We often miss the important role of prediction error.

1. PDCA
LEARNING ALONG THE WAY
Mike Rother &
Bill Costantino
August 2012
Illustration from:
The Adventures of
Ned the Neuron
www.kizoomlabs.com
Copyright © 2013 Mike Rother, all rights reserved
1217 Baldwin Avenue / Ann Arbor, MI 48104 USA / tel: (734) 665-5411 / mrother@umich.edu
© Mike Rother TOYOTA KATA
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2. WHY THIS SLIDESHARE?
There are many challenges humans face, but we can handle
them... if we manage ourselves a little differently
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 the
steps weʼll take, determine whoʼs
responsible for each step, assign
Bill timing and execute the plan. Mike
We do need to make plans, but the
approach we may naturally prefer is
often not effective for meeting goals
in complex situations.
It takes a different approach and a little practice to mobilize our
astonishing collective capability for meeting challenging goals
© Mike Rother TOYOTA KATA
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3. TAKEAWAYS
One of the ways we learn is through the steps of
Plan --> 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.
© Mike Rother TOYOTA KATA
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4. SO LETʼS TAKE A LOOK
© Mike Rother TOYOTA KATA
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5. HOW DO YOU WORK TOWARD A GOAL?
Viewed from a distance, any human endeavor
seems to involve three steps:
Plan Action Evaluate
© Mike Rother TOYOTA KATA
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6. BUT OF COURSE IN REALITY YOUʼRE CONSTANTLY
CYCLING THROUGH THOSE THREE STEPS
When you try to reach any objective
you repeat the steps many times
P P P P P P P P P
A A A A A A A A A
E E E E E E E E E
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.
© Mike Rother TOYOTA KATA
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7. A LEARNING PROCESS IS GOING ON
AS 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
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8. HOW DO WE LEARN?
Believe it or not... An essential
mechanism of learning is Prediction Error
What do you
(Plan) expect to happen?
Prediction
Evaluate Action
What actually happened?
What did you learn?
A main way we learn new things is when an actual
outcome differs from the predicted outcome
© Mike Rother TOYOTA KATA
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9. HOW PREDICTION ERROR DRIVES LEARNING
Itʼs the scientific approach: When a result is as-predicted it
confirms something you already thought. When a result is
different than predicted you are about to learn something new.
Strengthens
Confirmed current thinking.
Like re-walking a
Prediction path in the snow.
Surprise. Potential
for new knowledge,
Error learning & discovery.
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.
© Mike Rother TOYOTA KATA
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10. THIS IS EVEN HOW SOME OF
YOUR 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
Strengthen Actual
or weaken result Rapid adaptation to reality
this one
synapse Synapse (learning) via a cycle of
prediction, met or not,
evaluated by dopamine.
Neuron Neuron
© Mike Rother TOYOTA KATA
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11. PREDICTION --> ACTION --> EVALUATE
This cycle is a natural building block of anything
that humans achieve as they operate in systems
P P P P P P P P Big
A A A A A A A A
E E E E E E E E Project
P P P P P Landing an
A A A A A
E E E E E Airplane
P P P Driving
A A A
Plan E E E to Work
P
A Cooking
Evaluate E an Egg
Action
Synapse
© Mike Rother TOYOTA KATA
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12. PLAN DO CHECK ACT
PDCA (or PDSA) is a 4-step learning cycle
that echoes our neural learning process
Wow... a version of PDCA
may be baked into our
neurons that are involved
in learning new behavior!
PDCA drawing
by Jurgen Appelo
© Mike Rother TOYOTA KATA
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13. HOW PDCA WORKS
The “C” of PDCA
The “P” of is a reflection...
PDCA is an
expectation or What are we
a prediction... learning from
this?
...a hypothesis
What do we
need to adjust?
Illustration from The Team Handbook, page 3-33
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.
© Mike Rother TOYOTA KATA
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14. PDCA EXPERIMENTS ARE DONE
AT KNOWLEDGE THRESHOLDS
Thereʼs always a knowledge threshold... look for it
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.
Predictable Zone Uncertainty / Learning Zone
tacl
e s
?
Ob s
? Next
Target
r Condition
nclea ry
The plan is Current
? U ito
Terr
We want
made here Knowledge to get
Threshold here
© Mike Rother TOYOTA KATA
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15. FOR EXAMPLE
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. Youʼll Target
have to experiment your way forward and iteratively Condition
learn how to make your pull system operate as desired.
Predictable Zone Uncertainty / Learning Zone
Pull system
n g
rimenti
between
Expe processes A & B
working as
designed,
The plan is by (date)
Current
made here Knowledge
Threshold
Spot the knowledge threshold and
conduct your next PDCA experiment
here as quickly & cheaply as possible!
© Mike Rother TOYOTA KATA
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16. OUR NEURONS MAY UNDERSTAND THE
ROLE OF “PLANNING” BETTER THAN WE DO
Many of us think finding the best path to a goal involves
developing 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 should
also learn a good iteration process.
Imagine what would happen if the neurons in
your brain only planned and executed and
didnʼt constantly adjust based on micro
results. You probably wouldnʼt be alive
reading this today!
A plan is only a prediction of how things
will go. A plan is a hypothesis.
© Mike Rother TOYOTA KATA
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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.
Complex
Interconnected
System
A
Mechanical System H B
A B C D
G C
F D
In business schools we teach how to manage E
businesses as if they were mechanical systems, not
the 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.
© Mike Rother TOYOTA KATA
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18. WHAT IS SCIENTIFIC THINKING?
What we • Quantification and precision
may think • Objective and certain
scientific is • Reveals what is there
Example: We have made the right plan
• Involves uncertainty,
What ambiguity & incompleteness
scientific • Never free from error
really is • A process of discovery, via
systematic trial and error
Example: Our plan is a hypothesis
© Mike Rother TOYOTA KATA
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19. WHAT SHOULD WE DO NEXT?
© Mike Rother TOYOTA KATA
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20. PRACTICE A DIFFERENT WAY!
We can practice routines (kata) to develop new habits that
take us beyond mechanistic thinking, to iterative thinking
thatʼs more in line with how interconnected systems work
Illustration by Meryl Runion Rose
© Mike Rother TOYOTA KATA
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21. ONE STEP TO PRACTICING ITERATION
Whenever youʼre working toward a goal
ask yourselves the Five Coaching Kata Questions daily
PRESCRIPTION
5 Basic Coaching Kata Questions
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?
© Mike Rother TOYOTA KATA
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22. ANOTHER STEP TO PRACTICING ITERATION
Conduct experiments inside the PDCA Zone
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.
© Mike Rother TOYOTA KATA
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23. FOR GOOD ORGANIZATIONAL HEALTH...
Take a daily dose of Improvement Kata practice
Bill Mike
Practicing the Improvement Kata is perhaps the best way
we've found so far for actualizing PDCA in an organization.
~ John Shook, Chairman and CEO, Lean Enterprise Institute
© Mike Rother TOYOTA KATA
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24. Remember... any plan
is only a hypothesis,
so be prepared to
learn on the journey!
© Mike Rother TOYOTA KATA
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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
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26. WEʼD LIKE TO THANK...
 Pat Boutier
 Emiel Van Est
 Dennis Gawlik
 H. Thomas Johnson
 Jeff Liker
 Bernd Mittelhuber
 R.R.
... for their thoughtful and insightful
input to this SlideShare
© Mike Rother TOYOTA KATA
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