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-  Lack of a "biased" viewpoint. A forecast error or de­
viation is defined as the difference between the de­
mand that was forecast and the actual demand that
occurred. It is generally measured once a month by
family or by product. Sometimes quarterly or year-
to-date totals are also reviewed. With the prolifera­
tion of markets, products, and customer-sensitive
options, it becomes very difficult to analyze all these
forecasting errors in companies with growing prod­
uct lines. So, in the interest of time and expedience,
often the only errors that are analyzed are the most
significant variances that occurred, last month. This
is far too narrow a perspective.

-  There are two significant but very different con­
tributors to any forecast error. The first is called bias.
Bias represents a consistent forecast error in the
same direction (actual sales usually being above fore­
cast or below forecast) over a period of time. This is
the most difficult and harmful form of forecast in­
accuracy, because over time either inventories will
build or customer service problems will proliferate.
The only way to detect bias is to look at the actual
sales vs. the forecast over a longer period of time in
an attempt to identify the pattern. Often bias can
be caused by undetected seasonal demand patterns,
cycles, or long-term trends. Sometimes the bias is
exaggerated by organizational or interpersonal con­
siderations such as always forecasting low so that
sales and marketing can "beat the numbers." In
other cases forecasting is always high to justify new
product introductions, capacity expansion, inven­
tory availability, meeting an aggressive revenue
growth target from top management, or to justify
an advertising, spending, or staffing budget.

-  It is critical to identify and adjust for these or any
other causes of bias, since it will have the largest cu­
mulative impact on costs, inventory, and service.

-  "Random" or "normal" deviation above and below
the mean or average demand is the other portion of
forecast error. Once the bias is removed, it means that
the forecast is just as likely to be low as it is to be
high in any time period. And, over time, the plusses
and minuses will balance out and the total forecast
will closely approximate the total actual sales. This
"lack of linear demand pattern" is more prevalent in
some products than others. It is affected by the be­
haviors, practices, and economics of demand chain
partners that affect the timing and quantity of the
product being ordered.

For example, low-volume, low-cost products are
much more likely to have non-linear demand since it
is easier and more cost efficient for the demand chain
customers to lot size or order the materials less of­
ten, without paying the price of a large investment
in inventory. Critically needed is the ability to ana­
lyze forecast vs. actual sales over longer periods of
time to allow the responsible people to differentiate
between bias and "normal variation," and then act
appropriately. Otherwise, just looking at last month's
sales could trigger adjustments of forecasts up and
down in response to normal variations when, in fact,
the average forecast is accurate and what is needed is
the ability to accommodate normal variations. Oth­
erwise, undetected bias may not be noticed for sev­
eral months, until inventories or backorders are
excessively high.

•         Cleaning the "greasepit': In the military (at least in
my era of the '60s), one of the worst assignments was
"KP" or "kitchen police." This meant working in the
mess hall helping to prepare, but mostly clean up after
meals. The worst part of KP used to be "cleaning the
grease pit." This involved the maintenance and cleanup
activities of the containers where excess and discarded
cooking oils and grease were stored.

Forecasting is the "grease pit" of modern manufac­turing and distribution organizations. By its very na­ture, forecast analysis is a very detailed, quantitative job. For the reasons listed above, it's difficult, if not impos­sible, to do it well, or at least as well as eveiyone would like it to be done.

Because often effective tools are not made available to help focus attention on the real problems, forecast­ing can feel like "drudge work," having to hunt through hundreds and thousands of pieces of data to find the real root causes of the problems. Responsibility for fore­casting usually rests with sales and marketing person­nel, since they are "closest to the customer" and should have die best handle on, and the earliest warning of shifts in market forces that can affect demand. They then be­come the unhappy "cleaners of the grease pit." Though many effective sales and marketing professionals have very good quantitative and analytical skills, their pro­fessional interests and preferences more usually involve strategic and tactical planning, and communication with the customers regarding their needs. They usually don't look forward to analyzing historical data and adjusting numbers that will never be accurate enough to make anyone happy. It's often the part of the job they like the least, and therefore the part that's put off and given the lowest priority.

To Be Continued

For balance of this article, click on the below link:

Lean Manufacturing Articles and click on Series 12


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