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Standing on the shoulders of giants: production concepts versus production applications. The Hitachi Tool Engineering example

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This article analyzes the methodologies of Henry Ford, Taiichi Ohno, and Eliyahu Goldratt and presents explicitly the four principles of flow management according to these operations management philosophies. The differences among them are related to different instances of the same principles in different environments: Ford in his industry focusing on mass production of few (or one) products, Ohno at Toyota with the Toyota production System (TPS), and Goldratt in a wide range of production environments. The concepts are illustrated and tested in a practical case of implementation in the Hitachi Tool Engineering company. Here, we have the classic case of an unlikely successful attempt to implement a methodology (Lean), and how the Theory of Constraints (TOC) solved this issue. Finally, the limits for the solution proposed by Goldratt for operations management (DBR) are described.
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Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
be explained by a lack of serious efforts. This company had
repeatedly tried to implement Lean but the deterioration
in production performance forced them to go back to the
more traditional ways of managing production.
Likewise, the fact that most of Japanese industry did not
implement Lean cannot be attributed to a lack of sufficient
knowledge. Toyota was more than generous in sharing their
knowledge. This company put all the TPS knowledge in the
public domain and even went as far as inviting their direct
competitors to visit their plants. Hitachi, like so many other
companies, was using the available knowledge and was not
shy about hiring the help of the best experts available.
There is an apparent explanation to these companies’
failure to implement Lean; an explanation that is apparent
to any objective observer of a company like Hitachi Tool
Engineering. The failure is due to the fundamental difference
in the production environments. When Taiichi Ohno
developed TPS, he didn’t do it in abstract; he developed it for
his company. It is no wonder that the powerful application
that Ohno developed might not work in fundamentally
different production environments.
But that doesn’t mean that Ohno’s work cannot be
extremely valuable for other environments. The genius of
Ohno is fully revealed when we realize that he faced the
1 Introduction
It is easy to trace the popularity of Lean production
to Toyota’s success. Toyota’s success is undeniable.
Toyota now manufactures as many cars as the traditional
leader GM and does it while making profits. Over
the last five years, Toyota’s average net profit over sales
was 70% higher than the industry average, while GM is
losing money. The success of Toyota is fully attributed to
the Toyota Production System (TPS, this system became
known worldwide first under the name Just-In-Time (JIT)
and later as Lean production. Toyota itself claims that Lean
production does not fully capture its TPS spirit due to
distortions in communications and implementations.). At
least this is the conviction of Toyota’s management – the
stated number one challenge of Toyota is to pass TPS on
as the company’s DNA to the next generation.
Given that Toyota is the flagship of Japan’s industry, one
should expect that Lean would be widely implemented in
Japan. Surprisingly, this is not the case. It is commonly
known in Japan that less than 20% of the manufacturers
have implemented Lean. How come?
It is not because they did not try to implement it. Many
companies in Japan put serious efforts into trying to
implement Lean but failed. One such company is Hitachi
Tool Engineering. Their inability to implement Lean cannot
Eliyahu M. Goldratt
Abstract
This article analyzes the methodologies of Henry Ford, Taiichi Ohno, and Eliyahu
Goldratt and presents explicitly the four principles of flow management according to
these operations management philosophies. The differences among them are related to
different instances of the same principles in different environments: Ford in his industry
focusing on mass production of few (or one) products, Ohno at Toyota with the Toyota
production System (TPS), and Goldratt in a wide range of production environments.
The concepts are illustrated and tested in a practical case of implementation in the
Hitachi Tool Engineering company. Here, we have the classic case of an unlikely
successful attempt to implement a methodology (Lean), and how the Theory of
Constraints (TOC) solved this issue. Finally, the limits for the solution proposed by
Goldratt for operations management (DBR) are described.
Keywords: Operations research. Theory of Constraints TOC. Lean. Toyota Production
System – TPS. Goldratt. Ford. Ohno. Production flow. Flow principles.
Standing on the Shoulders of Giants –
Production concepts versus production applications
The Hitachi Tool Engineering example
334 Goldratt
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
flow Ford limited the space allotted for work-in-process
between each two work centers. That is the essence of the
flow lines as can be verified by the fact that the first flow
lines didn’t have any mechanical means, like conveyers,
to move inventory from one work center to another.
The daring nature of Ford’s method is revealed when one
realizes that a direct consequence of limiting the space is
that when the allotted space is full, the workers feeding it
must stop producing. Therefore, in order to achieve flow,
Ford had to abolish local efficiencies. In other words, flow
lines are flying in the face of conventional wisdom; the
convention that to be effective, every worker and every
work center have to be busy 100% of the time.
One might think that preventing resources from working
continuously will decrease throughput (output) of the
operation. That undesirable effect might have been the
result if Ford would have been satisfied with just limiting
the space. But there is another effect that stems from
restricting the accumulation of inventory. It makes it
very visible to spot the real problems that jeopardize the
flow when one work center in a line stops producing for
more than a short while, soon the whole line stops. Ford
took advantage of the resulting clear visibility to better
balance the flow by addressing and eliminating the apparent
stoppages (Balancing the flow is not equal to balancing the
capacity having the capacity of each work center match
its load – a common mistake made when balancing flow
lines). The end result of abolishing local efficiencies and
balancing the flow is a substantial increase in throughput.
Henry Ford achieved the highest throughput per worker
of any car manufacturing company at the time.
In summary, Ford’s flow lines are based on the following
four concepts:
• Improvingow(orequivalentlyleadtime)isaprimary
objective of operations.
• Thisprimaryobjectiveshouldbetranslatedintoa
practical mechanism that guides the operation when
not to produce (prevents overproduction).
• Localefcienciesmustbeabolished.
• Afocusingprocesstobalanceowmustbeinplace.
Like Ford, Ohno’s primary objective was improving
flow – decreasing lead time as indicated in his response
to the question about what Toyota is doing (OHNO, 1988,
p. ix):
All we are doing is looking at the time line from the
moment the customer gives us an order to the point
when we collect the cash. And we are reducing that
time line… [...]
Ohno faced an almost insurmountable obstacle when
he came to apply the second concept. When the demand
for a single product is high, dedicating a line to producing
each component, as Ford did, is justified. However, at that
exact same situation. At that time, the production system
that revolutionized production was the flow line method
that Henry Ford had developed. Ford’s method was already
used not only in almost all vehicle assemblies, but also in
very different industries like beverages and ammunition.
Also at that time, it was already accepted that flow lines
can and must be implemented only in environments where
the required quantities justify dedication of equipment to
a single product. Whenever the quantities were not big
enough, no one contemplated the possibility of using lines.
No one except for Ohno.
Ohno realized that the concepts that underlie Ford’s
system are generic that the application is restricted to
some type of environments, but the concepts are universal.
Ohno had the clear vision to start from the concepts, the
genius to design an application that is suitable for Toyota’s
environment, where it is not feasible to dedicate equipment to
the production of a component, and the tenacity to overcome
the huge obstacles standing in the way of implementing
such an application. The result is TPS.
Rather than refraining from using the right concepts or,
even worse, trying to force the application in environments
that are apparently too different, we should follow in
Ohno’s footsteps.
In this paper, we will present:
• Thefundamentalconceptsofsupplychains–the
concepts that Lean is based upon,
• Agenericapplicationoftheseconceptsthatcanbe
used in a much wider spectrum of environments, and
• TheimpressiveresultsHitachiToolEngineering
achieved with this broader application.
2 Historical perspective
The manufacturing industry has been shaped by two great
thinkers, Henry Ford and Taiichi Ohno. Ford revolutionized
mass production by introducing the flow lines. Ohno took
Ford’s ideas to the next level in his TPS, a system that
forced the entire industry to change its grasp of inventory
from an asset to a liability.
Ford’s starting point was that the key for effective
production is to concentrate on improving the overall flow
of products through the operations. His efforts to improve
flow were so successful that, by 1926, the lead time from
mining the iron ore to having a completed car, composed
of more than 5,000 parts on the train ready for delivery,
was 81 hours! (FORD, 1988) Eighty years later, no car
manufacturer in the world has been able to achieve, or
even come close, to such a short lead time.
Flow means that inventories in the operation are moving.
When inventory is not moving, inventory accumulates.
Accumulation of inventory takes up space. Therefore, an
intuitive way to achieve better flow is to limit the space
allowed for inventory to accumulate. To achieve better
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Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
to produce. No card no production. The Kanban system
is the practical mechanism that guides the operation when
not to produce (prevents overproduction). Ohno succeeded
to expand Ford’s concepts by changing the base of the
mechanism from space to inventory.
Adhering to the flow concept mandates the abolishment
of local efficiencies. Ohno addressed this issue again
and again in his books, stressing that there is no point
in encouraging people to produce if the products are not
needed in the very short-term. This emphasis is probably
the reason that outside Toyota TPS first became known as
Just-in-time production. (Nevertheless in the Lean literature
there is no explicit stress on the fact that TPS mandates
the abolishing of local efficiencies.)
Once the Kanban system the system that guides the
operation when not to produce was implemented on the
shop floor the immediate reduction in throughput mandated
the mammoth effort to balance the flow. The challenge
that Ohno faced was orders of magnitude bigger than the
one Ford faced. To realize how big the challenge was, it
is enough to highlight just one aspect out of many. Not
like in dedicated line environments, Ohno’s system forced
a work center to frequently switch from producing one
component to another. For most work centers every such
switch necessitates spending time to do the required setup.
Since the containers, by design, called for a relatively
small number of parts the production batches that they
dictated were, many times, ridiculously small relative to
the setup required. Initially for many work centers the time
required for setups was more than the time required for
production, resulting in a significant drop in throughput.
It is no wonder that Ohno faced enormous resistance so
much so that Ohno wrote that his system was referred to
as the ‘abominable Ohno system’ from the late 1940’s to
the early 1960’s (OHNO; MITO, 1988). Ohno (and his
superiors) certainly had an extraordinary determination
and vision to continue to push for the implementation of
a system that for any person who looked at it from a local
perspective, as most shop personnel must have, simply
didn’t make sense.
Ohno had to pave a new way to overcome the setup
obstacle. At the time, and until TPS became famous
worldwide, the traditional way to deal with setups was to
increase the batch size ‘economical batch quantity’ was
the popular name on which thousands of articles were
written (the first was HARRIS, 1913, since then more
articles on that subject are published almost every month).
Ohno ignored all that body of knowledge since yielding
to using ‘economical’ quantities would have doomed his
quest to reduce the lead times. Rather, he insisted that the
setups required are not cast in stone, that the processes can
be modified to drastically reduce the setup time required.
He led the efforts to develop and implement setup reduction
techniques that eventually reduced all setup times in Toyota
time in Japan, the market demand was for small quantities
of a variety of cars. Therefore, Ohno could not dedicate
lines at Toyota. As we already said, all other industries
that faced this situation simply did not contemplate using
lines. Ohno, however, was toying with the idea of using
lines when the equipment is not dedicated, when each work
center is producing a variety of components. The problem
was that in this case using the mechanism of limited space
would lead to gridlocks not all components are available
for assembly (assembly cannot work) while the allotted
space is already full (feeding lines are prevented from
working).
Ohno writes that he realized the solution when he
heard about supermarkets (much before he actually saw
a supermarket during his visit to the US in 1956). He
realized that both supermarkets and the feeding lines at
Toyota needed to manage a large variety of products. In
the supermarkets, products were not jam-packing the
aisles, rather most merchandise was held in the backroom
storage. In the store itself, each product was allocated a
limited shelf space. Only when a product was taken by
a client, replenishment from the backroom storage was
triggered to refill that product’s allotted shelf space. What
Ohno envisioned is the mechanism that would enable him
to guide Toyota’s operation when not to produce. Rather
than using a single limited space between work centers
to restrict work-in-process production, he had to limit
the amount allowed to accumulate of each component
specifically. Based on that realization Ohno designed the
Kanban system.
The Kanban system has been described in numerous
articles and books. In this article we’ll describe just the
essence to show how true Ohno was to the fundamental
concepts. Between each two work centers (to reduce
the number of places containers must be held, Ohno
extensively used U-cells rather than using work centers
that are composed of a single type of machines), and for
each component separately, the accumulation of inventory
is limited by setting a certain number of containers and
the number of units per container. These containers, like
every container in every industry contain also the relevant
paperwork. But, one page of the paperwork –usually a
card (kanban in Japanese) a page that specifies only
the component codename and the number of units per
container, is treated in an unconventional way. When the
succeeding work center withdraws a container for further
processing that card is not moved with the container,
rather it is passed back to the preceding work center. This
is the notification to that work center that a container was
withdrawn, that the allotted inventory is not full. Only in
that case is the preceding work center allowed to produce
(one container of parts specified by the card). In essence
the Kanban system directs each work center when and
what to produce but more importantly it directs when not
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cut the payroll of Toyota (the two main elements of cost);
rather he put all his energies to improve flow.
What is obliterating the picture is that the end result of
focusing on flow and ignoring local cost considerations is
a much lower cost per unit. Exactly like the end result of
abolishing local efficiencies is much higher efficiency of
the labor force. If it looks strange it is because managers
have not yet internalized the conceptual difference in
guiding operations to concentrate on improving throughput
rather than concentrating on reducing costs. One of the
ramifications of concentrating on cost reduction is that almost
all initiatives to foster a process of on-going improvement
quickly reach the point of diminishing returns and as a result
many deteriorate to lip service. But that issue is too broad
and too important to be squeezed into this article.
In summary, both Ford and Ohno followed four concepts
(from now on we’ll refer to them as the concepts of supply
chain):
• Improvingow(orequivalentlyleadtime)isaprimary
objective of operations.
• Thisprimaryobjectiveshouldbetranslatedintoa
practical mechanism that guides the operation when
not to produce (prevents overproduction). Ford used
space; Ohno used inventory.
• Localefcienciesmustbeabolished.
• Afocusingprocesstobalanceowmustbeinplace.
Ford used direct observation. Ohno used the gradual
reduction of the number of containers and then gradual
reduction of parts per container.
3 The boundaries of Toyota
Production SystemTPS
Ohno’s approach in developing Lean demonstrates an
important idea: there is a difference between an application
and the fundamental concepts on which the application is
based. The fundamental concepts are generic; the application
is the translation of the concepts to a specific environment.
As we have already seen the translation is not trivial and
necessitates a number of solution elements. What we have
to bear in mind is that the application makes assumptions
(sometimes hidden assumptions) about the environment. We
should not expect an application to work in environments
for which its assumptions are not valid. We can save a lot
of effort and frustration if we bother to explicitly verbalize
these assumptions.
The most demanding assumption that TPS is making
about the production environment is that it is a stable
environment. And it demands stability in three different
aspects.
The first aspect is revealed once we pay attention to the
fact that, even when an appropriate environment is chosen
and the best experts are supervising the implementation,
to be, at most, just a few minutes. For example, Toyota’s
die changes went from two to three hours in the 1940’s to
less than one hour and as low as 15 minutes in the 1950’s
to 3 minutes in the 1960’s (Ohno, 1988). It is no wonder
that Lean is now strongly associated with small batches
and setup reduction techniques.
But the need to balance the flow necessitated much more
than just dealing with the setup obstacle. The fact that most
work centers were not dedicated to a single component made
it almost impossible to spot by direct observation the real
problems which jeopardize the flow. Ohno was fully aware
that there were too many things that can be improved, that
without a way to focus the process improvement efforts it
would take too long to balance the flow.
The Kanban system provided him such a way. The rocks
and water analogy of Lean is useful for understanding how
this is done. The water level corresponds to the inventory
level, while the rocks are the problems disturbing the
flow. There are many rocks at the bottom of the river and
it takes time and effort to remove them. The question is
which rocks are important to remove. The answer is given
by reducing the water level; those rocks which emerge
above the water are the ones that should be removed. At
the initiation of the Kanban system, to achieve reasonable
throughput, Ohno had to start with many containers each
holding a non-negligible quantity of a particular part.
Gradually, Ohno reduced the number of containers and
then the quantities in each container. If the flow was not
noticeably disturbed, then the reduction of the number of
containers and quantities per container continued. When
the flow was disturbed the Five Why’s method was used to
pinpoint the root cause. It had to be fixed before reducing
quantities resumed. It took time but the end result was a
remarkable improvement in productivity.
It should be noted that even though, in the last twenty
years, every other car company has implemented one
version or another of the Toyota system and reaped major
benefits, the productivity of Toyota is unmatched by any
other car company. This fact points to the importance
of choosing correctly the process that focuses the local
improvement efforts. Unfortunately, the improvement
efforts of other companies are misguided since they are
aimed at achieving cost savings rather than being totally
focused on improving the flow.
Ohno did not invest so much effort in reducing the setup
times in order to gain some cost savings. If saving cost
would have been his target he would not have ‘wasted’ the
time saved by further reducing the batches and therefore
doing much more setups. Ohno did not try to reduce the
number of defective parts in order to save some (trivial)
costs; he did it to eliminate the major disruptions to flow
that result from having a defective part. Ohno did not even
try to squeeze better prices from Toyota suppliers or to
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a mode of receiving orders (and promised deliveries) that
restrict the mix change from one month to another. Most
companies are not able to enforce on their clients such
favorable conditions.
It is important to note that the required stability is outside
the power of production to improve. All three aspects of
stability have to do with the way the company designs and
sells its products and not with the way it produces them.
Unfortunately, the majority of companies suffer from at
least one aspect of instability if not from all three.
The above doesn’t mean that for environments in
which the assumptions of Lean are not valid, fragments
of Lean cannot be used (e.g. U-cells can be helpful in
many environments and setup reduction techniques can
be used in almost every environment). But, it does mean
that in such environments one should not expect to get the
same magnitude of results that Toyota achieves – results
that elevate that company into what it currently is. Using
some specific techniques of Lean, being satisfied with
some cost saving programs, shouldn’t be considered as
implementing Lean.
4 The importance of flow in relatively
unstable environments
Ford and Ohno opened our eyes to the fact that better
flow – reducing lead time – leads to much more effective
operations. They have demonstrated it on stable environments
but what is the impact of improved flow on relatively
unstable environments?
The first aspect of instability is instability due to short
products’ life. When the products’ life is short, overproduction
can become obsolescence. Moreover, since the lifetime is
short, long production lead-times leads to missing the
market demand. For example, suppose that the lifetime of
a product is about 6 months and the production lead-times
of that product is two months. The long production lead
time causes losing sales not because the demand is not there
but because, for a significant period of time, production
cannot satisfy the demand.
The second aspect of instability is instability in demand
over time per product. The common practice in environments
that have a large number of SKUs that are subject to sporadic
demand is to reduce the hassle by trying to satisfy this
demand from stock. The disadvantage of this practice is
high finished goods inventories that turn extremely slowly
coupled with high levels of shortages. A production system
that is capable to organize the shop floor to the extent that
much better flow is achieved has a drastic impact on these
environments.
Environments that suffer from the third aspect of instability
instability in the overall load are the ones that can gain
the most from much better flow. The temporary overloads
on the various resources cause these companies to usually
it takes considerable time to implement Lean. Liker
points out in The Toyota Way that Lean implementations
led by the Toyota Supplier Support Center (TSSC, the
organization Toyota created to teach U.S. companies TPS)
take a minimum of six to nine months per production line
(LIKER, 2004). This is not a surprise to anybody who is
aware of the number of disruptions to flow that exist in
almost any production environment and the sensitivity
of the Kanban system once it starts to reach its target of
low inventory. Since the Kanban system takes time to
implement, its assumption is that the environment is relatively
stable that the processes and the products do not change
significantly for a considerable length of time.
Toyota enjoys a relative stable environment. The car
industry allows changes only once a year (a model year
change) and usually from one year to another the vast
majority of the components are the same. That is not the case
for many other industries. For example, in major sections
of the electronics industry, the life span of most products
is shorter than six months. To some extent, instability of
products and processes exists in most other industries. For
example, Hitachi Tool Engineering is producing cutting tools,
a relatively stable type of product, but fierce competition
forces this company to launch new cutting tools, that require
new technology, every six months. It is a Sisyphean task
to implement Lean in such an environment.
A second aspect of the stability required by TPS is
stability in demand over time per product. Suppose that
the lead time to produce a certain product is two weeks but
the demand for that product is sporadic; on average there
is just one order per quarter for that product. Currently,
this product contributes to the work-in-process only during
two weeks in a quarter; the rest of the time it is not present
on the shop floor. But that will not be the case under Lean,
which mandates permanently holding containers for each
product between each two work centers.
Hitachi Tool Engineering is producing over twenty
thousand different SKUs. For most SKUs the demand is
sporadic. The necessity to permanently hold, for each SKU,
inventory between each two work centers would lead, in the
Hitachi case, to holding considerably more work-in-process
inventory than what they hold today. This is apparently not
a suitable environment for Ohno’s application.
But the most demanding aspect of the stability required
by TPS is stability in total load placed by the orders on
the various types of resources. Suppose that, like in most
companies, the orders are not uniform throughout. It is very
likely that the load placed this week on a particular work
center is considerably lower than its capacity while next
week the load is slightly higher than its capacity. In this very
common case, the Kanban system, that is preventing build
ahead preventing producing ahead of time will lead to
missed due dates in the second week. Toyota’s orders are
relatively stable and nevertheless, Toyota had to establish
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The difficulty in using a time-based system is that, for each
order, we should restrict the release of the corresponding
material to be an appropriate time before the due date
of the order. But how does one go about computing the
appropriate time?
When computers appeared on the industrial scene (in the
early sixties) it looked like we, at last, had the proper tool
to handle the immense amount of details and calculations
needed to compute the appropriate times for each material
and order. Within ten years many computer programs, to do
just that, were developed in numerous companies around
the world. Unfortunately, the expected results of better flow
and less work-in-process did not materialize.
The problem is that the time it takes material to be
converted to a finished product, ready for delivery to the
client, is depending more on the time it has to wait in queues
(waiting for a resource that is busy processing another order
or waiting in front of assembly for another part) and not so
much on the touch time to process the order. It is commonly
known that in almost any industrial operation (except for
process lines and companies that use the Kanban system)
the time that a batch of parts spends being processed is
only about 10% of the lead time. As a result the decision
on when to release the material determines where and how
big the queues will be, which in turns determines how much
time it will take to complete the order, which determines
when to release the material. We were facing a chicken and
egg problem. In the seventies it was suggested to handle
that problem by reiterating the procedure (closed loop
MRP) to run the computer system, to check the resulting
planned overloads on the various resources (the size of the
queues), to adjust the due dates to eliminate the overloads,
and to repeat this process until all meaningful overloads
were eliminated. This suggestion did not last long since
experience showed that the process doesn’t converge; that
no matter how many iterations are done the overloads just
move from one resource type to another.
As a result, already in the seventies, the usage of these
computer systems was not to guide the precise timing
of release of material to the shop floor but rather it was
confined to giving better information on the quantities
(and timing) to order material from the suppliers. The
official name of these systems was coined to reflect their
major usage [...] Material Requirements Planning MRP,
(ORLICKY, 1975).
The fact that such a mammoth effort did not yield
a practical time-based mechanism to guide operations
when not to produce, should not be taken as a proof that
such a mechanism cannot be developed for the less stable
environments environments that must meet the due-dates
of an uneven flow of clients’ orders. It should not even
discourage us from attempting to use time as the base for
a practical mechanism. But it should be a warning against
an approach that tries to develop such a mechanism through
have relatively poor due date performance (<90%) and as
a result they are inclined to add more capacity. Experience
show that when such companies succeed to drastically
improve flow, not only that their due-dates reach the high
nineties but excess capacity, as high as 50%, is revealed
(MABIN; BALDERSTONE, 2000).
Ohno had demonstrated that the concepts that Ford
introduced are not restricted to mass production of a
single type of product. Even though the obstacles to apply
these concepts to a less restrictive environment looked
insurmountable, Ohno’s genius and tenacity proved to us,
not only that it can be done but how to do it.
We now realize that:
• TPSisrestrictedtorelativelystableenvironments,
• Mostenvironmentssufferfrominstability,and
• Relativelyunstableenvironmentshavemuchmoreto
gain from better flow than even stable environments.
Now that we realize the above shouldn’t we follow in
the footsteps of Taiichi Ohno? Shouldn’t we go back to the
supply chain concepts and derive an effective application
that is suitable for the relatively unstable environments?
5 A time-based application
of the supply chain concepts
The most intuitive base for the mechanism to restrict
over-production is not space or inventory but time if
one wants to prevent production ahead of time one should
not release the material ahead of time. Using time as the
base is not only more intuitive, and therefore more easily
accepted by the shop floor; it has an advantage that makes
it suitable for unstable environments it is much less
sensitive to disruptions in flow.
The robustness of the time-based mechanism stems from
the fact that it directly restricts the overall amount of work
in the system rather than doing it through restricting the
amount of work between each two work centers. In flow lines
or Kanban-based systems the allotted inventories between
work centers is restricted to the bare minimum (usually
corresponds to much less than one hour of work). Therefore,
when a work center is down for more than a short while the
succeeding work centers are almost immediately starved
for work and the preceding work centers are “blocked”
from working. When, for any of the work centers, the
accumulation of all the time consumed by starvation and
blockage is more than the excess capacity of that work
center, the throughput of the company is reduced. The
sensitivity of flow lines and Kanban-based systems stems
from the fact that a disruption that occurs in one work center
consumes capacity also from the upstream and downstream
work centers – a phenomenon that (almost) doesn’t exist
for the time-based systems since the work, once released
to the floor, is not artificially restrained.
339
Standing on the Shoulders of Giants – Production concepts versus production applications...
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
jams, the more management attention is needed to sort out
the priorities. The magnitude of the required management
attention as a function of the length of the chosen time
buffer is shown schematically in figure 1.
Operations that implemented Ford’s or Ohno’s systems
enjoy an average lead time which is only a few times longer
than the actual touch time and management do not have to
invest almost any attention to guide the shop floor personnel
on what to work on now. They definitely reside at the left
hand side of the low plateau of that graph. But where on
the graph are the vast majority of operations, operations
that are using the more conventional practice?
As we said, in conventional plants batches of parts spend
only about 10% of the time being processed. About 90%
of the time the batches are either waiting in a queue for a
resource or waiting for another type of part to be assembled
together. What we learned from Ford and more so from
Ohno is that we shouldn’t accept the size of batches as
given; that economical batch quantities are not economical
and instead we should and can strive to reach a one-piece
flow. Armed with that conviction it is easy to realize
that when a batch of parts is being processed (except in
processes like mixing or curing) only one item is actually
worked on while the other items in the batch are waiting.
That means that in conventional companies that use batch
sizes of more than ten units in a batch (which is the case in
the majority of production environments) the touch time
is actually less than 1% of the lead time. There is another
phenomenon that typifies these companies; whatever the
formal priority system is, if a formal priority system exists at
all, the actual priority system is: “hot”, “red hot” and “drop
everything – do it now”. These companies are apparently
high on the right hand side slope of the management
attention versus time buffer graph.
handling the immense amount of details and calculations.
What is needed is a more of a bird’s eye view approach.
Going back to basics, following the concepts of supply
chain, the objective is to improve flow to reduce the
lead time. Taking time (rather than space or inventory) as
the base for the mechanism to guide the operation when
not to produce mandates that we should strive to release
the corresponding material an appropriate short time,
just-in-time, before the due date of the order. But, what
do we mean by ‘just-in time’? Even though the term
‘just in time’ is a key concept in Lean its use is figurative
and not quantitative. In Lean, by production just-in-time
we certainly don’t mean that the part that was worked
on just now is needed to be at the loading dock ready
for shipment in the next second… or minute… or hour.
Actually, it is likely, that even under the best Kanban
systems, this part will not be worked on right away by
the succeeding work center (as can be deduced from the
fact that full containers are routinely waiting between
work centers). So, what time interval will we consider to
be ‘just in time’? More explicitly: if we want to restrict
overproduction by restricting the release of the material,
how much time before the due date of an order should we
release the material for that order?
One way to reach a reasonable answer is through
examining the impact the choice of that time interval has
on the magnitude of the required management attention to
meet all due-dates. Suppose that we release material before
the due date just the time it actually takes to process the
order. Such a choice will necessitate a lot of management
attention to closely monitor operations, since any delay
in any operation or even a delay in moving the parts
between operations will result in missing the due date.
Moreover, precise scheduling will be needed to ensure
that no queues will occur since any queue causes a delay
for the parts waiting in the queue. This is certainly not a
practical choice, even infinite management attention will
not be sufficient to meet all due-dates. We must choose
a longer interval of time; an interval that contains safety
to accommodate delays. The need to include safety is
the reason for referring to the time interval of release of
material before the due-date as the ‘time buffer’.
Choosing longer time buffers elongates the lead time
and increases work in process, but since longer time buffers
means more safety time, expectations are that with much
less management time a higher percentage of orders will
be completed on or before their respective due-dates. This
is correct for relatively short time buffers, but when the
time buffers are considerable, another phenomenon starts
to raise its ugly head. What we have to bear in mind is that
the longer the chosen time buffer, the earlier material is
released which means that more orders are simultaneously
present on the shop floor. When there are too many orders
on the floor, traffic jams start to occur. The more traffic
Time buffer size
Jams,
missed
priorities
Insufficient
reaction time
Management attention
Figure 1. The relationship between management time required and
the time buffer size
340 Goldratt
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
The lesson that Shewhart brought to manufacturing from
Physics, and Deming made known worldwide, is that trying
to be more accurate than the noise (in our case, trying to
use sophisticated algorithms that consider every possible
parameter in an environment of high variability) does not
improve things but makes them worse the results will
most certainly not be an improvement but a deterioration
in due-date performance.
A straightforward priority system emerges when we
recognize that the time buffer, being half of the current
lead time, is still much longer than the touch time and
since it dramatically reduces the traffic jams, without
any interference, many orders will be finished within just
one-third of the time buffer and the majority will be finished
within the first two-thirds of the time buffer. Based on that
realization, priorities are assigned by ‘buffer-management’.
Per batch the time that has passed since its release is
tracked. If less than one third of the time buffer has passed
the priority color is green, if more than one-third but less
than two-thirds the priority color is yellow, if more than
two thirds the color is red, if the due date has passed the
color is black. Blacks have higher priority than reds, etc. If
two batches have the same color, to try and decide which
one should be worked on first is an excellent example of
trying to be more accurate than the noise.
Putting such a system on the shop floor is relatively easy.
In the first step there is no need to do any physical changes,
just to choke the release of material to be half the historical
lead time before the corresponding due-date and guiding
the shop floor to follow the color code priority system.
Compared to the efforts, the impact is impressive. To get
a first hand impression of the impact (and the speed) from
just the first step, Figure 2 gives the actual percentage of
late orders of a 2,000 worker plant that produces thousands
of different metal kitchenware.
Of course local efficiencies must be abolished, otherwise
the pressure to release material too early will continue.
Experience shows that the speed at which everybody on the
shop floor realizes the positive impact, makes that change
almost resistance free.
But, in most environments there are still orders that
miss their due dates and there is still enormous potential
for improvement to capitalize on. The fourth concept must
also be translated into practice a focusing process to
balance flow must be in place.
The first step in balancing the flow is relatively easy.
Choking the release of material exposes the abundant excess
capacity that was masked before. But it is likely that some
work centers have less excess capacity than the rest. These
work centers are flagged since they do have a queue of
inventory in front of them. The fact that local efficiencies
are abolished helps to identify the simple actions required
to increase their capacity simple actions such as ensuring
that a capacity-constrained work center will not stay idle
Being on the right hand side slope means being in a
lose-lose situation; lead times are very long (relative to
the touch time), inventories are high and in many cases the
company suffers from poor due-date performance (<90%)
in spite of high management attention. Bearing in mind that
if management would have chosen a shorter time buffer
(moving into the wide plateau region of the graph) the
situation would be remarkably better, how can it be that
the vast majority of conventionally run companies are in
that lose-lose situation?
The answer was given by Ford and Ohno. Through their
work they, decisively, proved that contrary to the common
belief, striving to constantly activate all resources all the time
is not a recipe for effective operations. On the contrary, the
exact opposite is true; to reach effective operations, local
efficiencies must be abolished. But conventional companies
do try to reach full activation of resources. Whenever the
upstream resources are not bottlenecks (and that is the case
in the vast majority of environments) they will, from time
to time, run out of work. To prevent it, material is released;
material that is needed for more remote orders (or even
for forecasted orders). The unavoidable consequence is
longer queues. Longer queues cause some orders not to be
fulfilled on time which in turn is interpreted as: we should
release the material earlier. And is also interpreted as: we
don’t have enough capacity. It is not difficult to envision
how such forces push companies up the slope.
A good starting point for improving flow will be to
choose the time buffer to be equal to half the current lead
time; such a choice will ensure that the company will find
itself somewhere on the plateau of the graph. There is no
point wasting time by trying to find the optimum point,
the immediate benefits are too significant to postpone
and the next efforts to balance the flow will modify the
graph itself.
Restricting the release of material to be just the time
buffer (half the current lead time) before the corresponding
due-date of the orders will considerably improve the due
date performance, will reduce the lead time to half of what
it is now, and therefore as the excess inventories are flushed
out, will shrink the work-in-process inventory to less than
half of its current level.
But one cannot expect that this change alone will bring
the due-date performance to the high nineties. Simply there
are still many orders on the shop floor, there are queues in
front of resources and leaving to chance the sequence in
which the work is processed will cause many orders not to
finish on time. A priority system is needed. The need for a
priority system should not open the gates for sophisticated
algorithms to set the priorities. Simply, the number of orders
coming in is constantly changing, the content of work
differs from one order to another, the length of the queue is
constantly changing and let’s not forget that disruptions still
occur; in short, this is an environment with high variability.
341
Standing on the Shoulders of Giants – Production concepts versus production applications...
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
than the starting point. Hopefully such top management
behavior is behind us.
The more sensible way to deal with the exposed excess
capacity is to capitalize on it; to encourage the sales
force to take advantage of the improved performance to
gain more sales. The increased sales can easily cause the
emergence of a real bottleneck. Ignoring the bottleneck’s
limited capacity when giving due-date commitments to
new orders will deteriorate the due-date performance and
sales from disappointed clients will plummet. It is essential
to strengthen the tie between sales and operations that is
the real challenge. A system must be put in place to ensure
that every due-date commitment is given only according
to the yet unallocated capacity of the bottleneck.
The bottleneck becomes the ‘drum beat’ for the orders,
the ‘time buffer’ translates due-dates into release dates and
the action of choking the release becomes the ‘rope’ that
ties the order to the release of work. That is the reason
this time-based application of the Theory of Constraints
became known as the Drum-Buffer-Rope system or in
short DBR.
Currently there is widespread experimentation to polish a
process of further improving operations based on recording
and analyzing the reasons for the red orders.
during lunch break or shift changes, offloading work to
less efficient work centers that have ample excess capacity,
etc. (GOLDRATT; COX, 1984).
Since the above actions add effective capacity to the work
centers that cause queues, the queues become shorter and
fewer orders reach the red status. This means that the time
buffer becomes unnecessarily long. An effective rule to adjust
the time buffer, without taking a risk of deteriorating the
high due date performance, is to decrease the time buffer
when the number of red orders is smaller than 5% of the
number of total released orders and to increase it when the
number of red orders is more than 10%.
A company that follows the above will find itself,
within a few months, with very high due date performance,
considerably shorter lead times and ample excess capacity.
This is when the real challenge begins. In the past, sometimes
(too many times) the reaction of top managers to the fully
exposed excess capacity was to ‘right size’ the capacity
and gain cost savings. This is a grave mistake. The ‘excess
capacity’ is employees. Employees that just helped the
company to improve and as a direct consequence are
‘rewarded’ by losing their, or their friends’, jobs. In all the
cases that such an action had been taken, the unavoidable
reaction quickly deteriorated the plant performance to worse
Choking release
0,0%
5,0%
10,0%
15,0%
25,0%
20,0%
30,0%
Late orders
10/5
10/14
10/13
10/19
10/16
10/17
10/18
10/7
10/6
10/9
10/10
10/11
10/12
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11/29
11/28
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11/30
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12/2
12/7
12/5
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11/22
11/21
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11/27
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10/28
11/3
10/31
11/1
11/2
10/23
10/21
10/24
10/25
10/26
10/27
1 month
Figure 2. Real life example of the effect of choking the release on the due date performance.
342 Goldratt
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
Ltd increased from 1.1 billion yen in the fiscal year ending
March 2002 to 5.3 billion yen in the year ending March
2007 – a five-fold increase in net profit in five years. The
profit ratio of Hitachi Tool Engineering Ltd increased
from 7.2% in 2002 to 21.9% in 2007, the highest ratio
ever reported in this type of industry (MURAKAMI,
TAKAHASHI; KOBAYASHI, 2008).
7 The boundaries of DBR
As was highlighted before, an application makes
assumptions (sometimes hidden assumptions) about the
environment and we should not expect the application to
work in environments for which its assumptions are not valid.
The assumption that DBR makes is apparent, it assumes
that the touch time is very small (<10%) compared to the
current lead time. This assumption is valid for many, if not
most, typical production environments. But, definitely it is
not valid for a very broad range of environments that are
traditionally called project environments.
In project environments the touch time is relatively
long and the eagerness of the clients to get the project
completed forces operations to promise lead times which
are only twice (or rarely three times) longer than the
touch time. No wonder that in projects the performance
is bad to the extent that no one expects to get the project
completed on time and on budget and with the full
content; something is expected to give. But, that fact
shouldn’t distract us from the conclusion that since DBR’s
assumption is not valid, DBR is inappropriate for project
environments. A different application, an application that
directly addresses the relatively long touch time is needed
(GOLDRATT, 1996).
6 Example of Hitachi
Hitachi Tool Engineering Ltd., a 24 billion yen company,
designs and manufactures over 20,000 different cutting
tools. The demand for most products is sporadic, and the
industry standards force them to launch new product families
of tools every six months. When new product families are
launched, the older families become obsolete. No wonder
that their efforts to implement Lean were unsuccessful
(UMBLE, M. UMBLE, E.; MURAKAMI, 2006).
Hitachi started to implement DBR in one of their four
plants in Japan in 2000. The jump in due-date performance
(from 40% to 85%) associated with cutting WIP and lead
times in half and the ability to ship 20% more products
with the same labor force encouraged them to expand the
implementation. By 2003, they had implemented DBR in
all four plants (Umble, Umble & Murakami, 2006).
The drastic reduction in lead time and the much better
responsiveness enabled a reduction of inventory in the
supply chain the distributors from 8 months to 2.4
months. The reduction of inventory improved dramatically
the distributors’ return on investment, freed up their cash
and strengthened the relationships between Hitachi and the
distributors. No wonder that the distributors expanded the
product range of Hitachi tools that they were offering, leading
to an increase of 20% in sales (in a stable market).
The true impact is revealed when we evaluate this
company’s bottom line performance in light of the fact
that during the period of 2002 to 2007 the price of raw
materials (metals) increased much more than the increase in
the selling price of cutting tools. Under such conditions the
profits of the company should have vanished. Instead, the
annual net profit before taxes of Hitachi Tool Engineering
Sobre Ombros de Gigantes – Conceitos de produção versus
aplicações na produção. O exemplo da Hitachi Tool Engineering
Resumo
Este artigo avalia as metodologias de Henry Ford, Taiichi Ohno e Elyahu Goldratt e apresenta de maneira explícita
os quatros princípios de gestão do fluxo que estão por trás destas filosofias de gestão de operações. As diferenças
entre estas três são traçadas para diferentes instâncias dos mesmos princípios em ambientes específicos: Ford em sua
indústria, com foco na produção em massa de poucos (ou um) produtos; Ohno na Toyota, com o Toyota Production
System (TPS); e Goldratt, em uma classe ampla de ambientes de produção. Os conceitos são ilustrados e postos à prova
no caso prático da Hitachi Tool Engineering. Este é o caso clássico de tentativa de implementação de uma metodologia
(Lean) que não se aplica e como isto foi resolvido pela Teoria das Restrições (TOC). Finalmente os limites da solução
proposta por Goldratt para a gestão de operações (TPC) são explicitados.
Palavras chave: Pesquisa operacional. Teoria das Restrições. Lean. Sistema de Produção Toyota. TPS. Goldratt. Ford. Ohno.
Fluxo produtivo. Princípios do fluxo.
343
Standing on the Shoulders of Giants – Production concepts versus production applications...
Gest. Prod., São Carlos, v. 16, n. 3, p. 333-343, jul.-set. 2009
References
FORD, H. Today and tomorrow. Oregon: Productivity Press, 1988.
GOLDRATT, E. M.; COX, J. The goal: a process of ongoing
improvement. USA: North River Press, 1984.
GOLDRATT, E. M. Critical Chain. USA: North River Press,
1996.
HARRIS, F. W. Factory. The Magazine of Management, v. 10, n.
2, p. 135-152, 1913.
LIKER, J. K. The Toyota Way. New York: McGraw-Hill, 2004.
MABIN, V. J.; BALDERSTONE, S. J. The world of the theory of
constraints: a review of the international literature. Boca Raton:
CRC Press LLC, 2000.
MURAKAMI, S.; TAKAHASHI, J.; KOBAYASHI, S. A guide to
making ever flourishing company: production, distribution,
marketing and sales. Japan: Chukei Publishing, 2008.
p. 196-207.
OHNO, T. Toyota Production System: beyond large-scale production.
Oregon: Productivity Press, 1988.
OHNO, T.; MITO, S. Just in time for today and tomorrow. Oregon:
Productivity Press, 1988.
ORLICKY, J. Material Requirements Planning. New York:
McGraw-Hill, 1975.
UMBLE, M.; UMBLE E.; MURAKAMI, S. Implementing theory of
constraints in a traditional Japanese manufacturing environment:
the case of Hitachi Tool Engineering. International Journal of
Production Research, v. 44, n. 10, p. 1863-1880, 2006.
Sobre os autores
Eliyahu M. Goldratt
PO Box 204
Yehud, 56100
Israel
e-mail: info@goldrattgroup.com
InvIted PaPer
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Purpose Drum-Buffer-Rope (DBR) is a method to adjust production flows, synchronize the release of materials and enable a process of focused improvement in production systems. Literature on DBR applications in engineer-to-order (ETO) production systems, where customers participate in product design decisions and, consequently, in the way production is planned and executed, is rare. However, the interest in improving production management in ETO systems has received attention from the scientific and business communities. The goal of this research was to evaluate the implementation of DBR in an ETO productive system, critically analyzing the necessary adaptations for its use. Design/methodology/approach This research was conducted through a case study in a company that manufactures electronic equipment, known as avionics, in the aerospace sector. Findings In this context, the contribution of this study consists of evaluation of the implementation of DBR in an ETO productive system, describing the implementation and the necessary adaptations of the DBR to the ETO productive system explored, comparing it with the DBR theoretical proposals and Simplified Drum-Buffer-Rope (S-DBR) methods. Originality/value The study contributes to knowledge by expanding the field of the DBR application to make it more precise, and by applying the theory of constraints, in a general manner, to this type of productive environment.
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The purpose of this article is to investigate the impact of theory of constraints (TOC) and total productive maintenance (TPM) practices on operational performance, and their interlinkage between each other. Constructs that are critical to auto manufacturer’s operational performance have been identified with the help of literature and experts from industry. The impact of TOC and TPM on operational performance has been evaluated. Similarly, impact of competitiveness on operational performance has been evaluated. Further, alternate models are tested and evaluated through structural equation model. It was observed during testing of alternate models that TOC and TPM have a direct impact on operational performance. However, TOC and TPM practices also directly impact overall operational performance, which in turn, influences competitiveness. In comparison of alternate models, the model in which TOC and TPM affect overall equipment efficiency (OEE), human total participation and commitment (HTPC), throughput (T), inventory (I), and operating expense (OE) practices and these further affect the operational performance, is found most appropriate. This study provides some useful implications from industry point of view. TOC and TPM practices are crucial to auto manufacturing industries. TOC and TPM are the core of attaining sustenance in crucial factors, which will have greater impact to achieve operational performance. Overall equipment efficiency, Human total participation, T, I, and operating expense practices are driven by TOC and TPM practices. This crucial factor linkage helps to achieve the desired operation performance. There are very limited studies that have considered both the continuous improvement practices together to achieve better operational performance. In auto manufacturing industry, both TOC and TPM are crucial continuous improvement practices for any organization to drive its growth.
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To cite this version: Hind Bril El-Haouzi, Etienne Valette. Human system integration as a key approach to design manufacturing control system for Industry 4.0: Challenges, barriers, and opportunities. 17th IFAC Symposium on Information Abstract: This paper is intended to discuss the works and challenges raised by Human-System Integration (HSI) as a key approach to deal with the changes that Industry 4.0 will bring to Smart Manufacturing Control System (SMCS). Industry 4.0 has come with many technological advances allowing significant possibilities to enhance flexibility, efficiency and human well-being but also increasing the complexity and the lack of exhaustive view on the behaviour of autonomous agents. This leads to the questions of acceptability, comprehension, or adaptation between humans and systems. This paper suggests some challenging perspectives from the organization, technology, and social dimensions' triptych. An overview of works related to organisation theory, HSI & SMCS have been done to evaluate the barriers, opportunities, and action levers for the future smart manufacturing control systems' challenges.
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This research presents a case study of a virtual ‘textbook’ application of the theory of constraints (TOC) in a Japanese tool manufacturing company. Hitachi Tool Engineering uses state-of-the-art technology to design and manufacture cutting tools known as End-mills. The plant described in this study is a classic V-plant and exhibited all of the standard problems of a traditionally managed V-plant, existing within the unique framework of Japanese work culture. Plant management applied the five focusing steps and used the operations strategy tools, including drum-buffer-rope and buffer management, to improve the system. Following the approach recommended by Eli Goldratt, the thinking process tools of current reality tree and evaporating clouds were used to help identify and resolve problems when the implementation encountered major obstacles. While the implementation was a huge success, the devastating effect of a core problem being left unresolved is well documented. The implementation generated significant improvements in work-in-process inventory, production lead time, on-time delivery, productive capacity, inventory turnover, product quality, sales volume, and profitability. Moreover, management has extended the introduction of TOC to the non-manufacturing functions and TOC is becoming the common company culture that bridges four culturally diverse manufacturing plants.
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Conventional approaches to Material Requirements Planning (MRP) entail an inherent massive data-handling task. Consequently, batch-oriented MRP systems tend to be untimely in their response to change since replanning can be done only periodically—at best, probably once a week. A method of continuous replanning that minimizes the number of accesses to inventory records and bills of material at any given time is presented in this paper. Called net change, this approach offers the user the ability to replan at high frequency, or continuously in a transaction-driven system. The COPICS manuals describe a system that is based on net change material requirements planning.
A guide to making ever flourishing company: production, distribution, marketing and sales
  • S Murakami
  • J Takahashi
  • S Kobayashi
MURAKAMI, S.; TAKAHASHI, J.; KOBAyASHI, S. A guide to making ever flourishing company: production, distribution, marketing and sales. Japan: Chukei Publishing, 2008. p. 196-207.
The Magazine of Management
  • F W Harris
  • Factory
HARRIS, F. W. Factory. The Magazine of Management, v. 10, n. 2, p. 135-152, 1913.
Just in time for today and tomorrow
  • T Ohno
  • S Mito
OHNO, T.; MITO, S. Just in time for today and tomorrow. Oregon: Productivity Press, 1988.