Data Driven Supply Allocation to Individual Customers Considering Forecast Bias

Abstract

We propose a data driven allocation planning (DDAP) approach exploiting increasingly available data on individual customers and products by allocating supply on a highly granular level at high planning frequencies. The method considers the demand forecast bias of customers, supports an efficient supply allocation and incentivises the customers to communicate truthful forecasts. Using the approach in a numerical study based on the semiconductor industry, we demonstrate that the approach increases overall service levels, especially for customers with truthful forecasts, and reduces excess allocations, leading to lower inventory levels. The analysis further shows that the allocation efficiency increases with the granularity level and the predictive quality of the available data.