Supply Chain Analytics最新文献

Communication and collaboration between supply chain partners is more important than ever. To achieve this, visibility between different supply chain tiers is essential. Recent literature has discussed the benefits of increased supply chain visibility, but more research is necessary to provide concrete evidence. The main question this article aims to answer is about what parts of a supply chain are critical for establishing and increasing visibility. Toward this end, this study uses the amount of unmet customer demand as a performance measure, and performs simulations and empirical analysis on multi-tier supply chains of various sizes. Results indicate that the customers (i.e., downstream supply chain) are the most critical components, and the managers must focus on increasing visibility with them. In addition, visibility in the downstream can be nearly as effective as full visibility in specific settings: The maximum gap between the amounts of unmet demand for the two settings is about 7%. However, the main value of full visibility becomes more apparent when significant deviations exist between forecasted and actual customer demand amounts. As the experiments demonstrate, full visibility in the entire supply chain is the most effective level of visibility.

Green Supply Chain Management requires coordinated decisions between the strategic and operational organization layers to address strict green goals. Furthermore, linking CO2 emissions to supply chain operations is not always easy. This study proposes a new mathematical model to minimize CO2 emissions in a three-layered supply chain. The model foresees using a financial budget to mitigate emissions contributions and optimize supply chain operations planning. The three-stage supply chain analyzed has inbound logistics and handling operations at the intermediate level. We assume that these operations contribute to emissions quadratically. The resulting bilevel programming problem is solved by transforming it into a nonlinear mixed-integer program by applying the Karush-Kuhn-Tucker conditions. We show, on different sets of synthetic data and on a case study, how our proposal produces solutions with a different flow of goods than a modified linear model version. This results in lower CO₂ emissions and more efficient budget expenditure.

The Internet of Things (IoT) has attracted the attention of researchers and practitioners in supply chains and logistics (LSCs). IoT improves the monitoring, controlling, optimizing, and planning of LSCs. Several researchers have reviewed the IoT-based LSCs publications indexed by academic journals focusing on decision-making. Decision support systems (DSS) are in the infancy stage in IoT-based LSCs. This paper reviews the IoT-LSCs from the DSS perspective. We propose a new framework for helping decision-makers implement IoT based on the decisions that need to be made by describing a transition scheme from simple, if-then decisions to analytical decision-making approaches in IoT-LSCs. The IoT Adopter II is an extension of the IoT Adopter framework, in which a new layer called ‘decision’ has been added to enable decision-makers implementing IoT to improve the list of predefined decision-making processes in LSCs. Although academic literature review analysis provides valuable insights, a wide range of related information is available online. This study also utilizes a web content mining approach for the first time to analyze the IoT-LSCs in the decision-making context. The results show that the IoT-LSC field involves two emerging themes, blockchain supply chains and supply chain 5.0, and two mainstream themes, i.e., big data analytics and supply chain management.

Sustainable Supply Chain and Industry 5.0 are two important concepts reshaping how businesses operate in the modern world. Together, these two concepts drive the advancement of a highly sustainable and robust worldwide economy. Companies are now becoming more sustainable in supply chain management, using technologies like blockchain and co-bots to track the origin of goods, ensure ethical and sustainable sourcing, and work with humans safely and effectively. This study develops a theoretical model highlighting the determinants of Industry 5.0, Sustainable Supply Chain Practices, by combining theoretical frameworks from the manufacturing, supply chain, and information systems literature. The study's analytic sample comprises 342 responses collected from professionals working in the electronics industry's supply chain. Hypotheses were constructed employing deductive reasoning, leveraging insights gleaned from prior research. The study is conducted utilizing the Structural Equation Modeling (SEM) to substantiate the presumed connections among various constructs, namely, Industry 5.0 innovations, Sustainable Supply Chain Practices (SSCP), Sustainable Supply Chain Performance (SCP), and Supply Chain Risks (SCR). The Structural Equation Modeling analysis results show a direct impact of Industry 5.0 technologies through Sustainable Supply Chain Practices can enhance Supply Chain Performance and mitigate Supply Chain Risks. Combining the two paradigms can foster the development of new business models that prioritize sustainability and contribute to a more equitable and environmentally friendly economy that brings positive change for both businesses and society.

The weather affects the sales of many retail products worldwide. As the weather becomes more erratic due to climate change, retail organizations must respond by incorporating weather information into their sales forecasting models. This study proposes a modeling framework for identifying, quantifying, and evaluating the use of weather information in forecasting models. The models are developed using several time-shifted weather features and machine-learning techniques. Our method is applied to a dataset encompassing individual products and product categories obtained from a large Canadian retail organization. We find that using weather information improves the accuracy of sales forecasts significantly, explaining up to an additional 47% of the variance for the individual products and up to an additional 56% for the product categories, on top of the variance explained by a baseline model. By analyzing the parameters of the trained models, we can also determine the importance and influence of each weather feature, including time-shifted features. Our research findings contribute to both the literature on forecasting in the retail sector and the decision-making of retail organizations. By comparing a model developed with and without weather information, the organization can better determine the value of weather in its planning. Customer expectations of future weather significantly influence sales and should be considered for future studies. Our work provides a basis for researchers and retail organizations to forecast sales of individual products using weather information.

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.