In his book The
Goal, Eliyahu Goldratt presents Alex Rogo, a plant manager who received the
task of improving a plant’s results in no more than three months, otherwise it
will be closed. What to do in such a short schedule? He found the answer while
accompanying his son's Boy Scout troop in a 20-mile hike, where he faced the
following problem:
Ahead of the
group, fast-walking Andy was trying to set a new speed record. Behind, fat
Herbie was setting the slowest pace. So, the group started to spread in the
woods (like an increasing inventory level in a plant). They were supposed to
cover 10 miles in five hours, but they are going just half the necessary speed.
What to do to improve their efficiency?
To solve the Boy
Scout troop problem, Alex Rogo asked the group to stop and join hands. Than,
like dragging a chain, he passed through the line until the entire troop had
exactly the opposite order they had before. They started walking again with the
condition that nobody could pass anybody.
This time the
group stuck together, but some of the boys started to complain that they were
going too slow. In fact, keeping that pace they couldn’t accomplish the planned
schedule. So it was necessary to elevate the constrain, the first boy in the
line, the fat Herbie. They realized that Herbie’s pack was too heavy, full of
things like soda, spaghetti, candy bars, pickles, and even an iron skillet.
Then they decided to split the load among others members of the team and, this
time, they really improved their performance as a group.
In The Goal, the company’s traditional cost accounting
and variance reporting system was responsible for many of the problems the
factory was experiencing. Instead of focusing efforts on activities that would
increase profits, the company’s
traditional management accounting system focused attention mainly on
counterproductive efforts to reduce unit product costs. If real improvements
had been made in operations, the management accounting system almost invariably
would have sent inappropriate signals in the form of unfavorable cost
variances. Therefore, as a precondition to improving,
Alex had to throw out the old cost accounting and variance reporting systems.
He then completely redesigned the accounting and performance reporting system
from the ground. (Noreen et al., 1995, pp. 2-3)
Israeli physicist
Eliyahu Goldratt notice that there was plenty of room to improve the
exploitation of resources in manufactures plants. With his associates he
created a company named Creative Output, in the beginning of 1980’s, developing
a software to schedule jobs through manufacturing processes, focusing on
bottleneck operations.
(...) Goldratt has a doctorate in physics and
became involved in business almost accidentally. A friend was having difficulty
scheduling work at a plant that built chicken coops. Goldratt was intrigued by
the problem and conceived an innovative scheduling system that permitted a
dramatic increase in completed chicken coops with no increase in operating
expenses. Goldratt discovered that there was no satisfactory job shop
scheduling software available on the market, so he incorporated his ideas in a
software product called OPT that was launched in 1978. (Noreen et al.,
1995, pp. 2-3)
The Theory of
Constraints is the result of broadening this scope, applying a problem-solving
approach which seeks to improve the global objective of a system through the
understanding of the underlying causes and effects dependency and variations.
The core idea in the Theory of Constraints (TOC) is
that every real system such as a profit-making enterprise must have at least
one constraint. If it were not true, then the system would produce an infinite
amount of whatever it strives for. In the case of a profit-making enterprise,
it would be infinite profits. Because a constraint is a factor that limits the
system from getting more of
whatever it strives for, then a business manager who wants more profits must
manage constraints. There really is no choice in this matter. Either you manage
constraints or they manage you. The constraints will determine the output of
the system whether they are acknowledged and managed or not. (Noreen et al.,
1995, p. xix)
At first glance it
may looks like simple, but this is not exactly the case considering the way
people used to think and evaluate problems, as stated by Peter Senge:
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. (Senge, 1990, p.
3)
The first step is
to break the current view (focusing on local optima) and start to consider the
effects of local changes in the whole results.
This is exactly the
same principle created by Justus von Liebig (1803-1873), known as the “Law of
the Minimum”, which states that the yield potential of a crop is determined by
the poorer of all nutrients. If a nutrient is deficient or lacking, plant
growth will be poor even if all the other nutritive elements are abundant. The
growth of the crop can only be improved by increasing the amount of the
limiting nutrient. Goldratt’s original contribution was to emphasize the same
principle to business management:
The first step is to recognize that every system was
built for a purpose, we didn’t create our organizations juts for the sake of
their existence. Thus, every action
take by any organ – any part of the organization – should be judge by its
impact on the overall purpose. This immediately implies that, before we can deal with the improvement of
any section of a system, we must first define the system’s global goal; and the
measurements that will enable us to judge the impact of any subsystem and any
local decision, on this global goal. (Goldratt, 1990, p. 4)
This vision
inspired the analogy of the system as a chain. If we imagine a system as a
chain, the only way to increase its strength is to improve the weakest link.
This is the point where the chain will broke soon or later. If we improve any
other link, letting the weakest one aside, the strength of the chain will keep
absolutely the same as before (as stated in the “Law of the Minimum”). Only
when the weakest link is strengthen the overall system will be improved. And when
the weakest link is strengthen to the degree that it is no longer the weakest
link, there will be another weakest link limiting the system performance.
The concept behind
the “Law of the Minimum” and the view of the system as a chain are the basis
for the Theory of Constraints. This primary approach constitutes a valuable
orientation defining which actions should be taken to improve the overall
performance of a system. The constraint can be found inside the organization
(lack of hardware, software, people, machines, etc.), can be the result of a
controlling policy, or can be located outside the company (market, suppliers,
infrastructure, etc.).
Focusing local
optima, not considering the overall impact of compartmentalized efforts, is
just waste of time and money. Unfortunately, focusing local performance,
without identification of neither the weakest link nor the overall results, is
common sense in organizations. That’s why managers are asked to improve local
performance and demand the same to their subordinates. But the sad true is that
the system will increase only at the same rate of improvement in its constrain
(the weakest link). All other efforts, increasing output in non-constraint
parts, will only result in waste of time and money. Instead of focusing on the
best solution for each component it is necessary to look for the best possible
solution for the entire system (global optimum), avoiding emphasis on local
optimum. The overall result, in an ongoing system, is not exactly the sum of
the results of each part, it is the result the weakest part makes possible in
interaction with the others.
To make things
clear, let’s consider a system of five resources (can be a production line,
sales department, etc.), as presented in figure 1:
Figure 1: A linear system of 5 resources.
Now suppose, for
simplicity, that this system produces only one item. Let’s say it can produce
10 units of item A per hour. But there is only market for selling 8 units. In
this case, the system should produce only 8 items per hour, according to the
constraint in this case (the market). Production above 8 units per hour will
only represent cost since it cannot be sold, at least not without some kind of
promotion which could affect profit margins. In a situation like that company’s
efforts should concentrate in marketing and sales, not in improving the
production output.
Suppose now the
demand for product A increased to 12 units per hour. The system can only
produces 10 units/ hour, so these 2 extra units will represent unsatisfied
customers, damaging the image of the company and attracting new competitors to
the market. A fast solution would be expand the working time with extra hours
(which would be costly and can negatively affect productivity and quality) or
even outsource part of the production (if possible). But suppose these
alternatives are not available. What should be done?
Let's consider in
more detail the capacity of each resource. The fact that the system is
producing 10 units per hour doesn’t imply that each one of the resources
produces exactly 10 units per hour. If this is the case, the system should be
absolutely perfect, with each resource producing in a harmoniously constant way
where each unit produced requires the same time (zero variations). There is no
rework to be done, nor unexpected setups, problems in machines or with workers.
But, if any variance occurs in one machine, all the sections downstream would
be affected and the system could not produce 10 units per hour.
The fact that the
system is able to produce 10 units/ hour doesn’t mean that all resources can
(or should) produce only at this rate. In fact the system produces 10 units/
hour just because there might be one resource – and probably just one – that
could not produce at a higher rate. And this one is the constraint, the weakest
link of the system. A local analysis reveals the real capacity of each
resource, as presented in figure 2:
Figure 2:
Capacity level in each resource of a linear system.
As we see, only
resource 4 has the capacity of 10 units/ hour. This is the resource which is
limiting the system’s performance. If we want to increase the throughput it is
mandatory to improve the capacity of resource 4. Any effort to improve capacity
in other resource will only represents increasing costs, not affecting overall
performance.
Before analyzing
what could be done to improve capacity in resource 4, we should consider the
flow of work through the system (how it is balanced). Imagine what would happen
if the production schedule does not consider the impact of different capacity
in resources? Unfortunately this is a common situation, because managers are
asked to improve local optima. In fact, we are used to think the way that local
performance anywhere improves overall results.
So, what would be
the result of exploiting capacity in any resource of this system except
resource 4? Probably only increase of inventory in process. The throughput rate
will keep the same at 10 units per hour. There is absolutely nonsense trying to
reach local optimum everywhere. But who never observed a manufacture plant
piled up of material in process?
In our example, if
we schedule production for 10 units per hour rate in each resource, the overall
result will certainly be less than 10 units/ hour. Simply because any variance
which results in less than 10 units/ hour in one resource would not be recovery
by the others – this “balanced” system wouldn’t have any inventory in process
for safety. And if at some time the system does not produce 10 units it also
would not produce a higher rate afterwards in order to recover the production
level.
In synchronous manufacturing thinking, however, making
all capacities the same is viewed as a bad decision. Such a balance would be
possible only if the output times of all stations were constant or had a very
narrow distribution. A normal variation in output times causes downstream
stations to have idle time when upstream stations take longer to process.
Conversely, when upstream stations process in a shorter time, inventory builds
up between the stations. The effect of the statistical variation is cumulative.
The only way that this variation can be smoothed is by increasing
work-in-process to absorb the variation (a bad choice because we should be
trying to reduce work-in-process) or increasing capacities downstream to be
able to make up for the longer upstream times. The rule here is that capacities
within the process sequence should not be balanced to the same levels. Rather,
attempts should be made to balance the flow of products through the system.
When flow is balanced, capacities are unbalanced.
(...) Rather than balancing capacities, the flow of
product through the system should be balanced. (Aquilano et al., 2001, pp.
668-669)
How can we balance
the system? For that purpose Theory of Constraints has a methodology called
drum-buffer-rope. Since we assume variability in the system as natural it
should be important to keep inventory of finished goods, if possible. This
inventory of finished goods, called shipping buffer, provides safety against
problems in production line that could affect the sales of the company. This
way the shipping buffer signals what level of output is necessary in the
system. If finished goods inventory increases, than sales are decreasing and
might not be necessary to keep the same production rate of 10 units/ hour. The
shipping buffer dictates the output rate of the system (like a drum dictates
the cadence for rowers on a boat), and signals how much material should be
released into the system to accomplish the necessary output. Figure 3
exemplifies this drum-buffer-rope system:
Figure 3:
Drum-buffer-rope system.
But there is still
a missing point. As stated before, the market now is not the constraint of the
system. In fact, if demand is higher than supply (as indicated by the fact that
the system could sell 12 units/ hour if it would have productive capacity) it
will not be possible to keep a shipping buffer, which would also not be
necessary, since everything produced would sell immediately (output rate is not
dictated by the market, but by resource number 4).
Next figure
presents the situation where the drum is represented by resource 4, which
determines the amount of material that should be released in the system
(represented by a rope). But if there is a problem at resources 1, 2, or 3,
resource 4 wouldn’t be able to keep the output rate of 10 units/ hour and the
entire system would be affected. Resource 5 will suffer the lack of material
and, without a good shipping buffer, the sales level will decrease.
Figure 4:
Drum-buffer-rope system with constraint at resource 4.
Situation gets
even worse when the market is seeking for more products than it is possible to
produce. In this case there is not a shipping buffer, so any delay in
production represents a real decrease in sales.
Since the market
is buying, let’s forget the shipping buffer (otherwise we would keep it). Now
we need to do something to protect the output rate of the system – and as the
output rate is dictated by the constraint (drum), we should provide a shelter
for the performance of resource 4. The solution is to keep inventory of
material ready to use just in front of resource 4. If anything goes wrong at
resources 1, 2, or 3, the system will not be immediately affected, since the
constrained resource 4 could use this inventory to keep with the rate of 10
units/ hour. As soon as the problem is solved, the resources upstream should
use their extra capacity (each of them can produce more than the constraint
resource) to recover the previous level of the constraint buffer. When market
is not the constraint, we should focus on keeping resource 4 producing without
interruptions (the real problem is that if anything goes wrong exactly at
resource 4).
In the same way,
if something happens to resource 5 the constrained resource 4 should not stop.
So it would be necessary to leave free space in front of resource 4 (space
buffer) in order to place its completed parts. Afterwards, resource 5 can use
its extra capacity to eliminate excess inventory. Now the system is balanced
according to the drum-buffer-rope methodology. On one hand, the output rate is
dictated by resource 4 (drum) which regulates the production of upstream
resources and the quantity of material to be released into the system (this is
also regulated by the level of the constraint buffer, in case of any problem).
On the other hand, the market regulates the necessary output for the constraint
(depending on the system it might be necessary a shipping buffer – probably not
the case if market seeks to buy more than the system can produce).
Unfortunately,
most systems are not simple as this example. There might be different products
and occasions which could also imply in different constraints (keep the system
balanced is not an easy task). But the point to highlight is that the overall
system output rate is dictated by the constrained resource and not by the
capacity of each resource individually.
For instance,
consider again resource 1 in the example. It has capacity for producing 25
units per hour, even though the system output rate is only 10 units/ hour. So,
we could say that resource 1 is working only at 40% of its capacity. What is
the current view about it? Bad productivity, low efficiency. Let’s suppose one
hour of resource 1 costs $50. Producing 10 units/ hour means that each unit
costs $5 at this resource. It would cost only $2 if it produces at 100%
capacity. Most managers would increase production rate in this resource
concerned with “saving costs”. But what will be the result for the entire
system? Sales will increase? No, the overall output rate will keep at 10 units/
hour due to constraint in resource 4. Costs will decrease? On the contrary,
since certainly the inventory level throughout the system will be higher (more
money invested to produce the same as before).
The local measure
of cost per unit is just an illusion. But to overcome the assumption that a
resource idle just represents waste is a heresy. It’s necessary to break a
paradigm and states that if we want to exploit a system efficiently the first
step is to forget local efficiency, then identify the constraint and work to
elevate its output rate.
Appropriate performance measures are essential to
organizational control. Production workers are typically evaluated by efficiency
to standard time or rate. The use of these measures encourages workers to
maximize output at each resource. Worse, these measures encourage a worker to
make more pieces than are
currently needed rather than to waste time setting up to make parts that are
needed. Clearly, non-constraints should produce only in quantities sufficient
to supply the constraint for short term requirements. Any additional production
will be excess inventory that may never be sold. The assumption behind the use
of local efficiency measures is that the system’s output will be maximize (or
cost minimized) if each resource’s output is maximized. Local productivity
measures, however, do not support the system’s productivity. In fact, the
production of excess inventory increases expense and lead time and causes order
due dates to be missed. Thus, these local productivity measures are not
consistent with the firm’s financial objectives. (Gardiner et al., 2001, p.
15)
BIBLIOGRAPHY:
AQUILANO, N.J.;
JACOBS, F.R; CHASE, R.B. Operations
Management For Competitive
Advantage. 10th ed. Irwin McGraw-Hill, 2001.
GARDINER, Stanley
C.; BLACKSTONE Jr.; GARDINER, Lorraine. The
Evolution of The Theory of Constraints. International Journal of
Management, 2001, pp. 13-16.
GOLDRATT, Eliyahu
M. What is This Thing Called Theory of
Constraints. New York: North
River, 1990.
OREEN, Eric;
SMITH, Debra, MACKEY, James T. The Theory
of Constraints And Its Implications For
Management Accounting. North River Press, 1995.
SENGE, Peter M. The Fifth Discipline: the art and
practice of the learning organization. Randoum House, 1990.
