Forecasting service parts demand for a discontinued product

Hong, Jung Sik; Koo, Hoon-Young; Lee, Chin-Seung; Ahn, JaeKyoung

doi:10.1080/07408170701745337

Cited by 37 publications

(41 citation statements)

References 11 publications

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“…As a result, neither safety stocks nor service levels or stock-out costs can be computed. The latter aspects have been analyzed by Ritchie and Wilcox (1977); Fortuin (1980Fortuin ( ,1981; Klein Haneveld and Teunter (1998);and Hong et al (2008) for several model variants. Leifker et al (2012) study LTB problems in a continuous setting without any service period restriction, while there is limited information on the customers, and the only alternative is buying a part.…”

Section: Literature Reviewmentioning

confidence: 99%

Last time buy and repair decisions for spare parts

Behfard

Heijden

Hanbali

et al. 2015

European Journal of Operational Research

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a b s t r a c tOriginal Equipment Manufacturers (OEM's) of advanced capital goods often offer service contracts for system support to their customers, for which spare parts are needed. Due to technological changes, suppliers of spare parts may stop production at some point in time. As a reaction to that decision, an OEM may place a so-called Last Time Buy (LTB) order to cover demand for spare parts during the remaining service period, which may last for many years. The fact that there might be other alternative sources of supply in the next periods complicates the decision on the LTB. In this paper, we develop a heuristic method to find the near-optimal LTB quantity in presence of an imperfect repair option of the failed parts that can be returned from the field. Comparison of our method to simulation shows high approximation accuracy. Numerical experiments reveal that repair is an excellent option as alternative sourcing, even if it is more expensive than buying a new part, because of the option to postpone the repair until the parts are needed. In addition, we show the impact of other key parameters on costs and LTB quantity.

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Section: Literature Reviewmentioning

confidence: 99%

Last time buy and repair decisions for spare parts

Behfard

Heijden

Hanbali

et al. 2015

European Journal of Operational Research

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“…To cope with the production planning problem in a lumpy demand environment, Quintana and Leung (2007) proposed a simple adaptive smoothing approach to replace the conventional industrial practice of choosing a smoothing factor largely based on the analyst or engineers' experience and subjective judgement. Hong et al (2008) identified for major factors influencing the service part demand forecasting and developed a stochastic demand forecasting method. Simultaneously, with the development of artificial intelligence, the artificial ANN is applied to predict customer demand, and the prediction result is acceptable (Chang, Wang, and Tsai 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction

Cao

Jiang

Wang

2015

International Journal of Production Research

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With the emergence of individualised and personalised customer demands, the interaction of service and product has come into the sight of manufacturers and thus promoted the arising of service-oriented manufacturing (SOM), a new business mode that combines manufacturing and service. Be similar to the conventional manufacturing, the customer demand prediction (CDP) of SOM is very important since it is the foundation of the following manufacturing stages. As there are always tight and frequent interactions between service providers and customers in SOM, the customer satisfaction would significantly influence the customer demand of the following purchasing periods. To cope with this issue, a novel CDP approach for SOM incorporating customer satisfaction is proposed. Firstly, the structural relationships among customer satisfaction index and the influence factors are quantitatively modelled by using the structural equation model. Secondly, to reduce the adverse effect of multiple structural input data and small sample size, the least square support vector mechanism is employed to predict customer demand. Finally, the CDP of the air conditioner compressor which is a typical SOM product is implemented as the real-case example, and the effectiveness and validity of the proposed approach is elaborated from the prediction results analysis and comparison.

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“…The model is not a strict final order model in that it is expected that multiple orders of parts will be given during the course of the service contracts but at a cost greater than that of the initial order. Hong et al (2008) calculate the final order amount by discretizing the postproduction phase of the product life cycle. Pourakbar and Dekker (2012) examine the end-of-life inventory problem with customer differentiation, in which different sets of customers are put into different classes.…”

Section: Literaturementioning

confidence: 99%

Determining Optimal Order Amount for End-of-Life Parts Acquisition with Possibility of Contract Extension

Leifker

Jones

Lowe

2014

The Engineering Economist

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Manufacturers often encounter difficulties in supplying an adequate number of spare parts for a product in its postproduction phase. As a result, manufacturers will sometimes make one final order of a model of spare part that is used to satisfy any demand for the spare part going forward. The problem is compounded when a customer may request an extension to the maintenance or supply contract for which the part is supplied. We present two possible approaches for dealing with the problem: one involves the use of a continuous-time dynamic program that allows for the possibility of salvage, and the other makes use of a two-stage stochastic algorithm that does not consider salvage.

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Forecasting service parts demand for a discontinued product

Cited by 37 publications

References 11 publications

Last time buy and repair decisions for spare parts

Last time buy and repair decisions for spare parts

Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction

Determining Optimal Order Amount for End-of-Life Parts Acquisition with Possibility of Contract Extension

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