Forecast reconciliation in the temporal hierarchy: Special case of intermittent demand with obsolescence. (15th May 2023)

• Record Type: Journal Article
• Authors: Sanguri, Kamal; Patra, Sabyasachi; Punia, Sushil
• Is Part Of: Expert systems with applications. Volume 218(2023)
• Publication Date: 2023-05-15
• Subjects: Intermittent demand -- Obsolescence -- Temporal hierarchy -- Forecast combination; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119566
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel framework of forecast reconciliation is proposed. Framework is suitable for intermittent demand items with risk of obsolescence. The proposed framework ensures improved non-negative reconciled forecasts. Efficacy of framework is analysed with the help of empirical and simulated dataset. Results establish the improved forecasting performance of the proposed framework. Abstract: Intermittent demand items are often exposed to a risk of obsolescence, which translates into a downward trend if related series are observed at reduced frequencies. However, the existing forecasting applications in the said context are limited to the modifications in the Croston method. Moreover, they ignore the potential benefits of utilising the information present at reduced frequencies. Accordingly, the study proposes using the temporal hierarchy (TH) framework to model the decreasing trend information for intermittent demand items efficiently. Additionally, with various optimal reconciliation techniques under the TH framework, the study attempts to provide an in-depth understanding of their expected performance in the obsolescence scenario. Further, the study argues that the reconciled forecast at the bottom level for these techniques can be expressed as a combination of disaggregated forecasts. Wherein the scheme of the combination depends on the set of assumptions followed by each approach. However, their application is limited by the tendency to produce negative reconciled forecasts, which is of particular concern for intermittent demand items. Therefore, the present study proposes using the constrained least square estimation framework to determine the desired optimal combination scheme for the bottom-level forecasts, which are then utilised to enforce coherency across the considered hierarchy. Besides, the study corroborates the suitability of the proposed framework in the obsolescence scenario with the help of simulated and empirical intermittent demand datasets. Finally, the results are validated with the help of relevant statistical analysis.