Academic journal article The Journal of Business Forecasting Methods & Systems

Fundamental Issues in Business Forecasting

Academic journal article The Journal of Business Forecasting Methods & Systems

Fundamental Issues in Business Forecasting

Article excerpt

True demand is most difficult to measure, it will be easier to measure if a company fills all orders in full and on time, which is not often the case ... you can make a significant improvement in forecasts, with little or no organizational resources, by identifying and eliminating steps and participants that make the forecast worse ... forecasts are more often an expression of an organization's targets or wishes.

Forecasting is a difficult and thankless endeavor. When accuracy is not quite where everyone wants it to be, we react by making significant new investment in technology, process, and people. Unfortunately, investment in the forecasting function is no guarantee of better forecasts. There are often fundamental issues that impact an organization's ability to forecast accurately. Until those issues are recognized and addressed, further investment in the function may be wasted. This article identifies several fundamental issues that should be of concern to organizations with a new forecasting function, and to those struggling to improve it.

WHAT IS DEMAND?

Perhaps the most fundamental question of all is, "What are we trying to forecast?" The usual answer is that we are trying to forecast customer "demand," with demand defined as "what the customers want and when they want it." A good forecast of demand, far enough into the future, allows the organization to invest only in the facilities, equipment, materials, and staffing that it needs. This usual definition is not problematic until we try to operationalize it, that is, when we start to describe the specific, systematic way to measure it.

If customers place orders to express their "demand," and if the organization services its customers perfectly by filling all orders in full and on time, then we have our operational definition. In this case, demand = orders = shipments. If both order and shipment data are readily available in the company's system, then we have the historical demand data, which we can use to feed our statistical forecasting models.

Unfortunately, few organizations service their customers perfectly. As such, orders are not a perfect reflection of true demand. This is because when customer service is less than perfect, orders are subject to all kinds of gamesmanship. Here are a few examples:

1. An unfilled order may be rolled ahead to a future time bucket.

2. If shortages are anticipated, customers may artificially inflate their orders to capture a larger share of an allocation.

3. If shortages are anticipated, customers may withhold orders, or direct their demand to alternative products or suppliers.

In the first example, demand (the order) appears in a time bucket later than when it was really wanted by the customer. Rolling unfilled orders causes demand to be overstated - the orders appear in the original time bucket, and again in future buckets until the demand is filled or the order is cancelled.

In the second example, the savvy customer (or sales rep) has advanced knowledge that product is scarce and will be allocated. If the allocation is based on some criterion such as "fill all orders at X%," the customer simply over-orders and ultimately may receive what it really wanted in the first place.

The third example not only contaminates the use of orders to reflect true demand, but it can also cause significant financial harm to your business. If you are in a situation of chronic supply shortages (due to either supply problems or much higher than anticipated demand), customers may simply go elsewhere. Customers may truly want your product (so there is real demand), but it won't be reflected in your historical data because no orders were placed. While orders are often perceived as "equal to or greater than" true demand, this third example shows that what is ordered may also be less than true demand.

As with orders, the use of shipments to represent demand has a number of potential problems. …

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