Search

Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels. [ABSTRACT FROM AUTHOR]

Like Kanban or classic inventory management, Demand Driven Material Requirements Planning (DDMRP) assumes that an order is created when the stock level drops below a given level. The assessment of this level can be continuous or periodic. A periodic evaluation can force better repartition of the load over time. DDMRP characterizes the demand for a product by its average daily demand. This aggregation specifies neither the average size of the elementary demands nor the average duration between two elementary demands. Based on a discrete event simulation model, this study suggests that, at least for a flow-shop organization, the DDMRP method, with continuous order generation, is suitable for large mean time between elementary orders, while periodic observation improves the method for small values of it. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) is a recent inventory management method that has generated considerable interest in both academia and industry. Many recent papers have demonstrated the superiority of DDMRP over classical methods like MRP or Kanban, an observation confirmed by companies that have implemented DDMRP. However, DDMRP depends on many parameters that affect its performance. Only general rules are given by the authors of the method to fix these parameters but no algorithm. The present paper aims to fill this gap by proposing a multi-objective optimisation algorithm to fix a set of eight identified parameters. The suggested genetic algorithm is coupled with a simulation algorithm that computes the objective functions. Two opposing objective functions are considered: first, the maximisation of orders delivered on-time to the customer and, second, the minimisation of on-hand inventory. A set of data instances was generated to test the suggested method. Fronts of non-dominated solutions are found for all these instances. [ABSTRACT FROM AUTHOR]

Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors. [ABSTRACT FROM AUTHOR]

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic growth in Indonesia. Inventory management is one of the key aspects in the operational sustainability of MSMEs, especially raw materials used in the production process. One of them is XYZ MSMEs which is a producer of meatball pentol and meatball tofu products which uses several types of raw materials in its production process. The problem currently being faced is that there are often shortages and excesses in purchasing raw materials during procurement. This research aims to analyze the cost of raw material inventory at XYZ MSMEs using five different Lot Sizing methods, namely Economic Order Quantity (EOQ), Period Order Quantity (POQ), Silver Meal (SM), Least Unit Cost (LUC), and Part Period Balancing (PPB). This research uses historical data from MSME XYZ to identify differences in raw material inventory costs between the five MRP methods used. The results of this research show that the Silver Meal (SM) method produces the lowest total inventory costs for beef and tapioca starch as raw materials. Meanwhile, the Part Period Balancing (PPB) method produces the lowest total inventory costs for MSG raw materials. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact in terms of Fill Rate of production control policies in a two-product, two-echelon supply chain dynamic problem with production capacity. The factory node is subject to two different disruptive occurrences (i.e. failure events and changeovers) that can cause unforeseen problems in the supply chain. To control these adverse events, the factory can adopt different production control policies. We compare the well-known Hedging Corridor Policy with two variants, namely Modified Hedging Corridor Policy and Improved Modified Hedging Corridor Policy, and Demand-Driven Material Requirements Planning policy. Firstly, we use the Response Surface Methodology to calibrate the endogenous factors for each strategy. Then, through an extended full-factorial Design Of Experiments, we evaluate the effectiveness of the production control policies for several operational and market scenarios defined by varying exogenous factors. Interestingly, our study reveals that the Hedging Corridor Policy represents the best rule to increase the Fill Rate. The policies have been compared also in terms of indicators related to the factory inventory level. The results seem to point out that efficient production control policies (measured in terms of operational efficiency) do not necessarily yield the best results when measured in terms of supply chain efficiency. [ABSTRACT FROM AUTHOR]

Many companies in practice want to dynamically adjust planned lead times in their production planning and control systems in response to demand fluctuations. But for decades it has been recognised that this can lead to escalating planned lead times and realised throughput times. Authors have highlighted the negative impact of this 'lead time syndrome', especially in the context of Material Requirements Planning systems, prompting the development of alternative concepts intended to overcome its vicious cycle, such as Workload Control. Yet some authors have shown that increasing planned lead times has advantages – it can improve end-item service levels. To resolve this paradox, we conjecture that the effects of the lead time syndrome are limited when demand is independent of internally planned lead times, such as in make-to-order companies, and subsequently use simulation to prove this conjecture. We show that although dynamic planned lead times have a detrimental effect on performance in make-to-order systems, it is not an increase in planned lead times that leads to a performance loss. Rather, it is the decrease of lead times in low load periods that increases workloads in upcoming periods of high load. This questions the use of upper bounds (WIP-cap) in these contexts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When dealing with stochastic multi-state production and distribution systems, determining the appropriate quantity of safety stocks on hand may be tricky. We often limit existing solutions for maximizing safety stock levels while keeping cost, objectives and service level constraints in mind to a subset of multi-stage inventory challenges, such as strictly serial architectures or specialist two-stage production/distribution systems. This may make achieving ideal stock levels while meeting cost objectives and sustaining service levels problematic. This is due to the underlying assumption in these systems that cost and service level are directly proportional to one another. Controlling unpredictability and satisfying customers are inventory management's most important tasks. DDMRP (Demand-Driven Material Requirements Planning) dynamically adjusts inventories to handle unpredictability and improve customer service. This is done to improve crisis management. This achieves "demand-driven material requirements planning." This study presents a DDMRP-specific safety stock calculation. The inclusion of this formula aids in the preservation of mathematical consistency. When dealing with a model based on normal probability distributions, the major focus is discovering and applying the parts necessary for safety stock computation. One of the most well-known fast-moving consumer goods (FMCG) firms in South East Asia is the major focus of a case study to validate the proposed strategy. To build a scientific model, the study adopts a probabilistic strategy. The primary goal of this model is to aid in the process of identifying appropriate levels of safety stock and inventory at the distributor level, which will ultimately allow for a customer fill rate of 99%. A distribution network will be employed to achieve this purpose. The model provides a complete framework for estimating optimum safety stock levels and reorder points while accounting for the stochastic nature of demand and including critical elements. This enables more precise forecasting of future demand. This contributes to better inventory management practices and promotes customer satisfaction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Integrating the E-commerce system with an enterprise resource planning tool can help the firm improve performance, maintain customers, and increase sales. In Enterprise Resource Planning, integration features can be provided either as developed features or as separate assignments and contributions. Problems with the online platform, improper addresses, rejected payments, and especially apparent transactions are frequent problems for online buyers. The enhanced Adaptive Ant Colony Optimization is utilized to optimize the rural E-commerce express of transportation. Several innovative routes can lower the downlink transportation cost and reach all collecting places with a fast delivery route. Convolutional Neural Networks were utilized to increase the collective innovation of the E-commerce platform and simplify network communication. E-commerce is a mechanism used to market information services and products. Hence, ERP-AACO-CNN has been designed to integrate Enterprise Resource Planning and E-commerce, and business operations can stream smoothly from the front to the back of the business. Statistics on sales orders, customers, stock levels, price, and essential performance measurement systems. The automated invoices, frequent communications, financial report preparation, product and service delivery, and material requirements planning. The most significant results will likely finance businesses that employ it as a stimulant for a wide-ranging process improvement. In addition, E-commerce is a valuable innovation that connects buyers and sellers in various corners of the globe. Customer satisfaction is projected to be more significant than fault detection at 95.2 % accuracy for the proposed method's E-commerce system with the superior value. According to client demand, an E-commerce system is the most accurate development at a given input level, and a future ERP is 64.9% efficient. The proposed approach has a 24.5% random error rate and a 13.2% mean square error rate. A comparison of E-commerce and enterprise ERP precision to the proposed technique yields 83.8% better results. [ABSTRACT FROM AUTHOR]

Spare-parts provisioning strategy is crucial for airlines or Maintenance Repair Overhauls (MRO). The strategy is divided into multiple classes that refer to several criteria, then uses Material Requirements Planning (MRP) to run those strategies. However, classifying spare parts into a strategy is onerous and requires innovation. Therefore, we employ three algorithms: Decision Tree, Random Forest, and Support Vector Machine (SVM), and use spare parts and aircraft utilization data to classify the strategy and optimize spare parts provisioning of MRO over time. The results showed Random Forest performs best by accuracy, sensitivity, and specificity with a score of more than 97%. [ABSTRACT FROM AUTHOR]

The features of various supply chain systems use at the enterprise are generalized in the article. The evolution of supply chain systems is studied starting from the material requirements planning (MRP) system, which became the basis for the construction of such systems, to the modern ERP II system with a CSRP unit, as well as DRP-I, DRP-II systems, which emphasize the distribution requirements planning taking into account the requirements and requests of consumers. The main advantages and a number of disadvantages in the context of using MRP, ERP and DRP supply chain systems are identified. The main advantages of the systems analyzed are the integration of data from different sources into a single digital environment, such as procurement plans, reports and other information bases; construction of a flexible system of planning and management of material flows, which provides an opportunity to quickly respond to the needs of supply chains in real time; automation of the main business processes related to the procurement system, production management and logistics, which contributes to the reduction of risks related to the human factor, which can negatively affect the company’s activities. The role of an ERP system is determined, enabling companies to gain a deeper understanding of their supply chain ecosystem. At the same time, the key tasks of the ERP system are the active management of various activities that take place throughout the product life cycle to maximize consumer value, the level of satisfaction of consumer expectations and achieving competitive advantage. The main stages of ERP implementation are studied and summarized. The necessity of resource planning in the ERP system, synchronized with customers, coverage of relations with customers by the formation of the CSRP concept, which involves the integration of the customer into the enterprise management system, is determined. The DRP-I and DRPII systems are characterized, which are implemented for the purpose of planning, accounting and management of transport operations related to the movement of material and technical resources and finished products. [ABSTRACT FROM AUTHOR]

Purpose: Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0. Design/methodology/approach: The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production. Findings: When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes. Originality/value: The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems. [ABSTRACT FROM AUTHOR]

Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice. [ABSTRACT FROM AUTHOR]

A fundamental construct of most planning systems is the planned lead time. A planned lead time is a control parameter that refers to the planned amount of time for a production, transportation or replenishment activity. The intent of this article is to provide a brief, critical examination of planned lead times. We first discuss why planned lead times are used and why they are important. We then discuss the trade-offs to be considered in setting the planned lead times. We follow this with a review of the research literature on how to specify the planned lead times. Finally, we offer a simple model to provide some insight on how the planned lead time for an activity depends on the stochastic variability of the resource requirements for the activity, and on the flexibility and utilisation of the resource associated with the activity. [ABSTRACT FROM AUTHOR]

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python's efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes. [ABSTRACT FROM AUTHOR]

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the small business industry, effective cost control is of paramount importance. A well-functioning Material Requirements Planning (MRP) process plays a vital role in enabling businesses to manage their costs efficiently. This research paper explores the improvement of MRP in the context of small businesses in Thailand through the implementation of a web-based application. The objective was to enhance the MRP process by developing an MRP system using ReactJS, NodeJS, and PostgreSQL. The system was evaluated using heuristic evaluation techniques and the results indicated a positive outcome, the mean value is 0.83. The developed web based MRP system proved beneficial for small businesses, as it effectively reduced stocking costs and facilitated efficient raw material procurement. This research provides valuable insights into the implementation of web based MRP systems, enabling small businesses to optimize inventory management and enhance operational efficiency.

Industry 4.0 is a basic step in the upgrade of manufacturing companies into digital enterprises. It allows more flexibility in manufacturing, as well as mass customization, provides better quality, and enhances productivity. As a result, it empowers companies to meet the challenges of smart manufacturing which is increasingly individualizing products with shorter time-to-market span and improving quality. Smart manufacturing has an important part in Industry 4.0. This study aims to characterize and analyze a smart manufacturing process designed for a company specialized in the production of brass accessories: spherical bushels. We basically set up a simulation tool to develop a numerical production platform for Industry 4.0 which is able to efficiently operate and manage the production and procurement through material requirement planning (MRP, master production program), logistics warehouse, and cyber-physical production system (CPPS). The findings have been optimized by a new redesigned approach of MRP 2: load-capacity adjustment for smart workshop and Industry 4.0 manufacturing planning. It is the setup of an integrated manufacturing system process, which resulted in reducing the spherical bushels' assembly time and controlling the production and the assembling process. It allowed us to increase the equipment utilization rate by comparing it with the company's former equipment running time prior to switching to smart manufacturing. The proposed model proves that we can successfully optimize the practical application of Industry 4.0 and therefore significantly increase the production efficiency. We are confident that this is the first work addressing the implementation of a simulation platform controlled by a dedicated cyber-physical production system (CPPS) and a master production program (MPP). A case study of a company manufacturing brass accessories is presented in this paper. The developed simulation platform offers the possibility to create a future digital twin of the company. [ABSTRACT FROM AUTHOR]

Material flow control in remanufacturing is an important issue in the field of disassembly. This paper deals with the potential of autonomous material release decisions for remanufacturing systems to balance the uncertainties related to changing bottlenecks, to maximise throughput ( TH ) and to minimise work-in-process ( WIP ). The goal is to achieve the highest possible throughput rate using real-time data while keeping costs to a minimum. Unlike traditional production systems, remanufacturing must consider and handle high uncertainties in the process. Up to now, classical methods such as CONWIP, Material Requirement Planning (MRP) and Kanban have been used for material flow control. However, these methods do not perform well in a system with high variation and uncertainties such as remanufacturing as they aim to find solutions for static environments. Crucial for optimal production in stochastic environments is finding the optimum pull or release rate which can vary over time in terms of maximising TH and minimising WIP . We propose a deep reinforcement learning approach that acts on the environment and can adapt to changing conditions. This ensures that changing bottlenecks are taken care of and that there is a minimum WIP in the system. [ABSTRACT FROM AUTHOR]

Push and pull methods have been adopted for specific volume production and uncertainty scenarios in order to plan and control production. The further development of hybrid or integrated methods allows benefit to be drawn from opposing approaches. The literature concerning Demand-Driven Material Requirements Planning (DDMRP) proves its superiority under conditions of internal and external uncertainty for high-volume production compared to the most implemented push method (manufacturing requirements planning MRPII). Companies that have adopted this method, manufacture on average 10–15 parts per product, with 2 or 3 levels of bills of materials. In this paper, we evaluate the applicability of DDMRP in a complex manufacturing environment (e.g. products of four levels of bill of materials) in terms of customer satisfaction and stock levels. Buffered and non-buffered items clustered in seven types of decoupling structures contributed to this complexity. We developed a DDMRP model for planning and execution purposes, which was simulated in ARENA's discrete events software. We analysed the model's on-hand stock and delayed orders. DDMRP works effectively under the manufacturing conditions considered. It is found to prevent inventory stockouts and overstocks, reduce lead time by 41% and reduce stock levels by 18%. The success of this method; however, depends on the strategic positioning of the buffers. [ABSTRACT FROM AUTHOR]

Purpose: Changes frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry. Design/methodology/approach: An exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs). Findings: Dynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation. Research limitations/implications: The multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research. Practical implications: In revealing antecedents of complexity's effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment. Originality/value: Building on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Since the beginning of computerised production management, material requirement planning has been a central part of optimising company processes. The reason for this is that the precise computation and optimisation of material requirements can result in significant cost savings for the company in the short term, which can have a significant impact on the overall efficiency of the company's processes. To this end, the definition of the material requirements for production has been complemented by new optimisation methods that can be used to further improve the solution of the problem of dynamic lot sizing. In this paper, the authors investigate the effectiveness of the Wagner- Whitin and Silver-Meal algorithms through a scenario analysis. The results demonstrate that significant cost savings can be achieved by using both algorithms. [ABSTRACT FROM AUTHOR]

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

Demand driven material requirements planning or DDMRP is a recent and promising material management method that has been developed and implemented in the practitioner world. Essentially, DDMRP represents a rethinking of the basic MRP logic. By incorporating elements drawn from Lean Systems and the Theory of Constraints and by introducing new features such as dynamic buffers, DDMRP modifies the basic MRP logic so that it is better able to satisfy customer demands in an increasingly demanding, turbulent and dynamic environment. Claims have been made by firms that DDMRP represents a superior planning approach. In this paper, we introduce and explore DDMRP. In addition, we evaluate its effectiveness relative to two other widely accepted approaches - MRP II and Kanban/Lean production - through a series of structured computer simulation experiments. The results strongly indicate that DDMRP does represent a superior approach - one that warrants further academic study. [ABSTRACT FROM AUTHOR]

Improper planning of inventory will affect the factory operating costs, building costs, the cost of loss, and the cost of product defects due to being stored for too long which will eventually become a loss. This research discusses the processing industry which is experiencing lumpy demand. In carrying out the production process, the company has never made plans for future demand, resulting in a waste of message costs due to repeated orders of raw materials ordered to suppliers. This paper contributes to overcoming this issue by simulating future demand by using the Material Requirement Planning (MRP) method with a probabilistic Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ) model. The demand in the coming period is determined using the Autoregressive Integrated Moving Average (ARIMA) method, and an aggregate plan is carried out to determine the regular cost of raw material production and optimal subcontracting. The final analysis states that the calculation of MRP on the selected items using POQ produces the lowest cost for planning S45C-F, SGT-R, and SKD11-R, while SLD-R uses the probabilistic EOQ method. [ABSTRACT FROM AUTHOR]

The use of the lithium-ion battery (LIB) in both traction and stationary applications has become ubiquitous. It is essential that retired LIBs are wisely treated, with a basis in the concept of the circular economy, to mitigate primary resource use. A closed-loop repurposing and recycling treatment is required. Thus, using the concept of total material requirement as an indicator of natural resource use based on mining activity, a dynamic material flow analysis was executed considering the degradation of the battery, its lifespan, and demand patterns under several scenarios. Then, the effect of circularity on the savings in global natural resource use involved across the entire lifecycles of LIBs was evaluated. It was found that the global resource use for LIBs will increase to between 10 and 48 Gt in 2050. Circularity has the potential to contribute to an 8–44% reduction in the global resource use associated with LIBs in 2050. It was also found that a longer lifespan in the years leading up to 2050 would have a greater impact on the reduction of resource use for LIBs, despite the lower effectiveness of circularity, because it would reduce the demand for LIBs. [ABSTRACT FROM AUTHOR]

This paper presents a digital material planning approach, utilizing simulation-based optimization to select and parametrize article specific demand forecasting methods. Demand forecasts are the basis of material requirements planning in consumption-based material planning, and are an essential lever for efficient inventory and order calculation. Despite their acknowledged potential, digital tools for optimized demand calculation are still lacking in practice. Thus, the goal of the presented approach to provide an application-oriented method to optimally select and parametrize state-of-the-art forecasting methods, based on product-specific demand data. In this approach, a rule-based selection heuristic is combined with static simulation of demand time-series and a metaheuristics-based optimization of forecasting parameters, to provide automatically optimized article-specific demand forecasts. Case studies for two companies in the capital goods industry evaluate and quantify the application potential. The results point to significantly improved, item-specific demand planning. [ABSTRACT FROM AUTHOR]

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression. [ABSTRACT FROM AUTHOR]