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

Nachiketas Waychal, Arnab Kumar Laha and Ankur Sinha

Journal of the Operational Research Society, 2024, vol. 75, issue 9, 1813-1825

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.

Date: 2024
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DOI: 10.1080/01605682.2023.2277865

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