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An adaptive multi-objective optimal forecast combination and its application for predicting intermittent demand

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  • Nachiketas Waychal
  • Arnab Kumar Laha
  • Ankur Sinha
Abstract
While time series forecasting models are generally trained by optimising certain forms of error, the end-user’s forecasting needs in a multi-objective setting can be broader, and often mutually conflicting. A production manager may prioritise high product fill rates and low average inventory resulting from a forecast over just low error. The conflict among multiple objectives is notably worrisome in intermittent demand forecasting, where error-minimising approaches can devalue the practitioner’s objectives. To address such forecasting problems, we propose an Adaptive Multi-objective Optimal Combination (AMOC) of forecasts which incorporates the end-user’s preferences across multiple objectives. We demonstrate the use of AMOC in a real-life application of intermittent demand forecasting for optimising four distinct inventory management objectives using five specialised forecasting methods across single-period and multi-period inventory handling scenarios. Additionally, we conduct a comprehensive experiment on a subset of M5 competition data to exhibit the robustness of the AMOC using 13 diverse forecasting methods and four statistical objectives.

Suggested Citation

  • Nachiketas Waychal & Arnab Kumar Laha & Ankur Sinha, 2024. "An adaptive multi-objective optimal forecast combination and its application for predicting intermittent demand," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 75(9), pages 1813-1825, September.
    • Handle: RePEc:taf:tjorxx:v:75:y:2024:i:9:p:1813-1825
    DOI: 10.1080/01605682.2023.2277865
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