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Data driven supply allocation to individual customers considering forecast bias

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  • Seitz, Alexander
  • Grunow, Martin
  • Akkerman, Renzo

Abstract

We propose a data-driven allocation planning approach, which is designed for use in advanced planning systems as they are widely used in industrial environments. The approach exploits increasingly available data on individual customers and products by allocating supply on a highly granular level at high planning frequencies. It counteracts rationing gaming by customers, which we assume to be the reason for demand forecast biases.

Suggested Citation

  • Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:proeco:v:227:y:2020:i:c:s0925527320300761
    DOI: 10.1016/j.ijpe.2020.107683
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    References listed on IDEAS

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