DDMRP as Production Control Policy in a Two-Product Closed-Loop Supply Chain

Corsini, Roberto Rosario; Costa, Antonio; Fichera, Sergio; Framinan, Jose M.

doi:10.1007/978-3-031-38165-2_13

Cited by 1 publication

(1 citation statement)

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“…Cuartas and Aguilar [33] described a replenishment algorithm using a learning algorithm based on Markov's decision-making process. The authors Dessevre et al [34], Xu et al [35], and Corsini et al [36] added part of capacity planning to the planning process in the DDMRP system. Just as unlimited capacities were considered in the MRP system, in the original DDMRP methodology [1], the authors did not consider capacity constraints.…”

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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