Demand Driven Material Requirements Planning (DDMRP): A systematic review and classification

Azzamouri, Ahlam; Baptiste, Pierre; Dessevre, Guillaume; Pellerin, Robert

doi:10.3926/jiem.3331

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…Both Erraoui et al (2019) and Ptak and Smith (2019) underline the importance of the inventory positioning step in a demand-driven distribution network. However, they didn't propose a tool to make positioning decisions (Azzamouri, Baptiste, Dessevre & Pellerin, 2021), in contrast to this paper. The principal contributions of this paper are i) to propose a mathematical model to optimize buffer positioning in a demand-driven distribution network, and ii) to evaluate the performance of the DDDRP approach compared to the conventional DRP approach.…”

Section: Ddmrp and Dddrp Conceptsmentioning

confidence: 89%

An optimization model for demand-driven distribution resource planning DDDRP

Mohammad

Ali

Baptiste

2022

JIEM

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Purpose: Demand-Driven Distribution Resource Planning (DDDRP) has recently been proposed in the literature to deal with higher supply networks complexity, shorter customer tolerance times, and inaccurate forecasts. The DDDRP requires to position inventory buffers in critical network nodes, where the inventory level in each buffer is replenished based on actual demands rather than on demand forecasts. This paper aims to identify optimal buffer positions in a distribution network driven by the DDDRP approach and to assess the performance of the DDDRP approach compared to the conventional Distribution Resource Planning (DRP) approach.Design/methodology/approach: First, a mixed-integer non-linear model is proposed to optimize buffer positioning under supply network constraints and with the objective of minimizing supply chain holding costs. Then, a case study is investigated to validate the optimization model and to evaluate the performance of the optimized distribution network driven by the DDDRP approach, compared to the DRP approach.Findings: Results of the considered case study demonstrate that the distribution network optimized and driven by the DDDRP approach achieves savings of 75% in terms of total holding costs and 67% in terms of inventory amounts, compared to a distribution network driven by the DRP approach.Research limitations/implications: Results of this paper cannot be generalized since several assumptions have been considered. Thus, addressing real case studies in different industrial contexts may be of theoretical and practical interest.Originality/value: This paper is the first to propose a mathematical model to optimize buffer positioning in a distribution network driven by the DDDRP approach.

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Section: Ddmrp and Dddrp Conceptsmentioning

confidence: 89%

An optimization model for demand-driven distribution resource planning DDDRP

Mohammad

Ali

Baptiste

2022

JIEM

Self Cite

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“…As recently emphasized by Azzamouri, Baptiste, Dessevre, and Pellerin (2021), the founders of the DDMRP gave only a few details on parameter definition. In Ptak and Smith (2016), they set the parameters as follows:…”

Section: Sizing the Ddmrp Buffersmentioning

confidence: 99%

“…Through their study of the literature, Azzamouri et al (2021) showed a tendency to use different types of simulations in order to obtain new scientific results on the DDMRP. The most prominent one is Discrete Event Simulation (DES), that allows to model complex workshops and specific DDMRP behaviors.…”

Section: Sizing the Ddmrp Buffersmentioning

confidence: 99%

Dynamical multi-parameter sizing of DDMRP buffers in finite capacity flow-shops

Martin

Lauras

Baptiste

2023

Computers & Industrial Engineering

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“…They conclude that the main criticality of the method is the high subjectivity affecting the positioning of the buffers. On the other hand, Azzamouri et al (2021), carry out a systematic literature review about research dealing with the DDMRP published between 2011 and 2020 in different languages. They analyze the extensions of the method in the literature, and present a taxonomy.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid algorithm based on reinforcement learning for smart inventory management

Cuartas

Aguilar

2022

J Intell Manuf

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This article proposes a hybrid algorithm based on reinforcement learning and the inventory management methodology called DDMRP (Demand Driven Material Requirement Planning) to determine the optimal time to buy a certain product, and how much quantity should be requested. For this, the inventory management problem is formulated as a Markov Decision Process where the environment with which the system interacts is designed from the concepts raised in the DDMRP methodology, and through the reinforcement learning algorithm—specifically, Q-Learning. The optimal policy is determined for making decisions about when and how much to buy. To determine the optimal policy, three approaches are proposed for the reward function: the first one is based on inventory levels; the second is an optimization function based on the distance of the inventory to its optimal level, and the third is a shaping function based on levels and distances to the optimal inventory. The results show that the proposed algorithm has promising results in scenarios with different characteristics, performing adequately in difficult case studies, with a diversity of situations such as scenarios with discontinuous or continuous demand, seasonal and non-seasonal behavior, and with high demand peaks, among others.

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Demand Driven Material Requirements Planning (DDMRP): A systematic review and classification

Cited by 19 publications

References 23 publications

An optimization model for demand-driven distribution resource planning DDDRP

An optimization model for demand-driven distribution resource planning DDDRP

Dynamical multi-parameter sizing of DDMRP buffers in finite capacity flow-shops

Hybrid algorithm based on reinforcement learning for smart inventory management

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