Case-Oriented Paper

Journal of the Operational Research Society advance online publication 25 June 2008; doi: 10.1057/palgrave.jors.2602637

Forecasting newspaper demand with censored regression

M Kiygi Calli1 and M Weverbergh1

1University of Antwerp, Antwerp, Belgium

Correspondence: M Kiygi Calli, Department of Marketing, University of Antwerp, Prinsstraat 13, Antwerp 2000, Belgium. E-mail: meltem.kiygicalli@ua.ac.be

Received May 2007; Accepted May 2008; Published online 25 June 2008.

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Abstract

Newspaper circulation has to be determined at the level of the individual retail outlets for each of the editions to be sold through such outlets. Traditional forecasting methods provide no insight into the impact of the service level: defined as the probability that no out-of-stock will occur. The service level results in out-of-stock situations, causing missed sales and oversupply or returns. In our application management sets a policy aiming at a 97% service level. The forecasting system developed provides estimates for excess deliveries and for the expected shortages. The results compare favourably to the traditional moving average approach previously employed by the publisher. Censored regression is a natural approach to the newspaper problem. It provides information on key policy variables and it is relatively simple to integrate into the distribution policy, with only small adaptations to the existing forecasting and distribution policy.

Keywords:

censored regression, decision analysis, forecasting, distribution

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