Framework for the Design of a Demand-Driven MRP (DDMRP) Model from Case Study

Efficient operation systems and supply chain management are critical for companies striving to enhance productivity and competitiveness [1, 2]. Historically, during the 1980s, the primary concern for businesses was the capacity of suppliers to adapt and innovate in response to customer demand [3, 4]. However, inventory management remains a significant challenge, particularly for small and medium-sized enterprises (SMEs), as it requires meticulous planning and control to optimize resources while meeting fluctuating market demands [5-7]. Effective inventory management and pricing strategies are essential for policymakers and business leaders to make informed decisions that enhance supply chain resilience [6-9].

The urgency of sustainable resource utilization and environmental preservation has intensified due to growing environmental concerns. Nevertheless, existing literature scarcely addresses inventory and pricing strategies from a synchronized and sustainability-oriented replenishment perspective [10-12]. To address these gaps, Demand-Driven Material Requirement Planning (DDMRP) has emerged as a consumption-based, pull-driven methodology designed to synchronize supply chain operations with real-time demand [8, 11, 13, 14]. DDMRP fundamentally shifts traditional supply chain management paradigms from a "Push and Promote" model to a "Position and Pull" approach, enabling precise procurement and manufacturing decisions based on a multi-level Bill of Materials (BOM) [14-17].

Despite the advantages of DDMRP, production planning often involves trade-offs between cost-efficiency metrics, such as average available inventory, and service-level indicators, like On-Time Delivery (OTD) [11]. Various production planning philosophies have evolved in response to distinct industrial challenges [6, 18]. However, existing studies on inventory management lack comprehensive frameworks or systematic procedures for sustainable inventory planning, particularly in environments characterized by supply chain disruptions and intermittent demand patterns. Additionally, the prioritization mechanism in DDMRP is inherently one-dimensional, focusing primarily on adherence to predefined stock targets (buffers) [19, 20]. In contrast, real-world supply chain decision-making is inherently multi-dimensional, requiring an integrated approach that considers multiple operational trade-offs [18, 21].

Recent research underscores the potential benefits of integrating inventory and transportation management [22-24]. However, improving inventory management without negatively impacting transportation efficiency remains a challenge, as misalignment between these functions can significantly increase logistics costs [11, 25]. Thus, a dynamic balance between inventory and transportation management is necessary to optimize overall supply chain performance. While prior studies Lahrichi et al. [7], Wesendrup nad Hellingrath [12], Kumar et al. [26] have explored inventory and transportation from various perspectives, there is still a lack of structured frameworks guiding the effective application of economic models in inventory planning.

This study aims to address these research gaps by making the following contributions: (1) identifying the theoretical foundations of DDMRP through a systematic literature review, (2) developing a framework for implementing Manufacturing Planning and Control (MPC) systems under DDMRP principles, and (3) analyzing the core components of the DDMRP methodology to enhance its applicability across different industrial contexts.

3.1 Literary gap

In the studies on the implementation of DDMRP methodology by El Marzougui et al. [1], Kortabarria et al. [3], Orue et al. [30], Azzamouri et al. [32], Lee and Rim [35], Erraoui et al. [36], Dimas Mukhlis et al. [45], indicated that the DDMRP framework is still difficult to implement due to its scarcity of information as it is a modern system and encompasses a more dynamic production. For this reason, as a result of this gap, it is required to study a framework and process for the implementation of the design under that facilitates companies to use, so that they can quantify the planning and also for inventory control through the DDMRP design by means of Buffers can control fluctuations. Given the events it is necessary to promote the study in order to establish a beneficial and effective framework for the design of the DDMRP model in the distribution companies.

3.2 Main DDMRP MPC systems

3.2.1 MRP

Butturi et al. [33], interpreted MRP as comprising a series of techniques that are used in the database such as BOM, inventory and master production schedule (MPS) to determine the material requirements, to generate decision making and to release the material replenishment order.

From the development of MRP came a transformation to MRP II and then to Enterprise Resource Planning (ERP) obtaining new components, since it has evolved so much in technology and has also changed the market requirements, this traditional method is the basic engine that is used to promote information systems, which was developed in the 1960s [3]. However, many studies concerning the MRP have identified several problems such as inaccuracy concerning the Master Production Schedule (MPS) forecast, complete execution concerning the BOM, order release for manufacturing, inaccuracies in the LT, among others [1]. In the same way, Kortabarria [3] has studied the MRP concluding that it is not one of the best MPC systems to face such a volatile and variable environment. Therefore, MRP is considered a traditional system and is not a suitable method in such an agile environment as the current ones [1].

3.2.2 Evolution of SC conditions

The SC Management (SCM) that works directly in the planning, inventory control and also the flow of information through suppliers to customers. Given a big step in evolution within the time period from 1999 to 2005, it works on multilevel inventory management, mainly in distribution networks, in relation to operational research [36].

Also the diverse parameters that it generates result with a greater variability within the production system referring to the MRP II so it makes it difficult to determine an accurate forecast, in the same way it is understood that the MRP so it alters the changes in demand, generating a whip effect and in the same way it presents a bimodal level of existence, so it requires this method a reliable network system in non-existence of variables and peaks of demand. The MRP has limits with the current market restrictions, i.e., at present the company is facing the market demand; in order to achieve this requirement, it must comply with greater efficiency in all operations and also using company resources, this causes fluctuations in demand [1]. Under this context, a modern approach such as DDMRP has emerged.

Variability:

The variability of DDMRP has four main sources that are identified as supply variability, demand variability, operational variability, uptime variability and quality problem management variability (Figure 3) [1]. The last one causes the whiplash effect and also the bimodal distribution according to the SC inventory [45], it should be emphasized that the SCM, both scientists and industrialists noticed the whiplash effect obtaining that the variability process increases as we change from one level to another concerning the SC [36].

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Figure 3. Main variabilities

Bullwhip effect:

It is an enlargement progress concerning the variability, within the perspective of the SC crossing from customer to producers. In particular, when the order according to the suppliers, delaying more concerning the buyer's sales (demand distortion), the distortion is amplified as water upstream in such an amplified way (disparity widening). The effect gives start by the lack concerning the SC synchronization as shown in Figure 4 where customer, retailer, distributor, manufacturer and supplier are shown [39], his focuses on the cause concerning the effect and determination to decrease its impact so five main causes are identified as of the whip effect, The use of demand forecasting, supply shortage, delivery time, batch order and price variation are included, thus providing some alternatives to reduce the impact of the whip effect, according to the consolidation of information related to demand.

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Figure 4. Diagram of the bullwhip effect

Bimodal distribution:

Applying the MPC system, the stock level of a company shows a bimodal distribution that varies between too little and too much inventory, according to Figure 5 we can observe two general points of the inventory (red zones) that identifies too little, where it points to lost sales and lack of stock, and at the other point where it indicates too much, where it points to excess cash [8, 39].

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Figure 5. BIMODAL distribution

3.2 DDMRP

In today's volatile and unpredictable manufacturing environment, traditional systems often prove too complex, necessitating the adoption of a demand-driven manufacturing methodology. This approach is designed to shorten lead times while aligning production efforts with market demand, enabling more effective planning, management, and modeling of the supply chain (SC) in a way that protects and enhances material flow. Additionally, it organizes replenishments and operational processes based on demand-driven models [46].

As an evolution of the traditional MRP system, DDMRP offers significant advantages over its predecessors, particularly in terms of reducing uncertainty and mitigating the bullwhip effect within the SC [33]. Furthermore, in response to fluctuations, DDMRP introduces new strategies for lead time compression, effectively aligning production with market demand [3]. Orue et al. [30] analyzed the DDMRP methodology, demonstrating its capacity to optimize inventory management and enhance service levels.

Figure 6 illustrates the fundamental pillars of DDMRP, highlighting its integration across various areas. It effectively manages material flow [31], providing a robust system for inventory control and production planning [10]. As a modern, hybrid model, DDMRP combines the best practices from MRP, DRP, Lean, Six Sigma, and the Theory of Constraints (TOC), incorporating key innovations that foster improved decision-making in the face of current market challenges [8, 45].

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Figure 6. DDMRP

This methodology creates its own analysis and has the ability to provide a solution to various problems, in the same way it provides a comprehensive approach to operations management [32]. This method is focused on SC, achieving flow optimization in relation to distribution [36]. In addition, a suitable way is determined in order to give a good decision making [33]. In this way the methodology also incorporates manufacturing control and planning [3]. For this reason, the method involves innovative aspects and also capacity, which aims to give solution to the whip effect in relation to SC [33]. However, the fluctuation manages to analyze the supply, management, demand, and operation, reaching to reduce the costs and also reducing the LT [36]. This model analyzes the fluctuations and manages to adjust the inventory and increase the service [32], achieving a better organization according to inventory management, production, and planning [39, 43].

To obtain a solution to the problem, this new model called DDMRP is generated, it is based on traditional methods such as: MRP; TOC and JIT: MRP; TOC and JIT. In order to obtain a better visibility in the company, the position according to the variability that corresponds to the demand, adjusting the stock levels, while maintaining or increasing the service level [47]. In order to use the DDMRP method, work is done in three areas: distribution, production and purchasing, and the model is carried out with five fundamental steps (Figure 7). To conclude we have defined five primary components as building blocks in DDMRP, which are designed to be introduced and also applied together, while ignoring components that would greatly reduce the value of the solution in most environments, in which we first identify the position, then for both the second and third is to protect, and finally the fourth and fifth is to pull [8, 39].

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Figure 7. Five phases

Azzamouri et al. [32] indicate the DDMRP model, which is comprised of five components, namely:

1. Buffer positioning

2. Profiles and buffer levels

3. Configurations and settings

4. Demand-driven planning

5. Visible and collaborative execution

3.3 Analysis framework

In the research related to DDMRP, there are 5 components which will be detailed in the following steps to obtain a better perspective [32].

3.3.1 Strategic inventory positioning

Thus, by placing the inventory in several parts to locate the difficulties, working on the 4 main fluctuations factors, and decreasing the LT, placing the inventory in several parts causes a huge problem in relation to the resource. However, removing the inventory and also the SC causes significant risk [8, 39].

3.3.2 Profiles and protection levels

In the second step with respect to the DDMRP, it is to identify the safe amount of the decoupling point [21]. In which it is identified in three damping zones (Figure 8).

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Figure 8. Buffer zone

Then the guidelines will be used as set out in Table 2 and 3, where the lead time factor (LTF) and also the variability factor (VF), which is determine at 3 level [35], are identified. Depending on the type in relation to the product and LT.

Table 2. FL factor

Table 3. VF factor

Figure 9 identifies each zone for the purpose of calculating demand and distribution data in relation to the DDMRP [36].

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Figure 9. Zones

3.3.3 Dynamic adjustments

Companies and their SCs must be prepared to swiftly adapt to market changes in order to ensure optimal customer service. Achieving this flexibility requires the use of dynamic buffers that can effectively respond to new requirements. In this context, the DDMRP method facilitates dynamic adjustments by incorporating operational parameters, market fluctuations, and the consideration of planned or anticipated future events. This approach ensures that planning systems remain aligned with market fluctuations, optimizing inventory management and enhancing the ability to respond to demand [3].

3.3.4 Demand-driven planning

According to this section, supply orders are generated with the name of production order, purchase order and also movement order related to the inventory. Production planning is concerned with determining the frequency and quantity of these replenishment orders [37]. Therefore, it interprets an immense importance and also an advantage of the sub-element in relation to other traditional models.

Azzamouri et al. [32], works with the original DDMRP planning design. Since it occurs under the supply order taking as reference the net flow position (NFP), when this is located in a zone of replenishments that is achieved to identify as the encoders in this case the yellow zone, if it is by quantity the coding of the green zone. Therefore, the decoupling point concerning the planning process this proceeds to generate explosions decoupling the BOM. Since a series of supply order occurs concerning the higher level, furthermore the decoupling is limited in the explosion in relation to the BOM. The explosion is interrupted because the decoupling point is at the NFP, which is determined independently at that location. The explosion continues only if the NFP at this point is below the yellow threshold (TOY). Figure 10 illustrates a clear example of the uncoupled explosion. While part 101 has an NFP below TOY, the explosion starts and stops once it reaches the decoupling point. In contrast, parts 304P, 301, and 203 will continue to explode regardless of whether their NFP reaches TOY. In conclusion, when the NFP of part 501P reaches TOY, the explosion will proceed indiscriminately [3].

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Figure 10. Uncoupling explosion

3.3.5 Execution-visible and collaborative

Finally, this step encompasses both the planning and execution phases. The DDMRP methodology makes a clear distinction between these two stages. The planning phase involves generating a supply order request based on the net flow item and concludes when the proposal is approved and converted into an official supply order. The execution phase, on the other hand, focuses on managing these open supply orders to ensure the protection and facilitation of inventory flow. DDMRP utilizes color-coded alerts to enhance visibility and prioritize orders effectively. These alerts highlight urgent situations that require immediate attention, enabling companies to prioritize orders based on the current status of available stock, rather than relying solely on scheduled delivery dates [30].

The thorough literature analysis has established the theoretical foundations supporting DDMRP, highlighting its evolution and distinction from traditional MRP approaches. It has been demonstrated that DDMRP is based on key principles such as the integration of real-time demand signals, the use of dynamic buffers, and flexible inventory management. This review has emphasized the advantages of the DDMRP approach in addressing uncertainty in modern supply chains, particularly in environments characterized by highly volatile demand. However, key challenges were identified due to the lack of standardization in its implementation, which limits its universal adoption.

The development of a framework for implementing MPC based on DDMRP has been a significant contribution to understanding how this approach can be operationalized in practice. This framework proposes a flexible structure that adapts to the specific needs of each industry, allowing for an effective transition to a demand-driven planning system. However, the findings revealed that the absence of clear guidelines and standardized procedures is a significant barrier to the successful implementation of DDMRP. It is recommended that future research focus on creating simulation tools and evidence-based implementation guides to help overcome these barriers.

The analysis of the core components of DDMRP revealed that, although this approach holds great promise for improving supply chain performance, its applicability varies depending on the characteristics of each industrial sector. Factors such as supply chain complexity, demand variability, and production responsiveness play a crucial role in determining the effectiveness of DDMRP in different contexts. This study highlights the need to adapt the model to the realities of each industry to maximize its potential. Additionally, the integration with emerging technologies, such as the Internet of Things (IoT) and machine learning, could strengthen DDMRP's ability to manage the complexity and dynamics of modern supply chains, making it a key area for future research.

Although the benefits of DDMRP are evident, implementing this approach faces several challenges, including the lack of formalized standards and resistance to change within organizations. These obstacles point to the urgent need for more robust, evidence-based implementation frameworks, as well as simulation tools that can predict variability in complex production systems. In terms of future research, it is crucial to develop advanced decision-support models that use predictive analytics and simulations to optimize buffer placement and dynamic inventory management. Furthermore, integrating emerging technologies offers a promising path to expand DDMRP's capabilities, enabling scalability and improving operational performance.