Service parts logistics for short lifecycle products

• Main Authors: Castillo, David V., Chua, Alvin John A., Ibuyan, Julianne Christine N.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Business logistics; Production management; Product life cycle; Spare parts; Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/6474
• Institution: De La Salle University

Service parts, or spare parts, management is more difficult than finished goods supply chain management because of the peculiarities of spare parts. Spare parts differ from finished goods because of their larger variety, slower usage, higher criticality and higher risk of obsolescence. Because of these characteristics, the management of spare parts is subjected to a number of challenges that include high demand, uncertainty, high cost uncertainty and high service requirements. The management of spare parts is made even more complicated by the consideration of short lifecycle products, because these products make the long lead times and unpredictable market response of spare parts more important to consider. The challenges present in managing spare parts for short lifecycle products make them more susceptible to a variety of supply chain risks. Research literature has revealed two main themes of research in spare parts: 1) that many risk minimization models in supply chain do not incorporate multi-echelon inventory equations and 2) that spare parts researchers do not delve into the analysis of the effects of risks and uncertainty on spare parts supply chain systems. Therefore, there is a need to formulate a mathematical model for the design of multi-echelon multi- indenture service parts supply chains that captures facility location, transportation and inventory decisions and that will minimize costs and meet service level requirements while considering various supply chain risks. The mathematical model formulated is a mixed integer nonlinear programming problem. In order to obtain a near-optimal solution for the model, a genetic algorithm was designed and programmed in Excel VBA. Validation of the model consisted of three response surface methodology experiments. The first experiment aimed to find out the behavior of spare parts systems in typical settings, whereas the second and third experiments aimed to determine the effect of demand and cost uncertainties, respectively on the spare parts supply chain. The objective of the second and third experiments is to obtain a supply chain design that is robust to both demand and cost uncertainties. From the validation and analysis of the behavior of the mathematical model, general findings can be derived such as primarily choosing to place inventory of downstream elements, lowering repair times and other types of delays anywhere led to more desirable supply chains in terms of cost, locating most of the repair operations at facilities that have shorter delays, and stocking low criticality spare parts in the most upstream element of the supply chain while optimizing the allocation of high critically parts across all elements of the supply chain. On top of the validation experiments, risk scenario analysis were also conducted to determine the most effective risk mitigation strategies that can be used to hedge against particular supply chain risks. In this case, increased responsiveness or an agile supply chain is found to be the best strategy to use for all the scenarios tested. The results of the research then include a set of guidelines that can be used for the management of spare parts supply chain specially in the face of demand and cost uncertainty, as well as a list of risk mitigation strategies that can be used to hedge against particular supply chain risks.