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Dear Dr. Jain,

We are currently working on improving our forecast accuracy. One of the ideas is to create an ‘all other’ group for the smaller customers with low volume. While it improves FA, my concern is the visibility we lose of that lower level downstream. For instance, each customer gets its own labels. In an all other group, how do we trigger procurement to buy the correct components and how does supply know where to make the item if 20 customers are now one entity? Any ideas on how that can work? Has anyone else used this method and how did they make it work?


It is true forecast accuracy improves as we forecast at a higher level of aggregation, but by doing that we lose visibility at a lower level. There is a way to get around this, though. For procurement, we certainly need forecasts by customer. The best way is to breakdown the aggregate forecast into customers by using rolling percentage shares of each customer, which can be computed from the sales data of last 9 or 12 months. The rolling percentages will give us, on average, what percentage of sales come from each customer. By applying these percentages to the aggregate forecast, we can get forecasts of each customer. This is not unusual—many companies do this. The only difference is they apply it to SKUs that are difficult to forecast at that level. They prepare their category level forecast, and then use these percentages to arrive at SKU level forecasts. How far back to go in calculating percentages depends on how quickly percentages change.

I hope this helps.

Dr. Chaman Jain,

St. John’s University


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