Intelligent Techniques for Safety Stock Optimization in Networked Manufacturing Systems

Desmet, Bram; Aghezzaf, El‐Houssaine; Vanmaele, Hendrik

doi:10.1007/978-1-84996-119-6_13

Cited by 2 publications

(1 citation statement)

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“…In this context, Hua and Willems (2016) optimized safety stocks for a two serial line supply chain and showed that the safety stock levels depend on the supply chain cost and lead time parameters. In contrast to the previous authors' work concerning a serial line supply chain, Desmet et al (2010) proposed a set of normal approximation models to optimize SSs in distribution systems, assembly systems and, more generally, networked manufacturing systems based on normal approximation models for the critical safety stock parameters. They benchmarked their approaches with discrete event simulation and validated them.…”

Section: Introductionmentioning

confidence: 99%

Determination of dynamic safety stocks for cyclic production schedules

Bahroun

Belgacem

2019

Oper Manag Res

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Safety stocks are necessary to accommodate supply or demand uncertainties. Typically, safety stocks are held constant throughout the management horizon. In this paper, we study the determination of dynamic safety stock levels under cyclic production schedules. Currently, companies often operate according to contract orders with fixed and cyclic delivery dates and varying quantities. In this context, companies have attempted to implement cyclic production modes for their cyclic delivery requirements. Indeed, cyclic production scheduling has many benefits, such as respecting just-in-time principles by synchronising production with demand and improving shop floor control. The objectives of this paper are twofold: (1) to propose a new dynamic approach for determining safety stock levels that is adapted to cyclic production and (2) to demonstrate the limitations of traditional approaches under nonstationary demands. Our dynamic approach is based on modelling a repetitive production sequence comprising many manufacturing orders. Our model estimates the dynamic safety stock levels necessary to accommodate cyclic manufacturing orders with uncertain quantities and start dates using a Monte Carlo simulation approach. For the case in which all demands follow normal probability distributions, we validated the results of our simulation model by comparing them to numerically approximated theoretical results. We then compared the proposed dynamic simulation approach with the traditional approach. The results demonstrate that the dynamic approach is more efficient in terms of simultaneously minimizing the required safety stocks and improving the service level by decreasing the probability of stock outs. Finally, we applied our simulation approach to a case in which all demands follow uniform probability distributions.

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Section: Introductionmentioning

confidence: 99%