Systems Concepts for Agile Practitioners
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Presented at Agile Open San Diego in 2014 with parts repeated at Agile Coach Camp West, 2015.
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Systems Concepts for Agile Practitioners
- 1. Systems Concepts for Agile Practitioners Agile San Diego 5/1/14 Roger Brown, CSC, CST
- 2. The significant problems we face today cannot be solved at the same level of thinking at which they were created. - Albert Einstein
- 3. Why? Chaotic Complex Complicated Simple Human History Technological advance and growing population in a finite space have increased the complexity of human interactive structures.
- 4. Systems are greater than the sum of their parts. System properties and behaviors emerge from the combination of its constituent parts Reductionism is not sufficient. Our systems are dynamic and driven by nonlinear effects that are not easily understood.
- 5. Helpful Theoretical Models Queuing Theory – Erlang 1910 Lean Thinking – Deming 1940 System Dynamics – Forrester 1950 Automata Theory – 1940 Ulam and von Neumann Network Theory - 1970 Complexity Theory - 1970 Learning Organization – 1990 Senge A model is a simplification of reality intended to promote understanding.
- 6. System Dynamics A system is an entity which maintains its existence through the mutual interaction of its parts. - Gene Bellinger Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. - Jay Forrester
- 7. Counter-intuitive Behavior System behaviors come from structures, not from coefficients
- 8. Feedback Mechanisms Reinforcing feedback Balancing feedback + Credit Card Balance Credit Card Interest + + Body Temp Sweat - 3 examples from www.beyondconnectingthedots.com
- 9. Feedback Delay Time DesiredState Long delay causes wider swings Short delay converges sooner Agile/Lean achieve smoother flow and reduced risk by shortening the delay time for feedback
- 10. Cost of Change and Feedback Delay
- 11. Lean Thinking 95% of variation in the performance of a system (organization) is caused by the system itself and only 5% is caused by the people. - W. Edwards Demming Misconception easily turns into common sense. - Taiichi Ohno
- 12. Push & Pull Systems Push systems overwhelm capacity, creating turbulence, rework, waste and delay Pull systems have a steady flow that provides predictability ♫ Push
- 13. Push Pull Make a plan Have a queue of work and a goal Track % completion of plan Measure throughput and work done Buffer plan for contingencies Small, frequent tasks to manage variety Plan decides what to do next People decide what to do next Long feedback delay Continuous short feedback loops Demand exceeds capacity Demand limited to capacity Fixed scope and time Fixed WIP Forecast based on estimates Forecast based on data
- 14. Little’s Law Cycle Time = Number of Items in Process/System Capacity
- 15. ? Single Piece Flow Do This Don’t Do This
- 17. Local Optimization A focus on one property can have unintended impact on the system as a whole
- 18. Goodhart’s Law The moment a measure becomes a target, it ceases to be useful as a measure. Story Points/Sprint
- 19. Complexity Theory Complicated Complex Click pictures to view examples. • Many different parts. • Can take it apart and reassemble it. • If one part fails, it all fails. • Many similar parts acting independently within social rules. • Aggregate behavior cannot be predicted from individual part behaviors. • Still “works” if a part is removed.
- 21. Sense Input What our senses tell us Probe How we use our senses to get new information Mental Models What sense we make of new information Actions, Experiments What makes sense to do next
- 22. Sense-Making Tools - anecdote.com - getreframer.com - sensemaker-suite.com We make decisions based on our patterns and mental models, not on information or theory.
- 23. Learning Organizations 1.Systems Thinking 2.Personal Mastery 3.Mental Models 4.Building Shared Vision 5.Team Learning
- 24. Working as a Team Organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free and where people are continually learning how to learn together. - Peter Senge The Fifth Discipline, 1990
- 25. Urgent Not Urgent Important I • Crises • Pressing Problems • Deadline Driven Projects, Meetings, etc. II • Preparations • Learning • Kaizen Events • Relationship Building • True Recreation NotImportant III • Interruptions • Some phone calls • Some email • Someone else’s emergency IV • Trivia • Busy work • Time wasters • “Escape” activities When do we get time to improve? If we don’t spend any time sharpening the saw, we will have to work harder and harder to get the same results. - Steven Covey, The 7 Habits of Highly Effective People, 2004 By intentionally creating downtime, or ‘slack’, management will find a much-needed opportunity to build a ‘capacity to change’ into an otherwise strained enterprise that will help companies respond more successfully to constantly evolving conditions. - Tom DeMarco, Slack, 2002
- 26. Learning Levels Single Loop: tweak the parameters Double Loop: experiment with the process Triple Loop: learn how to learn
- 27. Retrospectives Communities of Practice Book Club Brown Bag Seminars Shadowing Story Telling Knowledge Management Brainstorming Skills Exchange Team Learning Tools
- 28. References Books: • Thinking in Systems : A Primer – Meadows • The Fifth Discipline and its Fieldbook – Senge • Business Dynamics - Sterman • The Principles of Product Development Flow: Reinertsen • The Systems Bible – Gall • 10 Steps to a Learning Organization – Kline and Saunders • Learning in Action – Garvin • Systems Thinking Playbook – Sweeny and Meadows Websites - www.beyondconnectingthedots.com/ - Bellinger - www.cognitive-edge.com – Snowden - www.systemdynamics.org
- 29. Presenter Roger Brown • Agile Coach • Scrum Alliance • M.S. System Dynamics, Dartmouth College 1977 • Contact Email: roger@agilecrossing.com Twitter: rwbrown Blog: www.agileCoachJournal.com LinkedIn: http://www.linkedin.com/in/rogerwbrown
Editor's Notes
- The Chinese finger puzzle confounds our instincts. When we try to pull free, our fingers are more trapped. Complex systems have similarly counter-intuitive behaviors. These are inherent in the system structures – causal relationships, feedback loops. Attempts to increase or decrease individual parameters may cause a temporary or local change but cannot alter the overall system behavior. Example: Software developers are encouraged to work faster to meet a deadline. Late delivery will cost the company money due to lower sales. More code is written but less is tested because there is not enough time. The shipped product has many defects requiring hot-fixes. The later a defect is addressed, the more it costs to fix. So the result of hurrying to avoid reduced sales results in more costs due rework. Profit is less. There is less time to add more features in the next version.
- Reinforcing feedback causes something to grow. Here, credit card balance grows in the absence of any pay-down simply because interest increases. Ask about a counter example that we like better – bank interest compounding. Balancing feedback helps a system stay near equilibrium. Here a warm person sweats to get evaporative cooling. The goal is a steady body temperature.
- The feedback loop is really a “learning” loop. Delay time of feedback loop impacts ability to stay near target. What if your speedometer lagged by 60 seconds? I know driving in Mumbai it appears it wouldn’t matter, in fact it matters much more given the great variability in the driving patterns that we have observed. Relates to cadence, how often we receive and react to feedback.
- Compare to Push Impact on predictability System determines the rate vs. manager/customer determines the rate There are three basic types of pull system: replenishment pull, sequential pull, and mixed pull system with elements of the previous two combined In all three cases the important technical elements for systems to succeed are: 1. Flowing product in small batches (approaching one piece or single-piece flow as in batch of one) 2. Pacing the processes to the takt time (to stop overproduction) 3. Signaling replenishment via a signal Kanban 4. Leveling of product mix and quantity over time (in software development this mean mix of product development, code refactoring and fixes to maintain low technical debt) -- http://www.lean.org/Library/BatchProcessesByArtSmalley.pdf Drum-Buffer-Rope (Goldrat) is one type. http://www.focus5.com/html/drumbufferrope.html DBR is based on the TOC logistics approach and the TOC five steps of focusing. These are: 1. Identify the System's Constraint(s). 2. Decide How to Exploit the System's Constraint(s). 3. Subordinate Everything Else to the above Decisions. 4. Elevate the System's Constraint(s). 5. If, in the Previous Steps, a Constraint Has Been Broken, Go Back to Step One, but Do Not Allow Inertia to Cause a System's Constraint. and the definitions of DRUM, BUFFER and ROPE are: DRUM - A schedule for the constraint. BUFFER - A protection against Murphy. This is the time provided for parts to reach the protected area. The protected areas are the Drum, the due-dates and the assemblies of constraint parts with non-constraint parts. ROPE - A schedule for releasing raw materials to the floor. The Rope is derived according to the Drum and Buffers; its mission is to ensure the proper subordination of the non-constraints.
- What other differences?
- Cycle Time is the time between any two points in a work flow. So when we ask what is the Cycle Time customer experiences, then it equals the time from request to the time the request is fulfilled for the customer. i.e Lead time of the system. Queuing theory: Little's Law tells us that the average number of customers in the store, L, is the effective arrival rate, λ, times the average time that a customer spends in the store, W, or simply: L = λW. Example: A small Store The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm-)average time a customer spends in the system, W. ex: Average number of customers in a check out queue = 4, average rate of arrival is 16 per hour then 4=16*W hence W= 4/16 = 0.25 hrs, customers spend 0.25 hours on average checking out. You can use this throughput measure to tune your system to the conditions you desire. Imagine a small store with a single counter and an area for browsing, where only one person can be at the counter at a time, and no one leaves without buying something. So the system is roughly: Entrance → Browsing → Counter → Exit This is a stable system, so the rate at which people enter the store is the rate at which they arrive at the store, and the rate at which they exit as well. We call this the arrival rate. By contrast, an arrival rate exceeding an exit rate would represent an unstable system, where the number of waiting customers in the store will gradually increase towards infinity.
- Local optimization can sub-optimize the whole. What is being optimized here? What is the consequence to the overall goal of the truck owner? Picture: http://sedislogistic.files.wordpress.com/2011/04/overloaded-truck-in-shandong.jpg Dr. Deming had shown that problems in an organization (small or large) are attributed to 94% being system related and only 6% due to workers.
- Charles Goodhart: Professor of Economics at LSE, former adviser to Bank of England The law states: Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes. Also related to Campbell’s law - The more an indicator is used as a measure, the more it is apt to distortion and corruption of the process it is intended to monitor So it is better to identify the target condition under which the process is to operate, in a pattern, required to achieve the desired outcome – i.e. target Target: Inventory level, Lead time, Cost, Labor cost, Quality level, Output per hour Target condition: Describes how the process should operate in order to achieve the Target – its actionable
- Liquid-solid Muscle-bone Mobile-locked Sports Evolution – adaptability Interesting question and resonant for me this week. I attended a Cynefin workshop with Dave Snowdon this week and listened to his keynote at the South African Scrum Gathering. One of his slides in the keynote positioned SAFe at the cusp of Complicated and Simple in the Ordered Domain. In his model, it is a dangerous practice to try to drive Complex problems into the Simple portion of the Ordered domain. Neither Best Practice (Simple) nor Good Practice (Complicated) are viable ways to deal with the Complex problem of scaling Agile in organisations.
- Learning loops in the support value stream. Walk through then with some examples.