Forecast-Driven Enterprise

A second organizational strategy is the forecast-driven enterprise. Simply put, this strategy is one in which the nucleus firm, usually their manufacturer, utilizes a forecast, an estimate of future demand, as the basis of its organizational strategy.

Here is the complicating factor: It is difficult to know what customer requirements will be from day to day, month to month, quarter to quarter, and so on. For instance, if a manufacturer was guaranteed that its wholesale or retail customers were going to need 1,000 SKUs (stock keeping units) every Wednesday afternoon, then getting those products to customers at the right time and place would be a matter of simple calculation based upon lead times for production and delivery. In turn, the manufacturer would look at the bill of material, determine the lead time for each, and submit schedules to its suppliers. Unfortunately, it’s difficult to predict even the most stable demand—say, for a product like diapers. There is some variability in demand for diaper, even though they aren’t subject to seasonal style changes or rapid peaks and valleys in response to outside influences affecting ability to pay. (That’s why Procter & Gamble cooperates with Wal-Mart to plan for demand and replenishment of diapers.) The chain of demand begins at the far retail end of the supply chain and works its way back toward the source of raw materials used in making the product. The traditional way of attempting to satisfy this demand is to forecast it.

In this retail example, forecasting along the chain works like this:

• The retailer forecasts demand from parents who purchase diapers.
• The wholesaler forecasts demand from all its retailers.
• The manufacturer forecasts demand from the wholesale distributors.
• The component suppliers forecast demand from manufacturers.
• The raw materials suppliers forecast demand from the component manufacturers.

How effective is this supply chain strategy? Let’s say you don’t want to be placing large bets on the accuracy of all those forecasts. Here’s what actually happens:

• Parents vary their diaper-buying patterns in fairly small increments due to factors nobody fully understands. They may go to different stores for a change, shop on Tuesday instead of Wednesday, or buy two or three weeks’ worth at one time because the diapers are on sale. So, actual demand never quite meets the forecast
• Meanwhile the retailer had already ordered enough to allow a little extra “safety stock” to put in its storeroom. (For retailers, safety stock is a quantity of stock planned to be in inventory to protect against fluctuations in demand or supply.) Or maybe the retailer runs a promotion that is not communicated to the distributor, thus resulting in needing a larger order than was previously forecasted. These fluctuations impact forecasting for the distributor.
• The wholesale distributor had forecasted demand based on past orders from its retailers. But now those demand patterns have a wider variability than the demand pattern at the retailer’s checkout counters due to that safety stock the retailer held on to. Sometimes the safety stock accumulates because demand is less than the forecast, and this means that the retailer’s next order is for less than its forecast—or perhaps it doesn’t have to order at the usual time at all, because it has a glut of diapers—which it probably sells off in a promotion. The upshot of all this is that the small variations in end-user demand are magnified at the distributor.
• Up the chain, the manufacturer of those diapers looks at the demand pattern from the distributor and makes its own forecasts, which show an even wider swing in variability.
• And this variability goes up the chain with ever-wider swings.

As mentioned earlier, this pattern of variability is called the bull-whip effect, and it affects all manner of supply chains that are based on serial forecasting by each independent division or firm that touches the product as it travels from raw material to finished retail item.