The purpose of this study is to gain some insights into the number of shortages resulting from two alternative demand allocation schemes between a contractor (machine M1) and subcontractor (machine M2), on the one hand, and from inventory accumulation, on the other hand. The shortages stem from random machine breakdowns, and each machine undergoes preventive maintenance. The motivation behind inventory accumulation is to allow demand to be fulfilled even when both machines are down.
The number of shortages stemming from all scenarios under consideration was established via computer simulation with the Arena© language.
For demand allocation that remains unchanged for the duration of the planning horizon and constant reliability of M1, it was found that, the less reliable M2 is, the more biased in favour of M1 will be the optimal demand allocation and the greater will be the number of shortages. Moreover, both dynamic demand reallocation over the planning horizon and inventory accumulation result in a substantial reduction in shortages.
The results are representative of the specific data, which were assumed in the simulation models. Nevertheless, this methodology is recommended for this type of analysis, as it is highly flexible and can take into account many practical considerations, which an analytical approach cannot.
Within the context of unreliable production machines, the most important practical implication of this study is that the dynamic reallocation of demand between a contractor and subcontractor, along with inventory accumulation, both have the potential to yield important reductions in the number of shortages.
The subject‐matter of this paper was not previously reported in the literature. Furthermore, the insights gained as a result of this study can yield substantial benefits to companies in terms of improving their service levels as measured by reduced shortages.
Cormier, G. and Rezg, N. (2011), "On coordinating maintenance, production and subcontracting: insights from simulation models", Journal of Quality in Maintenance Engineering, Vol. 17 No. 3, pp. 268-280. https://doi.org/10.1108/13552511111157380
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