Supply Chain Analytics最新文献

Sustainability in supply chain management addresses various challenges, from waste minimization to resource efficiency maximization. Two-dimensional cutting problems are common problems in most supply chains where small rectangular items need to be cut from large rectangular stock sheets to meet production needs or customer demands. The large stock sheets, produced from materials such as paper, steel, or wood, often contain defects. An optimal cutting solution is needed to avoid overlap with any defects and minimize waste in the cutting process. We propose a supply chain waste reduction optimization model using beam search algorithms for two-dimensional cutting problems with defects. Our proposed solution leverages the power of advanced analytics through a dynamic programming approach. Our algorithms feature variable beam widths and heuristic rules to reduce computation times while yielding high-quality solutions. A simulation model is used to assess the performance of the proposed algorithms.

Additive manufacturing (AM) is an evolutionary manufacturing technology attracting many firms and governments. Its impact, however, on supply chains remains unexplored. To date, no research has investigated the implications of additive manufacturing on Supply Chain (SC) resilience utilizing a bibliometric analysis. This study presents a bibliometric analysis to investigate the scientific development and contribution of additive manufacturing to supply chain resilience. The investigation is conducted to unveil and characterize the ongoing and future research trends of AM in SC and its intersection in the literature. After pre-processing, two hundred and twenty-four articles are extracted from the Web of Science database utilizing relevant keywords and logical connections, the metadata collected from these articles are investigated and compared using bibliographic coupling, sources analysis, co-citation and co-word analysis through the Bibliometrix R software and VOSViewer. The bibliometric survey aims to classify the most pertinent thematic collections and publications in which AM and SC intersect. We show a noticeable focus on adopting AM to respond to SC disruptions and SC management in recent years. Insights and trends regarding AM & SC for practitioners and scholars are identified and proposed for future research directions.

This study presents a Data Envelopment Analysis (DEA) model to evaluate the efficiency of the exchange of regional import and export goods in a railway supply chain network. Unlike previous railway supply chain efficiency studies, the exchange efficiency incorporates railway supply chain operational input, import volume as input, and export volume as output to help policymakers review railway supply chain performance and local freight structure decisions. The transport service efficiency of transportation lines between provinces in the railway supply chain network is formulated with a two-stage DEA model. This transport service efficiency is further decomposed into the efficiency of the forward and reverse lines constituting the transportation lines between provinces. To provide decision-makers with a path to improve exchange efficiency, we measure the improvement potential for each input and output of a province. The empirical study of China’s railway supply chain shows (1) Shanxi, Tibet, Shanghai, and Hainan provinces have the highest exchange efficiency; (2) intra-regional transport service efficiency is higher than inter-regional transport service efficiency in North, East, and Southwestern China; and (3) provinces with lower exchange efficiency can also have transportation lines with high transport service efficiency.

Supplier selection is a difficult task imposing significant challenges for supply chain managers in today's competitive environment. Sustainability adds another layer of complexity to this already difficult problem, given the global concerns on social, economic, and environmental impacts, especially in emerging economies. Many multi-criteria decision-making (MCDM) methods have been proposed for sustainable supplier selection. However, insufficient emphasis in the literature is given to sustainable supplier selection for supporting decisions in healthcare testing facilities in emerging economies. This study proposes a supplier selection process for healthcare testing facilities from a sustainability perspective utilizing an integrated MCDM framework combining stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assessment (WASPAS). SWARA is used to rank the supplier selection criteria, and WASPAS is utilized to select the most suitable supplier. An Additive Ratio Assessment (ARAS) and Evaluation based on Distance from Average Solution (EDAS) are used to validate the results. A sensitivity analysis is conducted to test different scenarios of interest with the WASPAS method. Cost stability, continuous improvement and quality control, and past performance and reputation are the top-weighted criteria in the study. The findings of this research provide actionable insights to assist healthcare managers in responding to sustainability challenges more efficiently. The contributions of the study also inform policymakers to make more responsible decisions and establish regulations to improve sustainability in the healthcare industry in emerging economies.

Although Machine Learning (ML) in supply chain management (SCM) has become a popular topic, predictive uses of ML in SCM remain an understudied area. A specific area that needs further attention is the prediction of late deliveries by suppliers. Recent approaches showed promising results but remained limited in their use of classification algorithms and struggled with the curse of dimensionality, making them less applicable to low-volume-high-variety production settings. In this paper, we show that a prediction model using a regression algorithm is capable to predict the severity of late deliveries of suppliers in a representative case study of a low-volume-high-variety machinery manufacturer. Here, a detailed understanding of the manufacturer’s procurement process is built, relevant features are identified, and different ML algorithms are compared. In detail, our approach provides three key contributions: First, we develop an ML-based regression model predicting the severity of late deliveries by suppliers. Second, we demonstrate that prediction within the earlier phases of the purchasing process is possible. Third, we show that there is no need to reduce the dimensionality of high-dimensional input features. Nevertheless, our approach has scope for improvement. The inclusion of information such as component identifiers may improve the prediction quality.

The logistics center concept has been discussed in the literature for over four decades. Logistics centers simplify the logistics network and have many advantages, such as lower transportation costs, an economy of scale, and integrated service capabilities. We propose a spatial multi-criteria decision-making model for new logistic centers in metropolitan areas. The first focus of the study is identifying the logistic concerns, defining the factors affecting the replacement decisions and determining the weights of the factors in metropolitan areas with many expert opinions. The second focuses on spatial analysis to locate new logistics centers serving urban areas. We present a case study in Istanbul, the most populous metropolis in Europe, to demonstrate the applicability and exhibit efficacy of the method proposed in this study. Outputs of the study pointed out where the convenient places are to locate new logistics centers.