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Forecasting Problems
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Other Factors

Is your business influenced by the economy? Consider that in an up-cycle, more people will buy new cars, and repairs may drop. More people will build new homes or undertake remodeling, increasing de­mand for construction products. The time is also better for impulse items, like wheat-hull filled pillows, and fad items like virtual pets. Is your business influenced by government contracts? Mandated programs such as ADA facility upgrades can cause spikes in the demand. But when the spike is over, if the history still reflects the spike, it can cause forecast error as assumption of continued demand.

A successful forecast requires paying attention to what goes on around you as well as the data on your computer monitor. Understand­ing the above points and applying knowledge will almost always im­prove your forecasts, notwithstanding acts of God, such as floods or earthquakes, or other unusual events. Things do not just "keep happen­ing" on their own, nor are they likely to stay the same. Reliance on demand history alone is a recipe for disaster!

Now knowing what you learned about the assumptions used in de­veloping a good forecast, you can see why using demand nonperformance should be used as a trigger for corrective action for those at the top in an organization. It is important that they be kept informed of these variances before little problems become big nightmares, and seize op­portunities when they present themselves. It is also the time to consider what impact the variance could have on future forecast periods. If de­mand was lower than expected because a promotional flyer was mailed late, will it have the same impact in the later period, more, or less, per­haps because it is now too late in the season? If demand is higher than expected, should future discounts be cut back to improve profits? These are strategic decisions, and, when the red flags of variance are raised, the company can put itself on an even more prosperous track.

Good aggregate forecasts at product group levels, developed with knowledge, can be a boon to those with forecasting systems that have forcing features. Forcing allows a group-level increase or decrease to be applied proportionately to the items in the group without manually adjusting each one. They can also be used to develop effective group-level measures that allow informed decisions to be made on issues such as overtime, changes to the MPS, and the like. And who wouldn't be proud of a 95 percent aggregate forecast accuracy?

 

Well, I have some problems with all this reliance on aggregates alone. Do any of your customers order aggregates? Do any of them ask you to just ship a few pallets of whatever you've got? (I wish mine did! I've got some stuff I'd LOVE to send them, and I'll bet you do, too!) No, each and every one of your customers, I assure you, has absolutely no interest in your aggregate accuracy. All they want is exactly the item they want, exactly when they want it! They don't want to have to come back tomorrow (do you)? They are not happy when you give them the great news that the factory back order will be resolved in two weeks (would you, if you needed the item today?). And they don't care that you have a whole aisle brimming with the same item in another color!

Aggregate forecast measures can also be misleading and mask prob­lems. Consider an example product line with only two items, each with a forecast of 100 this month, for a total aggregate forecast of 200. Dur­ing the month, you sell 50 of one item, and sell 150 (or actually sell 100 and back order 50 because you ran out) of the other for a total of 200 units. Your aggregate accuracy was 100 percent, yet you are over­stocked 50 of one item and back ordered 50 of the other. Great, huh?

You've certainly heard the adage, "Garbage in, garbage out." What do you think happens when you force (the act of imposing group-level forecasts on the items) great aggregate forecasts on item forecasts that are not maintained and are themselves a mess? You get a bigger mess! Yet so many companies spend all kinds of time on the group forecasts, ignore the items, and wonder why the results are poor.

To Be Continued

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

Lean Manufacturing Articles and click on Series 10


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