Spare parts inventory management: New evidence from distribution fitting

Turrini, Laura; Meissner, Joern

doi:10.1016/j.ejor.2017.09.039

Cited by 56 publications

(31 citation statements)

References 38 publications

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“…In contrast, the MRO inventory has irregular demand due to which forecasting its demand is a difficult task compared to the regular demand inventory [9] and therefore, it has great uncertainty because its consumption only depends only on maintenance phenomena which are unpredictable events. Hence, MRO inventory management is a great challenge for businesses with respect to reducing access inventory and increasing service level towards maintenance phenomena [10].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Additionally, Basten and Ryan [13] compared two inventory management models based on intervals between maintenance sessions. Likewise, Turini and Meisser [14] used the Kolmogorov-Smirnov test to find the probability distribution that best fits spare part demand. In conclusion, current research works analyze the importance of maintenance and propose several types of models to predict machine part failures.…”

Section: Introductionmentioning

confidence: 99%

A System Dynamics Model to Evaluate the Impact of Production Process Disruption on Order Shipping

et al. 2019

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Multiple studies have analyzed the importance of preventive maintenance. Similarly, they have developed and/or evaluated various spare part inventory management policies that help companies reduce the duration of production disruptions resulting from sudden mechanical failures. All of these studies assess the impact of mechanical failures on local aspects, such as idle times or management policies. However, they do not holistically evaluate how machine failures affect key performance indicators. To address this gap, we proposed a System Dynamics (SD) model to systemically analyze the impact of machine part failures on order shipping, a key performance indicator. Similarly, we assessed the impact of machine part failures on the levels of the company’s finished goods inventory. The research was conducted in a glass bottle manufacturing company. To increase order shipping as a performance indicator, we identified the key variables of the production process and conducted a sensitivity analysis of the variables. Our results indicate that it is possible to reach the company’s 98–100% complete delivery policy.

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“…The standard assumption is that lead time demand follows a normal or Poisson distribution. This is often not true, particularly for high irregular demand patterns (e.g., see Turrini and Meissner 2017). The departures from the assumed distribution may lead to very unsatisfactory inventory performance, e.g., high inventory costs (Nenes et al 2010, Turrini andMeissner 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A number of different distribution models have been used in the literature, including the stuttering Poisson (or geometric Poisson e.g., see Chen 2012), zero-inflated Poisson (ZIP), hurdle shifted Poisson (HSP), negative binomial, normal and gamma distributions (e.g., see Snyder et al 2012, Costantino et al 2017, Turrini and Meissner 2017. Among these distributions, the stuttering Poisson is computationally intensive, ZIP, HSP and negative binomial distribution are only applicable for over-Poisson dispersive cases and the normal and gamma distributions are continuous distributions.…”

Section: Introductionmentioning

confidence: 99%

“…According to Turrini and Meissner (2017), most inventory applications involve calculating an upper percentile (e.g., 98%) of the forecasted demand distribution, implying that the right tail of the distribution provides more information for inventory management. Based on this observation, we propose a weighted least square method (LSM) for fitting a distribution to a set of demand data in order to achieve good inventory performance.…”

Section: Introductionmentioning

confidence: 99%

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Modelling spare part demand data using transmuted or exponentiated Poisson distribution

Jiang¹,

Chen²

2018

Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability

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Traditionally, it is assumed that the lead time spare part demand follows the Poisson or normal distribution. It is well-known that the mean of Poisson distribution equals to its variance, which is called Poisson dispersion. Actual demand data scarcely meet the Poisson dispersion. To overcome this problem, several two-parameter variants (e.g., zero-inflated Poisson and hurdle shifted Poisson) of the Poisson distribution have been used for modelling irregular spare part demand data. In this paper, we propose two new two-parameter alternatives, which are transmuted and exponentiated Poisson distributions. The proposed distributions can be over-or under-Poisson dispersive, and hence provide better flexibility without losing the simplicity of the Poisson distribution too much. The appropriateness of the proposed models is illustrated through three real-world examples. Based on the idea that a fitted distribution should provide a good goodness-of-fit to the right tail of the empirical demand distribution so as to achieve good inventory performance, a weighted least square method is also proposed to estimate the model parameters. The appropriateness of the proposed approach is also illustrated.

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Spare parts inventory management: New evidence from distribution fitting

Cited by 56 publications

References 38 publications

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

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

A System Dynamics Model to Evaluate the Impact of Production Process Disruption on Order Shipping

Modelling spare part demand data using transmuted or exponentiated Poisson distribution

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