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The aim of the supplier selection process is to reduce purchasing risks, boost customer profitability, and cultivate enduring, close relationships between suppliers and buyers. This process stands as a pivotal component of supply chain management (SCM), making supplier selection a critical concern for establishing an effective supply chain system. In this instance, a case study has been presented for supplier selection employing Analytic Hierarchy Process (AHP) in conjunction with the Multi-Criteria Decision Making (MCDM) technique. We have explained it using fuzzy data. It not only contributed on utility of fuzzy AHP methodology, but also provided comprehensive literature review of MCDM problems. Furthermore, by outlining the steps of fuzzy AHP in a straightforward numerical manner, this study can serve as a guide for implementing the methodology in other (MCDM) problems. In this study, we evaluate four suppliers using fuzzy AHP. This method can be used when one has to select one particular supplier from a number of suppliers in a short time span. It will be also helpful to the Pharmacists to select the best supplier who can fulfil all their needs. Despite the abundance of studies on supplier selection, there has been relatively less investigation into the evaluation and selection of suppliers using specific measures tailored to the healthcare sector. To bridge this gap, this research puts forth a guideline for selecting suppliers of baby hygiene products. [ABSTRACT FROM AUTHOR]

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources. [ABSTRACT FROM AUTHOR]

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Abstract Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources.

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI’s influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI’s transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth.

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI's influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI's transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth. [ABSTRACT FROM AUTHOR]

The United Nations Convention on Climate Change 2015 mandates reducing the carbon footprint to reduce global warming. Considering environmental concerns, electric vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an "EV only" scenario by 2030. Two wheelers, with almost 80% of the market share of the Indian automobile industry, will provide the primary thrust for EVs. The holistic sustainability of the Indian EV two-wheeler industry will depend upon its vendor partnerships and processes, which are examined in this paper through a systematic review of the literature available from all known sources. This study, after reviewing over 165 papers apart from government and independent reports, also explores how sustainability and allied topics like green supply chain management in business decision making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimizes risk management strategies and promotes profitability. Considering the restricted availability of the literature on the Indian automobile industry in general, and specifically on the Indian EV or EV two-wheeler industry, this work will help in bringing focus on this area of fast-burgeoning importance and will pave the way for the establishment of a conceptual framework for research. [ABSTRACT FROM AUTHOR]

In today's dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior's originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively. [ABSTRACT FROM AUTHOR]

In light of dynamic market conditions, exacerbated by the global COVID-19 pandemic, there is a pressing need for businesses to enhance their Supply Chain Management (SCM) strategies. The surge in e-Commerce, technological evolution, disruptions in supply chains, and the imperative for collaborative risk management underscore the critical necessity for innovative and resilient SCM practices, forming the basis for this study. This study explores the integrated concepts of supply chain management (SCM) and logistics, focusing on their symbiotic relationship and impact on logistics performance. Emphasizing the significance of efficient logistics in enhancing customer value and cost-effectiveness, the research investigates the potential benefits derived from lean and agile logistics systems. The study employs a comprehensive approach, integrating lean logistics to optimize material flow and reduce waste and costs, while agile logistics enhances flexibility and responsiveness to dynamic demands. The goal is to develop a robust framework for designing a lean-agile supply chain, ultimately aiming to improve supply chain partnerships and overall logistics performance. The findings of this research contribute valuable insights to logistics companies, guiding them toward more effective supply network management. By understanding and leveraging the synergies between lean and agile logistics within the SCM landscape, organizations can enhance their logistics performance and adapt to the evolving demands of the market. This research adds to the existing body of knowledge by elucidating the integrated dynamics of SCM and logistics, specifically emphasizing the coalescence of lean and agile logistics systems. The proposed framework provides a practical guide for logistics companies seeking to optimize their supply networks and improve overall logistics performance. [ABSTRACT FROM AUTHOR]

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods. [ABSTRACT FROM AUTHOR]

Abstract Purpose This research aims to study the impact of sustainable supply chain management on both competitive advantage and organizational performance in the Ethiopian manufacturing industry. Design/methodology/approach The objectives are achieved through collecting and analyzing data from 221 Ethiopian manufacturing industries. This research employs a quantitative approach, specifically descriptive and causal research methods. The data are collected by questionnaires administered directly to a sample of 221 respondents who are managers and supervisors in the manufacturing industry. In addition, data analysis was performed using structural equation modeling in the Smart-PLS Software version (SmartPLS 4.0). Findings The research reveals that SSCM substantially and positively impacts competitive advantage and organizational performance. Furthermore, statistical findings prove the connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly influences the relationship between SSCM and OP. The results suggest that successfully implementing SSCM can improve competitive advantage and OP. Originality/value Considering the triple-bottom-line approach and the mediating effects of competitive advantage, this study is the first to analyze the relationship between SSCM and manufacturing performance in Ethiopia. This study adds to the existing literature by providing empirical evidence on the impact of sustainable supply chain management (SSCM) on competitive advantage and organizational performance in the manufacturing industry of emerging markets. Research limitations/implications The research is based on a cross-sectional study, which may prevent the generalization of findings derived from the current study. The analyzed variable in this study quantified OP, which is widely recognized as a very dynamic concept.

Purpose: This research aims to study the impact of sustainable supply chain management on both competitive advantage and organizational performance in the Ethiopian manufacturing industry. Design/methodology/approach: The objectives are achieved through collecting and analyzing data from 221 Ethiopian manufacturing industries. This research employs a quantitative approach, specifically descriptive and causal research methods. The data are collected by questionnaires administered directly to a sample of 221 respondents who are managers and supervisors in the manufacturing industry. In addition, data analysis was performed using structural equation modeling in the Smart-PLS Software version (SmartPLS 4.0). Findings: The research reveals that SSCM substantially and positively impacts competitive advantage and organizational performance. Furthermore, statistical findings prove the connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly influences the relationship between SSCM and OP. The results suggest that successfully implementing SSCM can improve competitive advantage and OP. Originality/value: Considering the triple-bottom-line approach and the mediating effects of competitive advantage, this study is the first to analyze the relationship between SSCM and manufacturing performance in Ethiopia. This study adds to the existing literature by providing empirical evidence on the impact of sustainable supply chain management (SSCM) on competitive advantage and organizational performance in the manufacturing industry of emerging markets. Research limitations/implications: The research is based on a cross-sectional study, which may prevent the generalization of findings derived from the current study. The analyzed variable in this study quantified OP, which is widely recognized as a very dynamic concept. [ABSTRACT FROM AUTHOR]

Background: In the fourth-year period (2018–2019 to 2021–2022) of the Public Finance Management Act (PFMA) audit, the Auditor-General South Africa (AGSA) reported that the Department of Water and Sanitation (DWS) had incurred approximately R1.7 billion in irregular expenditure; patterns of such expenditure were identified. The AGSA indicated this as a clear non-compliance with the Supply Chain Management (SCM) legislation. Aim: This article evaluated the legislative frameworks to inhibit irregular expenditure and proposed a framework for its management. Setting: The study was conducted in the DWS in South Africa. Methods: The researchers referred to available documentation and adopted a qualitative research approach. Structured interviews were conducted with 10 out of 26 possible participants from the organisational structure of National Treasury, DWS SCM, Financial Management, Internal Audit, Risk Management, and Internal Controls with more than 10 years’ experience in SCM processes, PFMA (1999), Treasury Regulations (2005), Preferential Procurement Policy Framework Act (2000), Public Service Commission (1997), and the manifestation of public decision-making. Results: The ineffectiveness of implemented measures and a lack of consequence to hold officials responsible for transgressions were the primary causes of irregular expenditure. Conclusion: Based on these findings, recommendations aimed to strengthen the procurement process. This includes development and implementation of a standard operating procedure (SOP) manual, and implementing consequences for transgressions. A framework will help manage irregular expenditure and to identify corrective measures. Although the research was limited to the DWS, the results and recommendations are transferable to other departments with comparable challenges. Contribution: The study could help the DWS and other government departments or spheres with similar challenges in managing and reducing irregular expenditure.

Managing various aspects of the Supply Chain (SC) has become increasingly challenging in today’s complex business landscape. To improve profitability, boost sales, and enhance customer satisfaction, it is crucial to explore future possibilities by adjusting key relational parameters. However, traditional forecasting methods often fail to provide accurate insights and are time-consuming. These limitations can be overcome using Artificial Intelligence (AI) algorithms such as Machine Learning (ML) and Deep Learning (DL). CatBoost algorithm is an ensemble-based ML model that can handle categorical variables effectively in its architecture, whereas other ML and DL models fail and require explicit encoding techniques. In this study, a predictive modeling approach using CatBoost to optimize supply chain processes using a mathematical approach was proposed. CatBoost evaluates the model on an e-commerce dataset through empirical analysis by tuning various hyperparameters to enhance prediction efficiency. A computational time limit of ten minutes was used to ensure practicality. Using regression and classification frameworks, this approach involves predicting sales, profit, and delivery times, and identifying potential customers. Consequently, analyzing the behavior of the learning rate and its impact on the performance metrics indicated that increasing the learning rate can lead to improved model performance.

Background: Although Blockchain Technology (BT) is one of the innovations that has considerable potential to improve business processes and enable new services for potential users, its implementation in supply chain management (SCM) of the automotive industry is only at its beginnings. From the growing number of publications focused on this issue, it is evident that the application of BT would significantly contribute to the development of the automotive industry and improve the supply chain of automotive components. Purpose: In this regard, the paper aims to analyze the challenges to the implementation of BT in SCM in the automotive industry sector through a systematic review of the literature and precise definition of the advantages and limitations that appear in supply chains after the application of BT. Study design/methodology/approach: The research is based on the application of systematic literature review methods. The paper presents the results and conclusions of 21 studies based on the search criteria outlined by the Web of Science, Scopus, and SpringerLink index databases. Findings/conclusions: The results suggest that insufficiently developed technology, lack of clear guidelines for implementation, incomplete standardization, legislative ambiguity, conflicts and insufficient cooperation between chain members appear as the biggest challenges for BT implementation. On the other hand, BT has great potential in reducing costs, providing higher quality products and services, and improving chain visibility in the automotive industry. Limitations/future research: The analysis of the papers in the above mentioned databases exclusively in English and the absence of empirical research stand out as the most prominent shortcomings. However, the obtained results of this study represent a quality basis for future research, which, judging by the popularity of the issue, will increase in frequency.

With the rapid advancements in Information and Communication Technologies (ICT) and the widespread enthusiasm of both theoreticians and practitioners, the broader transition to Industry 4.0 (I4.0) in major industries appears imminent. This empirical study analyzes business data from 1140 automotive companies operating in Europe, utilizing various business intelligence platforms and employing decision tree analytics to establish connections between enablers, drivers, company size, and financial resources. The goal is to identify persistent barriers hindering the rational transition to Industry 4.0. The findings reveal an uneven transformation within the industry nexus. While larger companies possess the financial means to allocate collective intelligence, technical resources, and drive necessary for fulfilling I4.0 requirements, smaller members of the nexus lag behind despite their enthusiasm and intent. This imbalanced evolution poses a threat to the comprehensive transformation required for realizing all the benefits of Industry 4.0 within the sector. The primary discovery indicates that small to medium-sized enterprises do not exhibit the same rates of Industry 4.0 adoption, a lag highly correlated with their available financial and human resources for digital transition. The decision tree proposed in this study offers guidelines for achieving an Industry 4.0-compliant nexus. Given its diversity and substantial global impact, the case study from the automotive industry proves intriguing and may later be generalized to other sectors. The study's outcome could empower engineering managers and researchers to implement, execute, and assess the impact of digital strategies based on the financial capabilities of industrial institutions. [ABSTRACT FROM AUTHOR]

To investigate current trends and challenges related to supply chain management in Germany, we conduct an interview-based study involving 16 experts. The study focuses on three areas: supply chain transformations, software challenges, and the adoption of emerging technologies. Supply chain transformations are influenced by increasing supply chain complexity, the growing prevalence of e-commerce, and the increased importance allocated by customers to trust and transparency. Complexity is, in part, due to disruptions caused by policy responses to the pandemic and to software challenges in terms of the adaptability of ERP systems and the cybersecurity of cloud-based solutions. This study is relevant for practitioners, as it sheds light on pressing issues in supply chain management, and for researchers as it uncovers potential topics that require further research in the future. [ABSTRACT FROM AUTHOR]

Information system (IS) is the advent of technology which has helped many sectors of business in earning greater revenue and becoming sustainable in the long-term approach. Information systems are also strengthening the supply chain management (SCM) and the global context for better delivery in meeting consumer demands and generating profits. The study mainly highlights the internet applications which are applicable within the supply chain and the information sharing as well as communicational improvement which has been implemented to the SCM. Additionally, the information system has also enhanced the consumer service, reduction of cost during procurement, and accessibility to worldwide market in the supply chain. [ABSTRACT FROM AUTHOR]

In today’s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior’s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively.

Abstract Companies must be able to identify their suppliers appropriately and effectively in order to survive in the competitive market conditions. In order to fulfill and surpass the expectations of the consumers and clients, companies need to interact with the relevant suppliers. It is a tough manner for companies to select the best supplier from a large number of relevant alternatives. The selection process of the appropriate supplier involves multiple interacting and competing factors. Generally, the selection process and its results cause a waste of time and money. For this purpose, MCDM methodologies are utilized to manage this complex process efficiently. MCDMs allows for consistent and accurate decision-making as well as the selection of the most appropriate supplier. MCDM is one the most preferred tool to select the best alternative under the conflicting and competitive criteria when the evaluations are made in crisp numbers. Therefore, MCDM methods are preferred in various applications in academia and real life. However, the evaluations could not be always possible with crisp numbers, especially in vague environments or evaluations needs qualitative data. This study is one of the first to combine the AHP and COPRAS supplier selection methods with interval-valued Pythagorean fuzzy (IPF) logic. The effectiveness of these IPF-AHP and IPF-COPRAS evaluations for the supplier selection problem is compared and examined. The experimental results of case scenarios show that IPF is an effective way to apply in decision-making applications. In addition, sensitivity analysis is conducted to evaluate the proposed methodologies. According to sensitivity analysis, the IPF-AHP and IPF-COPRAS be able to illustrate the effects of small changings in criteria weights. Therefore, companies can use the IPF-AHP and IPF-COPRAS to assist their decision-makers in identifying and selecting the best suppliers.

The COVID-19 outbreak in 2020–2021 caused unprecedented disruptions to global supply networks. Companies worldwide faced significant challenges as they dealt with the unexpected surge in demand for specific goods and services. This study delves into the importance of supply chain coordination (SCCO), supply chain resilience (SCRE), and supply chain robustness (SCRB), considering supply chain flexibility (SCFL) and Internet of Things and Big Data Analytics (IoT-BDA) integration. We explore how SCFL influences SCCO, SCRE, and SCRB, enhancing supply chain performance (SCFP). Using a cross sectional approach, we collected survey-based responses to ensure comprehensive representation from the supply chain domain. A total of 217 complete responses were collected and analyzed using AMOS 20. The findings suggest that SCCO, SCRE, and SCRB act as mediators between SCFL and IoT-BDA. However, statistical significance between SCCO and SCRB with SCRE was not established. The study emphasizes the robust predictive nature of SCFL, highlighting its pivotal role in fostering SCCO, SCRE, and SCBR through empirical evidence. Furthermore, it emphasizes the influence of SCFL on enhancing SCFP, particularly in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Supply Chain Management (SCM) is a practice that has rapidly spread across industries. SCM may boost output while simultaneously satisfying customers. Despite SCM's recognition as a key factor in enhancing healthcare efficiency, widespread adoption remains in its infancy. Hospitals, a crucial element of the healthcare supply chain (HSC), have failed to fulfill the primary goals of lowering costs and providing high-quality treatment due to their inadequate knowledge of supply chain management (SCM). This research was conducted to fill in the blanks in the current HSC literature. Achieving the healthcare supply chain's goal of reducing costs will be greatly aided by the thorough literature study completed for this report. This review of healthcare supply chain management can quantify the benefits of supply chain initiatives and identify opportunities for improvement. Healthcare institutions can make informed decisions on optimizing their supply chains by understanding customer and supplier needs. This includes making strategic decisions on how to improve inventory management, streamline processes and reduce costs. The focus of this study is on the relationship between supply chain practices, the efficiency of supply chain performance, and the financial outcomes for healthcare organizations. By highlighting certain key research issues that are shared by supply chain management and healthcare management, this article contributes to the literature in both areas.

Blockchain is a groundbreaking technology widely adopted in industrial applications for improving supply chain management (SCM). The SCM and logistics communities have paid close attention to the development of blockchain technology. The primary purpose of employing a blockchain for SCM is to lower production costs while enhancing the system's security. In recent years, blockchain-related SCM research has drawn much interest, and it is fair to state that this technology is now the most promising option for delivering reliable services/goods in supply chain networks. This study uses rigorous methods to review the technical implementation aspects of SCM systems driven by Blockchain. To ensure the security of industrial applications, we primarily concentrated on developing SCM solutions with blockchain capabilities. In this study, the unique qualities of blockchain technology have been exploited to analyze the main effects of leveraging it in the SCM. Several security metrics are utilized to validate and compare the blockchain methodologies' effectiveness in SCM. The blockchain may alter the supply chain to make it more transparent and efficient by creating a useful tool for strategic planning and enhancing connections among the customers, suppliers, and accelerators. Moreover, the performance of traditional and blockchain-enabled SCM systems is compared in this study based on the parameters of efficiency, execution time, security level, and latency. [ABSTRACT FROM AUTHOR]

In recent years, applications of Blockchain technology (BCT) have been progressing at a galloping rate in miscellaneous fields, such as finance, education, travel, healthcare, and insurance. However, BCT has gained much popularity in the construction industry, especially in developed nations worldwide, as it can solve real-world problems, including poor payments, inadequate cooperation and collaboration, inappropriate data sharing among stakeholders, and poor efficiency. The current research employs a bibliometric and systematic literature review (SLR) on utilizing BCT in the construction industry. Using co-occurrence and co-citation studies, network visualization and other methodologies concerning the Web of Science (WOS) database and the research contacts’ patterns were investigated in 482 academic papers. Notable publications, conferences, significant writers, nations, organizations, and funding organizations have been acknowledged. Our research reveals that the primary study topics are BCT in the construction industry, supply chain management, smart contracts, sustainability, building information modeling (BIM), the Internet of Things (IoT) and energy efficiency. Several possible fields for further research are mentioned, including the use of BCT in: (i) circular economy, (ii) risk management, (iii) smart villages, and (iv) infrastructure construction projects.

Supplier Managed Inventory (SMI) can be considered an enabler for supply chain coordination in which the supplier takes over the customer’s inventory to optimize the supply chain. However, the successful implementation of SMI is centered on the high level of trust, accurate data transfer, and efficient interaction between parties. This requires the sharing of information through supply chain stakeholders which face resistance and challenges due to the fear that this information will be revealed to its competitors and transparency of data. This paper has investigated the application of Blockchain technology and its potential for successful SMI implementation. The paper has proposed a Blockchain framework for the coordination of suppliers and customers. The framework includes a mathematical model for multiple supplier-customer order fulfillment which is embedded in the blockchain framework. The paper has demonstrated case studies to evaluate the performance of the proposed model with literature discussing the details of its blockchain framework.