On the benefits of delayed ordering

Syntetos, Aris A.; Teunter, Ruud H.; Babaï, Mohamed Zied; Transchel, Sandra

doi:10.1016/j.ejor.2015.08.003

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…For the purpose of testing the impact of the type of the lead-time distribution on the performance of the strategies, two distributions are considered, namely: the Geometric with parameter p (including the first success), denoted by G(p), and the discrete Uniform with parameters a and b, denoted by et al, 2010;Syntetos et al, 2016b;Baker and Kharrat, 2018).…”

Section: Variance Estimates Comparisonmentioning

confidence: 99%

Forecasting of lead-time demand variance: Implications for safety stock calculations

Babaï

Dai

et al. 2022

European Journal of Operational Research

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Section: Variance Estimates Comparisonmentioning

confidence: 99%

Forecasting of lead-time demand variance: Implications for safety stock calculations

Babaï

Dai

et al. 2022

European Journal of Operational Research

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“…with S = 2 , increases the total inventory and holding costs with about 31 percent compared to the condition-based inventory policy, as spares are ordered too soon for relatively new components. Indeed, it has been shown in Syntetos, Teunter, Babai, and Transchel (2016) that under a continuous ( s , S ) policy spares are often ordered too early, indicating that delayed ordering could be profitable. Basing the decisions on when to order spares on the complete system state rather than applying a basic inventory policy (which only considers the number of spares on hand and for which the demand process is memoryless) is an effective way to delay orders, and thus to reduce costs significantly.…”

Section: Average Cost Per Time Unit Divided Per Cost Component For Different Strategiesmentioning

confidence: 99%

Joint condition-based maintenance and inventory optimization for systems with multiple components

Keizer

Teunter

Veldman

2017

European Journal of Operational Research

126

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Efficient (condition-based) maintenance planning and inventory control of spares for critical components jointly determine the effectiveness of a maintenance strategy and, thereby, balance system uptime and maintenance costs. Duplicating an optimal policy for a single-component system to a multi-component system is not necessarily optimal, while a separate or sequential optimization of the maintenance and inventory decisions is also not guaranteed to yield the lowest costs. We therefore consider the joint optimization of condition-based maintenance and spares planning for multi-component systems. We formulate our model as a Markov Decision Process, and minimize the long-run average cost per time unit. A key insight from our numerical results is that the ( s , S ) inventory policy, popular in theory as well as practice, can be far from optimal for systems consisting of few components. Significant savings can be obtained by basing both the maintenance decisions and the timing of ordering spare components on the system's condition.

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“…The length of the cycle depends on the nature of the industry and, in many industries, the fiscal year includes multiple cycles. From this perspective, the solidification of capital is a direct loss to the operations (Syntetos, Teunter, Babai, & Transchel, 2016). Moreover, in a number of developing countries, for numerous reasons, interest rates are extremely high.…”

Section: Introductionmentioning

confidence: 99%

On the optimization of maintenance storage cost in industry a fuzzy logic application

Khalil

Labib

2021

IJQRM

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PurposeThe purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. Achieving a balance between the unavailability and over-storage of spare parts is a problem with potentially significant consequences. That significance increases proportionally with the ever-increasing challenge of reducing overall cost. Either scenario can result in substantial financial losses because of the interruption of production or the costs of tied-up capital, also called the “solidification of capital.” Moreover, there is that additional problem of the expiry of parts on the shelf.Design/methodology/approachThe proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy. Two levels of decision are considered simultaneously. The first is whether a part should be stored or ordered when needed. The second involves comparing suppliers with their batch-size offers based on user-determined criteria. A mathematical model is developed in parallel for validation.FindingsThe results indicate that the fuzzy logic approach is accurate and satisfactory for this application and that it is advantageous because of its limited sensitivity to the inaccuracy and/or incompleteness of data. In addition, the approach is practical because it requires minimal user effort.Originality/valueTo the best of the authors’ knowledge, the exploitation of fuzzy-logic altogether with limited sensitivity experts' inputs were never combined for the solution of this particular problem; however, this approach's positive impact is expected to be highly significant in solving a chronic problem in industry.

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On the benefits of delayed ordering

Cited by 3 publications

References 27 publications

Forecasting of lead-time demand variance: Implications for safety stock calculations

Forecasting of lead-time demand variance: Implications for safety stock calculations

Joint condition-based maintenance and inventory optimization for systems with multiple components

On the optimization of maintenance storage cost in industry a fuzzy logic application

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