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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]

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]

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 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]

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.

The developments of the fourth industrial revolution have caused changes in all areas of society, including production. The changes in production caused by the fourth industrial revolution have also resulted in fundamental changes in the supply chain and have converted it to supply chain 4.0. Organisations must be receptive to supply chain 4.0 to maintain their competitive advantage. Therefore, this study aimed to investigate the relationships among supply chain 4.0 technologies so that, by learning and understanding these connections, industries can pave the way for the implementation of these technologies in their supply chains and use them in problem-solving. The literature review was used to identify the supply chain 4.0 technologies, and the Delphi technique was applied to extract them, including the Internet of Things (IoT), cyber-physical systems, cloud computing, big data, blockchain, artificial intelligence, Radio-frequency Identification (RFID), augmented reality, virtual reality, and simulation. The relationships of supply chain 4.0 technologies were examined using the DEMATEL technique and based on interpretive structural modelling (ISM), their deployment map was drawn. The type of technologies was determined using the MICMAC method. The MICMAC analysis found that the artificial intelligence technology is independent and, based on the findings through the DEMATEL technique, this technology is related to simulation, which belongs to the first level of the interpretive structural modelling technique, and IoT, cloud computing, big data, and blockchain technologies, which are at the second level. Based on the ISM method, RFID, virtual reality, augmented reality and simulation technologies are located at the first level; IoT, cyber-physical systems, cloud computing, big data and blockchain technologies are situated in the second level; and artificial intelligence technology belongs to the third level. According to the related literature, few studies have been conducted on the issues of supply chain 4.0 and the technologies that affect it.

This study focuses on the barriers to implementing supply chain management (SCM) 4.0 in the foundry industry and provides insights into its practical implementation in foundry units. Our study identifies barriers such as data privacy, legal issues, SCM complexity, and barriers related to implementing Industry 4.0 (I4.0)-enabled SCM in the foundry industry. It also provides insights into the role of digital infrastructure in building effective I4.0-enabled SCM. This study provides insights into how SCM 4.0 can be implemented in the foundry industry. The findings will be helpful to policymakers and foundry consultants in designing appropriate strategies for foundry units. [ABSTRACT FROM AUTHOR]

This paper is written to propose a new architecture of the smart manufacturing system from the perspective of information system theory by synthesizing the achievements and experiences gained in the process of researching and developing the smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea. Verifying the structure of a smart manufacturing system suitable for the situation of the Democratic People's Republic of Korea is an essential research task that presents the goals to be achieved in the R&D and introduction of smart manufacturing systems, which are put forward by different R&D groups. Based on a global study, synthesis, and analysis of the architectural features of smart manufacturing systems required by enterprises, the authors have identified a new structure of smart manufacturing system that meets the actual conditions of the Democratic People's Republic of Korea by verifying its rationality through the practical process of studying, developing, and constructing the smart manufacturing system in the digital book editing (virtual production) and printing (physical production) of a book printing factory. The findings are that (a) a smart manufacturing system is an informative and organic combination of management information systems and the internet of things with cyber-physical system as the core; (b) a product lifecycle management system is a comprehensive information integration tool of production and management related information systems to optimize the production and management activities of enterprises and put them on a high scientific and technological basis, and its implementation is essential; and (c) research on the smart manufacturing system should be continuously conducted to meet the demands of enterprise management practice that are constantly faced with new theoretical, practical, and information technology challenges, and such research results should be realized at a high level based on strong research, development, and introduction forces in the future. The finding of the new architecture of smart manufacturing system has been made in the context of the research on the smart manufacturing system, and it will serve as the theoretical basis for the smart manufacturing system to be built in various types of enterprises in the Democratic People's Republic of Korea. [ABSTRACT FROM AUTHOR]

The fierce competition in international markets and the rapid advancements in information technology result in shorter lead times, lower transportation capacity, and higher demand. The supply chain network is one of the most crucial areas of concentration in the majority of business circumstances. Blockchain technology is a promising option for safe information exchange in the supply chain network. Although preserving security at every level of the blockchain is somewhat important, cryptographic methodologies are frequently used in the existing works. The novel perceptive craving game search (PCGS) optimization algorithm is used to optimally generate the key for data sanitization, which assures the privacy of logistics data. Here, the original logistics data obtained from the manufacturer is sanitized with an optimal key generated by using the PCGS optimization algorithm, avoiding the risk of unauthorized access and data swarm that causes the system to lag. Moreover, the sanitized data obtained from the manufacturer is transmitted to the allowed parties via different sub-chains. The same generated key is used on the receiving customer side for reconstructing the original information from the sanitized data. The performance and results of the proposed blockchain-based privacy preservation model are validated using various parameters. [ABSTRACT FROM AUTHOR]

A blockchain is a decentralised, unchangeable ledger that makes it easier to track assets and record transactions in a corporate network. In a blockchain network, anything valuable may be recorded and traded, lowering the risk and increasing efficiency for all parties. It has the potential to drive cost-saving efficiencies and enhance the consumer experience through traceability, transparency, and tradeability. Blockchain-based Supply Chain Management (SCM) can provide services to participants to inquire about product details, cost, quantity, quality, availability of products, location and other significant information. It provides transparency and agreement-outsourced contract manufacturing and enhances an organization's position as the main leader in responsible manufacturing. In this study, blockchain and its effects on SCM are discussed, along with security issues and solutions. [ABSTRACT FROM AUTHOR]

Purpose – Mismanagement and corruption in disaster relief operations (DROs) have created a demand for transparency and visibility in humanitarian logistics and supply chains. The global relief organisations and recent research endorse the adoption of digital solutions in DROs. The purpose of this research is to examine the moderating role of digitalisation in enhancing transparency in humanitarian logistics and supply chains of DROs in Pakistan. Design/methodology/approach – Employing the quantitative research method, the data were collected from 340 disaster relief workers through survey questionnaires using the snowball sampling technique. The data were analysed in the SmartPLS3 software of PLS-SEM. Findings – The findings suggested that in Pakistan, where corruption and mismanagement in humanitarian logistics and supply chain have been the greatest concerns of all the stakeholders, digitalisation of the DROs is a way forward to create transparency in the system and build the trust of the donor organisations and public. Research limitations/implications – The sample included only 340 disaster relief workers, future researchers may test the proposed model on a larger sample size and from different stakeholders' perspectives such as the disaster victims, government agencies and NGOs. Social implications – The ultimate beneficiaries of a digitalised and transparent humanitarian logistics and supply chain will be the society as a whole and particularly the victims of the disasters. By adopting the appropriate technologies in DROs, the victims will receive timely and entitled resources, and early warnings will save many lives. Originality/value – The paper contributes to the body of knowledge by providing the first empirical evidence of examining the moderating role of digitalisation in creating transparency in humanitarian logistics from one of the top ten most disaster-affected nations.

The integration of supply chain has always been a challenge for many companies. Despite the agreement on the strategic importance of supply chain integration, limited researches have been done on the relationship between supply chain integration and its impact on performance. In this study, sample size was 75 managers and experts from agriculture supply chain sector. Hence, interpretative structural modeling and structural equation modeling were used for analyzing the data (smart PLS). The results infer that the integration of marketing and supply chain management would improve supply chain performance through some factors. The results indicated Knowledge Management (KM), Human Resource Management (HRM), Information Technology (IT), marketing planning and supply chain planning are the most important indicators in improving Guar supply chain management. The Guar supply chain performance would be improved through the relationship between IT and HRM. [ABSTRACT FROM AUTHOR]

Fast-growing technology and the development of IT services have yielded the idea of founding a new application of stochastic processes and their properties. We give a new connection between electronic process management and a relatively new stochastic process named the Non-central Polya-Aeppli process. This process is applied as a counting process in the mathematical construction of the given model, and it has been introduced as a counting process in electronic process management. [ABSTRACT FROM AUTHOR]

Due to its similarity to industrialized products, the offsite construction industry is seen as a focus for the transformation of Construction 4.0. Many digital technologies have been applied or have the potential to be applied to realize the integration of design, manufacturing, and assembly. The main objective of this review was to identify the current stage of applying digital technologies in offsite construction. In this review, 171 related papers from the last 10 years (i.e., 2013–2022) were obtained by collecting and filtering them. They were classified and analyzed according to the digital twin concept, application areas, and specific application directions. The results indicated that there are apparent differences in the utilization and development level of different technologies in different years. Meanwhile, the introduction, current stages, and benefits of different digital technologies are also discussed. Finally, this review summarizes the current popular fields and speculates on future research directions by analyzing article publication trends, which sheds light on future research. [ABSTRACT FROM AUTHOR]

Customer service plays a vital role in organizations. It can prove helpful to companies looking for having a competitive edge over others. With main objective of communicating at all stages with clients, it can also assist in resolving complex situations in a subtle manner. This research paper is based on secondary resources and examines the working platform, process flow and importance of logistics service providers with respect to client retention, loyalty and perception of service quality of logistics versus actual services offered by logistics service provider (LSP) to their customer (exporters/ importers). The derivation of service quality gap is client expectation minus the service offering to them. This paper highlights customer service aspects important for any logistics service provider to sustain in long run. The outcome of this study uncovers impact of customer service on logistics company and their customers' business performance. [ABSTRACT FROM AUTHOR]

Improving the quality of supply chain performance in manufacturing sectors, factories and businesses are one of the most important objectives of managers in each country. The economic future of each country depends on the performance of the supply chains. Also, the increasing development of the Internet of Things (IoT) has an important role in facilitating the process of businesses in different parts of the supply chain. Internet of things has many applications in this area. The aim of this study is to identify and prioritize the internet of things applications in supply chain management (SCM) with multi criteria decision making (MCDM) approach. In this research, COPRAS, a MCDM method has been used. The evaluation criteria are derived based on the opinions of experts in the Iranian society of information and communication technology and managers working in the companies that are using the IoT. These criteria are selected by thematic analysis method. The research method is applicable in terms of purpose and is descriptive in terms of means of data collection. Research shows that "monitoring and control of perishable food and medicine", "maintaining employee health in different industries", "smart manufacturing line and smart devices," and “smart maintenance and repair” are the most important application of IoT in SCM

Limited resources in the current competitive world have motivated the companies to use from technological tools in order to increase their productivity and efficiency. In this context, identification and selection of suppliers of raw materials and consumer and capital goods in production processes and services are a long step in achieving the goal of survival and excellence. On the other hand, organizations regard as a network of different companies in the supply chain that work together in order to create more added value for the final customer or consumer. Increasing added value by enhancing quality and reduces costs. Suppliers dramatically affect corporate performance in terms of price, quality, technology, and delivery. Therefore, in this study, practical criteria for the selection of suppliers in an Electricity Distribution Company are prioritized using an Analytical Hierarchical Process. An integration of the Analytical Hierarchy Process and Goal Programming use to goal restrictions in allocating an optimal order quantity to each supplier. In following, an integration of AHP and GP use in the case study to supply 500,000 m low-voltage three-phase self-sustaining cable (3 *50 + 25 + 25) to allocate an optimal order quantity to each supplier. The results indicated that a better solution could achieve using an interactive model. Overall, this study provided an appropriate model for using limited financial resources to provide better services to achieve a strategic and comparative advantage. Research results are also applicable in the following areas: 1. Provide appropriate solutions to increase the company's profitability by reducing supply costs. 2. Planning for investment and exploitation in the company's marketing department and supply of consumer goods. 3. Identify barriers in purchasing consumer goods and equipment and their impact on the proper marketing of the company. 4. Application of research in the supply of consumer goods in the electricity distribution company of Khorasan Razavi province and other large service and production companies and future researchers whose information is not accurate and whose criteria are diverse and vague. [ABSTRACT FROM AUTHOR]

Much research has been conducted on the effects of COVID-19 on company and supply chain resilience. However, few contributions have focused on small and medium-sized enterprises. These companies are claimed to be the drivers of economic growth but often lack access to resources and alternatives when interruptions occur, making them a bottleneck for supply chains. Using a multiple case study approach, this paper links resilience theory to the design of the relationships between eight German small and medium-sized enterprises and their suppliers and customers. It analyzes the way in which these companies combine contractual and relational investments across their supply chain flows of product, finance, and information in order to improve resilience. Company representatives were interviewed on three occasions between June 2018 and December 2020, that is, before COVID-19 and during the lockdowns. The results of the case study explain why and how companies of this type have been able to anticipate and manage the crisis. The interviews revealed that those companies that made the largest investments in the relational aspects of their partnerships while safeguarding product and financial flows through contracts performed best. In principle, contractual investments are higher in partnerships with suppliers. However, the precise combination of contractual and relational investments depends on the business model, the business philosophy of the CEO, and the allocation of power within the supply chain. These findings indicate that, when collaborating with small businesses, supply chain partners should focus on building relationships in order to create resilience in the supply chain. [ABSTRACT FROM AUTHOR]

The effects of climate change can be seen immediately in ecosystems. Recent events have resulted in a commitment to the Paris Agreement for the reduction of carbon emissions by a significant amount by the year 2030. Rapid urbanisation is taking place to provide room for an increasing number of people's residences. Increasing the size of a city and the number of people living there creates a daily need for consumable resources. In the areas of transportation, supply chains, and the utilisation of renewable energy sources, deliver on pledges that promote the accomplishment of the Sustainable Development Goals established by the United Nations. As a result, the supply chain needs to be handled effectively to meet the requirements of growing cities. Management of the supply chain should be in harmony with the environment; nevertheless, the question of how to manage a sustainable supply chain without having an impact on the environment is still mostly understood. The purpose of this study is to present a conceptual model that may be used to maintain a sustainable supply chain with electric vehicles in such a way that caters to both environmental concerns and human requirements. As part of the continual process of achieving sustainability, interrelationships between the various aspects that are being investigated, comprehended, and applied are provided by the model that was developed. It is self-evident that governmental and international organisations that are concerned with supply-demand side information will benefit from such a model, and these organisations will locate viable solutions in accordance with the model's recommendations. Beneficiaries consist of individuals who are active in the supply chain and are concerned with supply-demand side information. These individuals also need to understand how to effectively manage this information. [ABSTRACT FROM AUTHOR]

Since the global financial crisis of 2008, research on supply chain finance (SCF) based on supply chain management (SCM) has increased rapidly. The context of SCF development is continuously changing, which means that it cannot function in isolation and financial ecology must be taken into consideration. Previous research has shown that comprehensive SCF studies incorporating financial ecology are lacking; although it was mentioned, it was at a descriptive level with fragmented dimensions, limiting the broader understanding of SCF. Therefore, to address this research gap and reveal future study prospects, we conducted a systematic literature search, focusing on 132 selected papers published between 2002 and February 2022. The present study provides insights into the development stages of SCF, and the understanding of 'Supply Chain' and 'Finance' in the context of the financial ecosystem. This study reiterates the necessity of studying SCF from a financial ecology perspective and contributes to the SCF ecosystem understanding framework, bridging the theoretical inadequacies in investigating SCF. [ABSTRACT FROM AUTHOR]

The Supply Chain Management (SCM) of agricultural products (AP) is an important part of modern logistics, and cultivating talents with rich theoretical knowledge and practical operation skills of the SCM of agricultural products is particularly meaningful for China as a major agricultural country. However, at present, few studies have concerned about the training path for such talents, so to fill in this research blank, this paper studied the training path for AP-SCM talents and the assessment of their training quality, aiming to find and solve the defects in the existing training programs. At first, this paper proposed training path for AP-SCM talents and attained the conclusion that colleges and universities should pose requirements for AP-SCM talents from 9 training directions. Then, a prediction model was built for assessing the training quality of AP-SCM talents, and the specific model framework and modeling principles were given. At last, experimental results verified the effectiveness of the prediction made by the proposed model. [ABSTRACT FROM AUTHOR]