On a method to improve your service BOMs within spare parts management

Stip, Joan; Houtum, Geert‐Jan van

doi:10.1016/j.ijpe.2019.08.001

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…In a recent study by Stip & Van Houtum involved, a method has been developed to generate warnings for possible errors. With this method, the percentage of not finding spare parts in the relevant company has been reduced by approximately 4-5 percent during the year (Stip & Van Houtum, 2020). Boutselis and McNaught worked on the problem of forecasting spare parts demand related to equipment failures within some Logistics Support Organizations (LSOs) (Boutselis & McNaught, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

Yuna¹,

Erkayman²

2022

European Journal of Science and Technology

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Demand forecasting is a difficult field of study for intermittent demands. Spare parts demand structures also have an intermittent demand structure. Therefore, for companies operating in this field, this situation causes various problems (holding cost or cost of lost sale). Intermittent demands are inherently difficult to predict. Demands with a smooth structure provide a more suitable working environment for businesses. Because the more accurately the relevant demand is forecasted, the more smoothly the works that depend on demand forecasting are carried on. In this study, a randomly generated demand series with intermittent demand structure is examined. The estimation difficulty of intermittent demand is illustrated by an estimation made in Matlab. In order to avoid this difficulty, the costs were tried to be minimized by determining the inventory levels. An inventory model is proposed that determines stock levels using intermittent demands and calculates average profit by calculating costs. The related model was solved with Genetic Algorithm in Matlab and the results were recorded.

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Section: Literature Reviewmentioning

confidence: 99%

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

Yuna¹,

Erkayman²

2022

European Journal of Science and Technology

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“…The developed model by was applied for the Q-R estimation. Better results were achieved as compared to the traditional models [28]. For the critical manufacturing systems, a service contract with the equipment supplier is essential.…”

Section: Introductionmentioning

confidence: 98%

Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment

et al. 2020

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This study proposes a methodology for the oil and gas businesses to keep their production plant productive with a minimum investment in carrying maintenance, repair, and operating inventory planning. The goal is to assist the exploration and production companies in minimizing the investment in keeping maintenance, repair, and operating (MRO) inventory for improving production plant uptime. The MRO inventory is the most expensive asset and it requires substantial investment. It helps in keeping the oil and gas production plant productive by performing planned and unplanned maintenance activities. A (Q, r) model with a stock-out and backorder cost approach is combined with a continuous inventory review policy for the analysis of class A items of oil and gas production plant MRO inventory. The class A items are identified through popular ABC analysis based on annual dollar volume. The demand for the inventory is modeled through Poisson distribution with consideration of constant lead time. The (Q, r) model in both stock-out cost and backorder cost approaches assigned higher order frequency and lower service level to low annual demand and highly expensive items. The stock-out cost approach shows an 8.88% increase in the average service level and a 56.9% decrease in the company average inventory investment. The backorder cost approach results in a 7.77% increase in average service level and a 57% decrease in average inventory investment in contrast to the company’s existing inventory management system. The results have a direct impact on increasing plant uptime and productivity and reducing company maintenance cost through properly managing maintenance stock. The analysis is carried out on the oil and gas production plant’s MRO inventory data, but it can be applied to other companies’ inventory data as well. All the results reflected in this research are based on the inventory ordering policy of two orders per year. The inventory ordering frequency per year may be other than two orders per year depending on the type of organization.

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Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research

2023

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Data science and big data analytics (DS &BDA) methodologies and tools are used extensively in supply chains and logistics (SC &L). However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise DS &BDA application areas in the SC &L comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose an unique systematic review methodology for the field of DS &BDA in SC &L. Second, we use the methodology proposed for a systematic literature review on DS &BDA techniques in the SC &L fields aiming at classifying the existing DS &BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. We analyse 364 publications which use a variety of DS &BDA-driven modelling methods for SC &L processes across different decision-making levels. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of DS &BDA in SC &L.

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On a method to improve your service BOMs within spare parts management

Cited by 8 publications

References 19 publications

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

Improved MRO Inventory Management System in Oil and Gas Company: Increased Service Level and Reduced Average Inventory Investment

Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research

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