Supply Chain Analytics最新文献

Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.

Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.

Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.

Supplier selection has become increasingly complex regarding selection criteria caused by expanded data collection processes and supplier numbers due to globalisation effects. This complexity has led to the consideration of Artificial Intelligence (AI) techniques to facilitate and enhance supplier selection. However, the AI techniques most often applied are unfamiliar to stakeholders and have limited explainability, posing a significant barrier to adopting intelligent approaches in supply chains. To address this issue, we propose a hybrid supplier selection framework that combines interpretable data-driven AI techniques with multi-criteria decision-making (MCDM) approaches: the former aims to reduce the complexity of the supplier selection problem, while the latter ensures familiarity to supply chain stakeholders by retaining MCDM at the heart of the supplier selection process. The framework is validated through two real-world case studies supporting supplier selection decisions in oil, gas, and aerospace manufacturing companies. Preliminary results from our case studies suggest that the framework can achieve comparable performance to approaches utilising only machine learning while offering the added benefits of end-to-end explainability and increased familiarity.

Supply chain competitiveness and agility are matured areas in supply chain management. While there is an ongoing evolution in digital technology alongside supply chain competitiveness and agility, the literature appears to have limited bibliometric reviews on how digital technology impacts these aspects. This study examines supply chain competitiveness with bibliometric analysis, focusing on the critical elements of supply chain agility and the impact of rapidly advancing digital technologies. The study bridges the gap between management and technology disciplines. Employing the PRISMA methodology, 147 scholarly articles were meticulously selected and analysed, adopting a multifaceted analytical approach that combines bibliometric and descriptive analyses. This thorough literature synthesis reveals a profound and intricate connection between supply chain agility and digital technology, underscoring their joint significance in fostering competitive advantage within the dynamic business landscape. This investigation contributes to the existing body of knowledge by identifying seven distinct clusters, offering a detailed map of the current research landscape. This study charts a course for future academic inquiries into this critical area and provides valuable insights for practitioners. It underscores the importance of integrating agile supply chain practices and digital technologies to maintain and enhance competitive positioning in today’s fast-paced business environment.

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.