|
One such method is proposed while the research continues.
A note on computerised stock algorithms, both explicit and implicit.
A human forecasting system has an inbuilt gatekeeper. The forecaster decides whether to accept or
reject changes suggested by their investigations.
By this and other such methods the forecast is made explicit.
By contrast, many system forecasts are implicit, and never exposed to critical scrutiny. For example,
where the systemised outcome is a suggested Purchase Order the forecast and standard deviation
calculations are implicit.
Where the results mislead it's common to blame the forecast.
Maybe the forecast is as good as it can be, the problem is with the SD estimate?
The origins of this research.
When one company's figures show a $7m a
year gap between actual and ideal stockholding
with no discernable pattern in the product by
product differences, the problem must have
multiple causes. We needed to investigate and
challenge every component (human or system)
of the calculation. In this case, standard
deviation was just one (the second largest) of
more than 5 contributory causes. We estimate
SD was causing $750,000 of extra costs a year
in the UK alone.
The size of this prize was a spur to delve into
an area we felt had been under-researched.
Indeed the study was in part a legacy of a deep
seated unease with both the theoretical SD
calculation and its computer implementation.
We therefore set out to answer 2 questions
- Is past SD a good predictor of future SD?
- Even if it is a bad predictor, do the forecast
and SD errors cancel? In other words, does
it matter? Are we often right for the wrong
reason - the errors have cancelled or partly
cancelled.
Along the way we found just how much SD has
been overlooked; it had become the 'forgotten
partner' in the whole forecasting for stock
arena.
| |
Some light hearted comparisons are helpful for
the way they illuminate the difference in
mindshare.
- Demand forecasting has 2.18 million web
references, vs 7,500 for standard deviation.
- Forecasting has 57 books in Amazon, SD has
no books, one paper.
- Forecasting has an
institute (the [American] Institute of Business Forecasting) which - the last conference I
attended - had absolutely nothing on standard
deviation.
- Several professional societies have
forecasting SIG's (Special Interest Groups) -
ORS (Operation Research Society) is just one.
None have SD SIGs
Method.
The method simulates a common (though not
recommended) computerised safety stock
algorithm.
Monthly 'Sales' for a single product are
randomly generated about a mean from a
Poisson or similar skew distribution. In other
words, the sales have the demand variability
one would expect of a rational market with no
acquisition cost or history of shortage.
The sales populate 12 months in each of 1,000
notionally different years. In fact the underlying
demand is the same, each month or year's sales
are just samples from the population and
therefore vary purely through sampling error.
Using 8 different forecast methods, each year
calculates a month ahead forecast, and a
historic SD. These are used to calculate a cycle
+ safety stock target over an array of lead times
and service levels, always using the correct
transforms. The cycle and safety components
are kept separate so we can later determine
which component is causing what part of the
total error.
Since the 'correct' cycle and safety stock are
already known from the base data, we can now
compare and classify the computerised
predictions with this base. |
