Intermittent demand, inventory obsolescence, and temporal aggregation forecasts

Sanguri, Kamal; Patra, Sabyasachi; Nikolopoulos, Konstantinos; Punia, Sushil

Abstract

Forecasting for intermittent demand is considered a difficult task and becomes even more challenging in the presence of obsolescence. Traditionally the problem has been dealt with modifications in the conventional parametric methods such as Croston. However, these methods are generally applied at the observed frequency, ignoring any additional information, such as trend that becomes prominent at higher levels of aggregation. We evaluate established Temporal Aggregation (TA) methods: ADIDA, Forecast Combination, and Temporal Hierarchies in the said context. We further employ restricted least-squares estimation and propose two new combination approaches tailored to decreasing demand scenarios. Finally, we test our propositions on both simulated and real datasets. Our empirical findings support the use of variable forecast combination weights to improve TA’s performance in intermittent demand items with a risk of obsolescence.

Citation

Sanguri, K., Patra, S., Nikolopoulos, K., & Punia, S. (2024). Intermittent demand, inventory obsolescence, and temporal aggregation forecasts. International Journal of Production Research, 62(5), 1663-1685. https://doi.org/10.1080/00207543.2023.2199435

• Journal Article Type: Article
• Acceptance Date: Mar 27, 2023
• Online Publication Date: Apr 18, 2023
• Publication Date: 2024
• Deposit Date: Mar 30, 2023
• Publicly Available Date: May 16, 2023
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Issue: 5
• Pages: 1663-1685
• DOI: https://doi.org/10.1080/00207543.2023.2199435
• Public URL: https://durham-repository.worktribe.com/output/1178135