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Martin Thormann - One of the best experts on this subject based on the ideXlab platform.

  • Integration of Real-Time Demand Information and Spare Parts Distribution Planning for the Optimization of Spare Parts Supply in After-Sales Service Networks
    Operations and Supply Chain Management: An International Journal, 2015
    Co-Authors: Martin Thormann
    Abstract:

    Accurate spare parts Demand planning and effective distribution planning is essential for providers of after-sales services in the machine and plant engineering industry to ensure high spare parts availability for maintenance and failure orders (callouts) at a reasonable cost. Low spare parts availability is primarily the result of high uncertainty in spare parts Demand, leading to misallocation of parts within aftersales service networks. The lack of spare parts availability causes equipment downtime, resulting in customer dissatisfaction and possible penalty costs for after-sales service providers, if response times are contractually fixed. This paper proposes an approach and planning methods for integrating real-time status Information about equipment utilization and service conditions to determine optimal spare parts stocking strategies. For this purpose, spare parts stocking strategies and ordering policies for application in after-sales service networks are analyzed. Furthermore, a binary linear optimization model is developed for the assignment of stocking strategies to spare parts based on real-time Demand Information of the equipment to be serviced. This method uses data provided by an internationally operating elevator company.

Fangruo Chen - One of the best experts on this subject based on the ideXlab platform.

  • market segmentation advanced Demand Information and supply chain performance
    Manufacturing & Service Operations Management, 2000
    Co-Authors: Fangruo Chen
    Abstract:

    A monopolist sells a single product to a market where the customers may be enticed to accept a delay as to when their orders are shipped. The enticement is a discounted price for the product. The market consists of several segments with different degrees of aversion to delays. The firm offers a price schedule under which the customers each self-select the price they pay and when their orders are to be shipped. When a customer agrees to wait, the firm gains advanced Demand Information that can be used to reduce its supply chain costs. This article shows how an optimal pricing-replenishment strategy that balances the costs due to discounted prices and the benefits due to advanced Demand Information can be determined.

  • market segmentation advanced Demand Information and supply chain performance
    1999
    Co-Authors: Fangruo Chen
    Abstract:

    A monopolist firm sells a single product to a market where the customers may be enticed to accept a delay in when their orders are shipped. The enticement is a discounted price for the product. The market consists of several segments with different degrees of aversion to delays. The firm offers a price schedule under which the customers each self-select the price they pay and when their orders are to be shipped. When a customer agrees to wait, the firm gains advanced Demand Information which can be used to reduce its supply chain costs. This article explores the costs and benefits of this pricing strategy.

  • echelon reorder points installation reorder points and the value of centralized Demand Information
    Management Science, 1998
    Co-Authors: Martin I Reiman, Lawrence M Wein, Fangruo Chen
    Abstract:

    We consider a serial inventory system with N stages. The material flows from an outside supplier to stage N, then to stage N - 1, etc., and finally to stage 1 where random customer Demand arises. Each stage replenishes a stage-specific inventory position according to a stage-specific reorder point/order quantity policy. Two variations of this policy are considered. One is based on echelon stock, and the other installation stock. The former requires centralized Demand Information, while the latter does not. The relative cost difference between the two policies is called the value of centralized Demand Information. For fixed order quantities, we develop efficient algorithms for computing both the optimal echelon reorder points and the optimal installation reorder points. These algorithms enable us to conduct an extensive computational study to assess the value of centralized Demand Information and to understand how this value depends on several key system parameters, i.e., the number of stages, lead times, batch sizes, Demand variability, and the desired level of customer service.

Farzad Mahmoodi - One of the best experts on this subject based on the ideXlab platform.

  • safety stock determination based on parametric lead time and Demand Information
    International Journal of Production Research, 2010
    Co-Authors: Alex J Ruiztorres, Farzad Mahmoodi
    Abstract:

    In many production environments where Demand and lead times are variable, significant levels of safety stock inventory are required to assure timely production and delivery of the final product. Traditional models to determine the appropriate safety stock level may result in more safety stocks at sub-assembly and finished goods levels than necessary and thus lead to higher inventory carrying costs than desired. Such models generally incorrectly assume that the Demand during the lead time follows a normal distribution. This paper revisits and analyses a re-ordering point inventory model developed by Estes (1973) that accounts for Demand and lead time variability without making any particular distributional assumptions. Instead, it focuses on historical data to determine the possible outcomes of the replenishment cycle. We compare the proposed model with the traditional model by conducting simulation analysis using three data sets obtained from an electronics manufacturer. The results indicate that the prop...

Chung-piaw Teo - One of the best experts on this subject based on the ideXlab platform.

Dirk Pieter Van Donk - One of the best experts on this subject based on the ideXlab platform.

  • Safety stock or safety lead time: coping with unreliability in Demand and supply
    International Journal of Production Research, 2010
    Co-Authors: Tim J. Van Kampen, Dirk Pieter Van Donk
    Abstract:

    Safety stock and safety lead time are common measures used to cope with uncertainties in Demand and supply. Typically, these uncertainties are studied in isolated instances, ignoring settings with uncertainties both in Demand and in supply. The current literature largely neglects case study based contexts and, often, single product situations are investigated in which machine set-ups are not considered. Based on the problems and findings in a case study, we investigate the effects of safety stock and safety lead time on delivery performance in a multi-product setting. The outcomes of the extensive simulation study indicate that utilising a safety lead time results in a higher delivery performance where there is a variable supply, whereas having a safety stock results in a higher delivery performance where there is unreliable Demand Information. In contrast to earlier findings in the single product situation, this study shows that managers facing the combination of unreliability in Demand Information and supply variability in a multiple product situation should opt for a safety lead time as the most effective way of improving their delivery performance.