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A Guide to Implementing the Theory of
Constraints (TOC) |
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Quality Will Go Up! Implementing a Theory of Constraints solution will
improve quality. However the quality
improvement is generally a passive consequence. Passive in the sense that implementing the
solution will markedly improve quality even though improved quality was not
the objective. The drivers for this
are both implicit and explicit and revolve around the need to increase
throughput (1). Quality improvement can also be active and
independent of a solution but strongly focused by knowledge of the location
of the constraints, and the local performance measures that drive bottom line
results. This more active role for
quality has been termed TQM II (2, 3) and deserves much greater recognition
both within Theory of Constraints and quality management. Let’s examine both the passive and active aspects of
quality improvement in the context of Theory of Constraints. As inventory goes down quality goes up. If you search the literature you will find
little mention of this aspect.
However, Schonberger was quick to observe improvement in quality in
early just-in-time implementations at Kawasaki in the U.S.A., before the more
general adoption of total quality management (4). Goldratt and Fox also pointed out that “low
inventory equals high quality,” although this was just one of six attributes
that they considered was derived from low inventory (1). Why does product quality improve? In a nutshell, improvement in product quality is
related to work-in-process/lead time reduction, batch size reduction, and the
new-found importance of throughput at the constraint. In a pre-drum-buffer-rope situation for instance,
there is work in process everywhere, everybody is busy, and batch sizes are
large. It is no small wonder then that
if an error is made (and people do make errors) then the number of affected
parts can be quite large, and the time until detection at the next or subsequent
stages can be quite long. Therefore,
when the error is detected it may be hard to determine the conditions under
which it was made. This leads to a
high chance that it may re-occur. Because people are under pressure to maximize
efficiency at their section only, they may decide that it is best to pass-on
a known or suspected defect, knowing that it will be corrected later at a
subsequent section if necessary. When
detection does occur it most probably means a sizeable amount of rework is
needed. The whole batch may have to be
moved back to the source of the error and then expedited forward again. As work-in-process in general comes down in a
drum-buffer-rope implementation then the operators become more aware of
subsequent stages as being their customer.
They are also more aware that errors are more likely to be
attributable to specific situations – theirs.
Thus responsibility increases.
Because errors are detected and rectified earlier they provide a
faster feedback to the operators that something was wrong and the likelihood
of a repetition is decreased because the cause is more likely to be
known. More importantly, as the batch
size comes down, the number of erroneous pieces becomes smaller, even if it
is still an entire batch. Thus the
amount of rework becomes less and less and some “invisible capacity” becomes
available. Invisible, because rework
tends to be under-reported. An important rework factor related to the decrease
in batch size is the increase in frequency.
From an operator’s perspective, two small batches with the same error
are worse than one large batch with the same error. Thus even if the absolute number of errors
doesn’t increase, the frequency of the errors appears to increase and this
provides a very strong impetus for up-stream stations to improve their
quality quickly. These quality improvements are implicit and a
consequence of batch-size and work-in-process reductions. The explicit considerations come from the awareness
that as a part of exploiting the constraint, defective parts shouldn’t be
allowed to be processed by the constraint.
Also buffer management makes it obvious if material is late for the
constraint as a consequence of reworking a defect earlier in the process. This is a powerful feedback mechanism. Stein points out the obvious disconnect between many
TQM activities and increase in overall company profitability. For instance he quotes the following from
the 20 highest 1998 and 1999 American Malcolm Baldrige Awards; Increased
Product Reliability – 11% Reduced
Complaints – 11% Reduced
Processing Time – 12% Increased
Return of Assets – 1.3% Although certainly not the first person to point out
this problem, he then asks; “what would be the impact if a program were
developed which could systematically identify those things which, if
improved, would result in an immediate increase in profit. And, if placed end to end, would create a
process of ‘continuous profit improvement (2).’” The program for continuous profit
improvement is, of course, TQM II. TQM II postulates a set of 7 principles that serve
as guidelines to help in understanding how to focus efforts to maximize
profit through this approach. They
are; (1) Quality is a necessary condition. (2) Every solution will serve to
invalidate itself over time. (3) The throughput of the system is
determined by its constraints (4) The value of an activity is
determined by the limitations of the system (5) The utilization of any resource
may be determined by any other resource in a chain of events. (6) The level of inventory and
operating expense is determined by the attributes of the non-constraints (7) Resources are to be utilized in
the creation or protection of throughput, and not merely activated. TQM II presents a balanced and structured approach
to focusing the many very good quality tools on the business with the
objective of improving the bottom line.
Quality practitioners will find a ready made and accessible framework
with which they can substantially improve their impact. With knowledge of the system’s constraints
and the throughput generated we can actively pursue quality initiatives and
know the impact on the overall business.
As we have with both accounting systems and manufacturing systems
previously, we can capture quality systems in a diagram that better illustrates
there broader developmental relationships.
Quality improvement in Theory of Constraints doesn’t
just apply to manufacturing. In most
Theory of Constraint applications the negative feedback loops should become
positive; the feedback path should become shorter and more frequent. This will have a positive influence on
service quality regardless of whether it is in; supply chain distribution or
marshalling, sales, marketing, or project management.
(1) Goldratt, E. M., and Fox, R. E. (1986) The
Race. North River Press, pp 40-45. (2) Stein, R. E., (1994) The next phase of
total quality management: TQM II and the focus on profitability. Marcel Dekker, pp 2-3, 4, 103-105. (3) Stein, R. E., (1996)
Re-engineering the manufacturing system: applying the theory of constraints
(TOC). Marcel Dekker, 306 pp. (4) Schonberger, R. J., (1982) Japanese
manufacturing techniques: nine hidden lessons in simplicity. The Free Press, 260 pp. This Webpage Copyright © 2003-2009 by Dr K. J.
Youngman |
