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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. ...
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... • Securing sensitive information when handling extensive datasets is an absolute necessity for any DDMRP implementation. Compliance with data privacy regulations and safeguarding sensitive supply chain data should be paramount (Orue et al., 2020). Employing measures such as encryption, access controls, and data anonymization is indispensable for businesses. ...
Big Data integrated with DDMRP has turned a new leaf in managing the supply chain. The research elucidates the deep impact of Big Data in relation to DDMRP. Big Data from online transactions, sensor data, social interactions, and many other areas possesses the potential to bring a paradigm shift in the area of demand forecasting, inventory management, and supply chain optimization as a whole. It allows organizations to make informed, agile decisions for cost reduction, inventory optimization, and improved customer service by enabling real-time data analysis and predictive modelling. However, there are challenges to the adoption of Big Data in DDMRP, including data security and advanced analytics capability. The study examines the application, benefits, challenges, and impact of Big Data implementation in DDMRP. Therefore, the study also provides valuable input for organizations interested in making use of the potential of big data. Its findings point toward a decisive role of big data in reshaping supply chain strategy and enhancing responsiveness to market fluctuations for modern business.
... To address the shortcomings of conventional MRP, professionals and academic literature have invented many new approaches to material planning across decades. [5][6][7][8] One of them, demand driven MRP (DDMRP), is a relatively new approach that aims to improve material planning by emphasizing responsiveness to customer demand and reducing the risk of overstocking or stockouts. 6 The core intention of the planning method is to "position, protect and pull" (Ptak & Smith, 2019), 9 which has been identified as a crucial element in different segments of business. ...
... [5][6][7][8] One of them, demand driven MRP (DDMRP), is a relatively new approach that aims to improve material planning by emphasizing responsiveness to customer demand and reducing the risk of overstocking or stockouts. 6 The core intention of the planning method is to "position, protect and pull" (Ptak & Smith, 2019), 9 which has been identified as a crucial element in different segments of business. ...
... (2020), 6 and by Azzamouri et al. (2021). 14 The scarcity of concrete DDMRP applications reported in academic literature limits the validation of its implementation and integration strategies. ...
The objective of this study is to address existing study gaps by defining what materials are demand-driven material requirements planning (DDMRP) suitable and building a tool that helps to identify such materials. The research problems are approached with three different questions about suitability, features of the identification tool, and financial impact. The research methodology consists of a mixed method case study approach, where semi-structured interviews were conducted with supply chain professionals from the case company, and quantitative data related to the case company’s operations was analyzed. The data consisted of the relevant data of over 10,000 purchased materials. The findings of this study suggest that there are no certain characteristics that materials suitable for DDMRP have, but the potential must be defined individually in the case of every purchased material. The tool initially developed in this study helps to identify materials that meet the requirement of providing potential positive financial impact if brought into DDMRP scope by analyzing historical demand data, lead times, and inventory carrying costs.
... Por otro lado, para la producción de mezclas, las cuales se pueden tener en stock y cuyas mezclas son estandarizadas, se implementó la metodología Demand Driven [16] (Orue et al., 2020), que basa su teoría en la adaptación de la cadena logística, para reaccionar ante la disponibilidad en tiempo real, mediante pequeñas cajas de inventario que, según las características de la cadena, tiene distintas propiedades para garantizar siempre la disponibilidad de stock al menor costo y cantidad posible. ...
Medications are a fundamental part of patient care at all stages of their treatment. Magisterial preparations, which are a pharmaceutical product to meet a patient's medical prescription, require adequate management to provide individual safety, which makes it necessary to guarantee that the records corresponding to the production process are reliable and allow their traceability in the attention. The application of information technologies has shown a reduction in medication errors and adverse drug reactions, an impact that has also been documented in electronic prescription. In this study, the steps for the development and implementation of a technological tool through the application of elements of the SCRUM methodology, which allowed optimizing the process of managing medical orders for master mixtures, in a highly complex hospital. reducing times and reducing the risk of errors in the process. The number of orders received that required transcription decreased by 55%, from 2,219 to 999. The batch record consolidation time went from 6 hours to one.
... One of the differentiating elements of DDMRP is that instead of making forecasts to determine what should be done in production, it uses these forecasts to plan resources and feedstock materials that will be necessary in the process (Linares and Mayorga, 2017). This represents a significant advance in production planning and control systems, since it is capable of responding to the needs of a new paradigm (Orue et al. 2020). On one hand, in a study with data from an automobile company, it was shown that lead time was reduced by 94% and the inventory level became an effective stock (Shofa and Widyarto, 2017). ...
... Studies on DDMRP are both axiomatic and empirical (Bagni, Godinho-Filho, Thürer & Stevenson, 2021), but many issues remain to be tackled scientifically, especially from complex environments (Velasco Acosta, Mascle & Baptiste, 2020), industrial sectors (Dessevre, Lamothe, Pellerin, Ali, Baptiste & Pomponne, 2023), and about the implementation process (Orue, Lizarralde & Kortabarria, 2020). Azzamouri, Baptiste, Dessevre and Pellerin (2021) present more details in a systematic review. ...
Purpose: Although the authors of the Demand Driven Material Requirements Planning (DDMRP) argue that the method DDMRP is the solution to the limitations of traditional production management methods, its capacity management system remains unclear. Since DDMRP operates at infinite capacity, it is important to consider a capacity management approach to avoid under- or overloading production workshops.Design/methodology/approach: We propose a new dynamic capacity management approach for the DDMRP method. Our approach is based on the calculation of the anticipated workload, using DDMRP stock buffers and considering customer order spikes. Considering a real industrial case, we compare the proposed approach to a static one and a dynamic approach from the literature.Findings: The analysis of the results, supported by a two-way ANOVA, indicates that the proposed capacity management approach outperforms the performance of the other two approaches by maximizing the resource loading rate while ensuring a high customer service level.Originality/value: The originality of the article comes on the one hand from the capacity adjustment module by calculating the anticipated workload, and on the other hand from the comparison of this approach with two others, one of which comes from the literature.
... Several dozen scientific and professional papers have been published on the issue of DDMRP. A systematic review of scientific papers published up to 2020 devoted to DDMRP issues has been elaborated, for example, in publications by Azzamouri et al. [2] and Orue [3]. Many published papers are devoted to the standardisation and implementation of the DDMRP system in practice [4][5][6][7][8][9][10]. ...
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.
... In this section, a brief review of the existing studies on DDMRP performed between 2011 and 2023 is presented. For a comprehensive review of studies on DDMRP, readers are referred to El Marzougui et al. (2020), Orue, Lizarralde, andKortabarria (2020), Azzamouri et al. (2021), and Butturi et al. (2021). Our classification scheme is largely inspired from El Marzougui et al. (2020) and Lahrichi, Damand, and Barth (2022). ...
... Finally in the last years, because of COVID-19 the disruption of the supply chains has caused an important reduction of the service levels and an extension of the delivery time with related consequences on the cash flow and price of the products and services [1]. [2] explains that the general market behavior has evolved in the last 20 years: nowadays there are more demand instabilities, more sensibility to crisis and economic events, more product diversity, increasing competition, reduced customer lead times, and reduced time to market. These different parameters result in creating more variability in the production system and difficulties to establish accurate forecasts. ...
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.
... 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).