pdca - learning along the way
DESCRIPTION
We often miss the important role of prediction error.TRANSCRIPT
© Mike Rother TOYOTA KATA 1
Mike Rother &Bill CostantinoAugust 2012
Illustration from:The Adventures ofNed the Neuron www.kizoomlabs.com
PDCALEARNING ALONG THE WAY
Copyright © 2013 Mike Rother, all rights reserved1217 Baldwin Avenue / Ann Arbor, MI 48104 USA / tel: (734) 665-5411 / [email protected]
© Mike Rother TOYOTA KATA 2
MikeBill
Why should we manage ourselves differently? Because a scientific, iterative way of thinking & acting is not the typical way adults nor our business organizations think & act.We naturally prefer to define thesteps weʼll take, determine whoʼs responsible for each step, assign timing and execute the plan.We do need to make plans, but the approach we may naturally prefer is often not effective for meeting goals in complex situations.
WHY THIS SLIDESHARE?There are many challenges humans face, but we can handle
them... if we manage ourselves a little differently
It takes a different approach and a little practice to mobilize our astonishing collective capability for meeting challenging goals
© Mike Rother TOYOTA KATA 3
One of the ways we learn is through the steps ofPlan --> Act --> Evaluate. This is a natural cycle.
These steps are different from the mechanistic model that business is currently comfortable with.
Plan --> Act --> Evaluate is mirrored by the PDCA cycle.
When a step along the way goes differently than predicted or planned you often learn something new that helps you reach the goal. Although this is different from what business is currently comfortable with, we can practice a way to experiment in the “PDCA Zone.”
Practicing the Improvement Kata allows us to actualize the PDCA approach in business and everyday life.
TAKEAWAYS
© Mike Rother TOYOTA KATA 4
SO LETʼS TAKE A LOOK
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HOW DO YOU WORK TOWARD A GOAL?Viewed from a distance, any human endeavor
seems to involve three steps:
Plan Action Evaluate
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BUT OF COURSE IN REALITY YOUʼRE CONSTANTLYCYCLING THROUGH THOSE THREE STEPS
When you try to reach any objectiveyou repeat the steps many times
Why? Because we canʼt predict the future. No plan we make is 100% correct and nothing goes 100% according to plan.
Reaching an objective involves learning and making adjustments along the way.
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
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A LEARNING PROCESS IS GOING ONAS YOU TRY TO MOVE TOWARD A GOAL
Take the example of walking toward something. Constant sensory feedback and subconscious muscular adjustments are happening in order to generate effective locomotion.
While you walk, information from various sensors is used to adapt your posture and walking pattern to the dynamic characteristics of the task, the environment and your body.
© Mike Rother TOYOTA KATA 8
Evaluate
What do youexpect to happen?
What actually happened?What did you learn?
Action
Prediction(Plan)
A main way we learn new things is when an actual outcome differs from the predicted outcome
HOW DO WE LEARN?Believe it or not... An essential
mechanism of learning is Prediction Error
© Mike Rother TOYOTA KATA 9
Prediction
Confirmed
ErrorSurprise. Potential for new knowledge, learning & discovery.
Strengthenscurrent thinking.Like re-walking apath in the snow.
Prediction confirmation keeps you in place. Prediction error leads you out of your assumptions and forces exploration.This is because prediction error reveals a knowledge threshold.
HOW PREDICTION ERROR DRIVES LEARNINGItʼs the scientific approach: When a result is as-predicted itconfirms something you already thought. When a result is
different than predicted you are about to learn something new.
© Mike Rother TOYOTA KATA 10
THIS IS EVEN HOW SOME OFYOUR BRAINʼS NEURONS WORK
Learning goes on all the time in your brain. Every time you do something the synaptic connections between the involved dopamine neurons may be strengthened (+) or weakened (-) based on Reward Prediction accuracy (+) or inaccuracy (-)
Reward Prediction
Actualresult
Strengthenor weaken
this one synapse
Rapid adaptation to reality (learning) via a cycle of prediction, met or not, evaluated by dopamine.
Neuron Neuron
Synapse
© Mike Rother TOYOTA KATA 11
Plan
ActionEvaluate
PREDICTION --> ACTION --> EVALUATEThis cycle is a natural building block of anything that humans achieve as they operate in systems
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
P AE
BigProject
Landing an Airplane
Drivingto Work
P AE
Cookingan Egg
Synapse
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PLAN DO CHECK ACTPDCA (or PDSA) is a 4-step learning cyclethat echoes our neural learning process
PDCA drawingby Jurgen Appelo
Wow... a version of PDCA may be baked into our neurons that are involved in learning new behavior!
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HOW PDCA WORKS
The “P” of PDCA is an expectation or a prediction......a hypothesis
Unexpected results redirect your thinking, forcing new interpretations and steps. They put you at the learning edge.
When you reflect and attempt to understand why your prediction was inaccurate you discover new insights and build new knowledge.
The “C” of PDCA is a reflection...What are we learning from this?What do weneed to adjust?
Illustration from The Team Handbook, page 3-33
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Predictable Zone Uncertainty / Learning Zone
Current KnowledgeThreshold
We wantto gethere
Next Target
Condition
Obstacles
Unclear
Territory
? ?
?
The knowledge threshold is the point at which you have no facts & data and start guessing.This is where you should do the next PDCA experiment; learning where the facts run out.
The plan is made here
PDCA EXPERIMENTS ARE DONEAT KNOWLEDGE THRESHOLDS
Thereʼs always a knowledge threshold... look for it
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FOR EXAMPLE
Predictable Zone Uncertainty / Learning Zone
Pull system between
processes A & B working as designed,by (date)Current
KnowledgeThreshold
Spot the knowledge threshold and conduct your next PDCA experiment here as quickly & cheaply as possible!
We know how a pull system works, but we donʼt know in advance everything it will take to make your pull system work. This means you canʼt implement a pull system. Youʼllhave to experiment your way forward and iterativelylearn how to make your pull system operate as desired.
Experimenting
TargetCondition
The plan is made here
© Mike Rother TOYOTA KATA 16
Many of us think finding the best path to a goal involvesdeveloping the right plan and then executing it. Turns out, thatʼs incorrect when youʼre operating in an interconnected system. The way it works is that you make the best possible plan, and then you adjust along the way based on what you are learning along the way.
If you go to business school and learn a planning process, thatʼs only half of the matter. You shouldalso learn a good iteration process.
Imagine what would happen if the neurons inyour brain only planned and executed anddidnʼt constantly adjust based on microresults. You probably wouldnʼt be alivereading this today!
A plan is only a prediction of how thingswill go. A plan is a hypothesis.
OUR NEURONS MAY UNDERSTAND THEROLE OF “PLANNING” BETTER THAN WE DO
© Mike Rother TOYOTA KATA 17
WHAT ARE WE TEACHING?Too much certainty
Human capability, passion-driven endeavor and iterative / adaptive thinking are alive and well on the planet. But itʼs often overshadowed by an unscientific kind of thinking that has become habitual in many large business, financial, political and academic organizations.
In business schools we teach how to managebusinesses as if they were mechanical systems, notthe complex, interconnected systems they actually are. In particular we teach using accounting and financial control methods to make predictions and strive for financial results.Unfortunately, we have a tendency (certainty bias) to put too much faith in our predictions, and financial results are only an abstraction of reality.
A B C D
AB
C
DE
F
G
HMechanical System
ComplexInterconnected
System
© Mike Rother TOYOTA KATA 18
What we may think scientific is
What scientific really is
• Quantification and precision• Objective and certain• Reveals what is there
• Involves uncertainty,ambiguity & incompleteness
• Never free from error• A process of discovery, via
systematic trial and error
Example: We have made the right plan
Example: Our plan is a hypothesis
WHAT IS SCIENTIFIC THINKING?
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WHAT SHOULD WE DO NEXT?
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Illustration by Meryl Runion Rose
PRACTICE A DIFFERENT WAY!We can practice routines (kata) to develop new habits that take us beyond mechanistic thinking, to iterative thinkingthatʼs more in line with how interconnected systems work
© Mike Rother TOYOTA KATA 21
ONE STEP TO PRACTICING ITERATIONWhenever youʼre working toward a goal
ask yourselves the Five Coaching Kata Questions daily
1. What are we trying to achieve?2. Where are we now?3. Whatʼs currently in our way?4. Whatʼs our next step (the next
experiment) & what do we expect?5. When can we see what weʼve
learned from taking that step?
5 Basic Coaching Kata Questions
PRESCRIPTION
© Mike Rother TOYOTA KATA 22
Businesspersons are often understandably averse to experimenting because it feels uncontrolled; like you may never reach the goal.By using the Improvement Kata you can diminish this fear.Your Target Condition has a hard achieve-by date and is measureable. There are budget constraints and quality parameters. Itʼs within these defined limits that you design and conduct rapid successive experiments to reach your target condition.
ANOTHER STEP TO PRACTICING ITERATIONConduct experiments inside the PDCA Zone
© Mike Rother TOYOTA KATA 23
FOR GOOD ORGANIZATIONAL HEALTH...Take a daily dose of Improvement Kata practice
MikeBill
Practicing the Improvement Kata is perhaps the best waywe've found so far for actualizing PDCA in an organization.
~ John Shook, Chairman and CEO, Lean Enterprise Institute
© Mike Rother TOYOTA KATA 24
Remember... any plan is only a hypothesis, so be prepared to learn on the journey!
© Mike Rother TOYOTA KATA 25
I once believed that PDCA thinking is a natural phenomenon, like the way the brainʼs dopamine neurons behave.
But I also see all the evidence in front of me which points out that PDCA thinking is not as natural, automatic and widespread as I wish it were.
Just because our brainʼs dopamine neurons utilize prediction error to learn doesn't mean that we as humans have learned to utilize prediction error. It takes practice.
© Mike Rother TOYOTA KATA 26
Pat Boutier Emiel Van Est Dennis Gawlik H. Thomas Johnson Jeff Liker Bernd Mittelhuber R.R.
WEʼD LIKE TO THANK...
... for their thoughtful and insightfulinput to this SlideShare