Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies

Abstract This paper develops a supply chain (SC) model by integrating raw material ordering and production planning, and production capacity decisions based upon two case studies in manufacturing firms. Multiple types of uncertainties are considered; including: time-related uncertainty (that exists in lead-time and delay) and quantity-related uncertainty (that exists in information and material flows). The SC model consists of several sub-models, which are first formulated mathematically. Simulation (simulation-based stochastic approximation) and genetic algorithm tools are then developed to evaluate several non-parameterised strategies and optimise two parameterised strategies. Experiments are conducted to contrast these strategies, quantify their relative performance, and illustrate the value of information and the impact of uncertainties. These case studies provide useful insights into understanding to what degree the integrated planning model including production capacity decisions could benefit economically in different scenarios, which types of data should be shared, and how these data could be utilised to achieve a better SC system. This study provides insights for small and middle-sized firm management to make better decisions regarding production capacity issues with respect to external uncertainty and/or disruptions; e.g. trade wars and pandemics.</jats:p

Investigation On The Influence Of Remanufacturing On Production Planning And Control – A Systematic Literature Review

Production planning and control (PPC) is one of the focal operational tasks of a company, and it is used to design logistics services in a target-orientated manner so that individual customer requirements can be fulfilled. However, existing PPC framework models are still based on the prevailing linear economic procedure (take - make - dispose). Due to customers' increasing interest in sustainability and growing regulatory pressure, the Circular Economy (CE) meets these changing conditions by closing material cycles, improving resource efficiency and extending product life cycles. However, for a company to guarantee a high logistics performance, the operational PPC must be adapted to this new economic model. To this end, it needs to be investigated whether and how the adaptation of circular strategies influences existing PPC processes. This paper focuses on the circular strategy of remanufacturing and its influence on different PPC-main tasks. The latter will be examined using a systematic literature review. Finally, the results of this analysis are compared with the Hanoverian Supply Chain Model as a PPC framework model. This comparison shows which PPC tasks are affected and which existing approaches have already been developed. Ultimately, these results provide the basis for developing a framework model for operational PPC regarding the CE

A systematic review of decision-making in remanufacturing

Potential benefits have made remanufacturing attractive over the last decade. Nevertheless, the complexity and uncertainties associated with the process of managing returned products make remanufacturing challenging. Since this process involves enormous decision-making practices, various methods/techniques have been developed. This review is to specify the current challenges and opportunities for decision-making in remanufacturing. To achieve this, we perform a systematic review over decision-making in remanufacturing by classifying decisions into different managerial levels and areas. Adopting a systematic approach which provides a repeatable, transparent and scientific process, 241 key articles have been identified following a multi-stage review process. Our review indicates that most studies focuses on strategic-level(48%) and tactical-level (34%)with only 5% focusing on operational-level and the rest on two levels(13%). Regarding decision-making methods, most studies propose mathematical models (60%) followed by analytical models (31%). Furthermore, only 36% of the studies address uncertainties in which stochastic approach is mostly applied. A total of 21 knowledge gaps are highlighted to direct future research work

Design and financial aspects of the end-of-life management of telecommunications products

As a result of legislation the electronics industry faces product takeback and recycling. It is therefore important to understand the environmental burden caused by discarded consumer electronics and also how to better manage raw materials. The thesis begins with a review of current environmental issues from the viewpoint of the electronics industry. This shows that there are many complex interactions to be considered within any environmental framework particularly those between legislation, technology and business. Consideration of the drivers indicates that work should focus on the design understanding required to allow product life extension as well as current strategies addressing the reprocessing of used products. The body of the thesis therefore has two themes, both of which use telecommunications products, telephones, as their exemplar. The first theme, the design issues related to the end-of-life management is explored via a benchmarking study of eight telephones from European (UK and Germany) and Far Eastern suppliers (China and Malaysia). This study allowed the generation of design rules for such products. The work also examined the impact of design changes to improve end-of-life practices on manufacturing costs in Europe and the Pacific Rim to indicate the constraints of labour and investment costs. The second theme links the business and technological issues faced in the endof- life (EOL) management of electronic products. The EOL options considered are: resale, remanufacturing, recycling, disposal and to a limited extent, upgrading. Building on the technological understanding generated in the first theme accurate economic models are derived, based on commercial data, for exemplar telephone products that reflect the activities within each option. The potential revenue from each option indicates preferred design strategies and the models can therefore help resolve some of the uncertainties faced by decision makers. The thesis closes by identifying that the design rules and financial models are particularly appropriate for mature products such as the telephones used as exemplars, further research is therefore necessary to extend the existing work to high added value products

Optimising Supply Chain Performance via Information Sharing and Coordinated Management

Supply chain management has attracted much attention in the last decade. There has been a noticeable shift from a traditional individual organisation-based management to an integrated management across the supply chain network since the end of the last century. The shift contributes to better decision making in the supply chain context, as it is necessary for a company to cooperate with other supply chain members by utilising relevant information such as inventory, demand and resource capacity. In other words, information sharing and coordinated management are essential mechanisms to improve supply chain performance. Supply chains may differ significantly in terms of industry sectors, geographic locations, and firm sizes. This study was based on case studies from small and medium sized manufacturing supply chains in People Republic of China. The study was motivated by the following facts. Firstly, small and medium enterprises have made a big contribution to China’s economic growth. Several studies revealed that most of the Chinese manufacturing enterprises became aware of the importance of supply chain management, but compared to western firms, the supply chain management level of Chinese firms had been lagging behind. Research on supply chain management and performance optimisation in Chinese small and medium sized enterprises (SMEs) was very scarce. Secondly, there had been plenty of studies in the literature that focused on two or three level supply chains whilst considering a number of uncertain factors (e.g. customer demand) or a single supply chain performance indicator (e.g. cost). However, the research on multiple stage supply chain systems with multiple uncertainties and multiple objectives based on real industrial cases had been spared and deserved more attention. One reason was due to the lack of reliable industrial data that required an enormous effort to collect the primary data and there was a serious concern about data confidentiality from the industry aspect. This study employed two SME manufacturing companies as case studies. The first one was in the Aluminium industry and another was in the Chemical industry. The aim was to better understand the characteristics of the supply chains in Chinese SMEs through performing in-depth case studies, and built models and tools to evaluate different strategies for improving their supply chain performance. The main contributions of this study included the following aspects. Firstly, this study generalised a supply chain model including a domestic supply chain part and an international supply chain part based on deep case studies with the emphasis on identifying key characteristics in the case supply chains, such as uncertainties, constraints and cost elements in association with flows and activities in the domestic supply chain and the international supply chain. Secondly, two important SCM issues, i.e. the integrated raw material procurement and finished goods production planning, and the international sales planning, were identified. Thirdly, mathematical models were formulated to represent the supply chain model taking into account multiple uncertainties. Fourthly, several operational strategies utilising the concepts of just-in-time, safety-stock/capacity, Kanban, and vendor managed inventory, were evaluated and compared with the case company's original strategy in various scenarios through simulation methods, which enabled quantification of the impact of information sharing on supply chain performance. Fifthly, a single objective genetic algorithm was developed to optimise the integrated raw material ordering and finished goods production decisions under (s, S) policy (a dynamic inventory control policy), which enabled the impact of coordinated management on supply chain performance to be quantified. Finally, a multiple objectives genetic algorithm considering both total supply chain cost and customer service level was developed to optimise the integrated raw material ordering and finished goods production with the international sales plan decisions under (s, S) policy in various scenarios. This also enabled the quantification of the impact of coordinated management on supply chain performances

Best Environmental Management Practice in the Fabricated Metal Product manufacturing sector

This report encloses technical information pertinent to the development of Best Environmental Management Practices (BEMPs) for the Sectoral Reference Document on the Fabricated Metal Products manufacturing sector, to be produced by the European Commission according to Article 46 of Regulation (EC) No 1221/2009 (EMAS Regulation). The BEMPs, both of technological and management nature (identified in close cooperation with a technical working group) address all the relevant environmental aspects of the Fabricated Metal Products manufacturing facilities. The BEMPs described in this report provide guidance on the cross-cutting issues and optimisation of utilities of the manufacturing facilities. Moreover, the BEMPs cover also the most relevant manufacturing processes, looking at energy and material efficiency, protecting and enhancing biodiversity, using of renewable energy and using rationally and effectively chemicals e.g. for cooling of various machining processes. Each BEMP gives a wide range of information and outlines the achieved environmental benefits, appropriate environmental performance indicators to measure environmental performance against the proposed benchmarks of excellence, economics etc. aiming at giving inspiration and guidance to any company of the sector who wishes to improve its environmental performance.JRC.B.5-Circular Economy and Industrial Leadershi

STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL

Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage. In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems. The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics. To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry