicated for the individual manager for whom insight, value judgment, flair, acumen, maturity and experience count.
They are even more complicated for the social entity that is a management group.
Don't wonder why there is that little progress for going into the age of information processing.
It is not the technology that is the problem it is human nature in decision and control to adapt the change.
📚 E-2.5.3 Jabes Measuring maturity: each dimension out of 3
Technology Scope: Tools, Infrastructure
❌ I - processes & information ✅ T - Tools, Infrastructure ❌ C - Organization optimization
Physical and operating system components:
Maturity id
What
Context
CMM-4IT-1
Network
Communications, zone isolations. Virtualisations impact
Functions to hardware & network. Virtualisations impact
Tools, decision support and primary value stream system components:
Maturity id
What
Context
CMM-4IT-4
Tools Middelware
LCM without❗ organizational information
CMM-4IT-5
Operational plane
Classsic well known LCM as always has been done
CMM-4IT-6
Analytical plane
LCM always with organizational production ❗ information
Maintenance, service, management and compliancy system components:
Maturity id
What
Context
CMM-4IT-7
Up to date
Maintenance production planning, act on gaps
CMM-4IT-8
Cots vs "build"
Manage external purchased artifacts distinctly
CMM-4IT-9
Regulations
Being prepared for conforming compliancy: BIA CIA
Change Scope: Organisational Structure, Processes
✅ I - processes & information ✅ T - Tools, Infrastructure ❌ C - Organization optimization
Functional enablement support:
Maturity id
What
Context
CMM-4AS-1
Access Data
Provisioning information (data), limiting access
CMM-4AS-2
Platforms usage
Enabling processing lines, operational value stream
CMM-4AS-3
Monitoring
Enabling Monitoring , analytical plane what is happening
Functional realisations support:
Maturity id
What
Context
CMM-4AS-4
Data preparation
Adjust information (data quality) using ELT / ETL
CMM-4AS-5
Transformations
Chain management, operational value stream
CMM-4AS-6
Data delivery
Value stream results (data), limiting access
Functional architecture & compliancy:
Maturity id
What
Context
CMM-4AS-7
Corrective
Operational value stream, act on failures & mistakes
CMM-4AS-8
Algorithms
Knowledge rules & instructions for transformations
CMM-4AS-9
Regulations
Conforming regulations, directives with foreseen changes
Purposes Scope: organisation, managing missions
✅ I - processes & information ✅ T - Tools, Infrastructure ✅ C - Organization optimization
Holistic Organisation - Enterprise alignment:
Running the organisation (execution) in the now:
Maturity id
What
Context
CMM-4OO-1
OR Enable Metrics
Operational research, lean enabling in the organisation
CMM-4OO-2
Technology
Understanding technology processes value streams
CMM-4OO-3
Operational DMAIC
Existing value streams improvements, minimal adjustments
Running the organisation (execution) in the future:
Alignment to local environments social life circumstances
CMM-4OO-6
Tactical PDCA
Any value streams improvements, advanced adjustments
Running the organisation for a purpose:
Maturity id
What
Context
CMM-4OO-7
Plan structure
Decide or define. Make - refine - schedule/plan for execute
CMM-4OO-8
Let it happen
Versatile: "to do", "to perform", also "add" (pull-push)
CMM-4OO-9
Closed Loop
examine in order to determine its accuracy, quality, or condition
📚 E-2.5.4 Jabes Measuring maturity: human culture each of 3
Vision - Number of CMM controls
For enablement a culture, vision is not only about technology it is about humans, famous: "Culture Eats Strategy For Breakfast".
ndma
A vision of the content of your work (your team's deliverables, e.g., a future technology vision, or a future business position) is dangerous.
In a volatile world, we need dynamic organizations -- not organizations locked into a response to yesterday's challenges and opportunities, yesterday's business strategies.
Agility is the key.
⚠ Note: a vision is not a business goal (like market share or revenue growth), or a strategy (like acquisitions or digital business).
❗ The vision is: How your organization will work.
How it will address any and all challenges and opportunities that arise in an agile, dynamic manner.
Question: How to measure culture?
💡 There are no known positive objects.
Avoiding the three evils however, gives context in what should not be seen.
Split the important human aspects to three:
Serve IT Technology
Shape Data Driven
Steer Enterprise
Muda
CMM-4IT-0-Muda
CMM-4AS-0-Muda
CMM-4OO-0-Muda
Mura
CMM-4IT-0-Mura
CMM-4AS-0-Mura
CMM-4OO-0-Mura
Muri
CMM-4IT-0-Muri
CMM-4AS-0-Muri
CMM-4OO-0-Muri
The result: there will be twelve metrics for each of the three levelled stages.
The totals is 36 having each a value of 0 - 5. A visualisation in circle for a 360 retrospective (polar/radar diagram).
E-2.6 Experiencing the understanding journey
Depending on purpose three main categories:
Translation: source and target models for same contents with different languages.
Rephrasing: initial descriptions improvements using the same language.
Processing: source and target models describe successive steps along the development process.
The challenge in this: It are not the results being important, it is the journey of learning, understanding.
The knowledge of the journey to share ❗
📚 E-2.6.1 The why of learning organisations
The organisation in 6*6 approach
Once structuring my mind for information processing started for going into a 3*3 area's.
It did had a technical focus with the question for what is going on.
This clumsy tinkering draw up resulted in thinking in 6 columns for 3 rows where the contents in the rows went int0 a 2,2 setting.
The content went from technical to an abstracted non-technical level. The 2 dimensional model into a 3 dimensional one.
The figure:
Fractals in the organisation
Accepting recursiveness, fractals is breaking the classic knowledge hierarchy.
Only seeing this as an isolated component in a system doesn't help the system as a whole.
This is about human culture in many more aspects.
A promotion by ndma BWB :
The business-within-a-business (BWB) paradigm is foundational to NDMA's work.
It provides guiding principles for the design of every aspect of an organization's operating model.
Engagement ndma A=A
Empowerment comes down to simply this: Authority and accountability match.
What is missing:
The accuracy of prediction is an observable measure of knowledge.
Prediction, which can take the form of a plan, strategy, a decision, or any statement about the future, requires a theory.
accuracy of prediction depends on the extent to which a theory is aligned with the world to which the prediction refers.
(Edward Martin Baker).
Fractals when applying changes
A mismatch in accountability authority at some level in the system as a whole is a pitfall in projects.
Prediction for outcomes is measuring for what is intended to achieve.
Although this is well known the mistake is made over and over again.
Why Projects Fail - The Real Reason (LI G.Alleman 2025)
The measures of project success MUST start with two other measures.
Measure of Effectiveness (MOE)- The operational measures of success are closely related to the mission's achievements or objectives and are evaluated in the operational environment under a specific set of conditions.
Are stated in units meaningful to the buyer,
Focus on capabilities independent of any technical implementation,
Are connected to the mission's success.
MoE's belong to the End User. They define the units of measure
Measure of Performance (MOp)- Measures that characterize physical or functional attributes relating to the system operation, measured or estimated under specific conditions.
Attributes that assure the system can perform,
Assessment of the system to ensure it meets design requirements to satisfy the MoE.
MoPs belong to the Program.
The Systems Engineer develops them, measured by the Control Account Managers, and analyzed by program control staff.
📚 E-2.6.2 The what of learning organisations
Operational research (OR) and systems thinking
Whatever name is used it is the same abstracted goal (SB "Decision and Control" 1966, introduction)
The attack of modern science on complex problems arising in large systems of
men, machines, materials and money
in industry, business, government and defence.
Its distinctive approach is to develop a scientific model of the system, incorporating measurements of factors such as chance and risk, with which to predict and compare the outcomes of alternative decisions, strategies or controls.
The purpose is to help management determine its policy and actions scientifically.
The issue: another level of abstraction of science that is not common knowledge.
History of OR and systems thinking
With that lot of knowledge why it is failing to become common knowledge?
Fifty years of systems thinking for management (researchgate: MC Jackson University of Hull 2009)
Both operational research (OR) and applied systems thinking were born from the interdisciplinary ferment created during the Second World War when scientists from different disciplines found themselves working together on vital military problems.
Since that time the histories of OR and applied systems thinking have frequently come into contact and impacted upon one another.
For example, some of the early pioneers of OR (Ackoff, Churchman) later adopted the systems thinking label in preference to OR; soft systems thinking began life by defining itself in opposition to hard systems approaches such as OR (Ackoff, 1979; Checkland, 1978); and, more recently, both soft OR and soft systems thinkers have been involved in the development of problem structuring methods (see Rosenhead and Mingers, 2001).
🤔 The same origin is a natural similarity, but the thing as a whole falling apart.
To discover these points of interaction is not surprising because OR and applied systems thinking (AST - by which I mean, in this paper, systems thinking that has as its primary purpose the enhancement of management practice) have some crucial commonalities that draw them together and differentiate them from other approaches.
First, given the distinction forms of knowledge production, it is clear that both OR and AST are Mode 2.
In Mode 1 research is defined by particular scientific interests by scholars.
By contrast, Mode 2 research is produced to satisfy the demands of particular users.
It is (Gibbons et al, 1994) : Knowledge production carried out in the context of application and marked by its:
❶ transdisciplinarity;
❷ heterogeneity;
❸ organizational heterarchy and transience;
❹ social accountability and reflexivity;
❺ quality control which emphasises
❻ context- and use-dependence.
Tranfield and Starkey (1998) argue that management research generally should adopt a Mode 2 orientation, positioning itself in the social sciences as equivalent to engineering in the physical sciences and medicine in the biological sciences.
In fact, OR and AST have already occupied this space.
This explains their joint interest in 'clients', 'customers' and 'decision makers'.
Second, both OR and AST insist that rigour can be brought to Mode 2 research by building explicit models and using these during the course of an intervention and for later reflection.
🤔 The quest for management research is a reinventing the wheel.
This is going back for not commonly understanding of the additional abstraction level at OR AST.
Despite these crucial commonalities, that make OR and AST natural bed-fellows, advocates of the one often tend to know surprisingly little about the other.
They have their own textbooks, journals and conferences and relate to their own communities of practice.
Applied systems thinkers often refer to the classical textbooks and write off all OR as a form of hard systems thinking.
Operational researchers have been known to see systems thinkers as either unscientific, or impractical and too much in love with philosophizing.
This paper, by looking at the last 50 years of systems thinking in a manner that is relevant to OR, hopes to correct the distortion from at least the OR side.
Throughout, the importance of developments in AST for OR theory and practice is explained.
As a final point of introduction, however, it has to be said that any account of 'fifty years of systems thinking for management' will be partial.
I acknowledge the partiality of my account, particularly in respect of its bias to UK and US sources.
🤔 What is share is the problem of managing the complexity in systems.
A summary of Boulding's (1956) hierarchy of complexity, levels:
structures and frameworks which exhibit static behaviour and are studied by verbal or pictorial description in any discipline; an example being crystal structures
clockworks which exhibit predetermined motion and are studied by classical natural science; an example being the solar system
are control mechanisms which exhibit closed-loop control and are studied by cybernetics; an example being a thermostat
open systems which exhibit structural self-maintenance and are studied by theories of metabolism; an example being a biological cell
lower organisms which have functional parts, exhibit blue-printed growth and reproduction, and are studied by botany; an example being a plant
animals which have a brain to guide behaviour, are capable of learning, and are studied by zoology; an example being an elephant
people who possess self-consciousness, know that they know, employ symbolic language, and are studied by biology and psychology; an example being any human being
socio-cultural systems which are typified by the existence of roles, communications and the transmission of values, and are studied by history, sociology, anthropology and behavioural science; an example being a nation
transcendental systems, the home of 'inescapable unknowables', and which no scientific discipline can capture; an example being the idea of God
🤔 Other interesting notes from the paper:
Boulding uses the hierarchy to point out gaps in our knowledge, especially our lack of adequate systems models much above level-4.
That thermostat is in reality a level-2, when it is about temperature. A good regulator is an unsolved in classic science. Are there good understandable models at level-3?
are the systems models produced really applicable at higher levels of Boulding's hierarchy?
Structuralist explanations can, indeed, often seem 'reductionist' - pitched at the wrong level.
Much of the really innovative work takes place outside universities.
'Whole Systems Working': the process of involving all stakeholders of a domain in discus- sion about service change - all parties are encouraged to think about the way the whole service delivery system works, rather than focusing only upon their own service.
Vision misssons in a PDCA cycle
BSI 9-steps (LI A.Constable 2025 )
The 9-Steps to Success™ methodology, is a proven framework for turning strategy into results.
Each step builds upon the last, ensuring strategy is clearly defined, communicated, and continually improved.
Guiding by nine essential steps:
Assessment: Understand the current state.
Strategy: Define the mission, vision, and goals.
Strategic Objectives: Identify key focus areas.
Strategy Map: Visualize how objectives connect.
Performance Measures and Targets: Track progress with clear metrics.
Strategic Initiatives: Launch projects to drive change.
Performance Analysis: Monitor results and identify gaps.
Alignment: Ensure organization-wide coordination.
Evaluation: Refine the strategy based on insights.
🤔
The real strength of this approach lies in its ability to align mission, vision, and initiatives while promoting clarity and accountability.
It empowers better decision-making, fosters continuous improvement, and ensures strategies are practical and embraced throughout the organization.
in a figure:
See left side.
Balanced Score Card (institute )
The experts at Balanced Scorecard Institute (BSI) specialize in providing consultation, training, and professional certification services to all types of organizations.
There is a lot of well sophisticated underpinned planning and little rush for doing.
The roll-out is a small step.
📚 E-2.6.3 The how of learning organisations, Jabes Jabsa
Jabes goal: 🤔💡🎭 avoiding failures, wrong results?
The everlasting issue to tame "GIGO: Garbage in, garbage out".
"The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency.
The second is that automation applied to an inefficient operation will magnify the inefficiency". (‒ Bill Gates -)
Decreasing quality is an easy cost saving.
Effects by increasing failures - complaints, are not immediately measurable nor visible.
There are many constraints for a learning organisation.
Jabes goal: 🤔💡🎭 Add all compliancy aspects to artefacts.
The Goal for Jabes started by an idea for improvement in information processing, the scope the platform design cycle.
Immediate realised that information processing itself could benefit from the same (extended scope).
🤔 got blocked at that (no Do), switched to understanding of the why.
Fixed setting: what
Wanted / Searched: how to
Jabes - why
Technology
We learn what is known
improve to innovate functionality
Manage knowledge
Processes
dictated by what is done
improve to innovate functioning
Share knowledge
People
dictated by hierarchy
autonomy in line with missions
Support in interactions
Structure
known ideologies as usual
changing environments to adapt
Support for decisions
📚 E-2.6.4 Constraints in changing to learning organisations
Common fallacies, biases
In general, a bias is a distorted judgment that results from opinion, prejudice, and human cognitive limitations.
Biases rarely happen on purpose, and people are rarely aware of them. ...
And if people aren’t aware of them, wrong decisions are made.
🤔
Here are some examples of fallacies that play a crucial role in strategy:
Planning : vastly underestimating time and money it costs to achieve goals.
Sunk Cost: sticking to a strategy simply because you have already spent a lot on it.
Overconfidence: bias being too confident, being right or about the future will unfold.
There are many more biases and many of them exist for a reason.
They are shortcuts that help us make decisions fast and that tend to be right most of the time in ordinary life.
Common bubbles
Bubbles are the specific groups people are part of and by which they distinguish themselves from other groups.
Their main effect is that people are more exposed to people that are similar to them, than to people that are different from them.
🤔
In strategic decision-making at least the following three bubbles come to mind:
Company: employees of an organization tend to develop specific ways of thinking.
Industry: each industry has its own “rules of the game” telling you how it works.
Professional: each role in the organization has its own language, codes and habits.
Bubbles are great, because they bring likeminded people together and foster collaboration.
They are also misleading because, the stronger one identifies with a particular bubble, the more distorted one’s view on reality may become.
Common Blindspots
Blind spots are a category of cognitive limitations that distort people’s view on the world.
These are what people don’t get because of their particular viewpoint, position or perspective.
🤔
For strategic decision-making examples in not seeing new:
Technology coming because one is so invested in an existing technology.
Market: or customer type emerging because one is focused on another market.
Organizational: developments going on inside being so much focused on what’s current.
Everyone has blind spots. Because, as soon as one focuses one’s attention on something, the attention moves away from something else.
In general this is a strength. To get things done, people need to be able to focus and ignore a lot of the “noise” out there.
In strategy, though, the “noise” that may not seem relevant today, may be crucial to know toward the future.
As such, these blind spots distort strategic decision-making more than other types of decision-making.
Managing the 3Bs of bad strategic decisions
The common knowledge everybody knows but hardly anyone is applying in real life. There is another nasty question:
Biases, Bubbles & Blind Spots in Strategy (LI J.Kraaijenbrink 2025)
The bad news is that there is no way to avoid biases, bubbles and blind spots. They are part of life and we even need them to survive.
But, there are a couple of steps that you can take to reduce their impact on strategic decision-making:
Awareness, that you have them.
Make them explicit. Identify which biases, bubbles and blind spots.
Flip them. Deliberately generate alternative and opposing viewpoints.
Involve others. Diversity is key here, different people and perspectives.
🤔
While perfect strategic decisions do not exist, applying these four steps can help to significantly improve their quality.
E-3 Alignment impact on organisational systems as a whole
E-3.1 The state of information processing
It'ss difficult to see much progress in the undertanding of information processing, namely analysis for goals purposes to engineering.
As such imbalance creates a bottleneck that significantly hampers the potential benefits for the whole.
Strategic: these plannings ar going as long as risk-management schemes can cover for ill-fated turns of events.
Deterministic: unambiguous events into information, knowledge.
Stochastic: events in randomness whose range goes into information, knowledge.
The knowledge of the journey to share ❗
⚖ E-3.1.1 Executing information systems
ETL ELT recurring in new names by hypes
Data Products: A Case Against Medallion Architecture ,
The Medallion Architecture was coined as a way to describe a structured approach to organising data lakes into layers of quality.
The name is cleverly metaphorical, drawing from the idea of medals to represent increasing levels of data refinement and quality: Bronze, Silver, and Gold.
While data management seemed more doable, with different tiers having progressively different quality expectations, each benefitting from the former, it was apparent progress without real progress.
The misdirected purpose of each layer led each tier to inherently host poor data, which compounded in the next tier.
While the three-tier architecture is aesthetically pleasing and does divide the work across teams, it goes against the natural state of data and, in fact, obstructs the natural consumption patterns of business data. The product differnece
We don't waste much time before jumping into a case for the Data Product Ecosystem.
The lever we have used to build the case is a Push vs. Pull lens.
This lever enabled us to paint a very clear picture of the entire story through one end-to-end argument.
In contrast, a Data Product approach prioritises strong, self-contained foundations. Instead of relying on a hierarchical transformation model, data products are designed to be reusable and self-sufficient from the start.
Buzzwords a root cause for anti-patterns
DIB Guide: Detecting Agile BS
Agile is a buzzword of software development, and so all development projects are, almost by default, now declared to be “agile.”
The purpose of this document is to provide guidance to program executives and acquisition professionals on how to detect software projects that are really using agile development versus those that are simply waterfall or spiral development in agile clothing (“agile-scrum-fall”).
in a figure:
See right side.
AI only sees what you wrote, not what you thought.
AI only sees message response times, not friendship.
AI only sees your calendar events, not what they mean to you.
AI only sees what was implemented, not what was considered.
AI only sees the final decision, not bolts of inspiration.
AI only sees what worked before, not what will work next.
AI only sees what you did, not why you did it.
AI only sees your digital shadow, not the real you.
In case you missed it! AI isn't just transforming technology—it's redefining leadership.
As a senior leader, the spotlight is now on you: your ability to adapt, connect, and translate AI's complexity into real business value.
Here's your quick guide to thriving in this brave new world of GenAI:
Principle #1 - Get clarity on the who
Principle #2 - Get clarity on the what
Principle #3 - Be the author of meaning
Principle #4 - Think in terms of good enough
Principle #5 - Use human ratings as a proxy
Principle #6 - Try an experiment
Principle #7 - Tie it back to the business
Fallacies in Enterprise Architecture (EA)
Enterprise architecture is often confused with IT systems, which induces misguided understandings of business architecture.
The key confusion here is between architectures, supposedly stable and shared, and processes, which are meant to change and adapt to competitive environments.
But managing the dynamic alignment of assets (architecture capabilities) and supported business processes is at the core of enterprise architecture.
⚖ E-3.1.3 Egineering organisational systems
Understanding an Enterprise as viable system
These perspectives are hinting to understandable processes by processes and roles.
😉 For functioning by functionality there is the well known "DevOps".
Added to that is "folioPlan".
There are more to define in the structure.
😉 For audits evaluations there are 6 shown in the video.
The on/off boarding of resources, staff is part of floor 0-1.
There are more to define in the structure.
Functioning perspectives from the sides in a video:
The why of persistent change problems
Age of TPS, Lean, Toyota way
If the Toyota Way works in the USA, why do so many people make pilgrimages to Japan every year to see it in action?
TPS ? 80+ years old
Lean ? 35+ years old
Toyota Way ? Nearly 25 years old
Is it time to acknowledge that we need to adapt rather than simply adopt? (R.Kesterson).
An answer:
TPS wasn't designed as a universal toolkit, it evolved in a specific cultural and operational context.
After 80+ years, it's clear that simply adopting Toyota's methods doesn't guarantee success.
Lean has been around for decades, yet most Lean transformations fail outside Japan because the focus remains on tools instead of systems.
What's missing?
The Theory of Constraints (TOC).
TOC provides a universal, culture-independent approach to managing constraints and maximizing throughput.
At GM, the Throughput Improvement Process (TIP) used TOC to deliver $3 billion in documented results by adapting these principles to a North American environment, not copying Toyota's practices wholesale.
So yes, it's long past time to adapt.
Lean principles work—but only when integrated into our existing business culture using constraint-focused, profit-driven thinking.
This shift requires critical thinkers who understand that copying Toyota isn't the goal-improving performance is. (K.Kohls)
Ying and Yang - you always have both.
As manager it’s absolute important to know when you have production and when you have projects to manage.
If you use a project control to manage production – yes it will work, but you pay for it with additional planning and monitoring effort.
If you use a production control (and all agile methods are production controls) to manage projects, yes it will be much simpler to manage.
And yes, it will work, but you pay a dramatic high price: the lead time will be much longer than needed.
⚖ E-3.1.4 Architecting organisational systems
The moral of all this is to suggest that so long as the social, economic and industrial environments change slowly, the method of tenacity that our brains employ works well.
We adapt. Today, however, these environments are changing rapidly. The method of tenacity produces too slow an adaptation to cope with the revolutions that the world is undergoing in every sphere.
Unless those responsible for policy-making abandon this method, and turn to other ways of exploiting their cerebral equipment, our society will not adapt sufficiently quickly, and we shall become economically extinct.
Manifestly, the nation is moving towards this fate.
Governments are selected by the method of tenacity (the class vote); they operate by this method too (the British way of life).
Industry is managed by the method of tenacity (it was good enough for my grandfather). New thinking everywhere is blocked by the method of tenacity (this idea has not yet been tried out, let someone else make the mistakes).
Conversely, when new ideas about management and control have been discussed for a sufficiently long time, they too will be generally adopted, not by logic, but by the method of tenacity.
But this time, perhaps, it will be too late.
The second way of settling opinion without being scientific is by appeal to authority. In today's society, this mode of thinking is possibly the most important in fixing belief; it is the will of the institution.
On the face of it, this is a simple matter, with no subtle undertones; after all, people usually know when they are 'playing politics'.
The third and last category of non-science settling opinion.
An a priori argument in logic is one which begins from a set of axioms which are assumed to be true, rather than from experiences that have been undergone.
Some philosophers have argued that such axioms are innate in the mind, that they existed prior to experience (hence the name). ...
But whatever the philosophic issues, it is certainly the case that people do in real life produce all kinds of arguments which begin with unexpressed assumptions that they take to be selfevident.
It is tempting, perhaps, to say that the method of science is rational, whereas irrationality characterizes most human thinking, and yet one can be rational without being scientific.
It is better to attend to a special feature of the method of science, which might be called rigour.
Rigour is a precise formulation of method: something clear and definite, testable and repeatable.
If we want to use words carefully, in fact, the method of science is method. It follows from this that we ought not to have called the three modes of thinking already described 'methods' at all.
They are habits of thinking, and the most flattering word we can use to describe them is 'procedures'.
The challenge now being issued is of this form: 'You are a scientist (of some sort), which means that you have a mind trained to investigate natural phenomena, logically to take them apart with a whole set of highly sophisticated techniques, to reassemble them and to declare what makes them tick;
I have here an operation which is certainly a natural phenomenon; go ahead.' ... He is asking the scientist to research into operations and this, not surprisingly, is operational research.
... In short, if the whole team consists of the same sorts of people, a bias that has nothing to do with the work in hand will become evident.
So that is how and why operational research came traditionally, powerfully, and perhaps necessarily, to be interdisciplinary.
There is a ready example to hand; one that affects every kind of activity today.
This is the breakthrough in automation and computation.
Given that these new facilities and capabilities exist, it is not an exaggeration to say that no enterprise is the same as it was ten years ago. ...
It is very widely said, and fairly widely accepted, that there has been a mysterious lag in the exploitation of these technological advances.
People mumble about the slow-but-sure ways of practical evolution; they hope this explains the lag, and excuses it too.
But the reason is different in kind from this.
This organization is committed by its structure to an attempt to dress up the old system in modern clothes. ...
This is the recommended treatment for automation and computation today.
Instead, the technical breakthrough is used precisely to automate men: the payroll application, the stock-control application, the costing application, the programmed lathe, and so forth.
All these developments are good enough in themselves, but the new, higher-order strategic plan that is now possible has been largely overlooked. It is a job for operational research.
This point is underlined because there is a strong tendency in industry today to accept for OR work only the kind of scientist that one would expect to meet within the industry anyway.
In metallurgical industries, management is accustomed by now to meeting engineers, physicists, chemists and metallurgists.
They are puzzled by the arrival in OR teams of biologists and sociologists, for example.
But these are the very people who will be of most use in solving managerial problems of tactics and strategy.
They are neither stereotyped nor committed in advance to a point of view.
If a committee of responsible people is formed, they can commission an OR study of the problem before them.
This will mean that a group of scientists, which is interdisciplinary, which has access to all the facts, which has permission to investigate difficulties wherever they may lie and to pursue ramifications of the problem wherever they may lead, is engaged full-time in objective scientific pursuit of the right answer.
While this is going on, the members of the committee can happily go about their business.
The OR report is then produced to the committee, and will have roughly the following form.
The problem will be tentatively definedpossibly in terms rather different from those envisaged at the committee's first meetingand an attempt will have been made to describe the whole of the newly-discovered problem area in a systematic way.
The facts bearing on the problem will have been collected and collated, and fitted into this description.
Various scientific activities will have been undertaken with a view to examining suitable courses of action.
In the end, a number of possible answers will have been formulated which will be laid before the committee with an assessment of the probabilities, costs, risks and potential benefits of each.
In short, the precise problem requiring managerial decision will have been pinpointed, and the area of uncertainty that surrounds it narrowed as far as possible.
E-3.2 The state of technology for systems
Given that disproving convictions is typically easier than establishing alternative ones, it may be necessary to deal first with some fallacies that all too often clog the path to a sound assessment of system analyses.
Facts are not given but observed, which necessarily entails some observer, set on task if not with vested interests, and some apparatus, natural or made on purpose.
If they are to be recorded, even “pure” facts observed through the naked eyes of the innocent will have to be translated into some symbolic representation.
The knowledge of the journey to share ❗
⚖ E-3.2.1 Ideating information processes
Operations of processes, functionality for organisations
Bridging Strategy and Execution: Making Enterprise Architecture Actionable (LI Vijya Joshi 2025 )
Enterprise Architecture (EA) is often regarded as a strategic enabler that helps organizations align their technology landscape with their business objectives.
However, many organizations struggle to translate EA from theoretical frameworks into practical execution. ...
What could be the underlying factors?
Complex Frameworks: Many EA (Enterprise Architecture) models are often viewed as overly detailed, difficult, and challenging to implement.
Lack of Business Alignment: If EA does not directly address business goals, stakeholders may perceive it as merely an academic exercise.
Stakeholder Resistance: Teams may resist EA initiatives due to the belief that they introduce bureaucracy and slow down innovation.
Perception of EA as Artifacts Management: EA should guide technology and business decisions, but it is often reduced to the understanding of artifacts and policies management rather than an evolving strategy.
Operations of processes, functionality for organisations
The question in this in going back tot the origins: what is architecting and what is engineering?
The art of systems architecting (Mark W. Maier, Eberhardt Rechtin 2000 )
The continuing development of systems architecting Architecting, the planning and building of structures, is as old as human societies - and as modern as the exploration of the solar system. ...
Today's architecting must handle systems of types unknown until very recently; for example, systems that are very high quality, real-time, closed loop, reconfigurable, interactive, software-intensive, and, for all practical purposes, autonomous.
New domains like personal computers, intersatellite networks, health services, and joint service command and control are calling for new architectures - and for architects specializing in those domains.
Their needs and lessons learned are in turn leading to new architecting concepts and tools and to the acknowledgment of a new formalism, and evolving profession, called systems architecting; a combination of the principles and concepts of both systems and of architecting. ...
The nature of classical architecting changes as the project moves from phase to phase.
In the earliest stages of a project it is a structuring of an unstructured mix of dreams, hopes, needs, and technical possibilities when what is most needed has been called an inspired synthesizing of feasible technologies.
It is a time for the art of architecting.
Later on, architecting becomes an integration of, and mediation among, competing subsystems and interests - a time for rational and normative methodology.
And, finally, there comes certification to all that the system is suitable for use, when it may take all the art and science to that point to declare the system as built is complete and ready for use.
Not surprisingly, architecting is often individualistic, and the end results reflect it.
As Frederick P. Brooks put it in 1982 and Robert Spinrad stated in 1987, the greatest architectures are the product of a single mind - or of a very small, carefully structured team.
To which should be added, in all fairness: a responsible and patient client, a dedicated builder, and talented designers and engineers.
in a figure:
See right side.
The architect and the expanded waterfall. (Adapted from Rechtin 1991)
This waterfall is structural and impossible to avoid.
Only the scope is one of learning in iterations an option.
What could possible go wrong?
In architecting a new program, all the important mistakes are made in the first day. (Robert Spinrad, 1988).
Generally speaking, engineering deals almost entirely with measurables using analytic tools derived from mathematics and the hard sciences; that is, engineering is a deductive process.
Architecting deals largely with unmeasurables using nonquantitative tools and guidelines based on practical lessons learned; that is, architecting is an inductive process.
A systems approach is one that focuses on the system as a whole, particularly when making value judgments (what is required) and design decisions (what is feasible).
At the most fundamental level, systems are collections of different things which together produce results unachievable by the elements alone.
Processes, operations, serviced by platforms
what-is-platform-engineering
Platform engineering is the discipline of designing and building toolchains and workflows that enable self-service capabilities for software engineering organizations in the cloud-native era. Platform engineers provide an integrated product most often referred to as an “Internal Developer Platform” covering the operational necessities of the entire lifecycle of an application.
An Internal Developer Platform (IDP) encompasses a variety of technologies and tools, integrated in a manner that reduces cognitive load on developers while retaining essential context and underlying technologies. It helps operations structure their setup and enable developer self-service.
Platform engineering done right means providing golden paths and paved roads that match the preferred abstraction level of the individual developer, who interacts with the IDP.
This is about technology innovation, the surprise:
Promoting using lean in its classic principles.
Promoting to avoid: misaligned goals.
Avoidance of giant steps, go for agility.
Support for reliable data for informed decisions.
The problem of misalignment of organisations and technology is not at this component.
Over-Reliance on Ideation (Generating Ideas)
Ideas alone are not enough—narrative creativity emphasizes causality, strategy, and execution rather than just ideation.
Logic and Computation Cannot Fully Capture Creativity
Traditional creativity models treat creative thinking as a logical process similar to computation, relying on pattern recognition, analogy, and optimization.
Randomness Alone Does Not Drive Innovation
Narrative creativity provides structure and direction by connecting seemingly random ideas through causal storytelling.
Creativity is More Than Pattern Recognition
Simply recognizing a pattern does not guarantee originality or usefulness creativity requires a deeper understanding of how ideas work together dynamically.
Standardized Testing and School Curriculums Reduce Creativity
Students are trained to think in a convergent way (one correct answer) rather than exploring divergent and narrative driven solutions.
AI Cannot Teach Narrative Creativity
AI's output is based on statistical prediction, not true creativity.
Creativity Cannot Be Reduced to a Formula
Narrative creativity allows for more fluid, adaptive, and exploratory thinking, which cannot be captured in rigid frameworks.
True Creativity Requires Failure and Iteration
Many creativity programs do not emphasize failure, experimentation, and revision key aspects of real creative work.
We've been miseducated to see narrative as a product of creativity, when, in fact, narrative is an engine of creativity.
Narrative is what our brain uses to invent new plans, new tools, and new ways of doing.
Prototype Development: The goal of Prototype Development is to prove (or disprove) a hypothesis as quickly and cheaply as possible, such as “does this technology work?”.
Sustainable/Scalable Development: If you already know what you want to build and roughly how to build it (and have the money to fund it), this is probably the way to go.
If your application must scale quickly or is mission or safety-critical, this is your only option.
Prototypes are generally not designed to scale or stick around for a long time.
Observability, testability, and expensive in-house engineers are optional.
For many, outsourcing is their only option.
This might be the quickest and cheapest way to build working software, but with some serious limitations - it must be relatively simple and not mission/safety-critical.
These codebases also have very short shelf lives - possibly only weeks, not months.
Sustainable development starts slow.
Whether you use Waterfall or Agile, upfront work is required.
You don't need to solve every problem right away, but sustainable software must be based on good assumptions and abstractions.
It must be designed to scale on some basic level regarding load, complexity, and team size.
Best practices and observability infrastructure must be there from the beginning, or you will never catch up.
❗ The team that builds and manages this application must also be experienced and have a particular set of skills and discipline.
This isn’t software development, it’s software engineering.
Once in a while, refactors will be required to maintain team efficiency and reliability. ...
The reason for some of these refactors might be that an assumption may not pan out, or a business requirement changed, but that's fine because your software was designed to handle this.
in a figure:
See right side.
What is the opposite of alignment?
If you answered yes to any of the foloownge questions, then I would strongly consider sunsetting instead of refactoring.
Does more than 50% of your code need to be modified to clear your debt?
Is your software based on poor abstractions or incorrect data models?
Is most of your software not written using best practices?
Is your tech debt not limited to decoupled subsystems?
AI and the digital ecosystem
It's just distributed computing: Rethinking AI governance (Milton L. Mueller 2025)
The digital ecosystem is a distributed cybernetic system for the production and distribution of communication, information and control capabilities.
It is composed of four basic technical components: computing devices, networks, data and software. ...
The breakdown of the digital ecosystem into these four components is analytically useful.
From an economic standpoint, we can readily distinguish between markets for devices, data, software and networking services, and we can see how any information-communications product or service combines these elements in their design and supply chain.
From a technical standpoint, it allows us to see how specific manifestations of computing, such as platforms or machine learning applications, can be broken down into combinations of these components.
From a political or policy standpoint, it helps us to see how public control of each component requires different regulatory or governance tools.
...
The most important rationale for an ecosystem approach is that it highlights processes of evolution, co-evolution, and selection that lead to survival or extinction, growth or decline, of the socio-technical systems built around digital technologies, providing a framework for addressing the dynamics of distributed control, evolving capabilities and decentralized decision-making.
What we now call “AI” is actually a large, diverse set of machine learning applications. Machine learning applications use feedback loops from digital data (and humans) to train complex software models (an algorithm or some derivative of a neural network) to recognize inputs and produce or predict desired outputs; they are configurations of innovative software architectures, powerful processors, high-speed networks and abundant sources of digitized data.
The only thing all machine learning applications have in common is their dependence on the four elements of the digital ecosystem.
⚖ E-3.2.3 Ideating organisational processes
⚖ E-3.2.4 Ideating organisational systems
E-3.3 The state of organisations as systems
The mother of all fallacies is to think that models can describe some real-world truth.
Models necessarily reflect business and organizational concerns, expressed at a given time, and set within specific contexts.
If models were substitutes for code, or vice versa, that would make software engineering (and engineers) redundant.
Surprisingly, the illusion that the information contained in models is the same as the one contained in programs (and vice versa) has sometimes wrongly taken from the Model Driven Engineering paradigm.
The knowledge of the journey to share ❗
⚒ E-3.3.1 Descriptive analysis organisations as ViSM
Why descriptive analysis
Descriptive analysiss examines what happened in the past.
You're utilizing descriptive analytics when you examine past data sets for patterns and trends.
It functions by identifying what metrics you want to measure, collecting that data, and analyzing it.
It turns the stream of facts your business has collected into information you can act on, plan around, and measure.
Visions, missions, solutions
X-matrix: logical frameworks logframes Unlocking the Power of Logical Frameworks (LogFrames - Gavin Robers, Carla Owen 2025)
Logical Framework', or 'logframe', describes both a general approach to project or programme planning, monitoring and evaluation, and -in the form of a 'logframe matrix'- a discrete planning and monitoring tool for projects and programmes.
Logframe matrices are developed during project/programme design and appraisal stages, and are subsequently updated throughout implementation while remaining an essential resource for ex-post evaluation.
As a methodology, the 'Logical Framework Approach' (LFA) is a systematic, visual approach to designing, executing and assessing projects which encourages users to consider the relationships between available resources, planned activities, and desired changes or results.
At its core is a theory of change management which presents the logical flow of causal outcomes between achievement of a project/programme's activity targets, and the delivery of intended results.
Logframes, to this end, enable planners to establish a hierarchy of objective or result statements -i.e. a development pathway -which articulate their best understanding of how change can be achieved.
This is a monolithic idea.
A better one is using fractals in systems thinking.
The Logical Framework Approach elegantly weaves together top-down and bottom-up approaches to project management. It brings together the classical, top-down for identifying the activities in a project, with a rigorous bottom-up checking process to make sure that these activity lists are comprehensive.
Goal - what results do we expect?
Purpose - why are we doing this?
Outputs - what are the deliverables?
Activities - what will we do to deliver the outputs?
Indicators of Achievement - how will we know we've been successful?
Means of Verification - how will we check our reported results?
Risks and Assumptions - what assumptions underlie the structure of our project and what is the risk they will not prevail?
The fractal propagation of Why How What
The "Why" or purpose, identity is associated with a "How" and "What" in activities.
The next level in a logical frames is using that, but the context is changed.
"How" ➡ becomes the new "why".
"What" ➡ becomes the new "how".
new "what" to be investigated for activities.
This can only work when at a level the activities are only detailed to what is needed at that level (autonomy).
This kind of micromanagement: working on all details top-down , is not mentioned as a problematic pattern in VSM but it surely is.
Micromanagement is excessive and unhealthy need of control, where a manager closely observes and monitors the work of their subordinates, often with a lack of delegation.
It involves constant surveillance and making decisions without consultation, leading to a direct impact on the autonomy and creativity of the team.
⚒ E-3.3.2 Diagnostic analysis organisations as ViSM
Why diagnostic analysis
Diagnostic analysis helps to explain why things happened the way they did.
It's a more complex version of descriptive analytics, extending beyond what happened to why it happened.
CATWOE TASCOI
CATWOE is a mnemonic used in business analysis and problem-solving, especially within the context of Soft Systems Methodology (SSM).
It stands for:
Customers: Who are the beneficiaries or victims of the system? Who receives the output?
Actors: Who are the people involved in carrying out the activities within the system?
Transformation Process: What is the process that changes inputs into outputs?
Worldview: What is the broader context or the bigger picture into which the situation fits? How is the system perceived by the stakeholders?
Owner: Who has the authority to control or make decisions about the system?
Environmental Constraints: What are the external constraints that impact the system's operation?
By analyzing these elements, CATWOE helps in understanding different perspectives and ensures that all relevant factors are considered when defining and solving problems.
TASCOI is another mnemonic used in business analysis and problem-solving, similar to CATWOE.
It stands for:
Transformation: What is the process that changes inputs into outputs?
Actors: Who are the people involved in carrying out the activities within the system?
Suppliers: Who provides the necessary resources or inputs for the system?
Customers: Who are the beneficiaries or victims of the system? Who receives the output?
Owners: Who has the authority to control or make decisions about the system?
Interveners: Who are the people or entities that can influence the system but are not directly involved in its operation?
By analyzing these elements, TASCOI helps in understanding different perspectives and ensures that all relevant factors are considered when defining and solving problems.
Predictive analytics extends trends into the future to see possible outcomes.
This is a more complex version of data analytics because it uses probabilities for predictions instead of simply interpreting existing facts.
masterdata ViPlan PSM
Viability Planning: VIPLAN
It is a methodology developed by Raul Espejo (1988 1992) to handle messy situations by using a set of heuristic activities that guide thinking and actions.
The VIPLAN methodology emphasizes the context in which these messy situations are handled, paying particular attention to the cybernetics of the situation through the Viable System Model (VSM).
This approach involves activities such as creating rich pictures, naming elements, and structuring problem situations, which helps to navigate complex problems more effectively.
It's used in various fields to address issues that are not clearly defined and involve multiple perspectives.
Problem Structuring Methods: PSM
These are a family of approaches in operational research and systems thinking designed to tackle complex, "messy" problems that can't be clearly defined or easily solved.
These methods help to structure problems, facilitate understanding, and generate solutions by incorporating multiple perspectives and fostering participatory decision-making.
Some well-known PSMs include:
Soft Systems Methodology (SSM): Developed by Peter Checkland, this method uses rich pictures and conceptual models to explore complex situations.
Strategic Choice Approach (SCA): Developed by John Friend and Allen Hickling, this approach focuses on decision-making in uncertain and complex environments.
Strategic Options Development and Analysis (SODA): Developed by Colin Eden and Fran Ackermann, this method uses cognitive mapping to support group decision-making processes.
PSMs are particularly useful in situations where there are conflicting interests, high levels of uncertainty, and complex interdependencies.
Use case ViPlan
A ViPlan narrative Introducing the VIPLAN Methodology (with VSM) for Handling Messy Situations – Nine Lessons (Stephen Harwood 2021)
Does an explanation using a narrative.
It is presented as a heuristic and comprises a set of six activities whichact as ‘pointers’to guide thinking and actions.
The methodology’s contribution rests uponits explicit focus upon the context within which messy situations are handled.
This drawsattention to the cybernetics of the situation (Cybernetic Loop), which can be made senseof using the Viable System Model. ...
It is to the Operational Research (OR) domain that our attention turns, as it has its own well-developed tradition for the pragmatic handling of problems, in particular, ill-structuredproblems.
In contrast to design thinking withits focus upon artefacts, the OR orientation is more orientated to issues of organisation andhow that can be improved (e.g. logistics, production, social problems).
See figure.
The methodologycomprises six activities.
These are connected by two loops, the inner Cybernetic Loop and the Learning Loop.
These two loops denote, respectively, the operational and informational domains of the situation.
The operational domain is the realm of activity, interactions andgovernance, with emphasis upon the mechanisms of communications, where a mechanism is defined as an observed invariance in the dynamics of the operational domain (Harwood 2011).
Action
Thinking
1 Finding out: rich picture
2 Problem structuring: naming
4 Improve organisational conditions
3 Organisational diagnosis
6 Effect change
5 Modelling & Analyses
The figure in a table without the loops :
The informational domain is the realm of meaning, with emphasis upon the content of communications.
In practice they are intertwined.
Moreover:
the left hand side pertains to realworld engagement (action),
whilst the right side relates to the abstract world of conceptuali-sation (thinking),
Prescriptive analytics uses the data from a variety of sources — including statistics, machine learning, and data mining to identify possible future outcomes and show the best option.
Prescriptive analytics is the most advanced of the types because it provides actionable insights instead of raw data.
This methodology is how you determine what should happen, not just what could happen.
E-3.4 Processes at viable systems, internal
A knowledge perspective, systems usually distinguish between:
With a shared goal using a shared goal: planned changes, comprehensive and seamless model-based engineering applied to shared business functions.
The functional view of information can be further detailed across enterprise architecture layers int he now and future.
The knowledge of the journey to share ❗
⚒ E-3.4.1 Changing the static state, processes
The cycle of evolutionary value optimisation
Changing an existing situation requires understanding in what to change, strategy and how to change, engineering.
In simple obvious situations that is an overkill.
For complicated situations planning is not ambiguous but when it is complex it is by the unknowns.
Conformation of this for example at:
The Evo cycle is similar to SIAR but extended to 8 processes in continuous improvements.
Started with a process as input to be change into a process, clockwise, based on well known cycles.
In this the focus is on changing processes (input/ouput).
Too simple if it would be all.
Lean, Agile, The cycle of evolutionary value optimisation (EVO)
Another source is giving the continuing evolvement,
Tom Gilb Resources (A.Shalloway)
A large cost strategy (it takes a month-to-years) can be decomposed in two ways:
Task Decomposition:
This lists the tasks needed to do, before we can expect the strategy to deliver value.
No task alone delivers much value.
Tasks do not deliver improvement on their own for the planned Value Objective Goal.
Task are only components for a system where the functionality is only seen as the system as as a whole.
Sub-strategy decomposition, into steps.
This decomposes a strategy into a number of independently implementable sub-strategies:
which can be implemented in any sequence,
each of them will deliver value,
can be implemented in parallel.
These are steps; resulting in value.
A strategy can potentially be decomposed into steps, not always.
Decomposing into sub-strategies under the mentioned conditions avoids unnecessary dependencies.
❶ There is decomposing strategy. ❷ The dichotomy: functionality - functioning. ❸ Seeing the system as a whole is not only about components.
Lean, Agile, theory of constraints
There is a well known Theory of Constraints (TOC), in very simple terms: 'identify the factors, currently restricting your production flow, and ease them, or remove them, to improve productivity. '
This has always seemed to me to be ‘too simple’, in relation to my working environment.
So we, (Chris Dale, Al S, Steve T and I) decided that it might be useful to articulate these more-complex ideas, and see if we end up with something useful.
Looking back at his book, I conclude that we have substantially enriched the related ideas.
❹ Only partial requirements knowledge when starting. ❺ Assuming a level of autononmy for what is to be solved.
The 'Penta' Model insted of "iron triangles"
Problems with the Iron Triangle.
Poor definitions: “Quality” is not defined at all. It is not referred to in the plural (qualities like Usability, Security, Reliability…. and many more).
Insufficient Scope: One example: The Budget, or Financial Resources, is always defined and assumed to be the financial cost of a project until successful delivery.
It never even hints at the life-cycle maintenance costs (of great variety and magnitude).
This is irresponsible narrow-mindedness of the most dangerous kind. These later costs are often 10x or more, greater than initial capital costs.
And we ‘all' know that if you do not engineer the system up front, to have low maintenance costs, you can ‘save' on initial costs, and get disastrous maintenance costs, and failure rates, as a direct result.
Totally lacking critical factors: This is partly bove (maintenance costs, multiple qualities).
But the project management process is critical, and missing.
The notion of balancing multiple Stakeholder values, against all long-term and short-term resources [CE) is missing.
The notion of a prioritization policy is missing.
Not surprisingly, in PMBOK 4.0 the PMI came up with a suggestion of have six dimensions, with risk making it into the equation, with the “Project Management Star”.
Of the six risks: Schedule (time), Budget (cost), Scope and Quality, Risk. Resources in PMbok the time element is not present in PENTA.
🚧
Penta concept Co-invented by Al Shalloway and Tom Gilb, 2022 PENTA: Purposely Efficient Nodes for Top Attributes.
The PENTA is a simplified model of 5 basic conflicting forces in any system, which can be adjusted to give a more optimum balance.
Scope: is the specified set of stakeholder and system functions (what it must do) and constraints (what it must not do). Scope draws a border around a given system.
Values: is the specified set of stakeholder values ('wants', 'needs', 'wishes', 'visions') and system qualities, including system performance attributes ('potential values' for stakeholders).
Efficiency: is 'effectiveness-to-costs ratio'. Effectiveness includes all stakeholder-values actually delivered. The costs are life-cycle costs, not just ‘capital’ costs. This is a view outside the black box of Designs.
Resources: are any critical and prioritized, set of limited resources for the system lifecycle, such as time, money, people, space.
Designs: are any types of ‘implementable ideas’ (designs, strategies, architecture, solutions) which we use, in order to deliver a ‘best available’ balanced delivery of Values,Efficiency, Resources, and Scope, the other 4 Quints.
Imperfect: The Penta model is never complete, updated or fully detailed. It can be simplified and summarized.
It can view selected components, that are useful for consideration.
Obviously this model is just another model to help elaborate, evaluate and discuss all aspects of a project and it’s items.
The last piece of a puzzle is how to practical manage change.
Risk driven by focussing on 5 areas to solve
The question of when it is delivered
A 3d representation, dodecahedron: six areas top, botom, axis.
Having in the SIMF model for Jabes:
Only an orientation for tasks and roles in flows,
Two planes for the operations in the now and two for the future,
a representation of an extended cube, six areas three axis where two are doubled (five),
the approach of dodecahedron offers enough variety for changes to functionality and functioning.
❻ Project management (PM) activities into a regular dodecahedron.
⚒ E-3.4.3 Plan, prepare the future of states & processes
⚒ E-3.4.4 Enabling planned top-down changes with insight
Silent killer of change
The Silent Killer of Continuous Improvement (LI A.Ozel 2025)
Why Does Your OpEx Program Fails?
In boardrooms across the globe, leaders are scratching their heads, wondering why their meticulously planned Continuous Improvement initiatives are failing to deliver the promised results. The truth? It's not about the tools or techniques.
The real culprit often lurks in the shadows of change management.
Let's dive deep into why Operational Excellence programs falter and how to breathe new life into them using John Kotter's renowned 8-step change model:
Lack of Urgency:
Without a compelling reason for change, teams lose momentum. Create a sense of urgency by clearly communicating the need for improvement.
Weak Guiding Coalition:
OpEX programs require strong leadership support. Build a diverse team of influencers to champion the change.
Unclear Vision:
Without a clear direction, efforts become scattered. Develop and communicate a concise vision for your OpExprogram.
Poor Communication:
Change can't stick if people don't understand it. Overcommunicate your vision through multiple channels.
Failure to Remove Obstacles:
Identify and address barriers that hinder progress, whether they're processes, structures, or mindsets.
Neglecting Short-Term Wins:
Celebrate small victories to maintain momentum. Recognize and reward early successes.
Declaring Victory Too Soon:
OpEx is ongoing. Don't let up – keep pushing for deeper changes and broader adoption.
Not Anchoring Changes in Culture:
Ensure new practices become "the way we do things around here" by integrating them into your organizational DNA.
Risk-Based Thinking: RBT
Mastering Risk-Based Thinking in Quality Management (F.M.Boles 2004)
In today's dynamic business environment, the ability to anticipate and manage risks is critical to sustaining excellence and delivering quality products.
Risk-based thinking (RBT) is a proactive approach embedded in modern quality systems, particularly ISO 9001:2015.
How to Use Risk-Based Thinking:
Identify Risks: Analyze processes, stakeholders, and market trends.
Evaluate Impact: Assess the likelihood and consequences of identified risks.
Implement Mitigation Measures: Develop control plans to minimize risks.
Monitor and Review: Continuously track risk indicators and improve strategies.
in a figure:
See right side.
E-3.5 Services at viable systems, external
Given that services are by nature shared and symbolic, they can only be defined between systems.
The seamless integration of enterprise systems into digital organisational, business environments calls for a resetting of:
value chains in regard to enterprise architectures,
more specifically supporting assets.
Planning: the core of a systems survival.
Viable systems learning capabilities: planning should be defined by knowledge from the content in algedonic events.
The knowledge of the journey to share ❗
🎭 E-3.5.1 Promises for known products (goods, services)
DevOps double cycle
The DevOps double cycle model "DevOps" only covers two interest areas.
This a very limited alignment of all possible relevant interaction.
When the Operational value stream is on one plane the enginering buidling of products flows is on another plane.
A vertical double cycle is better than the usual horizontal.
The "DevOps" model:
Development is not about coding but: architecting engineering creating products, flows.
Operations is executing the flow mainly autonoom and initiates proactive improvements.
Another double cycle model "PortfolioPlan" are the dichotomous counterparts for "DevOps".
The "DevOps transitions are needing alignment wit the counterpart vice versa.
The "PortfolioPlan" gaps:
🕳 Product management the accountability for decisions what to engineer and build.
🕳 Financial budgets the financial enablement to do work for prodcucts flows.
🎭 E-3.5.2 Deliveries of known products (goods, services)
systems by micro-level
Analysing systems is usually done by looking at a complex macro-level situation and trying to understand that by simplifying.
There is that continuous duality change for information vs process.
Starting at a micro level, anatomical:
segmented areas, materialised information.
Processing units to do logical transformations.
Primary and secundory areas - processes.
The two components in the processing units have each a full classification in six attributes for safe usage.
The operational information flow
The operational value stream in a Pull and Push in all steps looks complex but is simple when understood.
The dichotomy of materialised information that is technical data and the transformations by processing is a continuously change in perspective.
Both are equal important in the flow.
Architecting cybersecurity safety:
Information safety is tailored to tasks/roles for a person.
Transformation safety is tailored to the skills of a person.
They have a different floorplan layout that should be combined to each other
There are four knowledge area's each of them dedicated for the process stage in the value stream.
The anatomy and physiology details are a next chapter. In a video:
🎭 E-3.5.3 Promises for unknown products (goods, services)
Frontend double cycle
The DevOps double cycle model "DevOps" only covers two interest areas.
This a very limited alignment of all possible relevant interaction.
When the Operational value stream is on one plane the enginering buidling of products flows is on another plane.
A vertical double cycle is better than the usual horizontal.
The "DevOps" model:
Development is not about coding but: architecting engineering creating products, flows.
Operations is executing the flow mainly autonoom and initiates proactive improvements.
Another double cycle model "PortfolioPlan" are the dichotomous counterparts for "DevOps".
The "DevOps transitions are needing alignment wit the counterpart vice versa.
The "PortfolioPlan" gaps:
🕳 Product management the accountability for decisions what to engineer and build.
🕳 Financial budgets the financial enablement to do work for prodcucts flows.
🎭 E-3.5.4 Deliveries of created products (goods, services)
The "DevOps" model:
Development is not about coding but: architecting engineering creating products, flows.
Operations is executing the flow mainly autonoom and initiates proactive improvements.
The operational information flow
The context interacton chain
Having the ViSM model aligned to Siar and all others there is that resulting 3d model with many fractals.
All four fractals seen from a plane should be a fractal on their own.
It is not a single interaction there are possible many chained. The chain is having two directions and has connections points.
At each plan a circular process either clockwise or counterclockwise is possible
These requirements result in a generic structure.
Organisation
Frontend
Technology
Backend
4
Plan (budgets)
1
community, integration
3
OPS
5
5
3
Buyer
1
economy, adaption
4
Assets
1
culture, latency
4
Pro-Vision
5
3
Motive
3
Portfolio
5
4
Dev
1
polity, goal attainment
4
Plan (budgets)
1
community, integration
3
OPS
5
5
3
Buyer
1
economy, adaption
4
Assets
1
culture, latency
4
Pro-Vision
5
3
Motive
3
Portfolio
5
4
Dev
1
polity, goal attainment
Although Agile is now to in end in the sizzle, the question is woudl it fit in this?
Individuals and interactions over processes and tools ➡ culture, latency
Working software over comprehensive documentation ➡ community, integration
Customer collaboration over contract negotiation ➡ polity, goal attainment
Responding to change over following a plan ➡ economy, adaption
This could accidental or intentional, it doesn't really matter. Increasing your Agility: An interview with Dave Thomas (2015)
How attempts for getting more efficient have developed in time it is logical.
The intent behind the Snowbird meeting was simply to explore what commonalities we could find between the different ways the 17 participants created software.
We discovered that, although the day-to-day executions were very different, we all shared a set of values—things that were important to us.
E-3.6 Retroperspective of the KA journey
Knowledge Assurance (KA) is a framework or set of practices designed to ensure:
accuracy,
reliability and
effectiveness
of knowledge within an organization.
It involves the systematic management of knowledge to maintain and enhance its quality.
The challenge in this: It are not the results being important, it is the journey of learning, understanding.
The knowledge of the journey to share ❗
🚧 E-3.6.1 The why of learning organisations
Knowledge Assurance KA
Key components of KA include:
Knowledge Creation: Developing new insights, ideas, solutions by research & innovation.
Knowledge Validation: Verifying the accuracy and reliability through peer reviews, audits and validations, testing.
Knowledge Dissemination: Effectively sharing and distributing knowledge within the organization ensuring it reaches the right people.
Knowledge Maintenance: Updating & refining knowledge keep it relevant and up-to-date.
Knowledge Utilization: Applying effectively for informed decisions & improved processes.
By implementing Knowledge Assurance, organizations can ensure that their knowledge assets are trustworthy, up-to-date, and valuable for achieving their goals.
Autopoiesis, rise - survival of the organisation
Luhmann’s theory of autopoietic social systems (David Seidl 2004)
The central concept around which the theory of social systems as developed by the later Niklas Luhmann is built is the concept of autopoiesis, originally developed by the two Chilean biologists Humberto Maturana and Francisco Varela.
Autopoiesis (< Greek: autos = self, poiein = to produce) means self-(re)production. Autopoietic systems thus are systems that reproduce themselves from within themselves, as for example a plant reproduces its own cells with its own cells.
Luhmann argued that the basic idea of autopoiesis applied not only to biological but also to a large number of non-biological systems. He thus appropriated the originally biological concept, modified it and applied it to the social domain.
In a similar way as biological systems social systems were thus conceptualised as systems that reproduced their own elements on the basis of it own elements.
Living systems are cognitive systems, and living as a process is a process of cognition. (Maturana and Varela 1980: 13)
On the level of its operations the autopoietic system does not receive any inputs from the environment but only perturbations (or irritations), which then might trigger internal operations in the system.
In other words, external events may trigger internal processes but they cannot determine those processes.
Luhmann (2000: 401) in this sense speaks of a “trigger-causality” [Auslösekausalität] instead of a “performance-causality” [Durchgriffskausalität].
In this sense the operations of an autopoietic system are defined as its cognitions; life and cognition are one and the same.
Hence, everything that has been said about life applies equally to cognition: cognition is a self-referential, autopoietic process.
This stance is generally known as Radical Constructivism (also: Operative Constructivism) expressing the idea that all cognitions (ideas) are constructs of the respective cognitive system and do not in any way reflect any kind of external reality. ...
According to Luhmann we can distinguish three types of social systems: society, face-to-face interaction and organisation.
All three systems are social systems insofar as they reproduce themselves on the basis of communications.
They are however different types of social systems insofar as they reproduce different types of communications.
autostatic, which relates to the concept of resisting change or maintaining stability automatically.
This term is often used in the context of systems or mechanisms that self-regulate to resist changes and maintain their current state.
homeostasis, is any self-regulating process by which an organism tends to maintain stability while adjusting to conditions that are best for its survival.
🚧 E-3.6.2 The what of learning organisations
Strategic themes
There should be an alignment in the components of strategy (system-4) and execction (system-3, sytem-1) in the product (services goods) domain. Why do strategic themes matter? (LI Andrew Constable)
Strategic themes are the backbone of an effective organisational strategy.
Focus: They drive alignment across the organization, concentrating efforts on high-impact areas.
Clarity: They simplify communication by organizing complex strategies into understandable areas.
Accountability: Each theme has clear ownership, ensuring responsibility for execution.
Alignment: Objectives and initiatives stay connected to overarching goals.
I recommend around three strategic themes. More than that can dilute focus, while fewer may not capture the full scope of the strategy.
When well-defined, strategic themes transform ambitious goals into actionable pathways, ensuring everyone in the organisation moves in the same direction.
Why strategy vs execution?
There should be an alignment in the components of strategy (system-4) and exaction (system-3, system-1) in the organisational domain. Strategy vs. Execution: A Galactic Struggle, Episode #25 (LI Randy Kesterson)
If your strategy deployment and execution plan feels like a choose-your-own-adventure novel with no ending, it might be time for a LEAN reality check.
The Strategy Guy is lost in a sea of PowerPoint slides, and the Lean/CI Leader is left wondering what strategy actually is.
Annual Strategic Planning: Where we launch 50+ initiatives, finish none, and then proudly start over!
Kaizen Mania: We optimize the breakroom coffee machine, but forget to fix the production line.
Strategy vs. Execution: Do they even know each other?
in a figure:
See right side.
What is the opposite of alignment?
Technical debt, missing alignment!
There should be an alignment in the components of strategy (system-4) and exaction (system-3, system-1) in the operational domain. Don’t get caught in refactor hell
I believe that most managers are aware of technical debt and that it is bad, but fail to understand its true impact.
This misunderstanding can lead to some very costly mistakes.
One of those mistakes is keeping a codebase around past its expiration date.
Yes, codebases have expiration dates.
Tech debt usually means that your code, abstractions, concepts, or data models no longer make the most logical sense.
If you have any outdated dependencies, areas of friction, or lack of testability - that also counts.
Was triggered by:
Underspending in fixing quality (LI Marc H Weiner 20235)
Tech debt isn’t an exceptional problem; it’s ordinary. Just like real infrastructure, it needs ongoing investment—not just an occasional “cleanup sprint” when things start breaking.
But when companies ignore it for too long, the system reaches what I call the Software Event Horizon: the point where the cost of making any change is so high that even the simplest features take forever, or result in bugs. And refactoring is impossible. There's no going back.
🚧 E-3.6.3 The how of learning organisations, Jabes Jabsa
the goal of ethical compliant processes
Several perspectives for compliant processes.
Safe environment Risk reducing: lost service, data breaches.
Information quality Avoiding: failures, wrong results.
Impact on persons Explainable personal data usage is mandatory.
Some of the details for compliant processes are organizational missions, others are legal regulations.
Structuring ideas and able to explain new idea´s to other persons is difficult. Using open source knowledge frameworks is helpful.
It must have:
all relevant information
simple to understand
left out what is not relevant
This is time consuming and possible a mission impossible.
🚧 E-3.6.4 Change constraints for learning organisations
System dynamics, Markov, Petri
What is system dynamics
Systems thinking is a way to describe and understand the causality and interrelations between variables within a system.
System Dynamics quantifies the impact of those interactions.
Systems thinking is a causality-driven, holistic approach to describing the interactive relationships between components inside a system as well as influences from outside the system. Its background emerges from various fields including philosophy, sociology, organizational theory, and feedback thought.
System Dynamics complements systems thinking by quantifying interactions and develops a time-dependent view of how the system behaves.
The approach focuses on building computer models that represent and simulate complex problems in which behavior changes.
These models bring to light less visible relationships, dynamic complexity, delays, and unintended consequences of interactions.
What is system dynamics
System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, table functions and time delays. Markov chains
In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC).
A continuous-time process is called a continuous-time Markov chain (CTMC).
Markov processes are named in honor of the Russian mathematician Andrey Markov. dependability models based on petri nets and markov chains
FPGA-based (Field Programmable Gate Arrays) designs are sensitive to many effects that can change their programmed function.
These changes are most unwelcome when designs are used in safety-critical applications, where the material loss or mortality can be caused because of their failure.
learn from others how to manage it better
The Uniqueness Trap (Bent Flyvbjerg, Alexander Budzier, M.D. Christodoulou and M. Zottoli 20235)"
Your project isn't one of a kind, and that's a good thing because it means you can learn from others how to manage it better.
Managers are indeed highly prone to believing that their projects are one of a kind even though few, if any, actually are.
This causes them to think they have nothing to learn from other projects.
Most important, it leads them to underestimate risk and overestimate opportunity and thus make poor decisions.
Specifically, the more distinctive managers consider a project to be, the more likely it is to exceed its budget and the more likely the overrun is to be considerable.
That led us to the conclusion that improving project performance has less to do with managing the activities involved and more to do with addressing how project managers make decisions.
The cure for uniqueness bias is to always assume that someone, somewhere has undertaken a project like yours, adopting what’s called an “outside view.”
If you can’t find any direct analogues, break the project down into modules and subprocesses, which may then prove comparable across projects.
Once you have found your analogues, be careful about how you process the information you glean from them. Even when taking an outside perspective, project managers making forecasts and decisions can fall prey to other biases that cause them to discount the risks attached.
Certainty has a way of preserving the world as it is. Those who are absolutely convinced of how the rightness of existing structures tend to defend them, even when those structures are flawed or harmful.
Certainty reinforces tradition, protects institutions over humans, and safeguards rules, even when those rules no longer serve a just purpose.
Certainty has a way of preserving the world as it is. Those who are absolutely convinced of how the rightness of existing structures tend to defend them, even when those structures are flawed or harmful.
Certainty reinforces tradition, protects institutions over humans, and safeguards rules, even when those rules no longer serve a just purpose.
Our anger in reaction to the world events of the moment is an act of defiance.
This is an anger of disruption for change.
True kindness requires the courage to challenge systems that do not serve our communities and their humanity.
It requires seeing beyond what is socially acceptable to what is fundamentally just.
Believe in the anger you feel when you see injustices.
Because there are doubts and uncertainties expanding your human-ness there.
And there is kindness waiting to shine through that pain.
There is loneliness in not finding the best answers when you refuse to accept things as they are.
There is exhaustion in constantly questioning, resisting, and pushing against forces larger than oneself.
Besides, the world does not always feel kind to those who challenge its structures.
Compliance is rewarded way more than intentional critique.
there is a bridge between your kindness and the anger we are experiencing now.
Kindness, at its core, is not an act of passive niceness but a radical openness to re-evaluating what we think we know when needed.
It is a willingness to live in the discomfort of uncertainty for the sake of our humanity.
I am not denying the burden this anger places on those who bear it.
This is not easy to carry, I agree.
We won’t realize then, that anger, while painful, is necessary. It sparks questioning that makes room for progress.
Aside the global explanation of thoughts there is an index contents. 
The communication with stakeholders is often forgotten or ignored.
Working on questions what kind of information processing is a difficult one without needed insight without communication. 
Leaders need to avoid micromanaging and stay connected to what is happening in order to spot a change in context. 
Conservative running as-is (vertical) 
bisl in three minutes
Guidance provided in Business Relationship Management Institute´s BRMBoK© with the ASL BiSL Foundation´s Business Information Services Library (BiSL©). 
level 1:
🤔 A mistake , adding the loops was not done fully, there should have been four of them.
The strategic loop should have been above the top and the tactical at what is shown as strategical.
What would got solved?
It´s an accepted convention that the monolithic data platform hosts and owns the data that logically belong to different domains.
Instead of flowing the data from domains into a centrally owned data lake or platform, domains need to host and serve their domain datasets in an easily consumable way. 
Indeed, this model was once characterized as being inherently conservative, because it allegedly assumed the stability and functionality of societal structures. 
Using ideas from cybernetics and systems theory, Parsons orders the four components of the system hierarchically, indicated by the zigzag sequence of directed links shown in bold in the figure. 
in a figure: 
The Logical Framework Approach elegantly weaves together top-down and bottom-up approaches to project management. It brings together the classical, top-down for identifying the activities in a project, with a rigorous bottom-up checking process to make sure that these activity lists are comprehensive.
This kind of micromanagement: working on all details top-down , is not mentioned as a problematic pattern in VSM but it surely is. 
Task Decomposition: 
Sub-strategy decomposition, into steps.
There is a well known Theory of Constraints (TOC), in very simple terms: 'identify the factors, currently restricting your production flow, and ease them, or remove them, to improve productivity. '
This has always seemed to me to be ‘too simple’, in relation to my working environment. 
Penta concept Co-invented by Al Shalloway and Tom Gilb, 2022 PENTA: Purposely Efficient Nodes for Top Attributes. 
