Sensing endogenous seasonality in the case of a coffee supply chain

Article


Shukla, V. and Naim, M. 2018. Sensing endogenous seasonality in the case of a coffee supply chain. International Journal of Logistics Research and Applications. 21 (3), pp. 279-299. https://doi.org/10.1080/13675567.2017.1395829
TypeArticle
TitleSensing endogenous seasonality in the case of a coffee supply chain
AuthorsShukla, V. and Naim, M.
Abstract

Rogue seasonality, or endogenously generated cyclicality (in variables), is common in supply chains and known to adversely affect performance. This paper explores a technique for sensing rogue seasonality at a supply chain echelon level. A signature and index based on cluster profiles of variables, which are meant to sense echelon-level generation and intensity of rogue seasonality, respectively, are proposed. Their validity is then established on echelons of a downstream coffee supply chain for five stock keeping units (SKUs) with contrasting rogue seasonality generation behaviour. The appropriateness of spectra as the domain for representing variables, data for which is daily sampled, is highlighted. Time-batching cycles which could corrupt the sensing are observed in variables, and the need to therefore filter them out in advance is also highlighted. The knowledge gained about the echelon location, intensity and time of generation of rogue seasonality could enable timely deployment of specific mitigation actions.

Research GroupInternational Business group
PublisherTaylor and Francis
JournalInternational Journal of Logistics Research and Applications
ISSN1367-5567
Publication dates
Online02 Nov 2017
Print04 May 2018
Publication process dates
Deposited23 Oct 2017
Accepted16 Oct 2017
Output statusPublished
Accepted author manuscript
Copyright Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Logistics Research and Applications on 2 November 2017, available online: http://www.tandfonline.com/10.1080/13675567.2017.1395829

Digital Object Identifier (DOI)https://doi.org/10.1080/13675567.2017.1395829
LanguageEnglish
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