A Guide to Implementing the Theory of
Competitive Advantage – Just Sufficient
We have seen in the previous section how we can
reduce lead time in a make-to-order environment and in particular one where
there is an external sales constraint.
If we can provide the same product or service as our competitors but
with a shorter lead time then we have a distinct competitive advantage. However, many organizations are not
clear-cut make-to-order entities but rather have a component of make-to-stock
in their process or they are purely make-to-stock.
In this situation something is completed ahead of
the customer requirement – even if that customer may in fact be internal to
the process. The business has made a decision to decouple the customer from the
process lead time by the use of inventory.
Indeed the only valid reason for making to stock is that the
process can’t make to order fast enough to satisfy customer expectations.
If we have excess process capacity relative to sales
and therefore quite a lot of finished goods; what is the problem? Well, let’s have a look. In a number of places we have addressed
deviations from the plan (1).
Deviations arise from;
doing what was supposed to
be done for the constraint.
(2) Doing what was not
supposed to be done for the constraint.
Here the constraint is sales, and in order to
exploit sales we need to make things that customers do want to buy in the
near future – we deviate by failing to make these things and thus produce
stock-outs. We also need not to make
things that customers do not want to buy in the near future, or at least not
make them in excess of the customer’s requirements – we deviate by making these
things and thus producing excess stock.
These are the issues that we need to address for finished goods.
We can gauge the amount of overstock by the weeks or
months of supply we have for each item relative to recent demand. We can gauge the amount of out-of-stock to
some extent by the need to expedite some items even though we have more than
sufficient capacity. The severity of
the problem will differ depending upon whether we are in a make-to-stock
environment, or a make-to-forecast environment, so we will deal with each of
these in turn.
The whole situation will be further exacerbated if
there is a distribution system attached to the process. However, we will leave that for a page of
its own, because in order to understand the solution we must first understand
a new concept for replenishment. We
will deal with replenishment on the next page. Here we will look at the two older
concepts; make-to-stock first, and then make-to-forecast.
Let’s begin by saying that in some instances it
should be quite possible for a make-to-stock process to migrate to
make-to-order if lead times are sufficiently reduced. To do this we would follow the suggestion
in the previous section on lead times; drying the system out, that is
reducing the excess work-in-process, and thus removing substantial amounts of
the queue time from the system, and addressing batch size issues. These together will substantially reduce
process lead time. Many businesses
will, however, still find that because of their product range (maybe many
thousands of standard items) or maybe large seasonal variation in demand,
they must manufacture to stock first, and then draw later from stock for
sales. We need to address these types
stock in a make-to-stock environment is the result of total work in process
(manufacturing lead time) and batching policy. This is primarily driven by;
(1) Excessive queue time/work-in-process.
(2) Batching of
(3) Batching in
Lets’ look at each of these in a little more detail.
Essentially the same mechanics apply here as was the
case in the make-to-order environment.
We must dry the system out by reducing the input into the system while
holding the output constant using drum-buffer-rope as the logistical system. Let’s repeat the diagram for good measure.
This will reduce the overall
work-in-process and thus lead time.
Batching of product, especially batching policy, is
of critical importance in a make-to-stock environment irrespective of the
lead time. You may want to check the
production section on batching once again to understand the differences in
batch type. In a make-to-order
environment we have seen that; either transfer batching – the splitting of batches into sub-batches while in the process, or process
batching – the release of smaller discrete batches more often allows for
considerable reduction in lead time.
However, process batch reduction has a much greater direct
contribution to finished goods reduction than transfer batching does. Let’s investigate this further in some
detail; it is important to understand this differentiation.
In the following diagram we can trace the inventory
levels for a particular product as finished goods in a make-to-stock
environment over several cycles. We
start with a new batch of goods arriving into the finished goods store from
manufacturing or processing. Over time
the inventory level decreases as the stock is drawn down for sales. At some stage a reorder point (ROP) is
triggered and a new batch is released to manufacturing. Inventory continues to be drawn down until
it is replaced with the new batch.
Variation in the rate of inventory
draw down and the variation in lead time required to produce the new batch is
taken into account by the allowance of some safety stock. The average stock level over time will be
half the maximum stock level.
Let’s have a
look then at the effect of introducing transfer batching.
Now, in a slightly exaggerated view, we can see the
original batch replacement arrives into the finished goods store as two
If we have already removed much of
the queue time from the system, then using two transfer batches will halve
the waiting within the run time at each section. The overall manufacturing lead time should
be reduced proportionally. As a
consequence we can supply the batch to the finished goods store faster;
therefore we can wait longer before triggering the reorder point. The reorder point is therefore lower. Also we don’t need as much safety stock and
therefore the safety stock level can also be lower. However, the average finished goods level
in most cases does not substantially decrease. This is important, failure to reduce
finished goods quality exacerbates our deviation of producing too much of
things that we don’t need.
Let’s then have look then at the effect of
introducing process batching.
Now we can see the original batch replacement
arrives into store as a sequence of two discrete and separate process
Again, if we have already removed
most of the queue time from the system, then using two process batches will
halve the waiting within the run time at each section. The overall manufacturing lead time should
be reduced proportionally. Once again
the reorder point is lower and we don’t need as much safety stock as we
initially did. Now, however, the
average finished goods level has also decreased in proportion to the decrease
in process batch size. We can maintain
the same level of service with half the finished goods! All we did was change a batching policy. In the production section on batching we
showed how to achieve this using a “truck and trailer” analogy. The cost of additional setups to the system
is not great – and in any case as sales is the constraint we already have
some excess capacity in processing to do this with.
This situation would seem to be a lean
manufacturer’s – and any financier’s – dream.
Short lead times, reduced work-in-process, and reduced finished goods
stock. In fact, you can see that there
is considerable cash to be liberated from the system that was previously tied
The reduced lead time will allow the system to much
more closely follow the sales trends and therefore reduce the number of
Lets return to a not uncommon situation that occurs
at the start of a drum-buffer-rope implementation in a make-to-stock
environment where there is a lot of finished goods and there are also
significant stock-outs. Once the
constraint has been identified and exploited we will have more capacity to
produce more than we did in the past.
There is thus a potential for some companies to make the error of
using this additional capacity to chase their stock-outs – they build even
more finished goods inventory in the mistaken belief that this will reduce
stock-outs, when in fact stock-outs are caused by long lead times and/or
The effect is the same as burying cash in the
system. Liquidity dries up, and
bankruptcy can become a real option.
This is a cautionary tale for the need to involve everyone; finance,
sales, marketing, as well as manufacturing at the earliest stages. Moreover, it is important to recognize that
the right solution to the right problem will avoid this situation from the
start. In this case the right soluiton
is process batching rather than transfer batching.
make-to-stock our finished goods level oscillates between some minimum value
protected by safety stock and a maximum value, usually separated by one
“batch-size” worth of material.
However, this is not always sufficient to protect our customers
especially where demand is highly variable or there is a considerable degree
of seasonality in the demand. The
alternative is to make-to-forecast.
Let’s have a look at that.
When do we
forecast? In the following situations;
(1) We make-to-order and we have insufficient forward orders
(2) We make-to-stock and our lead times are long/and or demand is variable
The first case
is tragic and really concerns make-to-order – we are trying to be “efficient”
by keeping people busy. We don’t need
to consider that case any further here but it is worthwhile to point it
out. The second case, however, does
concern us. “Many companies build stock because
their manufacturing processes aren't fast or agile enough to build to
order. But the stock level targets are
usually determined by forecasts, which are based on historical trends,
best-guesses about future trends in market preferences, known or anticipated
product introductions, planned sales or marketing campaigns, speculation
about what the competition will do, and the reading of crystal balls, bones,
and chicken entrails (2).”
Chicken entrails aside; forecasting introduces a new
and additional factor into our consideration of finished goods levels. Let’s look at this.
stock is now the result of total work in process (manufacturing lead time),
batching policy, and forecast policy.
This is primarily driven by;
(1) Excessive queue time/work-in-process.
(2) Batching of product.
(3) Batching in time.
(4) Forecast Accuracy
In fact, we often have a vicious cycle operating
prior to the introduction of drum-buffer-rope; batches are large,
work-in-process is high, and lead times are very long. This almost begs the use of forecasting to
“see” beyond the lead time horizon. We
know from our discussion on make-to-stock and previously on make-to-order
here how to break this cycle for all but the new entity – forecast accuracy.
It doesn’t seem to be difficult to obtain an average
estimate for a forecast value, but to know the variability inherent in that
estimated value is much more difficult to determine – and it gets worse the
longer the time horizon. “There are
two reasons why we don't see the forecasting error very often. First, when forecasts are based on the
intuition of the marketing people, the concept of error simply doesn't
exist.” “Second, even when the
forecasting error is available, and computerized algorithms are able to
assess it, the error may be so large that the whole notion of forecasting
seems dubious (2).” So it is not
necessarily the use of a forecast per se but
rather the noise in the system that gives rise to most of the problem. Let’s use a generalized example to
illustrate this further.
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? Now consider a product
line within total sales, what would the accuracy be on that? Twice the previous value perhaps – plus or
minus 10%? We might sell 90% of budget
or maybe 110% for a particular line.
How about a line item within a particular product line? 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 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. 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.
consequence of this is that sometime we are going to produce too much and
sometimes we are going to produce too little.
Consequently we carry stock that we can’t immediately sell and we miss
immediate sales that were certain. How
can we get around this problem?
We can reduce
the impact by reducing lead times substantially and we can reduce the impact
by reducing finished goods stock substantially. Indeed, we may be able to migrate from a
make-to-forecast system to a make-to-stock system. However, to really do much better in volatile
markets we must consider replenishment – replenishing our stocks as fast as
our market variability.
forecasting can be a cop-out. A
cop-out by manufacturing to marketing and sales. And a cop-out marketing and sales cop-out
to the market. No one needs to take
responsibility for making things we can’t sell and not making things that we
could sell. This need not be the
situation. We just have to be as agile
as our fickle market and for that we need replenishment.
In an external
constrained make-to-stock or a make-to-forecast environment it is easy to
make goods that can’t be sold, and not to make goods that could be sold. The net effect either way is to reduce
potential throughput. The primary
antidote to this is to use smaller process batches and thereby reduced total
finished goods inventory while at the same time increasing service
levels. However, in a supply chain
environment we can go one step further.
We can introduce replenishment.
Let’s have a look then at replenishment.
E. M., (1990) The
haystack syndrome: sifting information out of the data ocean. North River Press, pg 146.
(2) Schragenheim, E., and Dettmer, H. W. (2000) Manufacturing at warp speed: optimizing supply chain financial performance, pp 212 & 67.
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