Circular Economy of Plastic: Revisiting Material Requirements Planning Practices for Managing Uncertain Supply
Abstract
:1. Introduction
2. Material Requirements Planning
2.1. Uncertain Supply for MRP
2.2. MRP Strategies to Deal with Uncertain Supply
2.3. MRP Optimization Techniques for Uncertain Supply
3. Research Methodology
4. Challenges for Material Planning in a Recycled Plastics Supply Chain
4.1. Supply Chain Management
4.2. Production
4.3. Inventory Management
5. Design Requirements for an MRP Suitable for a Recycled Plastics Supply Chain
5.1. MRP Input
5.1.1. Bill of Material
5.1.2. Inventory Data
5.1.3. Lead Time
5.1.4. Master Production Schedule
5.2. MRP Processing
5.3. MRP Output
6. Discussion and Propositions
- The quality variation in the supply of the RP affects the production process and results in waste, maintenance, and the reduced quality of the output product. This variation cannot be handled at the material management level, and therefore production settings must be sufficiently flexible to deal with different compositions of plastic material with different recipes, also called alternative BOM, as explained in Section 5.1. Here, it is important to understand the properties of the material and the process parameters. The alternative BOM is designed to represent all possible alternative recipes of the end product from the combination of the raw materials. Many different additives can be introduced during the production phase to control the properties of the plastic and enable it to fulfil the requirements for use in specific applications. These include additives such as colorants, fillers, plasticizers, lubricants, and antioxidants [53]. The alternative BOM gives all information to the MRP that enables it to produce a reliable plan for the RP supply chain. This flexibility can be part of automation or a technological improvement, and will also provide agility to the production system to improve its resilience. It is also important to discuss the level of flexibility required to deal with the uncertain quality variation of the lots received from the upstream suppliers. Process flexibility and optimal process parameters are the limitations imposed on an alternative BOM, and the potential avenues of research related to managing the uncertain variety in quality are huge.Proposition01. An alternative BOM for process customization provides a viable solution to managing uncertain quality in the recycled plastic supply chain. However, it requires extensive knowledge of the process parameters and additives based on the composition of the RP. In addition, alternative BOMs also require flexibility in machine settings to control and manage the process of producing quality output products;
- Lead time is the main input into MRP, which is directly under the control of the management. The managers must set this lead time for each component, and need to understand how the performance has been affected by changing the lead time [75]. It becomes uncertain due to the immature supply market of RP, and as a result, the RP becomes short and will not be available on time. Lead time variability is therefore a key risk driver for the smooth flow of material through production, and MRP aims to avoid this issue and ensure on-time deliveries. A mitigation strategy is required to reduce its negative impact on the production system, and in the MRP context, there are various solutions and approaches given in Section 5.2. However, safety stock and safety lead time are the two major managerial approaches followed by MRP to reduce unwanted fluctuations in the lead time. The supply market of RP is underdeveloped for various reasons, e.g., competition against incineration, many small and new suppliers, lack of investment, and lack of technology. Therefore, big firms and the government should collaborate to develop legislation and subsidize RP processing to enable the development of the supply market. The other reason for the uncertainty in the lead time is the unavailability of the data of the RP at the required time. The uncontrolled nature of the lead time data in the case of RP requires the aid of emerging technologies, i.e., RFID, IOTs [76], and a digital product passport [77], to directly illuminate the uncertain changes in the supply chain and to estimate the lead time. Once one has collected data on time, then the manager can make effective decisions to mitigate the uncontrolled negative impact on the production system due to RP supply.Proposition 02. An uncertain lead time in the RP supply chain is the key input parameter of MRP, and mitigation strategies are required that use safety stock and safety lead time in the production, as well as supplying market maturity and using emerging technologies;
- The supply-oriented variation due to uncertain quantity is the biggest issue in MRP, and it is essential to select the best modeling technique to decide the right quantity order considering uncertain parameters. Researchers used several modeling techniques, i.e., fuzzy, stochastic, and robust approaches, to model uncertainties depending on the complexity of the problem and the company’s objectives. However, these improvements provide optimal plans in controllable uncertain environments. The nature of the RP, covering many input variables, constraints, and parameters under an uncertain environment, makes the MRP problem more complex. Therefore, it is required to look deeply into the applications of advanced techniques and analyze the impacts on managing RP supply uncertainties by performing an empirical study. This will help the organization find the best ordering schedule and production plan under uncertain supply variations for RP.Proposition 03. Recycled plastics supply chains operate at a higher level of uncertainty as compared to supply for virgin materials. As a consequence, MRP problems with uncertain input parameters become more complex, enabling them to formulate and calculate optimal re-ordering scheduling. The development of advanced modeling techniques based on stochastic, fuzzy, and robust approaches to meta-heuristic algorithms for optimization will enable MRP to deal with the uncertain supply of RP. However, the evaluation requires empirical studies to find the best-suited modeling approach for the RP problem;
- The firm must be externally aligned with stakeholders in the context of a circular economy [78] to manage the variations. Suppliers must be aware of the process capability and flexibility of the production firms providing the required recycled material. The processing firms are required to align the quality of raw material with the capability of the production system for the resilience of plastic supply chain management [79]. Prosman and Wæhrens [65] draw a relation of significance of integration between suppliers and manufacturers to manage variations in the quality of the received raw material in cases of industrial symbiosis, but they considered cement as a product for which different processing conditions are required. However, they derived better results in managing supply variation when integrating suppliers with the requirements and capability of the production. The production firms must integrate suppliers in their production planning process to ensure the quality of the supply with the capability and flexibility of the process to deal with the quality variations of RP. In addition, this also necessitates customer engagement to understand specific demand specifications at a granular level, and therefore to understand the important constraints and points where leeway for variation can be found and accepted. The organizations must help the customers understand the changes in the product due to the transition from a linear to a circular supply chain of RP.Proposition04. Collaboration with suppliers and engaging customers are significant measures in the process of reducing the supply variation in the recycled plastic supply chain.
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Author | Uncertain Environment | Approach |
---|---|---|
[35] | Uncertainty index: safety lead time and safety stock calculations | Fuzzy logic controllers and artificial neural networks |
[36] | Uncertain demand, capacity, and costs | Fuzzy approach |
[37] | Epistemic uncertainty and integrity conditions of the objective functions: cost, time, and backorder minimization | Fuzzy goal programming |
[38] | Procurement lead time: total cost minimization | Stochastic lead time: chance-constrained programming |
[39] | Uncertain lead time: fuzzy costs | Robust fuzzy suppliers’ lead time |
[40] | Uncertain lead time | Fuzzy multi-objective |
[41] | Uncertain order size and lead time | Stochastic variables |
[42] | High lead time, high computational time | Mixed Integer Linear Programming-based approach with solver |
[43] | Uncertain capacity, lead time, and inventory | Fuzzy systems |
[44] | Uncertain production system: optimal lot sizing and production plans | Fuzzy credibility-based double-sided chance-constrained programming |
[45] | Supply and demand uncertainty: safety stock and safety lead time | Stochastic lead time and dynamic demand |
Operations | Area of Change | Virgin Plastics | Recycled Plastic |
---|---|---|---|
Supply Chain Management | Quality (material properties) | Variations within a narrow range between batches | Can show high variations in material properties between and within batches |
Price | Following oil price (simple) Large-scale production processes with high-cost efficiency | Following several market drivers (complex) Often novel and complex production technologies and small-scale production | |
Number of suppliers | Globally consolidated—few big suppliers | Many small, local suppliers and a few large globally acting suppliers | |
Type of supplier partnerships | Transactional or strategic partnerships | Anything from transactional partnerships to industrial symbiosis | |
Production | Recipe—material composition | Stable composition of polymers and additives | Flexible composition of recycled and virgin polymers and additives to reach desired properties |
Recipe—process parameters | Stable, optimized process parameters (temperature, flow…) | Flexible adjustment of parameters to accommodate material properties | |
OEE | Relatively predictable | More variable and higher risk of disruptions due to contaminations | |
Production quality | Relatively predictable | More variability in quality, risk of higher quality reject rate | |
Inventory Management | Physical storage | Different batches can be stored in the same storage | Different batches might need separation |
Information systems | Different batches can be treated as the same stock items | Different batches might require different identifiers in the system | |
Buffer stock levels | Predictable supply streams allow for optimized buffer stock levels | Variable prices and material availability can influence buffer stock levels |
Input Data | Description/Information | Virgin Plastic | Recycled Plastic |
---|---|---|---|
Bill of Material (Recipe) | Relationship between product, intermediate components, and raw material. | Fixed relationship between materials and end-product | Complex relationships because of alternative possible recipes/BOM to produce the end product |
Inventory Data | On-hand Inventory
| Lower number of different items, stable allocation to different storage locations possible | A higher number of different items with changing storage locations (separate treatment of different batches). Higher risk of data inaccuracies |
Scheduled receipts
| High predictability/low variance for scheduled receipts due to the type of suppliers | Higher risk/higher variance due to the type of suppliers | |
Lead times | Expected time between purchase order and receipt of order | Relatively stable and short. | Risk of longer and more volatile lead times between different orders. |
Master Production Schedule | Customer demand due dates. Capacity input BOM information | A few given factors: due dates are fixed; capacity constraints are satisfied | More uncertain factors: changing due dates; incapable of processing varieties |
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Omair, M.; Stingl, V.; Wæhrens, B.V. Circular Economy of Plastic: Revisiting Material Requirements Planning Practices for Managing Uncertain Supply. Sustainability 2025, 17, 112. https://doi.org/10.3390/su17010112
Omair M, Stingl V, Wæhrens BV. Circular Economy of Plastic: Revisiting Material Requirements Planning Practices for Managing Uncertain Supply. Sustainability. 2025; 17(1):112. https://doi.org/10.3390/su17010112
Chicago/Turabian StyleOmair, Muhammad, Verena Stingl, and Brian Vejrum Wæhrens. 2025. "Circular Economy of Plastic: Revisiting Material Requirements Planning Practices for Managing Uncertain Supply" Sustainability 17, no. 1: 112. https://doi.org/10.3390/su17010112
APA StyleOmair, M., Stingl, V., & Wæhrens, B. V. (2025). Circular Economy of Plastic: Revisiting Material Requirements Planning Practices for Managing Uncertain Supply. Sustainability, 17(1), 112. https://doi.org/10.3390/su17010112