theleanmag — #18 — february 2024
LEAN AND DIGITIZATION:
PUTTING PEOPLE AT THE CENTER
by Jeff Liker, Jeremy Frank, Joe Li, and Yahya Kahn
Walk into a Toyota plant and the first
graphs. The performance of each line is vividly
overwhelming image is paper on walls and
displayed on huge computer screens updated
easels everywhere. Color coded charts,
automatically in real time and the numbers are
graphs, figures with places for data seem to fill
remarkable—making what was planned for the
every nook and cranny. Andon lights are firing
shift on time, without vehicles pulled off line
off constantly playing music and team leaders
for quality problems, 96% or more of the time.
come running to respond. The most common
Even old robots that many companies would
or biggest problems of each shift are worked
replace are running over 99% of the time.
on by teams at the front line using disciplined
What is missing, or at least very limited, are the
problem solving.
computer displays that have become popular
in the digitization of the factories. Toyota
believes strongly in the power of visual
For automated processes, skilled maintenance
management to highlight gaps between
team members perform detailed preventative
standard and actual and people’s brains to
maintenance for one full shift everyday, also
close those gaps. In the Toyota plant in the
guided by visual reminders on charts and
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theleanmag — #18 — february 2024
United Kingdom they operate with the motto:
humans and our endless capacity for creative
“Use brains not money.”
thinking and innovative problem solving. As
we will see, Toyota is indeed cautiously
stepping into the world of the Internet of
The lean movement views the Toyota
Things.
Production System as a benchmark for best
practice in manufacturing and even today
large companies typically have departments
The Nature of Problems and Problem
of continuous improvement experts roaming
Solving: Predictive versus Adaptive
the plants, leading improvement workshops,
We see too often lean advocates and IT
and preaching the virtues of lean. In the
people talking past each other about the
meantime, we have another movement
power and usefulness of advanced digital
sweeping manufacturing in the 21st century—
technologies. The IT people claim their
Industry 4.0. This is a movement to digitize
technology offers “solutions” that:
the factory promising a revolution that will
make yesterday’s manual methods obsolete,
· Predict where problems are likely to occur.
and companies that do not get on board also
· Immediately flag problems that have just
obsolete.
occurred.
· Closed-loop control systems automatically
Why does Toyota persist in using legacy
manual methods with paper? Why isn’t this
make adjustments to equipment to fix the
manufacturing leader leading the way with
problem.
· In cases where people are involved
digitization? And can these movements work
together—lean and digitization?
provide detailed guidance, perhaps
through augmented reality, to walk even
the novice through a recipe to fix the
We think the answer is yes they can, but it
problems.
requires stepping away from the orthodoxy of
· Provide real-time data on performance
lean and the bold, if sometimes arrogant,
promises of “solutions” provided by the digital
relative to targets so gaps can be quickly
factory. It requires understanding the reality of
and efficiently closed.
the types of problems that millions face in
typical days on the shop floor and how those
Meanwhile, lean advocates caution that the
problems can best be solved. It requires
real world is far more complex than that and
being realistic about the unlikely visions of
people, with our immense problem-solving
dark factories with most people gone, leaving
capability, still need to be in the center:
only engineers who develop the automated
systems. Most important, it requires valuing
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theleanmag — #18 — february 2024
theleanmag—15—May 2023
· In the highly variable and uncertain world
of manufacturing novel problems occur
These seemingly conflicting world views are
regularly, many of which have no known
actually focusing on two different problem
recipe.
solving scenarios—predictive versus adaptive.
Predictive problem solving are cases where
· The automation that is supposed to fix the
machine itself breaks down frequently
the problem can be easily diagnosed and the
and needs to be fixed.
solutions that will work predicted in advance
of actually taking action. Often these solutions
· The more complex the technology the
are based on engineering knowledge
more likely it will break down in
expressed in mathematical models.
unpredictable ways.
Essentially through prior knowledge the
· People with our unparalleled brain
people or system are trying to predict the
capacity and finely tuned senses can
future.
predict that a problem will occur and find
Adaptive problem solving are cases where the
creative ways to solve the problems
problem is vague, the solution is not clear,
outperforming even the most advanced AI
and it takes creative thinking and trials
systems.
organized as experiments to learn our way to
· Often simple, inexpensive solutions can
the solution. For these types of problems
be very effective and save a lot of money.
intimate knowledge of the conditions of
· People are visual creatures and to be
failure, and past experience with similar
most effective need simple displays of
problems, can be a great asset. People on
actual versus standard highlighting what
the front-line who live with the equipment
is green, yellow and red at a glance.
and processes have this knowledge.
Predic ve Problem
Solving
Adap ve Problem
Solving
Problem defini on
Clear
Requires Learning
Solu on
Well understood
Requires Learning
Problem Solvers
Experts or Authority
Stakeholders
Process
Efficient Decision Making
Act Experimentally
Timeline
Results ASAP
Expecta ons
Fix the Problem
A tude
Confidence and Skill
Time Needed to Follow
Process
Achieve Targets and
Develop People
Curiosity
Figure 1: Comparing Predictive and Adaptive Problem Solving
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theleanmag — #18 — february 2024
· If used effectively by well-trained people it
Table 1, adapted from O’Malley and Cebula,
contrasts these two problem solving
can lead to large increases in equipment
scenarios. If we labeled the “Predictive
performance very quickly.
Problem Solving” column as the Digital
· In many, maybe most cases, the insights
advocates view of the world and the
from the analysis require people skilled in
“Adaptive Problem Solving” column as the
adaptive problem solving to find the
lean view of the world we would have a
solutions that will work.
pretty good fit.
The Toyota Production System for
Who is right? Is it possible both are? There
Equipment Health
are many problems that can be solved
Equipment maintenance appears to be right
predictively by those with expert knowledge,
in the wheelhouse of the Industry 4.0
or from computer algorithms, though far
advocates. Industry 4.0 forecasts a world
fewer than the digital thinkers suggest in their
where repetitive manual processes are at
pitches for digitalization, and also many
some point fully replaced by automated
problems that require pure adaptation and
equipment. In a sense this is an engineering
learning by stakeholders experimenting,
paradise where the more predictable and
though fewer than the lean advocates
consistent programmable automation takes
suggest.
over for less consistent human operators who
have personal needs. The catch is that most
Let’s consider a case example of a class of
of maintenance is done by those
technologies that use sensors to measure
unpredictable humans who need deep
vibration, temperature, current, load, and time
knowledge, high levels of technical and
and have the following characteristics:
problem-solving skill, and creativity to identify
the cause of a breakdown and figure out how
· There is well established engineering
to do the repair.
science on how different pieces of
equipment function optimally that can use
these indicators to predict, detect and
The Toyota Production System is centered on
sometimes even diagnose problems.
people and usually applied to repetitive
manual work, like that on an auto assembly
· It is not so much AI as well understood
line. Just-in-Time is about working toward a
engineering equations.
high value-added flow of product to the
· When coupled with sensors and the
customer, without waste in the process. In
Internet of Things, the analysis provides
the plant this requires getting the right
information that is not always intuitive to
materials at the right time in the right amount
most operating personnel in factories and
to the team members who do the work.
the insights are often surprising.
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theleanmag — #18 — february 2024
theleanmag—15—May 2023
“[digitization]
requires valuing
humans and our
endless capacity for
creative thinking and
innovative problem
solving.
members pulled the “andon” cord when out
of standard condition were noticed which
could lead to line stops.
To have a consistent flow of the product and
its constituent parts you need stable
processes which requires Standardization.
There are standards for product design,
standards for process design, and
standardized work for people performing
manual tasks.
The reason People are in the center is they
not only perform the work, but are expected
to continuously improve the system, including
reacting to out of standard conditions with
creative problem solving. The ownership and
responsibility is at the front-line and work
groups comprised of team members, team
leaders, and group leaders are responsible for
achieving aggressive performance targets
and perform some of the simpler preventative
maintenance.1
Since there is always variation in various parts
So how does this apply to equipment
of the system, including the human, things
maintenance? The goal is to have healthy
will invariably be out of standard so corrective
equipment that is ready and able to produce
action is needed. Jidoka, the second pillar,
exactly the parts needed when they are
addresses out of standard conditions. It
needed with perfect quality.
began with Sakichi Toyoda who founded the
original loom works that led to Toyota Motor
Just-in-time (JIT) maintenance means the
Company. He ultimately invented a fully
right maintenance at the right time, neither
automated loom. Along the way he added
too early or too late. Preventative
physical sensors that stopped the loom when
maintenance is critical to Toyota’s high level
a single thread broke and signaled there was
of operational availability of equipment, but
a problem by a flag popping up. Later this led
one could argue that, without the right data, it
to identifying potential failure modes and
is often performed too early. That is, a broad
designing into equipment sensors to detect
set of maintenance procedures are done
out of standard conditions and stop the
whether the equipment needs it or not to run
equipment when certain thresholds were
at a high level Just-In-Case it might otherwise
reached. For manual processes team
lead to equipment breakdown.
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theleanmag — #18 — february 2024
Another approach to maintenance is to wait
Many of the equations that they use to predict
for it to fail, like the original sensor on the
equipment failure can be traced back
loom when a thread broke. This reactive
decades to fundamental engineering science.
When there is an analytical equation that
approach requires quick response to first
effectively predicts it is not necessary to have
contain the problem and get the equipment
AI systems scouring big data to find patterns.
running and then solve the problem at the
You simply plug the right numbers into the
root cause so it is unlikely to happen again.
right mathematical model. Toyota is one of
Arguably this is too late.
their clients.
Toyota is increasingly moving toward the
world of predictive maintenance. They wish to
know in advance for a particular machine that
Toyota Plant Example
it was going to fail in a specific way and take
Toyota has had great success with KCF
corrective action before the problem occurs.
technology for both predictive and adaptive
There are advanced methods, through the
problems.
“Internet of Things” and analytics for example,
As a predictive example, sensors were
that are increasing what we can predict.
installed on the drive motors of conveyors
Unfortunately, any digital system today can
and analysis of vibration patterns was able to
only measure certain variables, there are
predict impending failure. When the
limits to the analytical models, and because
vibrations exceed thresholds, the software
most factory systems are complex, unique
triggers a text and email. Team leaders and
and interconnected, there are a limited set of
members follow up immediately and
problems that can be managed purely on a
generally determine they need to schedule
predictive basis. Therefore, we still need
replacement of the motor by maintenance.
people to find the root cause and develop a
As the plant expanded it use, this type of
creative solution to solve the problem. Often
predictive maintenance action played out
solving the problem is a matter of trying
hundreds of times over thousands of pieces
things, experimenting, that is, adaptive
of critical plant machinery, and built
problem solving.
confidence in the system with the team
members.
Intelligent Sensors Case Examples
Going beyond predictive maintenance, the
KCF Technologies is one technology supplier
team members also realized many cases
where the predictive data enabled them to
that believes in the power of digital
make adaptive improvements to system
information to enhance, not replace creative
configurations and standard operating
problem solving by people. Their core
procedures. One example of this was in the
technology is advanced sensors, with the
paint shop. The KCF data consistently
computer power of advanced analytics.
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theleanmag — #18 — february 2024
showed imbalance on the paint shop exhaust
to figure out the best way to extend the
fans, related to build-up of paint on the fan
press’s operational life by an additional two
blades from the process.
years.
This data enabled the team members to
The team deployed a series of “IoT HUBS”,
assess and update the frequency and
which are specifically designed to capture
methods for proactively cleaning the fan
and process data on complex, intermittent
blades, thus eliminating the imbalance
machinery like stamping presses. The sensors
problem that would have otherwise led to
were installed on a press line to test its
premature failure of the fan support bearings
efficacy and data indicated severe
and the motor and structure. The solution was
deterioration in several key components of
not obvious immediately and took a more in-
the press, though the solutions were not
depth problem solving process with some
obvious. Through a process of going to see,
trial and error.
identifying several key causes of failure, and
There are even examples of a combination of
testing various ideas the team came up with
predictive and adaptive. An example of this
an alternative for the hydraulic brake pump
system—which was so out of date that
was at a Toyota facility that stamps out steel
replacement parts were no longer available—
parts. This was an older plant with aging
that resolved a number of the issues. The
equipment. Toward the end of 2022, they had
technology also was also able to anticipate
a debilitating equipment failure on a sub-
the need to replace the main motor bearings,
system of the stamping press. This resulted
which predictably failed 3 months later.
in significant hours of downtime, substantial
stress on the team members, and
But they found even this level of problem
considerable capital expenditures. The team
solving was not enough. The slide adjust
was caught off guard by unplanned failures
assembly required a more intensive problem
and consequently began a collaboration with
solving approach that built upon the
KCF in April 2023 to manage the health of
foundational current and vibration data
their assets more proactively.
supplied by the sensors. The team noticed a
change in the waveform that looked like it
could be a rotor bar fault in the making and
Simply adjusting their already robust
shortly after observed a spike in vibration on
maintenance practices would not be sufficient
the gearbox. As is often the case, there was
to address the particular problems that come
not much downtime to inspect the issue, and
with maintaining older equipment. Toyota’s
the slide adjust was hard to access. A cross-
team determined they needed a combination
functional team identified possible checks
of advanced technology and technical
and ranked them from easiest to hardest,
expertise. They formed an interdisciplinary
creating a feasible and optimized proactive
team of manufacturing engineers, corporate
maintenance plan, supported by remote
and site maintenance staff, and KCF’s analysts
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theleanmag — #18 — february 2024
continuous online monitoring until the
solving. Their experience also suggests that
planned downtime and scheduled inspection
over time, as the low-hanging fruit is picked,
several months later.
an increasing number of the problems require
adaptive problem solving.
Conclusion
We recommend that manufacturing
The lean movement started with its model,
companies that have not invested heavily in
the Toyota Production System. At the core of
the problem-solving capabilities of their front-
lean are people at the gemba, where the
line staff should do so in parallel with
work is done, who respond to abnormalities
introducing advanced digital technologies.
highlighted by the system.
This may mean going slower in introducing
the technologies, including piloting individual
Industry 4.0 is a vision for automated factories
modules before widespread deployment.
run largely by computers that plan and
Some companies are partnering lean
decide what to do and when. Presumably
practitioners with internal IT experts with
problems are solved by the computer
great results, much as we saw in the
systems, often with the help of advanced AI
collaboration between Toyota plant experts
and analytical systems.
and KCF technology experts.
We suspect that over time automation and
There is a different type of problem solving,
digital technologies will lead to fewer people
adaptive, in which the problem cannot be
in the factories doing manual work. But there
easily defined and solutions are not obvious.
will be an even greater need for thinking
Adaptive problem solving requires people at
people at the gemba solving problems as
the gemba who with their senses can find the
they are identified by software and people. In
root cause and solve the problem, often
this way the technology supports the people
through a series of experiments.
and process. n
We have given examples with one type of
technology, offered by KCF Technologies, of
sensors and systems based on engineering
equations that can predict when equipment
will fail and often provide solutions of the
predictive variety. “Often” is not always. KCF
looked at data from customers and estimated
References:
that roughly 50 percent of problems were
1.Jeffrey K. Liker, The Toyota Way, second
predictive and 50 required adaptive problem
edition, McGraw Hill, 2020.
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