Garbage In, Garbage Out
The best way to avoid the trap of overforecasting demand is to use point-of-sale information directly from the retailer. Since POS data is an accurate gauge of consumption, it improves the reliability of a forecast. That is how Scotts CIO Sengupta was able to improve his company's forecasting results. By using point-of-sale data, Scotts increased its forecast accuracy by more than 30 per cent in one year.
Before it started using POS data, Scotts would forecast the demand for its products at the national level - but not at the individual store level - for each of its customers, such as Wal-Mart and Home Depot. Each forecast would take into account how much each customer ordered of, say, a particular type of fertiliser in the past and combine that with other factors such as expected weather patterns. Since orders were simply an estimate of retail sales, the process left Scotts susceptible to the bullwhip effect. Furthermore, the sheer volume of the orders greatly inflated the forecasts' margin of error.
Now that Scotts has point-of-sale information from each retail outlet, its forecasts are more accurate and the risk of bullwhips is all but eliminated. Furthermore, the POS data lets Sengupta produce smaller, more detailed forecasts for each individual retail outlet if he wants (Sengupta says that Scotts actually forecasts in groups of stores to help reduce the impact of a one-time event in one store that wouldn't be replicated in another). Having many smaller, more accurate forecasts further reduces the overall margin of error.
In improving its forecasting process, Scotts has an advantage: The retail and consumer packaged goods industry is well ahead of the game when it comes to sharing data such as point-of-sale information. Most companies in this industry follow the blueprint laid out by the Voluntary Interindustry Commerce Standards Association subcommittee on collaborative planning, forecasting and replenishment.
In the rest of the world, however, most companies aren't in a position to get POS information from their customers. In the first place, few companies collect product-level data at the point of sale. And second, many aren't willing to share data that has traditionally been viewed as a closely guarded competitive secret.
But that doesn't mean you can't get better data and use it to improve your forecasts. In Europe, for example, Scotts gets POS information from only its three biggest customers or 20 per cent of its business there - the others either aren't able or willing to share it. "In Europe we understand that we can't get point of sale, but we still try to get as close to the point of final consumption as we can," Sengupta says. In this case, that's when sale items leave the distribution centres. While it isn't the same as point of sale, Scotts at least knows where its products are going and how much has actually been sold to retail outlets, which is more accurate than traditional order information. Furthermore, the distribution centre is several days closer to the eventual point of sale than Scotts' own warehouse, making that data a better indicator of current market trends.
There is other data available that can help CIOs improve the data in their forecasts. Imperial Sugar Vice President and CIO George Muller says that his company combines the order information it receives from its customers with market intelligence reports from Information Resources - data about what actually gets sold in stores. The IRI sales data, which will show, for example, how much sugar was sold in Atlanta area supermarkets, can serve as a surrogate for point-of-sale data. At the very least, it gives Imperial Sugar something to check its order data against.
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