Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

Mario José Basallo-Triana, Jesús Andrés Rodríguez-Sarasty and Hernán Darío Benitez-Restrepo

International Journal of Production Research, 2017, vol. 55, issue 8, 2336-2350

Abstract: In several industries, global competition, increasing customer expectations and technological innovations tend to accelerate product life-cycles. In this changing environment, traditional forecasting methods tend to be ineffective as a consequence of the transient and highly uncertain demand of short life-cycle products (SLCP), and the scarcity of sales data. To address this challenge, we present a methodology to forecast SLCP demand using time series of similar products referred to as analogies. Linear regression and clustering techniques are used for the selection and weighting of suitable analogies. The proposed methodology is tested against seven analogue-based forecasting methods, including two implementations of non-linear regression methods. In different sets of time series, our methodology attained more accurate forecasts with short processing times compared with state-of-the-art methods. Such results reveal promising applications of combined regression and clustering techniques as simple and effective forecasting tools for supporting replenishment decisions for SLCP.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2016.1241443 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:55:y:2017:i:8:p:2336-2350

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2016.1241443

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().