Supply Chain Analytics最新文献

The vehicle routing problem (VRP) is a combinatorial optimization problem that determines optimal routes to enhance distribution efficiency. One of the most popular strategies in freight distribution is multi-echelon distribution. Multi-echelon distribution networks often apply to supply chain management, land transportation, the maritime industry, aviation, etc., and rely on VRP. This comprehensive review systematically analyses 382 papers retrieved through the Scopus database. We use a bibliometric and network analysis tool to complete a systematic literature mapping identifying key interrelationships and research clusters. The analysis depicts five main research clusters: green logistics and decision analysis, scheduling and inventory optimization, VRP for city logistics, mathematical modeling and optimization, and outbound logistics and customer service, identified based on author keywords of the systematically derived paper pool. Each cluster is provided with foundational knowledge, concepts, theories, and employed techniques. Finally, future studies are suggested to explore more comprehensive investigation in highly discussed domains like city logistics problems in e-commerce, vehicle routing problems for sustainable logistics, and technological advancement-based applications.

This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.

Circular Supply Chain Management (CSCM) is gaining prominence among diverse stakeholders, practitioners, and scholars. However, its adoption remains limited, particularly within Small and Medium Enterprises (SMEs). This study employs Interpretative Structural Modeling (ISM), specifically tailored for SMEs, to elucidate the contextual relationships among CSCM indicators. Furthermore, it employs the Matrice d’Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis to categorize these indicators into driving- dependence power quadrants. Thirteen CSCM indicators are identified and classified into three sustainability dimensions: economic, environmental, and social. The ISM model comprises four levels, with employees’ exposure to hazardous materials at level one, followed by ten indicators at level two, one at level three (reuse, remanufacturing, recycling complexity), and one at level four (eco-material). MICMAC analysis reveals that none of the indicators falls into the autonomous quadrant. Employees’ exposure to hazardous materials is categorized in the dependent indicators’ quadrant, while ten indicators belong to the linkage quadrant. The independent quadrant includes two indicators: eco-material and reuse, remanufacturing, and recycling complexity. SMEs can utilize these CSCM indicators as an initial step toward circularity implementation. The recommended implementation sequence follows the ISM model hierarchy, starting with level four indicators and progressing through levels three, two, and one, acknowledging the influence of higher-level indicators on lower-level ones.

Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the core components of data-driven technologies of Industry 4.0, attracting much attention in digital supply chains and leading to a growing tide of academic publications. This study conducts a bibliometric analysis of data-driven technologies in digital supply chains. Additionally, some bibliometric methods, such as co-word analysis, are utilized to study the intellectual structure of the field and present a big picture. The co-word analysis maps data-driven technologies’ intellectual structure in digital supply chains and logistics. 3887 publications from the Web of Science (WoS) and Scopus between 2010 and 2021 were collected and analyzed. Then, a strategic diagram is employed on the co-occurrence network, indicating each theme’s current situation from two aspects of applicability and theory development. The study reveals that IoT and CPS technologies are in their infancy in digital supply chains and logistics, and additional studies are needed to fill the research gaps in this field.

The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the autoregressive integrated moving average (ARIMA) algorithm has the highest accuracy compared to other algorithms.

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.