Supply Chain Analytics最新文献

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.