Wednesday, August 13, 2008

Forecasting the future demand

Simple formulas for successful inventory management:

* Many buyers could not effectively deal with mathematical formulas or computers. Ten-key calculators were considered "state-of-the-art" technology. In fact, most purchasing decisions at the time were based on "SWAG" (silly, wild-ass guessing). Any formula (including Graham's) introduced to provide consistency in ordering had to be fairly simple and easily replicated on a calculator.
* Computers did not have the power to perform comprehensive forecasting formulas for thousands of parts within a reasonable period of time. Calculating Graham's simple average for thousands of items stretched the physical capabilities of most computer systems.These conditions present some unique challenges:

* Decreased margins tend to limit the amount of money a distributor has available to invest in inventory.
* Distributors must spread the money available to invest in inventory over a greater number of products.
* Customers are less tolerant if product availability does not meet their expectations.
You're obviously in trouble if you don't have the inventory your customers expect you to have. And if you've bought too much of an item, your money is tied up and can't be invested in the other products that allow you to take advantage of new sales opportunities.
When forecasting the usage of non-seasonal products with fairly consistent usage, we want to average the usage that was recorded during the past several inventory periods. But we also want to "weight," or place more emphasis on, the most recent month. Why?

1. There are often trends in a product's usage as it becomes more or less popular over time. For non-seasonal products, demand in the upcoming inventory period will more likely be similar to the usage recorded in the past several inventory periods than what happened six, eight, or twelve months ago.
2. At the same time, there is usually a certain amount of random variation in a product's usage from one inventory period to another. Notice how the usage of the item in the first example below has fluctuated over the past five months. This "up-and-down" pattern of usage is common for inventory items with moderate-to-high sales. If we were to use just the most recently completed one or two inventory periods in our calculations, the random fluctuations in usage would probably have too great an influence on the forecasted demand. We want to include enough history to ensure that random fluctuations do not have a significant impact on a product's forecast.

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