This paper addresses the problem of forecasting irregular demand, balancing the tradeoff between forecast accuracy and cost of collecting information. The literature suggests the adoption of a clustering approach, however it is not clear under which conditions this method is actually beneficial. We consider three kinds of demand variability, namely structural (e.g. seasonality), managerial (e.g. promotions) and random (i.e. unpredictable), and we investigate their impact on the correlation of demand within clusters of customers and thus on the clustering approach effectiveness. We develop an analytical model of this relationship and test it with real data in the fresh food industry. Results show that while structural and managerial variability make the clustering approach feasible, random variability works in the opposite direction, providing guidelines on when this forecasting method can be adopted.

Clustering customers to forecast demand

CANIATO, FEDERICO FRANCESCO ANGELO;RONCHI, STEFANO;VERGANTI, ROBERTO;ZOTTERI, GIULIO
2005-01-01

Abstract

This paper addresses the problem of forecasting irregular demand, balancing the tradeoff between forecast accuracy and cost of collecting information. The literature suggests the adoption of a clustering approach, however it is not clear under which conditions this method is actually beneficial. We consider three kinds of demand variability, namely structural (e.g. seasonality), managerial (e.g. promotions) and random (i.e. unpredictable), and we investigate their impact on the correlation of demand within clusters of customers and thus on the clustering approach effectiveness. We develop an analytical model of this relationship and test it with real data in the fresh food industry. Results show that while structural and managerial variability make the clustering approach feasible, random variability works in the opposite direction, providing guidelines on when this forecasting method can be adopted.
Demand forecast; Lumpy demand; Clustering customers
File in questo prodotto:
File Dimensione Formato  
Caniato et al. - PPC 2005.pdf

Accesso riservato

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 248.47 kB
Formato Adobe PDF
248.47 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/554997
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 17
social impact