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A Guide to Implementing the Theory of
Constraints (TOC) |
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How Can We Characterize Distribution? We are all familiar with distribution to some
extent. Whenever we go to buy
something off-the-shelf and it isn’t immediately available then we are made aware
of the existence, or in this case maybe the absence, of distribution. Of course it is best of all when
distribution is invisible to the end user.
Distribution systems are an integral part of many processes and
present their own unique problems and attendant opportunities. Let’s draw a generalized distribution system and
examine its properties in more detail.
Initially there may not be a plant warehouse, rather
orders may be batched and produced periodically and then shipped directly to
distributors or wholesalers. Therefore
the source node is often characterized for a particular product by periods
supply with long intervals of non-supply in-between. The other levels in the system, let’s call them
nodes, don’t produce anything. They
may purchase and on-sell or they may simply reflect a change of mode of
transportation. Although they don’t
have a manufacturing lead time, the do have a resupply lead time. Broadly this can be characterized as the
time taken to pick, pack, ship, unload, and enter the goods from one node to
another. The time taken between manufacturing at the source
node and consumption/purchase by the end user from the last node can be quite
considerable – multiples of months or quarters. The system as it is drawn could be geographically
local with production, wholesale and retail nodes. Or it might be national with regional, as
well as local warehouses involved, or for that matter international with
national warehouses in addition to regional and local warehouses. The distribution chain might be a part of a
whole integrated manufacturing business – the New Zealand diary industry
springs to mind – or it might be a stand-alone business itself making money
from buying and on-selling. In this
instance the manufacturer might not own the stock once it leaves the
premises, but until the end user has made a purchase the system hasn’t really
made a sale at all. A feature of traditional linear supply chains is
that we sometimes have too much of the wrong material which we can’t
sell. At the same time we also have
too little of other things that we can sell. In addition, diverging supply chains –
distribution systems – may have the right amount of the right material at the
right time, but it is in the wrong place.
Consequently sometimes we miss sales even though we have the material
in stock but not in the right place and at other times we miss sales because
we don’t have the material at all. In
a system which is not internally constrained by production, missing a sale
should be a crime. We need to address this problem; we need a plan of
attack. There is only one plan of attack, our 5 focusing
steps which we have employed in manufacturing and will continue to employ
here and in other places to develop simple and workable solutions to our
problems – both physical and policy. (1) Identify the system’s constraints. (2) Decide how to Exploit the system’s constraints. (3) Subordinate everything else
to the above decisions. (4) Elevate the system’s constraints. (5) If in the previous steps a constraint has been broken Go back to step 1, but do not allow inertia to cause a
system constraint. In other words; Don’t
Stop. What is the constraint in this system? The limited number of customers who come in
the door seeking to buy something from us.
That almost answers the second question – how to exploit the
constraint? We need to ensure that we
can always meet our customers’ demands and not miss any sales, or not sell
something of less value than our customers’ desires. We will need to deduce how to best do
this. We will also need to develop how
best to subordinate the system once the exploitation strategy is in place. The results of these activities might indeed
stimulate demand, and therefore also elevate the system. If, however, demand is still less than
desired we may have to resort to the next tool – the Mafia offer. A description of the Mafia offer forms the
last page to this section on supply chain.
We will limit ourselves here, however, to the first 3 steps; identify,
exploit, and subordinate. To enable us to determine the exploitation and
subordination tactics we need to examine the properties of distribution
networks in a little more detail.
Let’s do that. Senge describes a game, the beer game, which he uses
to illustrate and develop a number of points that are relevant to
distribution (1). This game isn’t
difficult to perform and is well worth while investigating. Essentially there are just 3 nodes, a
retailer, a wholesaler, and a plant.
The retailer has a stock of a particular beer and receives a steady
stream of orders from customers each week.
However, on one week only, a small jump in sales is introduced into
the system. There is a long feedback
period between retailer and wholesaler, and wholesaler and plant, essentially
the resupply time. This is sufficient
to introduce a major oscillation in the system that magnifies at each
subsequent stage quite out of proportion to the original small and unique
perturbation in sales. Why does this happen? The easy answer is human nature. People with good dynamic understanding will
resist the temptation and the system will remain stable. Most people, however, will not resist the temptation. Try it yourself with a group of people and
you will see. We tend to look at the
detail complexity and almost immediately lose focus on the dynamic
complexity. Play this game with
several groups and watch just how consistent the reaction is. The title of the chapter that describes
this game is called “Prisoners of the system, or prisoners of our own
thinking?” It is a learning exercise;
it is designed to make people aware of their own reactions to dynamic situations. In the beer game there are just 3 players,
the retailer, the wholesaler and the plant.
So it is a very simple chain of events, and yet within this simple
chain of events people wanting to do their very best quite soon produce a
system way out of control. If this is
the normal response in this simple and protected situation then consider the
ramifications in a multilayer distribution system in the real world. The beer game wouldn’t be so frightening if long
lead times weren’t endemic to most distribution systems. But they are. They are endemic because of the long signal
times that arise from using min-max systems to activate resupply. They are endemic because of the long
manufacturing lead times in many industries.
And they are endemic because of the perceived “lumpy” demand. We saw how this was so in the previous page
on replenishment. The long feedback
times cause perturbations at the plant when no such perturbations exist at
the retail nodes, this tends to cause the plant to chase peaks that don’t
exist, the overall capacity is sufficient to meet demand but the peak
capacity isn’t. This tends to be
viewed as insufficient internal capacity when in fact it isn’t. Essentially the beer game exposes to us the vices of
batching. When we have
batching and long lead times we tend to compensate by forecasting future
demand so that we can make “intelligent” decisions about current
production. However, we need to test
the validity of these forecasts.
Consider for a moment; what is the accuracy with which you can
estimate next months’ total sales? Is
it of the order of plus or minus 5%?
Sure it’s probably padded for safety, but if you had to put money on
it falling either way, would +/- 5% be sufficient? Probably.
Now consider a product line within next months’ total sales, what
would the accuracy be for a product line?
Twice the previous value perhaps – plus or minus 10%? We might sell 90% of budget or maybe 110%
for a particular line. Alright, then
how about a line item within a particular product line for next month? Would you be surprised to see plus or minus
20%? Customers, the economy, and
everything else are just so fickle. Let’s look at
this same problem from another perspective.
If the accuracy of the estimate for next month’s total sales is of the
order of plus or minus 5%, what is the accuracy for a quarter? It would be more than 5%, maybe plus or
minus 10%. What then of the accuracy
for 2 quarters out? Maybe plus or
minus 20%? The actual numbers aren’t
so important. Making our intuition
explicit is more important. It seems that
our intuition tells us that forecasting degrades as we increase the time
span, and forecasting degrades as we disaggregate the production down into
product lines and then line items.
There seems no doubt that the less aggregate the forecast the greater
the potential for forecast error, and the further out that we push the
forecast horizon, again the greater the potential for forecast error. Long lead
times cause us to compensate by forecasting.
Forecasting carries with it some uncertainty. We will call
this “forecasting error.” So far all of these characteristics are similar to
the problems that are generally experienced by make-to-stock and which we
discussed briefly in the section on finished goods. However a distribution system adds a
further dimension to our forecasting error due to the disaggregation of the
orders by geography. Let’s look at
this. What is the general accuracy of the estimate of
sales at the plant warehouse level?
Plus or minus 5% maybe? If
could be better and it could be worse, but let’s leave it at that. What then is the accuracy of the estimate
of sale at an individual distributor or wholesaler, better or worse? Worse of course. Let’s assign a plus or minus 10% accuracy
to line level volumes at these nodes.
What then is the accuracy of the sales estimate at an individual
retailer, better or worse? Worse of
course. Let’s assign a plus or minus
20% accuracy to line level volumes at these nodes. We have forecasting error due to; (1) Very long lead times. (2) Disaggregation by item. (3) Disaggregation by location. Moreover, if we look to manufacturing for an analogy
then distribution is similar to a “V-plant” as described in production
section. In V-plants under the
traditional approach expensive equipment must be utilized constantly to
absorb overhead and to insure that adequate value is received, since each
diverging point is an opportunity to misallocate material the V-plant is
dominated by this problem (2). Thus in addition to forecasting there is the chance
that the wrong material will go to the wrong place at the wrong time. It is misallocated. We will call this “allocation error.” Allocation error leads us to a number of undesirable
outcomes; there is channel stuffing, there is dead stock, and there are
stock-outs. In each case we are
failing to fully exploit the constraint, our consumer who wishes to make a
purchase. Let’s look at each of these
in turn. It would be nice to believe that in this day and age
of lean production and integrated logistics that channel stuffing no longer
occurs, but it does. Especially in
these times of very low interest rates and high liquidity when it is possible
for distributors to borrow against new inventory at very low cost. Moreover, in some industries a cash rebate
is offered for these “sales” to distributors, so in effect taking inventory
generates a small cash flow for the distributor even though the distributors
are yet to make a sale to their own customers. Moreover, the rebates cause “hockey stick”
production and the plants that service the process will have large quarterly
or 6 monthly swings in production demand as a consequence (depending upon
whether their financial reporting is quarterly or 6 monthly). Once again production plants feel the pinch
as they try to chase artificially induced peaks even though there overall
capacity is more than enough to meet demand. A specific example comes from the automotive
industry in the U.S.A., (3). At least
in the early 1990’s this mode of operation, channel stuffing, was still
endemic in the automotive industry; “… dealers are small, individually owned
businesses. Some 11,700 of them, or 47
percent, are still single-site operations.
In many cases they still pay cash up front for their cars and still
complain about the assemblers forcing them to take cars they don’t want. Inventories are still large – averaging
sixty-six days’ stock on hand over the last decade.” Of course channel stuffing also causes lead times to
increase providing a nice negative reinforcement loop to our forecasting
error. In this situation we might also expect to see quite
a bit of dead stock, however, this data is usually buried in the averages that
are presented to top management.
Management may become concerned when they see average months-of-supply
move from 3 to 4 months or from 6 to 7 months or whatever the figure might
be. However, within this story there
will be a tail, a long tail of months-of-supply that for some line items will
extend out to years. If we go through
our stock list item by item and divide it by recent demand, be that annual or
quarterly or 6 monthly, we will soon see the extent of the tail in the
stock. Consider how much capacity we
used to generate that stock. Go figure
out if we will sell it any time soon. Womack et al., quote an example where they visited a
divisional automotive headquarters facing the problem of how to sell 10,000
already built cars that no dealer wanted (3).
The company had built the cars based upon forecast of market demand
rather than actual orders from dealers or consumers. The market had change however and no one
wanted the cars. A dead stock problem
of monumental proportions. The above example is also a good indication of why
companies that have dead-stock will also have stock-outs; their valuable
manufacturing capacity was occupied building other things. But, why use automotive examples? Well, production/distribution chains in the
automotive industry don’t come much larger, tie up more money, or appear more
complex. Thus it makes a good
reference environment. All
distribution and allocation environments will share the same characteristics
however. We seem to be able to characterize
the problem without too much difficulty, let’s see if we can characterize a
solution with similar ease. We have been describing the supply chain here as
distribution. However, a more accurate
description would be distribution and allocation. The allocation is according to
manufacturing production-push. The
production-push is signaled by a forecast about the future based upon recent
past trends and intuition about the future. In the Theory of Constraints manufacturing
application – drum-buffer-rope – we saw how work is pulled by the constraint
schedules through the system. The
constraint schedules in turn are linked to actual customer demand. In the same manner, the kanban in
just-in-time also pull work through the system. We need to invoke the same principles here
in our distribution system; we need consumer, or customer, or end user
demand-pull. That way we only make
what is required by the system to satisfy demand, no more and no less. How can we do this however when we have such long
lead times from the source to end user?
Well, what if we were to consider just the next layer of nodes in the
chain rather than the whole chain from beginning to end? What if we were to consider resupply to
just the next level? Then lead times
would be much, much, shorter. Surely
that would help. In fact, isn’t this exactly what we did with our
replenishment buffers in the previous page?
Sure, there we only really considered one or two nodes at a time. But is there any reason why we can’t repeat
this – cut and paste it – from layer to layer in our distribution
system? Each node in the supply chain
pulls from the node above according to the signal that it receives from the
nodes that it supplies below. This
will enable us to invoke a pull-to-replenish system based upon demand by the
end user. Let’s try it. We are going to move from manufacturing production
push-to-forecast to customer demand pull-to-replenish. Let’s have a look at this solution. So far we have identified the constraint or the
leverage point – our limited number of customers. We have also now deduced an exploitation
strategy to overcome the current problems of our distribution system. We are going to use replenishment and buffer
management to make sure that we can always have the right material in the
right place at the right time – every time.
So what do we need to do and where do we start? Replenishment as you will recall from the previous
page is frequency driven, the more often we can replenish the smaller the
buffer that we will need to maintain at each node. What drives the frequency overall? The frequency that we manufacture at. It is no coincidence that supply chain
follows production on these pages because ultimately we must reduce the
production lead time, the batch size, and finished goods stock if we are to
increase production frequency in order to substantially improve the supply
chain. Indeed, we have assumed that since we have an
external sales or marketing constraint that we have already implemented
drum-buffer-rope in the manufacturing or processing stage. However, if we are looking at this as a
free-standing distribution situation – the processing stage is beyond our span
of control or sphere of influence – then we must treat that stage as an
external (and maybe less than reliable) vendor. We saw in the previous page how we can use
replenishment buffers to protect our sales under such conditions. In this situation we may not be able to
reduce supply chain stock levels as we would have if production was under
control. However, that is not the
objective, the objective is to increase throughput by not missing sales. In order to not miss sales where is the place where
we have the least allocation error and the least forecast error? Surely the finished goods stock of the
plant. This is then the place where we
must start. We must size the finished goods
stock buffers to adequately supply the downstream nodes while waiting for
upstream resupply from the plant. We
have moved safety to the place where it best protects the whole system. Let’s draw this.
Introducing replenishment buffers and increasing
resupply frequency automatically Once again the aim is to increase throughput, but
very often a consequence of that is a reduction in inventory locally, or even
within the whole system. If we
aggregate inventory at the plant warehouse and we can reduce the plant lead
time by half then we can also reduce finished goods at the plant warehouse by
half and in other parts of the system by much, much, more. Inventory reduction is a consequence of
meeting the objective of increasing throughput via increased resupply
frequency. In essence we have synchronized the whole supply
chain by buffering each node against the supply and variation in supply
leading into it and the demand and variation in demand leading out of it. Replenishment is the exploitation step, or the
motor, for supply chain solutions. We
need now to consider how to subordinate this particular supply chain in order
to ensure that we fully support the exploitation of the constraint. Let’s have a look at that. In our discussion of replenishment we described
buffer sizing, re-order duration, and resupply duration as the planning
functions which determine the characteristics of the system. Much earlier we also used the word “plan”
to describe how to exploit a system.
It was suggested that a plan was really is just a set of instructions
that provides for a timely and appropriate output. In distribution supply chains the
instructions, the plan, is embedded in the way we construct the system. In the
replenishment section we also described buffer management as the control function
once the buffers, our plan, are correctly sized and put in place. And again much earlier we described
subordination as deviation from our plan.
Thus the buffer management is our control system and our means of
determining deviation from our plan.
Buffer management ensures correct subordination. Goldratt considers that there are two ways in which
we can deviate from our plan (4); (1) Not
doing what was supposed to
be done. (2) Doing what was not
supposed to be done. Let’s see how we can relate this to buffering in
replenishment. Generally we can view not doing what was supposed to
be done as generating lateness. We
have ample protection at each stage of our distribution supply chain and
therefore if something is late to the next step it is most likely to have
arisen from something not having been done when it should have been
done. We can give this lateness a
value of throughput-dollar-days late.
The complement of guarding against what hasn’t been
done, is guarding against what has been done that shouldn’t have been
done. Generally we can view this as
having too much inventory sitting around for too long. We can give this waiting process a value of
inventory-dollar-days waiting. We
calculate this by taking the raw material cost only and multiplying by the
number of days resident in the system or subsystem. Material that sits around for too long
rapidly gains inventory-dollar-days.
Distribution is not simple replenishment, by
replacing forecasting with simple replenishment using buffers we avoid one source of error – forecasting error. Therefore, why can’t we just treat
distribution as a simple replenishment system through a linear supply chain
of dependent vendors or dependent nodes?
Well, there are a number of reasons for this; (1) We must consolidate demand from
numerous points of sale. (2) We must currently subordinate the
source nodes to the points of sale. (3) We must position the protection in
the place that best protects the whole system. Of these; positioning the protection for the system
in the place that does the most good is the most important; this avoids the other major source of error – allocation error. The best place for the protection is where
the aggregate volumes are greatest, closest to the plant. We can limit the overall inventory in the
plants finished goods stock and elsewhere by increasing the overall frequency
of reorder and decreasing the duration of resupply. Because distribution deals with a divergent
supply chain it is not a case of simple replenishment. We begin to determine the replenishment buffers for
individual line items at the point where we know the demand with the greatest
degree of certainty – at the plant warehouse.
The plant warehouse “sees” the aggregate demand of the whole system,
the peaks and the toughs smoothed out to the largest extent. Then we work through the individual nodes
in the next layer, and the next layer after that until we reach the point of
sale. Wherever possible, increasing
the reorder frequency and decreasing the resupply duration will allow us to
hold less stock while maintaining or improving customer service levels. We avoid forecasting error. We avoid allocation error. First-class distribution is predicated upon good
buffer management at the plant – because this is where most of the protection
is. The unavoidable outcomes are that we no longer have too little of the
right material in the right place in the right time because our plant
capacity is now “smoothed” and not wasted making unnecessary “emergency”
jobs. The unavoidable outcomes are; (1) Increased throughput at the point
of sale. (2) Decreased total inventory. (3) No stock-outs. (4) No over-stock. (5) Sufficient plant capacity In fact we should have just the right amount of just
the right material in just the right place – always. If this explanation seems quite simple and
straightforward then that is excellent; then we know that we have developed
an understanding of replenishment as applied to distribution. If experience tells us that reality is more
complicated than this generalized case, then that too is excellent. Now we are in a position to better
understand how to apply this methodology to our own particular situations. Let’s see then what have been the results in
specific implementations We used several examples from the U.S. automotive
industry earlier to show the magnitude of the problems faced by one of the
largest business distribution systems in commerce today. Automotive is important because it is a
hugely significant and competitive industry for many economies both
nationally and internationally. What
if we could provide a simple solution to distribution in that particular
environment using replenishment and buffer management? Well, the truth is that this has already
been done. Let’s have a look. The Cadillac division of General Motors successfully
implemented replenishment in the early 1990’s using the Florida region as the
testing ground. Instead of immediately
forcing finished cars onto dealers from the plant they placed them in a
regional buffer of 1400 cars at Orlando for delivery to the state’s 42
dealerships within 24 hours of an order – more than 95% of the time. Special orders can now be filled in 14 days
rather than taking several months as in the past (5). As a consequence of the success in Florida the
scheme was expanded to other major Cadillac regions in the country. In 1996 General Motors took the concept
nationally for Cadillac and expanded testing into the Chevrolet and GMC
sport-utility vehicles (6). A dealer
was reported to be happy “that they no longer have to stock ’10 versions of
the same car because if we had only one and sold it, it would take us six to
10 weeks to get another from the factory.
Now if we can sell one we can pull another one’ immediately from the
regional distribution center.” Overall then, popular model configuration delivery
time decreased from 6-10 weeks to 24 hours, special order configurations can
now be filled in 19 days guaranteed down from 10-12 weeks. Turnover at the regional distribution
centers was expected to of the order of 3 to 7 days (5, 6). Replenishment and buffer management brought
substantial and rapid improvement to General Motor’s Cadillac division’s
distribution system. It would be
difficult to imagine a more substantial or difficult environment to undertake
this type of exercise in. Think about
it. The distribution supply chain historically has been
one where goods have been produced and immediately pushed into the
distribution network as close to the customer as possible. This has lead to large inventories but none
the less not always the right things in the right place at the right
time. This arises from misallocation
in the divergent supply network and also forecasting error due to the large
work-in-process and hence long lead times. The Theory of Constraints supply chain solution,
replenishment, provides a motor to exploit the constraint – the customer – by
treating each node as a buffer and maintaining most goods where they will
protect the overall system best – at the plant warehouse. Reduced batch size in manufacturing leads
to more frequent resupply to the whole supply chain. More frequent reordering in the supply
chain itself and reduced resupply duration from node to node means that
substantially better levels of customer service can be maintained with less
overall inventory. Forecasting is no
longer required. (1) Senge, P. M.,
(1990) The fifth discipline: the art and practice of the learning
organization. Random House, pp 26-54. (2) Stein, R. E., (1994) The next phase of
total quality management: TQM II and the focus on profitability. Marcel Dekker, pg 37. (3) Womack, J. P., Jones, D. T., and Roos, D.,
(1990) The machine that changed the world.
Simon & Schuster Inc., pp 170-175. (4) Goldratt,
E. M., (1990) The
haystack syndrome: sifting information out of the data ocean. North River Press, pp 144-155. (5) Gabriella Stern, "GM expands its experiment
to improve Cadillac's distribution, cut inefficiency," The Wall Street
Journal (February 8, 1995). (6) Gabriella Stern and Rebecca Blumenstein,
"GM expands plans to speed cars to buyers," The Wall Street Journal
(October 21, 1996). This Webpage Copyright © 2003-2009 by Dr K. J.
Youngman |
