Sys6x6Logic

Reference	Topic	Squad
Intro	Explaining - optimizing, processing operations.	01.01
Why BPM	Why Engineering Organisations.	02.01
initiator-executor	The process circle in engineering organisations.	03.01
Process Mining	Process Mining.	04.01
cycle BPM	Elementary process cycle.	05.01
What next	Inventing changing Operations.	06.00
	Combined pages as single topic.	06.02

🤔 🎭	-💰-	⚙ ⌛
customer		delivery
requestor		performer
request		provide
initiator		executor
Pull		Push
service request	----	Service provider

AL-3.1

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⟲ AL-3.1.1 Info

butics

A case study for organizing the organizing is a source for good example for using Zarf and Jabes. Although nowhere Zachman is mentioned there is a remarkable similarity by the 4 disciples Steer Build Manage Drive in 6 levels. It is case study by observing what and how it is done, there is no attempt to align that to any scientific theory. Science for Policy Handbook (EU JRC july 2020).
Communities of Practice Playbook CoP (EU JRC july 2020) that is associated with a short (100 pages) handbook Science Communities of Practice Playbook light (EU JRC july 2020)
Communities of practice success wheel

In a figure:
see right side.

Scientists and policymakers need to work closer together to deliver progress for our societies, economies and our planet. For the last 60 years, the Joint Research Centre (JRC) has been working at this interface for the European Union. This handbook collects what we have learnt about the science–policy environment and shares our vision, best practice and innovative ideas to deliver impact for our societies.
The fact that we are an organisation working not only in a policy context but also inside a policymaking institution is an important lens through which the advice in this book has to be interpreted. We support policymaking at all stages of the policy cycle and often in direct collaboration with policy departments. While we take a policy-neutral stance, our mandate is to produce science relevant for policy and generating insights needed to create policy options.
Policymaking is a complex process that includes both an analytical and a normative dimension. This leads to a twofold problem. On the one hand, scientists and policymakers will define problems differently: one as something to solve technically, the other as a much more social process of negotiating solutions that have majority support. Therefore, their thinking and problem-solving strategies will be different, given that policymakers need to find the best knowledge and make sure it leads to a consensus or at least a majority on values, i.e., is both technically and politically feasible.

butics

It is case study by observing what and how it is done, there is no attempt to align that to any scientific theory. Science for Policy Handbook (EU JRC july 2020).
The isues: Chapter 1 page . Chapter 2 page 18 PNS Post Normal Science. In the social process of science, quality is achieved, not by a delusory pursuit of certainty, but by the skilled management of its uncertainties, involving all who have a concern for the issue.
Cocreation Chapter 3 page 22 lighthouse In the past, knowledge was produced by universities, mainly in North America and parts of Europe.
At the same time, the old, trusted, established knowledge outlets have lost or risk losing at least part of their authority. Just as in the media landscape, the old reference points are dissolving. ... Unlike the scientific method, which is based on the formation of hypotheses, these methods are inductive, based on picking out patterns and correlations in the data. Much of this work may not be reproducible, as the scientific method requires, because the data on which it is based are constantly evolving. ... The border line should not be made between "scientific" and "nonscientific" knowledge but between false and real facts. If science was able to embrace a role of a verifier of quality of arguments, it would strengthen its legitimacy to hold a privileged position in the policymaking process.
Chapter 3 page 24 Due to the growing complexity and importance of decisions, it seems to be wiser to put in place advisory groups or structures, where the constant interactions between scientists and policymakers are allowed. ... one type of organisation has a particularly important role to play, that is, "boundary organisations", i.e., those which sit at the intersection of science or knowledge and policy. ... ‘Wicked challenges’ rarely have definitive solutions, so scientists and policymakers have to stay in constant and ongoing dialogue with one another. This constant interaction is a key to mutual understanding, building trust and overcoming of the differences between scientific and policymaking cycles.
Chapter 3 page 26 Managing the existing knowledge, verifying it and making sense for different purposes should become an additional dimension of every knowledge organisation. ... Making sense of knowledge is a challenge for the whole society. If almost all players are now producing large amounts of data and knowledge, they are also all struggling to cope with the deluge that is being created.
Chapter 4 page 41 Skills co-creation In recommending eight skills, we argue that "?pure scientists" and "?professional politicians" cannot do this job alone. Scientists need "?knowledge brokers" and science advisors with the skills to increase policymakers' demand for evidence. Policymakers need help to understand and explain the evidence and its implications. Brokers are essential: scientists with a feel for policy and policymakers understanding how to manage science and scientists.
Chapter 5 page 51 Achieving policy impact Timing is a crucial driver of policymaking. Being on time and respecting the deadlines is key if one wants to receive any place in the policymaking process. However, figuring out the right timing and the expectations is not always easy or in control of the scientists. When policymakers are under time pressure to gather evidence, the strength of the existing relationship plays a role in whether scientists will have a window of opportunity.
Chapter 6 page 53 Co-creation (suggestions - ideas) Instead of attempting to "translate" research results at the end of a project, systemic cocreation of research questions early in the process helps mitigate that problem.
... Using cocreation in deciding what to study brings two main advantages. It improves the relevance of the science, both through microlevel discussions on specific questions and macrolevel strategic planning of broader research topics.
... Exchanging only dry assignments and working in isolation without a proper debate on what to study will not help either side achieve their goals. Chapter 6 page 56 It is also impossible to objectively verify whether the solution to them is "true" or "false", as in the case of science (where a hypothesis can be objectively verified). Defining a problem dictates the realm of solutions, and as such is a nonneutral exercise. Moreover, there are no criteria that enable one to prove that all solutions to a wicked problem have been identified and considered (p. 164), which makes the process imperfect by nature.
... Policymakers still need to answer the analytical parts ? to figure out what the technical possibilities to reach an objective are ? but they simultaneously need to reconcile conflicting values and objectives and also ensure that the course of action is approved by a majority, including of the public.
... Framing a policy problem is a political decision and an expression of power. However, it is not always a fully conscious choice. Particular definitions of a problem can seem obvious due to unconscious cognitive shortcuts, such as availability bias, confirmation bias and group reinforcement, among others (Hallsworth et al., 2018).
... Sometimes it means that the questions initially posed change radically because only through dialogue both sides realise the true extent of the knowledge gaps and evidence needed to bridge them. As policy questions and research questions are not interchangeable, there is a need for an iterative process of translation from policy questions to research questions. This implies active engagement, openness and attention to detail. It has to be done in a cocreative way, including opening up and ?questioning the question?.
Chapter 6 page 56 (scope limiting top-down) In the JRC, this approach is complemented by a top-down process, the Work Programme. Designed to provide an overview of the JRC?s annual research activity, its coordination process provides additional benefits. Indeed, the Work Programme goes through a set of formal processes throughout its yearly production cycle, which allows policy considerations to input into research planning. For instance, the Work Programme undergoes ex ante and ex post evaluations, whereby independent panels scrutinise respectively planned work and delivered outcome to check their policy relevance. At the ex ante stage, panels deliver opinions to senior staff on such relevance. These opinions trigger strategic discussion at the top level of the organisation and can lead to significant modifications.
Chapter 6 page 62 (other stakeholders) Done well, through an interaction and with the inclusion of citizens? and stakeholders? perspectives, these processes bring an increase in quality of science for policy in a complex world. A broader consensus, as opposed to singular decision of either scientists or policymakers, increases the legitimacy and relevance of the knowledge being produced.
Chapter 7 page 63 the wheel Offers a self-assessment tool for a community ? the CoP Success Wheel ? encompassing eight success factors. For a community to succeed, it is important to create the right environment which includes setting strategic goals and objectives, real governance and support from senior management, empowered community leaders, investment in the community management role and sound and user-friendly digital environments to host conversations, exchanges and repositories of knowledge.
To tap into a crowd?s wisdom and avoid tapping into a crowd?s madness (Bonabeau, 2009), it is important to get the mechanisms for harnessing the power of collective intelligence right. When the MIT Centre for Collective Intelligence looked into hundreds of examples of webenabled collective intelligence, researchers identified a small set of building blocks as the ?genes? of collective intelligence systems (see Fig. 1). Two dualties what / How-where and who-when / why.
Goal 1: Ensure coherent and coordinated mapping activities across JRC and EC 4 behaviours.
Behaviour 1 When working on a new map, members consult the CoP reference space to check for geographical correctness, mapping conventions and links to key resources (e.g., GISCO, ESTAT, etc.). Behaviour 2 Members share/point to their maps in the community. Behaviour 3 Facilitator creates reference library in the community, which covers geographical correctness, mapping conventions and links to key resources. Behaviour 4 Geographic information system users from policy departments join the community Goal 2: Improved efficiency and effectiveness of mapping 4 behaviours. Behaviour 1 Members post their questions and challenges.Facilitator responds to questions on geographical correctness by pointing to key resources and by inviting other members to respond. Behaviour 2 Members inform peers about their upcoming mapping activities and projects. Behaviour 3 Members attend regular face-to-face meetings to discuss topics of their interest. Behaviour 4 Members identify shared training needs.
Chapter 7 page 73 digital tools Digital tools hosting CoPs do not need to be complex, normally requiring functions such as forum, document libraries, expertise finder, teleconferencing and ideally video conferencing and online places where members can edit documents and discuss them. For example, the European Commission uses SharePoint, Yammer and Jive software for hosting internal CoPs and Confluence, Wikis and Drupal for CoPs involving members from outside the organisation. One can check some communities run with external partners on the JRC's Science Hub.
No dedciated tool metatdata oreiented for supporting the knowledge.
Chapter 7 page 73 figure 4 Four parties: Researchers (creating) , Community management (administration), Adivisory policy makers (processes), Decision making policy makers (strcuture)
Chapter 8 page 84 citizen engagement. Four activities: Adaption, Implementation, Application, Preparation and Outcomes, Methodology, Context, participants
There is a great deal of citizen engagement already out there, organised by other institutions and organisations, such as academia and NGOs but also grassroots initiatives in the form of activism, ?community? action, making and hacking. Chapter 8 page 86 citizen engagement. Identifying Who Should Be Involved Who are the citizens? Those concerned, the ?getroffen? (sufferers), stakeholders, voiceless and disempowered, community members? Chapter 9 page 99 (information age - Me:disappointing assumptions expectations) The interest in AI is rather recent even if the underlying methods date back to the 1950s. This new found interest in AI, and ML in particular, results from the convergence of three developments: (i) increased access to cheap computing storage and processing capabilities, (ii) increased availability of big data on which to train algorithms and address previously hard problems such as object recognition from digital images and machine translation and (iii) improved algorithms and increased access to specialised open source ML software libraries that make the creation and testing of ML algorithms easier.
Chapter 10 page 106 (the risk in trustworthiness) Nothing justifies playing with data to fit any particular interest in the policymaking ecosystem. It can happen that scientists may feel the need to artificially fit the scientific facts to policy agendas, to preserve a long-established relationship, gain new influence areas, and secure more impact or funding. The pressure can even directly come from policymakers. This is both ethically inadmissible and diminishes scientists? credibility given that policies and their background science are scrutinised by other actors and observers in the process.
There is a growing understanding that facts are established through a social, culturally-dependent process. Disrespecting the distinction between advocating for scientific evidence and promoting oneself and one's convictions damages the trust people have in reputable scientific expertise.
With many different stakeholders in the field, scientists and their organisations should learn a little bit about the main issues, opinions and constraints of each group in order to be able to identify them but also to better understand their positions and possibly benefit from their insights.
Backbone tools Chapter 11 page 119 Systems thinking It is necessary to change the perspective from the parts of the system to the connections between the parts and to the system as a whole, i.e., from reductionism to holism. Reductionism and holism are complementary. Many real-world systems show nonlinear interactions and are rarely in equilibrium.
They show phenomena such as adaptation (e.g., markets, innovation), the formation of hierarchies (e.g., public and private organisations, discussions), self-organisation (e.g., collective decisions, public opinion formation), robustness and fault tolerance (e.g., many biological systems), resilience (e.g., ecosystems, some technological ?ecosystems?), lack of central control (e.g., collective decisions in social insects, bird flocks, fish swarms, task allocation in computer systems) and other features well known from complexity science (CS). None of theseproperties can be understood on the basis of the properties of the parts of the system alone. Chapter 12 page 130 insight to foresight (predictive prescriptive) Foresight helps creating anticipatory governance capacity and awareness about future-related issues. It enables policymakers to make prudent long-term decisions, helps revealing the need for policy action and channel evidence into policy discussions.
A question then arises about how to be authoritative when acting to shape a future that does not exist yet. This is why public policy should take into account consciously a future time dimension even if its focus is on immediate public concerns. ...
Even if, by nature, VUCA elements are unpredictable, a well-prepared and resourced foresight approach can provide surprising levels of understanding about what could happen and how to deal with it (Da Costa et al., 2008). Short-sightedness, narrow-mindedness, simplistic lines of thought, linear thinking and lack of understanding of the past are probably the worst enemies of policymakers in a VUCA world. ...
While evidence is available for past and present developments (statistical data, scientific reports, etc.), there is no such thing as facts about the future as the future does not exist. The challenge is therefore to generate meaningful (and useful) knowledge about the future by interpreting facts about current developments and use imagination about the future (Duvka, 2020; EFFLA, no year). ...
Chapter 13 page 146 introducing design thinking byt simi to systems thinnking Design-based approaches are systemic in nature, explore complex context using visual methods, bring several disciplines together to make sense of the problem space and formulate proposals that focus on the quality of the experience from the point of view of the final beneficiaries. ...
The word ‘design’ has accumulated several different uses and its meanings shift according to the context in which it is used. ...
Chapter 14 page 153 measuring monitoring the policy process suffering in prejudice and pride When a research organisation covers a wide range of topics and policy areas, capturing the impact of its activities presents a methodological challenge due to the multitude of different impact pathways which are not always easily traceable. In our experience, this can be overcome by employing a range of tools to measure impact, starting with documenting impact pathways, building impact inventories and case studies and performing portfolio analyses. ...
Public research organisations need to demonstrate the impact of their research on policymaking or the society, and thus the value of public investment in research. ...
Finally, the inventory can be used to select related activities and impacts and analyse the portfolio in a given research or policy area. The feedback loop is closed when portfolio analyses discover gaps to be addressed by analysing new case studies of an organisation’s research activity. ...
It is thus paramount to imagine the possible effects of one’s research on how policy is designed, implemented or evaluated, as this provides accountability for the public funds spent on research. ...
"better" science portfolios […] would be achieved if science policy decisions reflected knowledge about the supply of science, the demand for science, and the relationship between the two’.
Chapter 15 page 168 to a Broader Audience (the nitwits) Often these audiences, especially citizens, can only be reached indirectly, via multipliers like the media or social media influencers. Furthermore, the co-creation and continuous engagement approach that Science for Policy 2.0 calls for with policymakers is only possible to a lesser extent with broader audiences as those approaches rarely scale to the level needed to reach them, and even if they do, they can be very resource intensive and thus out of reach for most organisations. Communication activities will thus vary according to the objective, the intended audience and the resources available. Specific areas: Chapter 16 page 183 Knowledge-Based Crisis and Emergency Management. A golden rule in crisis and emergency management is to handle crises as local as possible and as global as necessary ..
The most important lesson is that scientists, policymakers and practitioners must work together. ...
Scientists must learn what information to provide to whom at what time and in what format, so the information is directly relevant in the decision-making process of practitioners. ...
The Copernicus Emergency Mapping Rapid Mapping Service (http://emergency.copernicus.eu/mapping/) is the operational outcome of years of research at JRC. ...
Chapter 17 page 197 Behavioural Insights Behavioural insights are evidence-based conclusions about human behaviour in a given context. The behavioural sciences are all those disciplines that, through systematic observation and rigorous analysis, offer these insights. ...
The best starting point for appreciating the contribution of behavioural insights is the assumption, explicit in economics and implicit in much of policy thinking, that human beings are rational. They maximise their well-being, or utility in the economics nomenclature, taking a number of variables into account and following a series of axioms. The assumption of rationality refers mainly to the outcome of decisions, not the process (i.e., a decision is considered rational if it maximises utility, regardless of how that decision was arrived at). Circumspect analysts might take this assumption with a grain of salt but will nevertheless adhere to it. It is useful for economic analysis. However, as we know intuitively from observing our own behaviour and that of others, humans are far from rational. We exhibit consistent anomalies in our behaviour, oddities that cannot be properly explained by the assumption of rationality. These biases and heuristics have been identified through experimental studies and form the basis of behavioural economics (Kahneman and Tversky, 1979). ...
Chapter 17 page 199 People exhibit two ways of thinking. One (System 1) is characterised by being automatic, unconscious and frequent. It is prey to cognitive biases and strikingly prevalent in our everyday lives, accounting for about 95% of our thinking according to some estimates (Zaltman, 2003). ...
Why does the distinction between System 1 and System 2 matter and how is it relevant to policymaking? Because the design of many a policy initiative is based on the assumption that citizens routinely use System 2, whereas in fact, they may be using System 1. ...
Chapter 17 page 203 Moreover, there is the problem of diversity. Unlike national or regional policies, EU policies have to be effective in 28 different member states. And results from RCTs are not necessarily transferable to other contexts (Cartwright, 2007). Interventions that work in Denmark might easily fail in Italy. ...
Apart from experiments, qualitative methods have also proven useful. These include in-depth interviews, focus groups and participant observation, among others. They are different to experiments in their approach and fulfil a different purpose. For one, they do not seek to test hypotheses, but rather generate them. Their findings are not ‘objective’ (i.e., the perspective of the researcher must always be taken into account) or statistically generalisable. ...
Uncovering meaning is important, but not easy. It requires a rigorous, reflective and systematic process. Good qualitative evidence is not anecdotal. ...
Chapter 18 page 207 Quantitative Methods in the Policy Cycle for any policy modelling exercise, there is a need to decide what is important for different social actors as well as what is relevant for the representation of the real-world entity described in the model (see, e.g., Frame and O’Connor, 2011; Jerrim and de Vries, 2017). ...
However, theoretical models have their limits and can never fully capture reality in all of its complexity. Ultimately, policymaking is bounded by a reality check expressed by the simple question: what worked and for whom? ...
For example, in the European Commission, more than 200 models are applied within various policy departments and services with hundreds of scientific and administrative users in various countries. The list of domains ranges from greenhouse gas (GHG) emissions, energy consumption and economy to structural integrity assessment, to name but a few. ...
In order to address some of these challenges, the corporate modelling inventory and knowledge management system, MIDAS contains descriptions of models previously or currently in use in support of the EU policy cycle (Ostlaender et al., 2015). Chapter 19 page

The systems thinking triad (Graham Berrisford 2024)

A confusing mix of: work system theory (WST), domain-specific systems theory (DSST) and general systems theory (GST). In pursuit of systems theories for describing and analyzing systems in organizations (Twenty-Sixth European Conference on Information Systems (ECIS2018), Alter, Steven 2018)
The visibility and application of systems theories in the IS discipline have not come close to achieving the promise implied by the name of discipline. ...
The treatment or omission of systems theories in recent articles about perspectives or approaches in IS research provided an additional impetus to discuss systems theories in a more direct and specific manner. Burton-Jones et al. (2015) compares variance, process, and systems perspectives in IS research, each of which represents “a researcher’s choice of the types of concepts and relationships used to construct a theory.” It notes that papers comparing theoretical perspectives tend to “emphasize the variance/process dichotomy without mentioning the systems perspective.” It describes an unproductive tendency to keep the different perspectives separate and proposes ways to combine aspects of variance, process, and systems approaches. ...
GST purports to be a transdisciplinary theory that identifies properties shared by all systems (see above). The next two use the term system as a rough synonym for a tool that is adopted or liked. Soft systems theory is really an alternative name for soft systems methodology, less a theory and more a system-oriented method for problem identification and problem solving. WST will serve as this paper’s primary example for illustrating the potential of systems theories in IS. The IS Theories Wiki did not include several widely used systems theories that that will be mentioned later, i.e., activity theory, sociotechnical systems theory, and the viable systems model.

The Illusion of Resilience

Leadership at the Edge of Chaos for safety (D.Nichols DVMS 2025)
Executives and board members often assume their organizations are resilient because they’ve invested in the right technologies, hired skilled staff, or achieved compliance certifications. Yet every crisis tells us the same story: what fails first is not the firewall, the supply chain contract, or the policy binder: it’s leadership.
Hidden in plain sight, leadership gaps are revealed when stress hits the system. Failed digital transformations, high-profile breaches, and supply chain shocks aren’t stories of technical incompetence. They are stories of intent that weren’t matched with structure, culture that betrayed stated values, and boards that assumed when they should have demanded proof.
But ask yourself:

When was the last time your board demanded evidence — not assurances — that your suppliers could deliver under stress?
How often do you test whether your organizational structures reflect your strategic intent?
Do your cultural behaviors under pressure match the values you’ve put on the wall — or do they collapse when a crisis arrives?

If you can’t answer with confidence, your resilience is an illusion.

Representing ideas in visuals orientation distractors.

The practical thinking in thinking

'Crossing the chasm' model developed by G.A. Moore helps to successfully drive the adoption of the product in the market, across different customer segments.
The most challenging step is making the transition between the chasm of Visionaries and the Pragmatists.

Tech Enthusiasts (Innovators): They look forward to get the latest technology, and are less concerned with functionality and benefit.
Visionaries (Early Adopters): Similar to Tech Enthusiasts, the Visionaries group is all about trying out new products. They have a direct need for the product, and need less of convincing to adopt the new product, if it meets their need.
Pragmatists (Early Majority): They are open to innovation and embrace changes, however they only buy when the product is proven to provide the said benefit. They are less tolerant of technical issues than Visionaries. There exists a wide Chasm (gap) between the Visionaries and Pragmatists group of people.
Conservatives (Late Majority): This group adopts a new product only when they feel a loss by not adopting the product. Most users in this stage are aware of the product, however, they are hesitant to adopt (and spend money on) something new.
Skeptics (Laggards): They only buy product when it's forced upon them. They are very resistant to change, risk-averse, and not easily persuaded. They finally come around once all of the buzz about your product has quieted down.

Technical documentation The developer is responsible to create the technical documentation of the product from both internal and user perspective. Internal documentation are prepared over the course of entire development.
User documentation is created at the end of the User Acceptance testing (UAT) and released along with the Product.

butics

Amplio Factors for effective value streams enable us to accurately predict what practices will work best in our situation. This segment is part of Amplio Foundations. Amplio Axiom’s - Why theory is so important.

Standing on the Shoulders of Giants
The Importance of systems thinking
First principles of knowledge work and managing complexity
Understanding Value Streams.
Factors for Effective Value Streams
How Amplio Uses Patterns

Factors for Effective Value Streams Factors for effective value streams are derived from first principles and enable us to discern how effective our value streams are. They are based on Eli Goldratt’s theory of inherent simplicity that suggests “If we dive deep enough, we’ll find that there are very few elements at the base – the root causes – which through cause-and-effect connections are governing the whole system.” They are framed as questions to ask and can be used to decide between optional practices. See Amplio books for more.

Working on the most valuable items to achieve success for your critical stakeholders.
Understanding the acceptance criteria before writing any code.
Getting actionable feedback quickly on your product and on how you are working.
Working in small increments.
Using pull to keep workload within capacity.
Managing work in process at all levels to reduce delays in your workflow.
Organizing people to avoid multi-tasking and delays in people being available.
Having clarity on how people work together.
Making all work visible.
Focusing on the quality of the product

Role	Function	Metaphor
system-1	Operational units	Muscles doing the work
system-2	Coordination	Nervous system—managing tension
system-3	Control & optimization	Liver—resource distribution
system-4	Intelligence/Adaptation	Brain—future planning
system-5	Policy/Identity	Prefrontal cortex—purpose & values

Reference	Topic	<\|>	Topic	Reference
👓 info types	different types of information (data)	👓
👓 Value Stream	Value Stream, working cell shop floor	👓
👓 transform information	The Transformation job in details	👓
👓 bi tech	deep dive technology usage	👓

AL-3.6

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⟲ AL-3.6.1 Info

butics

The end of the hierarchical organisation in the scope of decision making

In the information age, centralized decision-making becomes a bottleneck.
Alberts/Hayes argue that information-rich environments demand empowered actors at the edge who can interpret, decide, and act without waiting for top-down directives. Dismantling traditional hierarchies challenges entrenched power structures, cultural norms, and comfort zones.
But the payoff, greater innovation, ownership, and responsiveness—is transformative. Its a human-centered philosophy for leadership, collaboration, and organizational design in the 21st century.
There are strategic goals by “Power to the Edge” (Alberts/Hayes)

Decentralized Decision-Making: Shifting authority from central command to the edge of the organization—where real-time information and action intersect.
Agility and Adaptability: Empowering individuals and teams to respond quickly to dynamic environments.
Trust-Based Networks: Building systems where collaboration and shared awareness replace rigid hierarchies.

The tacticals for adapting the strategy:

From Management to Mastery: The idea that leadership is not a formal role but a personal journey, empowering individuals at the edge.
Facilitators over Bosses: Replacing traditional managers with facilitators or rotating team leads mirrors the shift from centralized control to distributed leadership.
Trust and Autonomy: Emphasizes trust and dialogue over hierarchy—core tenets

Nature as a Model: References to ecosystems (e.g., fish schools, bird flocks, forests) reinforce the idea that decentralized systems can be highly adaptive and resilient. Dismantling traditional hierarchies challenges entrenched power structures, cultural norms, and comfort zones. But the payoff—greater innovation, ownership, and responsiveness—is transformative.
Bringing it to life: Map Your Value Streams, Build a Facilitation Cadre, Layers in Feedback Mechanisms.
A glimpse beyond: Nature-Inspired Networks, Dynamic Governance (Explore lightweight “policy as code”), Human-Tech Co-evolution (Experiment with AI assistants that surface context and risk at the edge). By weaving these tactics into your own context, you transform abstract strategy into everyday muscle memory—ultimately unlocking that human-centered, responsive organization the information age demands.
The challlenge in the operations is adaption to the tactics. Sometimes it is a natural adatpive change by the way the operational work is done. The operational work however is a result of education, learning, training. The crux of the challenge lies in translating strategic tactics into everyday operational behaviors, especially when those behaviors spring from formal training, ingrained habits, and learned processes. To make adaptation more than a “one-off” shift, organizations must weave continuous learning and real-time feedback directly into the flow of work.

Embed Learning in the Flow of Work
- Microlearning Moments
  - Deliver bite-sized tutorials, videos, or checklists at the point of need (e.g., via mobile apps or digital dashboards).
  - Tie each micro-lesson to a tactical shift (e.g., “How to run a rapid A3 experiment” or “Facilitation prompts for daily huddles”).
- On-the-Job Apprenticeship
  - Pair less experienced operators with rotating “tactical champions” who co-solve real problems.
  - Encourage shadowing and peer-led debriefs immediately after an adaptation is tried.
- Digital Twins & Simulations
  - Use lightweight, scenario-based simulators to let teams practice new tactics in a risk-free virtual environment.
  - Enable rapid “what-if?” iterations so lessons learned inform the next real-world cycle.
Create Continuous Feedback Loops
- Real-Time Metrics & Visual Controls
  - Surface leading indicators (cycle time, decision latency, experiment throughput) on shop-floor or digital war-rooms.
  - Use simple “traffic-light” signals or Kanban boards to spotlight where the new tactics are stuck or succeeding.
- Rapid PDCA Cadences
  - Shorten Plan-Do-Check-Act cycles from monthly to weekly (or even daily) so teams can adapt within days, not quarters.
  - Document small wins and failures in a shared repository for cross-team learning.
- Voice of the Edge
  - Empower frontline teams to propose tactical tweaks and vote on the most promising ones.
  - Route their insights directly into leadership forums to keep strategy grounded in operational reality.
Cultivate a Learning Culture
- Communities of Practice
  - Form cross-functional cohorts around each new tactic (e.g., “Lean Experimenters Guild” or “Edge Decision Network”)
  - Host regular show-and-tell sessions where practitioners demonstrate how they applied the tactic and what they learned.
- “Training as a Journey,” Not an Event
  - Replace one-off workshops with a structured curriculum that combines e-learning modules, coaching clinics, and peer reviews over months.
  - Award digital badges or rotating leadership roles to spotlight mastery of each tactic.
- Leader as Learning Sponsor
  - Have executives and managers attend the same micro-learning and simulation sessions as their teams.
  - Encourage them to coach, unblock, and publicly celebrate the adoption of new behaviors.

Align Incentives and Accountability
- Edge level metrics
  - Tie metrics directly to tactical adoption (e.g., “Edge teams reduce approval latency by 50% this quarter”).
  - Review these metrics in every retrospective, pivoting the tactic or the support model as needed.
- Safe-to-Fail Experiments
  - Architect “innovation sprints” within operations where teams can trial unorthodox approaches without penalty.
  - Debrief failures candidly—treat them as data to refine the next iteration.
- Knowledge-Capture Rituals
  - Make frontline retrospectives mandatory after every major operational shift.
  - Store structured learnings (what worked, what didn’t, what’s next) in a searchable knowledge base, tagged by tactic.

Select one operational area (e.g., procurement, customer support) and launch an integrated adaptation program: microlearning, feedback loops, community forums, and metrics.
Track both behavioral adoption (how often teams use the new tactic) and outcome impact (cycle time, error rates, engagement scores). Use these insights to refine the playbook before wider rollout.
Evolve your learning platform into a living “Tactics Library”—ever-growing with new practices, case studies, and coaching clinics. Keep the organization in a perpetual beta of its own best practices. (Institutionalize Continuous Adaptation)
By fusing education, on-the-job practice, real-time feedback, and cultural supports, you turn tactical shifts from isolated reforms into self-reinforcing operational habits. The result is a truly adaptive engine, where new strategies are mastered—and improved upon—right at the edge.

👐	C-Steer	C-Serve	C-Shape	👁	I-C6isr	I-Jabes	I-Know	👐
👐	r-steer	r-serve	r-shape	👁	r-c6isr	r-jabes	r-know	👐

📚	Reference Frames		Info types		info flows		📚
🎭	Concerns & Indices	◎	Elucidation	👁	Summary	◎	Vitae	🎭

Shape fractal: technology of a smart systems

AL-1 Improving processes, change transitions, using the 6*6 reference framework

Explaining - optimizing, processing operations.

Contents

Combined pages as single topic.

Progress

Why Engineering Organisations.

Demo - Design and Engineering Methodology for Organizations.

The process circle.

The process circle in engineering organisations.

The glossary by words is not consistent.

Operation, Information, Documentation

Requestor Informing by predictive, prescriptive reporting.

Process Mining.

Big data - IOT 4.0

Reverse engineering processes.

Disc over your processes.

Big data - Analyzing events

Elementary process cycle..

Process cycles at ICT

Practical example using the process cycle

Inventing change Operations.

Adding value

Two powers data and processes

Combined pages as single topic.

AL-2 Details systems ZARF internals 6x6 reference framework

AL-2.1

⟲ AL-2.2.1 Info

butics

AL-2.2

⟲ AL-2.3.1 Info

butics

AL-2.3

⟲ AL-2.3.1 Info

butics

AL-2.4

⟲ AL-2.4.1 Info

butics

AL-2.5

⟲ AL-2.5.1 Info

butics

AL-2.6

⟲ AL-2.6.1 Info

butics

AL-3 Details systems ZARF externals 6x6 reference framework

AL-3.1

⟲ AL-3.1.1 Info

butics

butics

The Illusion of Resilience

Representing ideas in visuals orientation distractors.

The practical thinking in thinking

butics

AL-3.2

⟲ AL-3.2.1 Info

butics

VSM metaphore

The Lean Problem

The Change Problem

Variations on: Machines, People, Processes, Structure

AL-3.3

⟲ AL-3.3.1 Info

butics

AL-3.4

⟲ AL-3.4.1 Info

butics

Intra References

Intra References by topics

AL-3.5

⟲ AL-3.5.1 Info

butics

AL-3.6

⟲ AL-3.6.1 Info

butics

The end of the hierarchical organisation in the scope of decision making