Formation of seasonal groups and application of seasonal indices

J. E. Boylan, Huijing Chen, M. Mohammadipour, A. Syntetos

Research output: Contribution to journalArticlepeer-review

Abstract

Estimating seasonal variations in demand is a challenging task faced by many organisations. There may be many stock-keeping units (SKUs) to forecast, but often data histories are short, with very few complete seasonal cycles. It has been suggested in the literature that group seasonal indices (GSI) methods should be used to take advantage of information on similar SKUs. This paper addresses two research questions: (1 ) how should groups be formed in order to use the GSI methods? and (2) when should the GSI methods and the individual seasonal indices (ISI) method be used? Theoretical results are presented, showing that seasonal grouping and forecasting may be unified, based on a Mean Square Error criterion, and K-mean s clustering . A heuristic K-means method is presented , which is competitive with the Average Linkage method. It offers a viable alternative to a company’s own grouping method or may be used with confidence if a company lacks a grouping method. The paper gives empirical findings that confirm earlier theoretical results that greater accuracy may be obtained by employing a rule that assigns the GSI method to some SKUs and the ISI method to the remainder.
Original languageEnglish
Pages (from-to)227-241
Number of pages15
JournalJournal of the Operational Research Society
Volume65
Issue number2
DOIs
Publication statusPublished - 1 Feb 2014

Keywords

  • forecasting
  • seasonality
  • grouping
  • clustering

Fingerprint

Dive into the research topics of 'Formation of seasonal groups and application of seasonal indices'. Together they form a unique fingerprint.

Cite this