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A Guide to Implementing the Theory of
Constraints (TOC) |
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Let’s Start By Stopping For A Moment Slow
down, block out the rest of the world for a moment, and take the time to
consider the following. Have you ever
found yourself in a situation where you were waiting for someone to provide
you with something, something that you must work on, and in-turn, which you
had to pass on to
someone else? Someone else who may
have also been waiting for that particular piece of work? And it wasn’t as though you had no other
work – correct? In fact, it is most
likely that you were up to your eyeballs in work, other work – but not the
work that you needed at that moment.
In fact its quite likely that you were continuing to receive work that
you didn’t particularly need or want at the moment, and you were passing on
work that others didn’t seem particularly enthused to receive, even if they
had been complaining about it only a few days previous. You probably wondered why you hurried. I
doubt that there are very many who can’t relate to this. In fact, for many of us, in our day-to-day
working lives, it is the rule rather than the exception.
It doesn’t really matter if you are on the shop floor or in corporate,
manufacturing or services, it pretty much seems universal. Universal
or not, does it need to be like this? I
hope to show you that, indeed, it does not need to be like this at all. What
this short moment of reflection shows is that most of us already have a
pretty good intuition for the concepts of dependency, variation, and an
understanding of systems and therefore the necessary intuition to find the
needed solutions. And really that is
all you require to understand the Theory of Constraints. Theory
of Constraints is the invention of Dr Eliyahu Goldratt, an Israeli physicist,
educator, and management specialist. It’s
a business philosophy
which seeks to strive towards the global objective, or goal, of a system
through an understanding of the underlying cause and effect dependency and
variation of the system in question. It
is equally applicable to for-profit and not-for-profit organizations for; strategy, personnel,
marketing, sales, distribution, manufacturing, design, and project
management. This
web site is designed to be an introduction to some of the important concepts
of the Theory of Constraints and its successful implementation. It is biased a little toward the manufacturing end of the
spectrum. But if you run a hospital
for instance and can’t see the parallels, then please let me know. It
is likely that you will have arrived at this material from a number of
different directions; (1)
Companies that are already very successful (we
aren’t making enough money). (2)
Companies that are not successful and in need
of turnaround (we aren’t making any money). (3)
Not-for-profit ventures (we can’t achieve
sufficient output with our current funding). In each case we are making
insufficient of whatever it is that the organization needs to make in order
to be successful. In other words, we
are, in some way, constrained. Although
Theory of Constraints developed out of for-profit based manufacturing, it has a much broader and more general
applicability. People in
not-for-profit organizations, or service organizations, or a paper-based
transaction will find the principles equally relevant and applicable. Indeed you probably suffer from “flavor
of the month syndrome” – and we all do to some extent – then get your very
best “healthy skepticism hat” out and read on. But consider, also, that the very existence
of something called flavor of the month syndrome must demonstrate a deeper
and on-going need or a desire to improve something – our organizations – our
working lives. The problem seems to be
that we are constantly seeking something out, and constantly being forced to
abandon those methods that fail to deliver the results that we seek. Equally,
be aware that it is almost 30 years since the beginnings of the solution to
the problem that has given rise to the concept of Theory of Constraints, and
yet it is still with us (1). And think
of just-in-time with its beginnings by Eiji Toyoda and Taiichi Ohno in the early 1950’s, and yet
it is still with us (2). What then of
statistical process control with an even deeper past extending back to Walter
Shewhart’s work in the 1920’s, and yet it too is still with us (3). Why
is it that after 30, or 50, or 80 years, these 3 particular methodologies are
still with us, offering solutions that are just as valid today as they were
at the time of their inception? Think
about it. Why
is this? Well
all of these
approaches are systemic. Note the
spelling; it’s not a mistake, systemic rather than systematic. Certainly these approaches are also
systematic as well as we will shall see, however, systemic approaches are
concerned with the system as a whole, not with parts of a system in
isolation. Think
back for a moment to the introduction when we imagined that we were stuck
somewhere in a process. Did the
process look something like this?
“From a very early age,
we are taught to break apart problems, to fragment the world. This apparently makes the complex tasks and
subjects more manageable, but we pay a hidden, enormous price. We can no longer see the consequences of
our actions; we lose our intrinsic sense of connection to the larger whole.” So
we do seem to have an innate systemic understanding but we tend to still
break things down into sub-parts. Just-in-time, statistical
process control, and Theory of Constraints are all system approaches. Goldratt characterizes a system approach as
a warning against “concentrating on a local optima (in place or time) and, by
that, jeopardizing the performance of the system as a whole (5).” More specifically,
statistical process control tells us that “as long as the deviation of a
process is within the statistical boundaries, the biggest mistake a person
can make is to re-align the process.
It is a warning against local optima in time; at this instant you may
think that you have brought the process to be closer to the average but
actually you enlarged the deviation of the system.” With regard to kanban in just-in-time “what
is it if not a technique that is built to prevent the local optima of a
worker producing all the time, producing even when down-stream operations do
not now need the results of his/her work?” In
Theory of Constraints the key to not concentrating on local optima
everywhere is described by a special term – subordination. We will return to subordination issues often. We
mentioned that all of these methods were systematic as well as systemic. In order to be systematic we need a
focusing mechanism to guide us where to place our attention first, second,
and so forth. The focusing mechanism
for just-in-time and
statistical process control is Pareto analysis; the focusing system for
Theory of Constraints is the 5 focusing steps which we will describe in full
in the page on process of change. Each
of these methods while acknowledging the overall system also acknowledges that within the
system there is dependency and variation.
It is acknowledgement of variation that sets these particular
approaches far apart from many other business approaches. Have you ever seen a budget prepared with a
target goal +/- some variation for error?
I don’t think so. The variation
is there without doubt, but it is not acknowledged. And if it is not acknowledged it can’t be
managed. In fact, we manage to try and
ignore it by consistently estimating on the low side with lots of safety tucked
away everywhere. If we have lots of
safety tucked away everywhere then once and a while we should exceed our
target goal – ever wondered why that doesn’t seem to happen? It seems then that we never get to really
know our true potential. Almost by default,
because we have defined a system with boundaries of some sort, we have also
defined that there must be finite capacity within that system. Usually defined by the slowest step in the
process in just-in-time and Theory of Constraints. It is sometimes useful to consider this
step as the weakest link in a chain.
And just as you can ignore the weakest link in a chain, but the
weakest link won’t ignore you, so too can you ignore the slowest step in a
process but the slowest step won’t ignore you. In other words, regardless of what you do,
the slowest step or weakest link will determine the rate or strength of the
whole system. Batching is something
manufacturing people do – right? Well
then consider this. Have you ever
heard someone say to you; “Well, we like your idea but we can’t consider your
proposal/request until next week when we usually consider these?” Ah, that sounds like batching to me,
batching in time. How about; “Yes, we like
your idea but we can’t process your application until we have a sufficient
number, please put it over there.” Ah,
that sounds like batching too, batching in quantity. You see we are all surrounded by batching
actions, some so common we hardly think about it at all. Is this important? Well I think so, especially if you consider
the comments about dependency and variation.
Batching increases the variation in the system and exacerbates the
dependencies. Imagine you are a
downstream step from a batching operation of some sort. Maybe towards the end of the current batch
you are beginning to run out of work, you slow down, it’s important to keep
busy after all. But it’s also like the
calm before the storm; you know that when the next lot of work is released
then suddenly you will have a lot of work on your hands and you will have to
work quite quickly to move some of it through. So the work load is variable for no other
reason than we had to batch before your position in the process. Variation increases with batching. The batching also
highlights dependency. You probably
had productive time towards the end of the last batch – that calm before the
storm – but you couldn’t use it. It
was lost to the system, and now that you are in the middle of the current
batch – up to your neck in work – you have no excess or spare capacity to
call upon to help you. If only you could
have somehow magically stored that capacity from the quiet period and shipped
it to the busy period when it would have been useful. So, batching also highlights dependency. There
are strong parallels between the systemic approaches we have been talking
about and the concepts of detail and dynamic complexity of Senge. In the Fifth Discipline he describes these two types of complexity
(4); (1)
Detail complexity – the sort where there are
very many different variables to consider. (2)
Dynamic complexity – the sort where cause and
effect are subtle and the effect over time is not obvious. In
operating a process, regardless of whether it is a small job shop or a huge
factory, and regardless of whether it contains highly variable human patients
or uniform mechanical parts, is full of detail complexity. However the underlying dynamics are limited in variety and not
particularly complex. When
someone tells you that there are 17 different ways to grind a drill shank
they are telling you about detail complexity.
When someone tells you there are 3000 stock items, again they are telling you about detail
complexity. However,
when an action has different effects over the short run and the long run we
are really looking at dynamic complexity.
When an action has one set of consequences locally and a very
different set of consequences in another part of the system, then there is dynamic complexity. When obvious interventions produce non-obvious
consequences, there is dynamic complexity. “The
real leverage in most management situations lies in understanding dynamic
complexity, not detail
complexity (4).” Restated; the real
leverage in most management situations lies in understanding cause and effect
dependency and variation. Let’s
be clear then, there is nothing wrong with detail complexity when it is
applied to various attributes of products in a process. In
fact, you already have the expertise to deal with this; otherwise you
wouldn’t be in business. The error, if
you like, is when we attempt to apply detail complexity to the process that
produces the products. This is the
domain of dynamic complexity. We
can combine the dynamic and detail complexity concepts of Senge with the
global objective versus local objective concepts of Goldratt in a simple 2 by
2 matrix. This is what we get.
This
gives us two diametrically opposed concepts.
The first is a combination of dynamic complexity and global
optimization which we will call the systemic/global optimum approach. The second is the combination of detail
complexity and local optimization which we will call the reductionist/local
optima approach. These are two key
concepts that underline much of the discussion and development in the following pages. Let’s list these for further clarity; (1)
Reductionist/local
optima approach. (2)
Systemic/global
optimum approach. They
represent two different lenses through which we can view our organizations. Let’s
return to Senge once more; “The most powerful learning comes from direct
experience. Indeed we learn eating,
crawling, walking, and communicating through direct trial and error – through
taking an action and seeing the consequences of that action; then taking a
new and different
action. But what happens when we can
no longer observe the consequences of our actions? What happens if the primary consequences of
our actions are in the distant future or in a distant part of the larger
system within which we operate? We
each have a ‘learning horizon,’ a breadth of vision in time and space within
which we assess our effectiveness.
When our actions have consequences beyond our learning horizon, it
becomes impossible to learn from direct experience (4).” “Herein
lies the core
learning dilemma that confronts organizations: we learn best from experience
but we never directly experience the consequences of many of our most
important decisions.” What
if there was a simple way to verbalize and capture the cause and effect which we never directly experience, but
which we none-the-less have the intuition for? What if we could overcome this core
learning dilemma? Senge proposed
computer-based “microworlds,” but what if we could do it on the back of an
envelope – pen and paper? There is a
mechanism that allows us to verbalize, construct, analyze, and communicate
these cause and effect relationships, and moreover, to propose workable
solutions to the problems that they cause.
This is known as the Thinking Process. Mention of the Thinking Process brings
us to the concept of elegance. In the
sciences the meaning of elegance is one of simplicity and ingeniousness. An elegant solution to a problem is a cause
for considerable respect. Theory of
Constraints seeks elegant solutions to problems, rather than sophisticated
ones. Elegant solutions are more
likely to have broken some deeper core or underlying problem; sophisticated
solutions are likely to have addressed a limited number of higher order
problems (symptoms) while leaving the underlying core problem unresolved. The
solutions may be elegant, but they are also incredibly robust. This robustness means that doing something,
anything, which is aligned with the direction of the solution, is most likely
to bring about an improvement
or movement towards that improvement.
In contrast the surest way to fail is to sit around measuring and data
gathering and waiting until you are sure your implementation will be
perfect. It won’t be perfect because
it will never start. Robust solutions
can stand a lot of rough handling. The
best thing to do then is to do something. If
a picture is worth a 1000 words, then please accept the following as a visual
summary.
Most
of all,
Theory
of Constraints is a work-in-progress.
It continues
to evolve into new areas as people discover its broader applicability and it
also continues to improve in delivery in established areas as people refine
their approaches. Hopefully
you have been armed with some key concepts.
We want to improve systems. We need to recognize that
dependency, variation, and finite capacity exist in these systems. We need to understand that seemingly
complex cause and effect occurs via these dependencies, both in time and in
space, and this has been termed dynamic complexity. Understanding these features is the key to
gaining control and moving our system in the direction that we desire. Let’s
have look then at the effect of this on the bottom line. (1)
McMullen, T. B., (1998) Introduction to the Theory of Constraints (TOC)
management system. St. Lucie Press, pg
114. (2)
Ohno, T., (1978) Toyota production system: beyond large-scale
production. English translation,
Productivity Press, pp 75-92. (3)
Lepore, D., and Cohen,
O., (1999) Deming and Goldratt: the Theory of Constraints and The System of
Profound Knowledge. North River Press,
pg 45. (4)
Senge, P. M., (1990) The fifth discipline: the art & practice of the
learning organization. Random House,
pp 3, 23, 71-72. (5)
Goldratt, E. M., In: Cox, J. F., and Spencer, M. S., (1998) The constraints
management handbook. St Lucie Press,
pp ix-xi. This
Webpage Copyright © 2003-2009 by Dr K. J. Youngman |
