Modelling and prediction of intermittent demand distributions

The management of spare part inventories is an issue of strategic concern for mostindustrial firms. However, the demand for spare parts is typically intermittent in naturemeaning that orders arrive sporadically and the order sizes may be highly variable. Anumber of authors have suggested that compound distributions could be used to modelintermittent demand patterns. There is however a lack of theoretical analysis and relevantempirical evidence on this issue. In this work, we assess whether compound Poissondistributions provide a good fit for the demand distributions of spare part items. Aframework that links demand classification and the distributional properties of demand isproposed and the empirical validity of the framework is assessed by means ofexperimentation with real data.This study also examines a number of different approaches for managing inventory itemswith intermittent demand. The literature on inventory management is dominated by the'frequentist-approach'; this is the term that is being used in this thesis to refer to all thesolutions that rely on frequentist inference in order to obtain the demand distribution. Thefrequentist-based approach is characterised by a reliance on a number of assumptions(including, at a minimum, that the demand distribution and the associated parameters areknown). As demonstrated in this study, such assumptions may pose considerable practicalproblems when demand is intermittent. An argument is being made in favour of otherinventory management approaches that rely on fewer, less restrictive, assumptions. Thealternative approaches considered in this study include the bootstrapping-based solutionproposed by Willemain et al. (1994), a new solution based on the work by Efron (1979)and a new approach that is based on the Bayesian paradigm. A comparison of the stockcontrol performance of these alternatives suggests that non-frequentist approaches mayperform as well as the frequentist one.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2012

Author dc:creator

• Lengu, D

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1338276

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1338276