A first MILP model for the parameterization of Demand-Driven MRP

Lahrichi, Youssef; Damand, David; Barth, Marc

doi:10.1016/j.cie.2022.108769

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…Duhem et al [27] discussed the dynamic determination of control parameters OST (order spike threshold) and OSH (order spike horizon). Lahrichi et al [28] suggested the use of the MILP model in determining strategic reservoir parameters. In their publications [29,30], authors Damand et al presented a procedure for applying genetic algorithms to determine DDMRP system parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Setting for Strategic Buffers in Demand-Driven Material Resource Planning through Statistical Analysis and Optimisation of Buffer Levels

Krajčovič,

Gabajová,

Gašo

et al. 2024

Applied Sciences

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The Demand-Driven Material Resource Planning (DDMRP) method is one of the newer methods of inventory management in an enterprise. Its creation was initiated by a change in the business environment and the characteristics of today’s supply chains. DDMRP brings a combined pull/push approach to inventory management based on creating strategic stacks in the supply chain and managing inventory at these strategic points based on customer orders. The DDMRP system provides a simple methodology that is easy to apply, even in smaller businesses, without the need for advanced information systems. However, a simple methodology also has its limitations because, in many cases, intuitive and subjective approaches are used to set inventory management parameters (variability factor, running time factor, seasonality factor, thresholds, etc.). Simplified parameter determination may, under certain conditions, lead to some storage tanks being too high or too low for certain periods of time. We know from classical inventory management, in the conditions of setting stack parameters in DDMRP, that the deficiency can be eliminated by the use of statistical–analytical approaches and optimisation techniques. This article deals with the issue of setting optimal values of storage tanks in DDMRP, while the correctness of the methodology is verified through simulation of the demand-driven planning process. The correctness and usability of the proposed approaches in sizing strategic reservoirs in DDMRP was confirmed through the results of stimulation experiments.

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