Supply Chain Optimization Research Articles

Redefining supplier selection in healthcare sector: a novel framework for supplier classification.

This paper aims to delve into the critical aspect of supplier selection in the healthcare sector, emphasizing the significance of strategic sourcing in enhancing operational efficiency and quality of services. The primary aim is to develop a comprehensive framework for supplier evaluation that aligns with the unique requirements of hospitals, ultimately improving procurement processes and patient care outcomes. The study leverages the renowned Carter's 7 C model as a foundational framework for supplier assessment, supplemented by insights gathered from interviews with experts in the New Product Introduction, Purchasing and Procurement departments of a leading hospital in India. Data collection involved a combination of primary interactions through Google Forms and secondary data extraction from Purchase Inquisition Brochures and Hospital Information Systems. The weighted average method was employed to determine the significance of various criteria based on expert recommendations, ensuring a fair and consensus-driven evaluation process. Through the synthesis of expert inputs and data analysis, the study unveils a nuanced set of criteria weights that guide supplier selection decisions in the healthcare setting. The research highlights the dynamic nature of supplier evaluation criteria, underscoring the importance of tailoring assessments to suit the specific needs of each hospital. By emphasizing effective communication, technological advancements and collaborative supplier relationships, the study underscores the potential for strategic supplier selection to optimize supply chains, drive innovation and enhance patient care delivery in the healthcare sector. The framework for supplier selection in the healthcare sector has limitations, including limited generalizability, subjectivity in expert opinions and a static framework. The framework assumes the constant importance of selection criteria over time, which could be affected by dynamic changes in the industry. Its focus on the healthcare sector may require adaptation to other sectors. For hospitals, it also supports cost optimization by focusing on critical variables like cost efficiency and quality. For suppliers, the framework provides clear expectations, performance benchmarking and collaborative growth. It encourages performance benchmarking and encourages collaboration between the hospital and its supply chain partners. The framework is a replicable model that can be adapted by other hospitals or organizations to optimize supplier selection processes. It promotes standardization in supplier evaluation, fostering consistency across the sector. For patients, the framework improves care delivery by ensuring reliable access to high-quality supplies and services. This research intends to provide a novel framework for the classification of suppliers in the healthcare sector for operational excellence which enhances the strategic sourcing decision in the domain.

Research on Cross-Border e-Commerce Supply Chain Prediction and Optimization Model Based on Convolutional Neural Network Algorithm

Enhancing the precision of supply chain management and reducing operational costs are crucial for the development of the cross-border e-commerce market. However, existing research often overlooks the demand uncertainty caused by seasonal variations and the challenges of handling returns in logistics. Therefore, this paper proposes a SARIMA-CNN-BiLSTM prediction model that effectively captures both the seasonal and nonlinear characteristics of cross-border e-commerce supply chains. Additionally, by incorporating the returns process, a supply chain distribution optimization model is developed with the objective of minimizing total operational costs. The model is solved using an improved whale optimization algorithm. In validation with real-world data, the SARIMA-CNN-BiLSTM model achieved a mean absolute percentage error reduction of 6.479 and 7.703 compared to convolutional neural network (CNN) and BiLSTM models, respectively. Moreover, the chosen optimization algorithm reduced the cost by 231,310 CNY, 62,564 CNY, and 131,632 CNY compared to the whale optimization algorithm, genetic algorithm, and particle swarm optimization, respectively. The proposed approach provides robust support for cross-border e-commerce enterprises in reducing costs and enhancing efficiency in their operations.

Recent Advancements in Anomaly Detection for Manufacturing Systems

Recent advancements in anomaly detection and root cause analysis have revolutionized manufacturing processes, enabling improved efficiency, quality control, and cost reduction. This review explores the latest developments in these fields, focusing on supervised and unsupervised anomaly detection methods, statistical techniques for root cause analysis, and predictive modeling approaches. Supervised anomaly detection methods, such as classification-based approaches, support vector machines, and decision trees, leverage labeled data to identify anomalies. Unsupervised techniques, including clustering-based methods (e.g., k-means and DBSCAN), density-based approaches (e.g., local outlier factor), and dimensionality reduction (e.g., PCA and autoencoders), detect anomalies without prior knowledge of anomalous instances. Statistical methods for root cause analysis, such as statistical process control, hypothesis testing, correlation analysis, and multivariate techniques (e.g., factor analysis), enable the identification of the underlying causes of manufacturing issues. Predictive modeling techniques, including time-series forecasting, machine learning for predictive maintenance, and optimization methods for process improvement, further enhance manufacturing efficiency. The integration of these techniques has led to significant improvements in quality control, equipment failure prediction, and supply chain optimization. However, challenges remain in handling complex, high-dimensional data and integrating domain knowledge with data-driven approaches. Future research directions include the development of explainable AI, edge computing, and federated learning for anomaly detection in manufacturing. This analysis offers a thorough examination of cutting-edge techniques for detecting anomalies and analyzing root causes, emphasizing their capacity to transform manufacturing operations and stimulate future advancements in these research areas. Keywords—anomaly detection, manufacturing, supervised and unsupervised anomaly detection

Optimizing Supply Chains in the Food and Beverage Industry through Digital Transformation

Optimizing Supply Chains in the Food and Beverage Industry through Digital Transformation

Adoption and impact of generative artificial intelligence on blockchain-enabled supply chain efficiency

Purpose The purpose of this study is to investigate the primary determinants influencing the acceptance of generative artificial intelligence (GAI) adoption within Blockchain-enabled environments. Further research will examine the impact of GAI adoption on supply chain efficiency (SCE) through the enhancement of Blockchain. Design/methodology/approach Drawing on innovation diffusion theory (IDT), this study used partial least square structural equation modelling (PLS-SEM) to look into the hypotheses. The data were gathered via online questionnaires from employers of Chinese supply chain enterprises that have already integrated Blockchain. Findings The findings of this study demonstrate that relative advantages (RAs), compatibility, trialability and observability have a significant positive effect on GAI adoption, while complexity harms GAI adoption. Above all, the GAI adoption has significantly enhanced Blockchain, thus effectively improving SCE. Practical implications The outcomes from this study furnish enterprises and organizations with valuable insights to proficiently integrate GAI and Blockchain capability, optimize supply chain management and bolster market competitiveness. Also, this study will help accelerate the successful integration of business processes and attain Sustainability Development Goals 9, industrial growth and industrial diversification. Originality/value To the extent of the author’s knowledge, the current status of the GAI study remains largely exploratory, and there is limited empirical evidence on integrating Blockchain capability and GAI. This research bridges the knowledge gap by fully revealing the optimal integration of these two transformative technologies to leverage their potential advantages in supply chain management.

Integrating life cycle assessment into supply chain optimization.

Integrating Supply Chain Optimization (SCO) with Life Cycle Assessment (LCA) is essential for creating supply chains that are both economically efficient and environmentally sustainable. While SCO focuses on optimizing network structures and decisions related to product and service delivery, LCA systematically assesses the environmental impacts across the entire supply chain. The existing literature treats SCO and LCA as separate, sequential steps, often leading to inconsistencies in scope and challenges in data transfer and rescaling. Our research presents a novel Supply Chain Life Cycle Optimization (SCLCO) model that integrates SCO and LCA. Our SCLCO model is based on LCA data structures, incorporates multi-time period, closed-loop SCO decisions (e.g. reverse chain management, inventory control, network design), and is capable of considering the three pillars of sustainability: environmental, economic, and social. It includes harmonizing principles, terminology, and notation, thereby bridging the gap between the SCO and LCA communities through a generalized formulation. Computational experiments on a selected SCO model from Operations Research literature validate the SCLCO and demonstrate its effectiveness in providing valuable insights to both SCO and LCA practitioners and researchers. The results emphasize that the simultaneous execution of SCO and LCA in SCLCO minimizes the risk of overlooking decision impacts and facilitates data transfer from existing LCA databases.

Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models

The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. Both models demonstrated strong predictive accuracy, with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), offering reliable and computationally efficient solutions for mid-sized operations. The findings underscore the novelty of combining ARIMA’s time-series capabilities with MLR’s multivariable analysis to address complex demand drivers. By aligning with Resource-Based View (RBV) and Supply Chain Resilience Theory, this research advances the understanding of AI-driven predictive models as strategic tools for enhancing operational efficiency, reducing waste, and promoting sustainability in cold chain logistics. This work sets the stage for future innovations in AI-driven supply chain optimization.

Optimizing Supply Chain Management with Real-Time Data Integration Platform

Real-time data integration platforms have emerged as transformative solutions in modern supply chain management, revolutionizing how organizations process, analyze, and act on critical business information. These platforms enable seamless connectivity between suppliers, manufacturers, distributors, retailers, and customers, providing end-to-end visibility and enhanced decision-making capabilities. The evolution from traditional batch processing to real-time integration represents a fundamental shift in supply chain operations, addressing challenges such as data latency, system fragmentation, and operational inefficiencies. Through advanced technological components including sophisticated data ingestion layers, powerful processing engines, and efficient distribution systems, organizations can achieve substantial improvements in inventory management, order fulfillment, and customer satisfaction. The integration of artificial intelligence, edge computing, and blockchain technologies further enhances these capabilities, enabling predictive analytics, automated decision-making, and enhanced security measures. The implementation of these platforms, while challenging, offers significant strategic advantages including improved operational efficiency, reduced costs, and strengthened competitive positioning in the global marketplace.

The Supply Chain Management of Live Streaming E-Commerce

This paper provides a comprehensive analysis of the management mode, supply chain management, and future prospects of the live streaming e-commerce industry. The article begins by outlining the fundamental concept and development process of live streaming e-commerce, emphasizing its significant role as a novel business model in the digital economy. Subsequently, it delves into a detailed analysis of the management mode of live streaming e-commerce, encompassing anchor team construction, content planning, marketing promotion, data analysis and optimization, after-sales service, and customer relationship management. Drawing on successful cases such as Li Jiaqi and Wei Ya, it underscores the critical importance of meticulous supply chain management and astute market insight. Simultaneously, the article also highlights the challenges and issues encountered by the live streaming e-commerce industry, including difficulties in inventory management, product quality control, and logistics distribution efficiency. Ultimately, the paper anticipates the future of live streaming e-commerce, asserting that technological innovation and model innovation will serve as pivotal drivers for industry advancement. It further proposes measures to enhance technology research and development, optimize supply chain management, monitor shifts in consumer demand, and reinforce industry self-regulation and oversight. In summary, this comprehensive analysis offers readers an opportunity to gain a profound understanding of the live streaming e-commerce industry while providing valuable insights for relevant practitioners.

Corporate entrepreneurship, supply chain management strategies and performance in the setting of Saudi Arabia: Empirical investigation

The objective of this study is to assess the joint effect of corporate entrepreneurship (CE) and supply chain management (SCM) strategies on firm performance in the circumstance of Saudi Arabian manufacturing firms. This is interesting in the sense that while previous scholars have focused on CE and SCM separately, the combinative aspect of the two particularly in nonwestern countries seems still to be short of attention. In this respect, this paper attempts to apply a resource-based view and suggest that CE and SCM complement each other and in the end performance of the firm will be increased. Out of the 84 Saudi manufacturing firms covered in the structured survey, data was analyzed using Ordinary Least Squares (OLS) regression. The results indicate that CE, particularly dimensions such as new business venturing, product innovation, technological entrepreneurship, mission reformulation, reorganization and system-wide changes, significantly enhances firm performance. Additionally, SCM strategies were found to have a positive and statistically significant impact on firm performance, highlighting the critical role of effective supply chain management in operational efficiency and competitive positioning. The findings contribute to the literature by providing empirical evidence from a Middle Eastern context, thereby enhancing the generalizability of existing theories on CE and SCM. The study also offers practical implications for managers and policymakers, suggesting that aligning CE initiatives with SCM strategies can drive sustained competitive advantage. These insights are particularly relevant for firms operating in emerging markets, where economic reforms and diversification efforts, such as Saudi Arabia's Vision 2030, necessitate strategic innovation and supply chain optimization.

Machine Learning in Industrial Applications: An In-Depth Review and Future Directions

As industries undergo a paradigm shift towards automation and data-driven decision-making, the integration of machine learning (ML) technologies has emerged as a pivotal force in optimizing industrial processes. This paper presents a comprehensive review of the current state of ML applications in various industrial domains, encompassing manufacturing, logistics, energy, and more. The analysis delves into the diverse methodologies and algorithms employed, highlighting their strengths and limitations. The review explores the impact of ML on enhancing efficiency, reducing costs, and improving overall performance in industrial settings. Real-world case studies demonstrate successful implementations of ML techniques for predictive maintenance, quality control, supply chain optimization, and process optimization. Additionally, the paper investigates the challenges and bottlenecks encountered during the deployment of ML in industrial contexts, such as data quality issues, interpretability, and scalability. Furthermore, this review identifies key trends and emerging technologies shaping the future of ML in industrial applications. Areas such as edge computing, federated learning, and explainable AI are discussed as potential solutions to current challenges. The paper also explores the integration of ML with other emerging technologies like the Internet of Things (IoT) and blockchain to create holistic and robust industrial ecosystems. In conclusion, this in-depth review provides a comprehensive understanding of the current landscape of ML in industrial applications, offering valuable insights for researchers, practitioners, and decision-makers. The outlined future directions aim to guide the development and implementation of ML solutions in the industrial sector, fostering innovation and efficiency in the evolving landscape of smart and connected industries.

Interconnected Supply Chain Management and Logistics: Key to Driving Business Success

Introduction: Supply Chain Management (SCM) and logistics are crucial elements in the success of any business, particularly in today's interconnected global economy. Effective integration of these functions enhances operational efficiency, reduces costs, and creates a competitive advantage.Objective: This paper explores the role of interconnection in supply chain management and logistics, highlighting the importance of seamless collaboration among all stakeholders. With the rise of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), the connectivity between various parts of the supply chain is improving, enabling real-time data sharing and greater responsiveness.Methodology: The Kaggle kernel "gelarerouzbahani/data-analysis-supply-chain-optimization" performs data analysis with a focus on optimizing supply chain processes.Result: By examining case studies and industry trends, this research emphasizes how interconnected supply chains are key to business growth and resilience in a dynamic marketplace.Conclusion: The analysis of the supply chain dataset has provided valuable insights into sales performance, revenue distribution, stock levels, and shipping costs

Research on Supply Chain Optimization of Health Care Vinegar Beverage Company under E-Commerce Model

Research on Supply Chain Optimization of Health Care Vinegar Beverage Company under E-Commerce Model

Optimizing supply chain performance in Somali SMEs: The role of internal and external integration

This study explores how supply chain integration (SCI) affects the performance of small and medium-sized enterprises (SMEs). Data were gathered through surveys from SMEs in Mogadishu, Somalia, involving supply, logistics, and operations managers as well as employees. The findings indicate that SCI significantly enhances performance in areas such as cost efficiency, product quality, delivery timeliness, and operational effectiveness. The study highlights the importance of not only internal integration but also strong connections with suppliers and customers. It adds to existing research by providing empirical evidence on the performance benefits of supply network integration and the factors driving these improvements. The study recommends that managers adopt a comprehensive strategy to strengthen both internal integration and collaboration with external supply chain partners.

Supply Chain Optimization for Environmental Sustainability

Supply Chain Optimization for Environmental Sustainability

A nexus between human capital management and lean supply chain in an organization

This study investigates the impact of key human resource management (HRM) practices—learning and development, recruitment and selection, and performance management—on the lean supply chain performance of organizations. A sample size of 100 participants was selected using a simple random sampling technique, targeting individuals with direct experience in both HR and supply chain management. Data were analyzed using SPSS software, applying regression analysis to examine the relationships between variables. The findings provide insights into how HCM contributes to optimizing supply chain performance, offering practical implications for organizations striving for lean supply chain success. The findings confirm the positive influence of these HRM practices on supply chain efficiency and effectiveness. Learning and development (L&D) emerged as critical for fostering continuous improvement and waste reduction in lean supply chains, equipping employees with the necessary skills to support operational excellence. Recruitment and selection were shown to play a fundamental role in hiring individuals aligned with lean principles such as waste minimization and process efficiency. Performance management further contributed by aligning employee performance with lean objectives, ensuring continuous progress in supply chain performance. The study emphasizes the need for strategic integration of HRM practices into lean supply chain initiatives for sustained organizational competitiveness. Limitations include a small sample size, highlighting the need for future research to expand the respondent pool and explore additional organizational development tools to better define the connection between human capital management and lean supply chains.

INNOVATIONS IN AN AGILE ORGANIZATION AS A DETERMINANT OF FUNCTIONING ON THE GLOBAL MARKET

The aim of the article is to analyze the role of innovation in modern enterprises as a challenge on the global market, taking into account the case study of Tesla. The hypothesis assumes that the ability to implement innovations is crucial to maintaining competitiveness and success on the international market. The Tesla case study shows that successfully innovating requires overcoming many technological, logistical, regulatory and cultural challenges. The most important conclusions indicate the need for continuous investments in research and development, building infrastructure supporting innovation, optimizing supply chains and adapting to local markets and regulations. The added value of the article is to provide practical recommendations for companies that can help effectively manage innovation in the global market.

Oil palm FFB supply chain for wetland and dryland plantations: Case Study in Banyuasin Regency

This research aims to identify the optimal marketing supply chain strategy for palm oil FFB in Banyuasin Regency using the SWOT method. It evaluates strengths, weaknesses, opportunities, and threats in the supply chain, prioritizing the best strategies. Data was collected through questionnaires and interviews, combining quantitative and qualitative insights. The findings place the smallholder palm oil industry in Quadrant 1 of the IFAS (3.28) and EFAS (2.69) matrix, indicating strong internal strengths and external opportunities. The proposed Strength-Opportunity (SO) strategy includes production efficiency, continuous training, adoption of best practices and technologies, and leveraging regulatory support such as subsidies and incentives. This approach aims to optimize distribution, improve FFB quality, and enhance the competitiveness of the smallholder palm oil industry in Banyuasin Regency.

Supplier selection for the halal instant food and export-oriented industry: a case study using ANP

In a competitive business environment, companies must carefully evaluate a range of complex factors to select suppliers that align with operational requirements and market demands. This study employs the Analytic Network Process (ANP) to identify and prioritize critical criteria for supplier selection, including halal certification, quality, sustainability, cost, delivery, flexibility, responsiveness, and technical capabilities. Data were collected through semi-structured interviews with supply chain managers and questionnaire-based surveys, integrating qualitative and quantitative insights. The ANP model was used to analyze the interdependence between these criteria, revealing that quality ranks highest (0.331), followed by halal certification (0.231) and cost (0.157). These findings highlight the priority given to maintaining high product quality and strict adherence to halal standards over short-term cost considerations. Supporting factors such as delivery, flexibility, responsiveness, technical capabilities, and sustainability were also evaluated for their roles in maintaining an efficient supply chain and meeting international logistics requirements. The structured ANP framework provides practical insights for halal instant food manufacturers, enabling them to optimize supply chain management and align with regulatory standards, market demands, and sustainability objectives. This approach enhances export capabilities and strengthens the competitive position of halal food producers in the global market

Big Supply Chain Analytics Enhances Decision-Making: A Literature Review Approach

The way organizations function in today's dynamic market landscape has been changed by the integration of data analytics inside supply chain management. Within the supply chain domain, data analytics pertains to be methodical procedure of gathering, analyzing, and interpreting diverse data kinds produced along the chain in order to extract significant understandings and practical intelligence. It entails applying methods from the fields of descriptive, predictive, and prescriptive analytics to boost operational effectiveness, optimize decision-making, and raise overall performance in the ecosystem of the supply chain. Organizations may make well-informed decisions, spot trends, and proactively handle problems by analyzing data from a variety of sources, including suppliers, manufacturers, distributors, and retailers. This results in more efficient resource allocation and streamlined procedures. Data analytics is playing a more and more important role as businesses look to improve operational efficiency, cut costs, and successfully satisfy customer needs. This study examines the role that data analytics plays in supply chain optimization, helping firms to increase the precision of their forecasts, simplify their logistical procedures, and make well-informed decisions through literature review. Most of the literature that is currently available agrees that big data analytics goes far beyond simply reimagining the supply chain. It may help the next generation of multinational corporations function more adaptably in a setting that is becoming more demanding and unpredictable. This conceptual paper applies relevant review literature and draw conclusions. DOI: https://doi.org/10.52783/pst.1315