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p class="TtuloAbstract">Since the creation of the demand-driven material requirement planning (DDMRP) model, numerous studies have analysed the methodology’s significant impact on different organisations. Several successful cases and research studies into DDMRP have demonstrated that the methodology is beneficial to organisations because it increas...
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... a company uses a traditional production planning and control system, their inventory level has a bimodal distribution that alternates from too high to too low. This change in distribution results in a high-cost inventory level and a low service level (Figure 2) (Ptak & Smith, 2016). To respond to this problem, Ptak and Smith (2016) introduced a new methodology known as DDMRP. ...
Citations
... They also conducted research on issues such as bottleneck resource-based hybrid manufacturing system scheduling, batch control and scheduling based on hybrid parallel processing lines, and integrated rolling planning and scheduling for hybrid production lines based on DBR and established mathematical optimization models with various complex constraints. In summary, each manufacturing company should formulate and choose appropriate production planning and control methods according to the current market environment and its own production status, which is crucial for the efficient operation of the enterprise (Orue et al., 2020). ...
Production plans based on Material Requirement Planning (MRP) frequently fall short in reflecting actual customer demand and coping with demand fluctuations, mainly due to the rising complexity of the production environment and the challenge of making precise predictions. At the same time, MRP is deficient in effective adjustment strategies and has inadequate operability in plan optimization. To address material management challenges in a volatile supply-demand environment, this paper creates a make-to-stock (MTS) material production planning model that is based on customer demand and the demand-driven production planning and control framework. The objective of the model is to optimize material planning output under resource constraints (capacity and storage space constraints) to meet the fluctuating demand of customers. To solve constrained optimization problems, the demand-driven material requirements planning (DDMRP) management concept is integrated with the grey wolf optimization (GWO) algorithm and proposed the DDMRP-GWO algorithm. The proposed DDMRP-GWO algorithm is used to optimize the inventory levels, shortage rates, and production line capacity utilization simultaneously. To validate the effectiveness of the proposed approach, two sets of customer demand data with different levels of volatility are used in experiments. The results demonstrate that the DDMRP-GWO algorithm can optimize the production capacity allocation of different types of parts under the resource constraints, enhance the material supply level, reduce the shortage rate, and maintain a stable production process.
... Enfin, la revue de littérature de (Orue et al., 2020) démontre qu'aucune publication ne traite le sujet de la standardisation du processus d'implantation de DDMRP. ...
L’environnement économique est souvent caractérisé par l’acronyme VUCA (Volatile, Incertain, Complexe et Ambigu) pour décrire les fortes perturbations qu’il subit. Les conséquences sur les entreprises industrielles sont fortes et poussent les entreprises à investiguer de nouvelles solutions pour maintenir ou améliorer leur performance.
Parmi elles, l’adaptation ou le changement du système de pilotage de la production offre des perspectives intéressantes, conduisant à l’émergence de nouvelles méthodes de planification intégrées au sein des systèmes de Production Planning and Control (PPC). La question du contexte de performance de ces nouvelles méthodes se pose afin de les intégrer dans les choix potentiels. La revue de littérature montre que les principaux PPC peuvent être utilisés dans plusieurs contextes industriels ce qui laisse supposer qu’une évaluation plus fine est nécessaire pour choisir un PPC adapté. Dès lors, il parait essentiel d’évaluer leur performance de façon globale en intégrant à la fois les retours d’expériences des utilisateurs et une analyse objective de leur comportement.
Face à cette problématique, nous avons développé un cadre méthodologique répondant à ce besoin. Nous proposons une approche basée sur 3 phases dont l’utilisation et le contenu sont adaptables en fonction du contexte industriel et des objectifs fixés. La proposition faite comprend le cadre lui-même mais également un ensemble d’outils et de méthodes permettant de comprendre, positionner et évaluer qualitativement et quantitativement le PPC étudié. L’utilisation de ce cadre est illustrée à travers l’étude de la méthode Demand Driven Material Requirement Planning (DDMRP).
... Although studies on DDMRP are both axiomatic and empirical (Bagni et al. 2021), many issues remain to be scientifically addressed, like questioning its applicability to specific industrial sectors or complex environments (Velasco Acosta, Mascle, and Baptiste 2019) and its implementation process (Orue, Lizarralde, and Kortabarria 2020). More details can be found in the systematic review of Azzamouri et al. (2021). ...
Production planning and scheduling for companies with divergent processes, where a single component can be transformed into several finished products, are challenging as planners might face material misallocation issues. In this paper, we address the problem of managing a divergent process with DDMRP stock buffers, where different finished products are bottled with the same component having a fixed batch size. An allocation decision needs to be made to determine the quantities of finished products to be bottled. This study is motivated by a real-life problem faced by a dermo-cosmetic company. We compare and analyze by simulation nine different policies triggering allocation decisions. The first policy is the classic DDMRP rule, while the others are new policies, including a virtual buffer of a generic finished product and ConWIP loops, delaying the allocation decision. Our results show that the policy combining the classic DDMRP rule and a ConWIP loop surrounding a part of the process reduces the work-in-process by 34% compared to the classic DDMRP while ensuring high customer service rates and control of flow times.
... Measures the number of times changes or variations are made to raw material purchase schedules over total planned orders. Studies indicate that this indicator can be reduced to less than 13% [34] (%) = ℎ ℎ 100 ...
... However, there are still many issues to be addressed scientifically, while more and more companies are developing DDMRP in many industrial sectors (Bahu, Bironneau, and Hovelaque 2019). Therefore, researchers aim to study the method in more complex environments (Acosta et al. 2019), raising new questioning from particular industrial sectors (Dessevre et al. 2020), and bringing the need of a standardised implementation process for the method DDMRP (Orue, Lizarralde, and Kortabarria 2020). ...
Demand Driven Material Requirements Planning (DDMRP) is a recent method mixing push and pull flow management. Although it claims to be the solution to traditional methods’ limitations, the DDMRP method works at infinite capacity: manufacturing or supply orders are launched according to a logic of replenishment of stocks defined as buffers. This article proposes an evaluation of capacity management using visual charts developed by simulation. These charts correlate the bottleneck resource's loading rate to a service rate by considering one of the DDMRP method parameters, the Decoupled Lead Time (DLT). The charts are a decision support tool. They allow identifying to which loading rate the DLTs are representative of the flow times of manufacturing orders and which capacity level to use. We study different workshops, including a real industrial case. Our results show that it is better to control the flow times by adjusting capacity rather than adjust the DLT parameter.
... • Implementation methodology used: Implementation guidelines or methodologies have not yet been addressed in the literature. Indeed, Orue et al. (2020) presented a systematic literature review to analyze studies that investigate the standardization of the implementation process of the DDMRP model. They have found no evidence of a standardized implementation process for DDMRP that could maximize its potential. ...
... Some authors only provide an explanation or an illustration of the method (Román-Cuadra, 2017;Bahu et al., 2018;Bahu et al., 2019;Erraoui, Charkaoui & Echchatbi, 2019;Favaretto & Marin, 2018;Garzón Hernández, 2018;Marin, 2018;Meinzel, 2019;Pekarčíková, Trebuňa, Kliment & Trojan, 2019). Note as well that only one systematic literature review paper has been published(Orue, Lizarralde & Kortabarria, 2020) and one traditional literature review(Balcioglu & Tanyas, 2019). We also note that there is a lack of work-oriented development of the method itself.Conclusion-Component 1: The DDMRP method has still not reached a significant publication level in the scientific literature, but it has emerged in different languages and has evolved and been used in different countries and organizations.It is surprising to note that even though the DDMRP method originated in the United States, we do not find many papers published in its country of origin. ...
Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to measure the spatiotemporal evolution of the DDMRP, its scope and context of implementation, and the research lines studied in that field in order to identify areas that still need to be addressed by future researchers.
Design/methodology/approach: The systematic literature review approach adopted in this paper examines research dealing with the DDMRP approach published in different languages between 2009 and 2020. To-date papers focused on the performance analysis and comparison, what differentiates this study is the focus on the scientific evolution level of DDMRP, the parameters, and contexts that should be more studied.
Findings: The results show that DDMRP is not yet a mature method and that the robustness of the approach still needs to be tested. More research is also required to determine scientifically some setting parameters, how the proposed DDMRP could be implemented in different industrial contexts with existing information systems.
Originality/value: Based on the evolution analysis of DDMRP, this study outlines its current state of maturity and its different shortcomings under a broader vision to make this method more complete on the scientific and industrial level.
Great dynamism and uncertainty characterize today's market situation, increasing the supply chain's complexity. This imposes the improvement of the classical production control systems. The present production environment is challenging, as traditional manufacturing planning and control systems were not developed to work in this context. The Demand Driven Material Requirements Planning (DDMRP) is a recently introduced method, proposed as an upgrade of the traditional methodologies which are widely used in today’s industry, capable of overcoming the nervousness and the bullwhip effect affecting supply chains under uncertainties. The DDMRP approach, however, is still not well established since the conditions for its application have been investigated more closely only in the last few years. In fact, there is a lack of literature in this field and only a few studies have scientifically proven the performance of DDMRP by applying this innovative method in real-world contexts. The aim of the study is to analyze the characteristics of this innovative methodology through the study of its basic principles and the evaluation of its performance. In this regard, the behavior of the DDMRP is simulated at varying demand conditions and the results are compared with those obtained from applying a classical methodology, i.e., the reorder point method. It emerged that substantial differences between the two analyzed methodologies are in the objective function (cost minimization vs service level maximization), and in the responsiveness to demand and lead time variability. Furthermore, it has been demonstrated that the breakeven point, at which the two models equally perform, exists.
A recent heuristic called Demand Driven MRP, widely implemented using modern ERP systems, proposes reorder policy based on buffers. Buffers are amounts of inventory positioned and set to control the net flow position, responding to stochastic demand and lead time. Our primary goal is to propose a theoretical foundation for such a heuristic approach. To this aim, we develop an optimization model inspired by the main principles behind the heuristic algorithm. Specifically, optimal policies are of the type (s(t), S(t)) with time-varying thresholds that react to short-run real orders. We introduce constraints related to the service levels, that are written as tail risk measures to ensure fulfillment of realized demand with a predetermined probability. Interestingly, it turns out that such constraints allow to analytically justify an empirical rule that the DDMRP employs to set the risk parameters used in the heuristic. Finally, we use our model as a benchmark to theoretically validate and contextualize the aforementioned heuristic.
