👐    metier   👐    design: bpm    sdlc    bianl    sense    data    meta    math    👐    devops: bpm    sdlc    bianl    sense    data    meta    math 👐
👐    Intro    quadrant use    Opt proces    4q innovation    Proces cycle    What next    👐    top bottom   👐

Design - Business Proces Management


Contents & topics BPM

BPM introduction my viewpoints

Mindmap start Business process management (BPM) is a discipline in operations management in which people use various methods to discover, model, analyze, measure, improve, optimize, and automate business processes. BPM focuses on improving corporate performance. (wikipedia)

🔰 Down to earth .. epilogue.


Progress


Contents

Reference Topic Squad
Intro BPM introduction my viewpoints 01.01
quadrant use Quadrant Usage 02.01
Conflict of interests 02.02
Opt proces Optimizing the Proces 03.01
Process engineering - operations research - Machine learning 03.02
👓 ml prc01 Historical developments 03.xx
Innovation X stable operations 04.01
👓 4q inn01 four quadrants: Innovation X stable Operations 04.xx
Business value X Complexity Simplicity 04.02
Proces cycle The proces life cycle 05.01
Analytics Life Cycle - Machine learning 05.02
👓 ml lcm01 Combining several concepts to a new complete circular one 05.xx
What next Strategy Working to Operations by Tactics (SWOT) 06.01
Following steps 06.02

Quadrant Usage

I will use an oriëntation that is according to Gartner, Forrester and more in their tools, suppliers analist reports. The top right corner is the preferred location while strong strategy (value long term) is at the right side.
Gartner Magic Quadrant
Gartner Magic quadrant A Magic Quadrant provides a graphical competitive positioning of four types of technology providers, in markets where growth is high and provider differentiation is distinct:
  1. Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.
  2. Leaders execute well against their current vision and are well positioned for tomorrow.
  3. Niche Players focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others.
  4. Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.

Those two dimensions are the best fit in human understanding trade-offs. There are however many more dimensions than just two. Need for multiple of those quadrants.

Conflict of interests

As soon as more than one person is in the organisation and more departments are getting created there are more different interest on each and all personal interests to be managed.

Hiearchy strategy, tactics, operations
There is a long history with management approaches.

The Amsterdam Information Model is more recent.
It is a split up in: Strategy, Tactics, Operations.

Im will use this in triangle representation according hierarchy of control authority.
This AIM model is often used in advice at reorganisations by consultancy companies. This is going along wiht a split up in the operations layer. The generic technical issues to be outsourced and keeping the staff that are aligned with business goals. The question is what the core competenties of an organisations are, at a moment in time.

Hiearchy strategy, tactics, operations
The hierarchical:
  1. Strategy,
  2. Tactics,
  3. Operational
segregation, is a similar one.
The top of the hierarchy, strategy, is the top of the pyramid.
This cause siloed organisations when the shared goal of an organization is obscured, replaced by a lot others.

Optimizing the Proces

There is a long history with management approaches. Taylor, Fayol, Deming and Drucker are often mentioned as major influencers.

Aside of those influencers the question is what did change in the proces.
  1. A long time mainly agriculture proces. The yearly cycle is limiting improvements by one person in about 30 trials. A failure can have an big impact.
  2. Industrialisation got disconnected form that yearly cycle, introducing many options for trials doing thing better.
  3. Automating the trials as goal for generating searched logic. Being able to make exact copies from production. Then number of testcases going unlimited.

Process engineering - operations research - Machine learning

This figure is the last one of those three historical changed approaches.
This one is showing the business proces using machine learning (ML, 👓 click figure)
Changing to using machine learning
Ancient automatization
The change that is giving most resistance is: placing the logic as being a result of a proces and not that human invented only anymore. It is human controlled but with the support of machines.

Negative sentiment:
  1. Making up is the idea: machines are taken over the world
  2. Automatization is not without failures bias, any mistake is a big failure
  3. Humans are disposable by using machines

Positive sentiment:
  1. machines helping humans by doing repeating monotonously labor work.
  2. Automatization ia able to decreasae failures and avoids bias as much as possible
  3. machines helping humans by supporting them at complex algorithms.

Innovation X Stable Operations

All organisations wants to be:
  1. a game changer == new products, dropping old ones
  2. being predictable and reliable. == same trustworthy product
The business usually wants it all at the same moment with no investment.
A conflict of interests that will last forever.
Innovation X Stable operations
(4q, 👓 click figure) The choice:

Doing two major changes at the same time often results in unwanted surprises
Scheduling actions spread in time, doing evalutions on each, makes more sense.

Business value X Complexity Simplicity

This is Prioritizing Value versus effort. Optimzing profit or being effective by:
  1. doing the most processes with high value at low cost as possible.
  2. prefering OpEx instead of CapEx
  3. not being bothered by legal requirements
Changing to using machine learning
The choice:

Summarizing planning journeys:
  1. Low value, simple = park them for later / reconsider.
  2. high value, simple = way to win customers / easy win.
  3. Low value, complex = not worth the effort / drop them?.
  4. high value, complex = do these first / strategic - split them.

It may be unexpected to start as soon as possible with the complex ones.
They are needing to be ready in-time, because the delivery will take longer you should start in time.
The easy quick wins are helpful in getting acknowledgement from the business. Doing some of those will give good feeling. The pitfall on addiction on those "good feelings" is loosing the outlook for the long term goals.

These are two dimensional representations. This a very limited view, there are many more dimensions. Three are possible showing a dimensional (x,y,z) figure, then we have: size of scape, color of shape.
📚 , 🎭 Innovation X ⚙ Stable Operations X 🎭 , ⚖ Complexity Simplicity
giving: 💰 Business value (8q, 👓)

The proces life cycle

In industry, product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products. PLM integrates people, data, processes and business systems and provides a product information backbone for companies and their extended enterprise. (wikipedia)

I will limit myself to ICT (Information Communication technology) I got involved at and did working at. The most interesting one is the analytics lifecycle proces althoug thos steps are very genereic, alos applicable for more traditional ICT processes.
Common ICT delopment stages:
  1. Defining and documenting business requirements, guessing their business value.
  2. Researching and possible do "prove of concepts" solving business requests.
  3. Building & running the solutions as needed for running the organisation.

Analytics Life Cycle - Machine learning

This figure is the last one of those three historical changed approaches.
This one is showing the business proces using machine learning (ML, 👓 click figure)


This proces is developed as a combination from the PDCA cycle, The AIM model and Crisp-DM adding deployment, monitoring and compliance all equally parts of the cyclus.

Strategy Working to Operations by Tactics (SWOT)

Having those ideas wiht design thinking the challenge is how to get it being practice.
There are a lot of management advisors. As infographic, for example PwC (2019) :
PWC proces steps
  1. Put customer value first
  2. Simplify your architecture
  3. Design for flexibility and speed
  4. Engage with your workforce and culture
  5. Adopt a services mindset
  6. Plot the journey before starting
  7. Organize by capabilities
  8. Be agile and user-centric
  9. Invest in resources that make the change stick
  10. Partner based on shared values and trust
Making SWOT analyses (Strengths Weaknesses Opportunities Threats) is a good practice. The management of those kind of processes is out of my scope.

💰 OpEx and CapEx is an financial accounting choice. That choice can have big impact on the organisation long term life expectations.
Legal requirements can be ignored when there is no cost or continuation impact. Wiht fines penalties and even taken out of business the are of high value in not an immediate fiancial figure way.

Following steps

Missing link

These are high level considerations

The proces, concepts SDLC, ALM, ALC (Software Development Life Cycle) 👓 details wiht PDCA DMAIC.

What is not there are: 👓 how to do technicals in real life (devops).



👐    Intro    quadrant use    Opt proces    4q innovation    Proces cycle    What next    👐    top bottom   👐
👐    metier   👐    design: bpm    sdlc    bianl    sense    data    meta    math    👐    devops: bpm    sdlc    bianl    sense    data    meta    math 👐

© 2012,2019 J.A.Karman