Supply Chain Analytics最新文献

Waste production is growing in most communities due to population expansion. Given the stated issue, managing the Solid Waste (SW) created worldwide would be vital. Effective Waste Management (WM) is essential to preserving the environment and lowering pollution. It aids in resource preservation, greenhouse gas emission reduction, and ecosystem protection. Additionally, the promotion of public health and sanitation is significantly aided by WM procedures. This study presents an integrated procedure to enhance the operations of a WM network for recycling SW. We propose a mathematical model to find the optimal sustainable vehicle routes, allocation, and Sequence Scheduling (SS) problem in the recycling industry to reduce costs and CO₂ emissions and increase job opportunities. The fundamental innovation of this work is considering waste-vehicle and waste-technology compatibility and Internet of Things (IoT) systems in the model to decrease CO₂ emissions and identify compatible waste for recycling centers to produce more final products. An LP-metric and an Epsilon Constraint (EC) approach are used to solve the suggested model. By comparing the two approaches, we have found EC performs better in results and CPU time. As a result, various test problems of different sizes are offered. Accordingly, sensitivity analyses are recommended to assess the suggested model’s effectiveness. Using vehicles compatible with waste reduces CO₂ emissions. Utilizing IoT technology and optimization methods makes it feasible to save costs (20%), have a less destructive impact on the environment (36%), and ultimately increase the sustainability of the WM process.

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

This study presents optimization models for large vehicle routing problems using a spreadsheet solver and Python programming language with extended graphic card boosting computing power. Near optimality is feasible and attainable with spreadsheet tools and models for solving real-life problems. However, increasing the availability of additional computing power through graphics processing and visualization is now a viable option for decision-makers and problem-solvers. This study shows that decision-makers can solve vehicle routing optimization problems with limited access to high-end optimization tools. This study shows managers and decision-makers can use vehicle routing optimization even with limited access to sophisticated optimization tools.

A systematic literature review is conducted to analyze and synthesize studies on supply chain collaboration in Industry 4.0 between 2010 and 2023. 152 documents were selected from various databases. The meta-synthesis method categorizes 8 Initiators, 8 Barriers, 7 dimensions, and 4 Outcomes of collaboration. The findings show that collaboration in supply chain 4.0, with the activation of drivers and enablers such as Industry 4.0 technologies, Information and communication technology infrastructure, and with the control of barriers such as Personal benefits and Operational and structural issues can utilize information and communication technologies to highlight sustainable performance and trust throughout the supply chain. The study develops the existing literature and persuades businesses and the scientific community to investigate the power of collaboration in supply chain partner activities. The analytical model in this study focusing on four main sections, can serve as a basis for conducting new research in the development of collaboration.

This study proposes an integrated framework for successfully implementing collaboration strategies in supply chain (SC) management using a fuzzy decision-making trial and evaluation laboratory (DEMATEL) and multi-criteria decision-making (MCDM) approach. The study analyses and measures the successful possibility of SC collaboration implementation in small and medium enterprises (SMEs). The fuzzy DEMATEL is used to evaluate the weight of each criterion, and fuzzy MCDM is used to assess the possible success rates of the collaboration strategies. The result shows that top management support and commitment, SC strategic planning, information sharing, goal congruence, organizational compatibility, and decision synchronization are major factors responsible for SC collaboration success in SMEs. This study helps predict the effectiveness of collaboration strategies and indicates remedial activities in the existing collaborative environment by undertaking corrective actions and measures to increase implementation success.

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploiting multi-to-multi relationship between tiers of a supply chain. The resulting formulation is flexible such that quantity discounts, inventory holding, and transport costs can be included. The framework introduces a new trade-off between tiers, leading to cost reductions in one tier but increased costs in the other, which helps to reduce the overall procurement cost in the supply chain. A mixed integer linear programming model is developed and tested with a range of small to large-scale test problems from aerospace manufacturing. Our comparison to benchmark results shows that there is indeed a cost trade-off between two tiers, and that its reduction can be achieved using a holistic approach to reconfiguration. Costs are decreased when second tier fixed ordering costs and the number of machining options increase. Consolidation results in reduced inventory holding costs in all scenarios. Several secondary effects such as simplified supplier selection may also be observed.

Public health agencies and medical organizations have increased their relentless efforts to control the outbreaks of mosquitoes causing West Nile disease epidemics with the re-emergence of deadly disease in the United States. Such measures include a large-scale spray operation of insecticides that can counter the population growth of mosquitoes. This study proposes a business analytics tool that helps determine the optimal number and location of airfields housing insecticide-spraying aircraft, the type (mode) and the number of insecticide-spraying airplanes, and their flight patterns. This study also identifies factors that can enhance and hinder the efficiency of insecticide spray operations by combining a bi-objective integer programming model with a geographic information system and forecasting models within a business analytics framework for the first time.

This paper develops a coordination model for a closed-loop supply chain (CLSC) system consisting of a single manufacturer and retailer where market demand depends on the green technology level, retailer’s selling price, and promotional efforts. The manufacturer manufactures products from raw materials to finished products and adapts them for sale to meet key market demands. Used products from consumers will be returned to the retailer to be processed by remanufacturing under a technology license, and the rest will be returned to the manufacturer for remanufacturing, refurbishing, recycling, or waste disposal. The models are formulated mathematically and constructed under three scenarios – centralized, decentralized, and a Stackelberg game led by the manufacturer. Numerical examples are given to illustrate the results of the developed model. The result suggests that both types of investment can assist the closed-loop supply chain in enhancing its financial and environmental performance. It also suggests that lowering emissions levels through green technology might increase product selling prices, hence increasing sales volume.