Does Forecast‐Accuracy‐Based Allocation Induce Customers to Share Truthful Order Forecasts?

What is it about?

In many industries, buyers (who may sell to end consumers or other buyers) often submit forecasts for future orders (also referred to as “soft orders”) to their supplier to help with capacity planning. Since soft orders represent the intent of purchasing and are often not legally binding, buyers tend to submit inflated order forecasts to secure capacity from their supplier. The supplier, knowing this forecast inflation behavior of the buyers, may discount or even ignore the forecasts received, leading to lower service levels and longer delivery times, which may further drive the buyers to inflate their forecasts. Capital‐intensive industries, such as semiconductor and aerospace manufacturing, particularly suffer from this. In this study, we investigate buyers’ strategic order forecasting behavior in a multi‐period setting under a forecast accuracy‐based allocation policy, where the supplier allocates (proportionally) more inventory to the buyer with the better order forecast accuracy in case of scarce supply.

Featured Image

Why is it important?

It could be quite costly, or even infeasible, for companies to test the impact of an allocation policy in a real‐world setting. Thus, we use the lab environment as a “test bed” to investigate our proposed allocation policy. We developed an interactive game that simulates a supply chain in which one supplier sells a key component to two buyers, who in turn sell to consumers. In each period, buyers share forecasts of future orders with their supplier. The participants in the game play the role of a buyer, while the supplier is automated. Our experimental findings suggest that rewarding forecast accuracy in allocating inventory can significantly improve the order forecast accuracy of the buyers and reduce their forecast inflation behavior. Interestingly, even without communication of the policy, buyers learn over time that more accurate forecasts lead to better service from their supplier and improve their order forecast accuracy.