Research of Guanxi in Transport Supply Chain and Value Chain Issues

Clarification of a term transport chain

Due to the emergence of the global economy and increased competition, many of the modern fishing companies have recognized that their fisheries rapid product introduction and service innovation to market the importance of supply chain management. To improve their competitiveness, many modern fishing companies have accepted supply chain management to improve organizational effectiveness and achievement of organizational objectives, increase customer value, better use of resources, and improve profitability. Consider adhere to enterprise operational efficiency to improve collaboration and client response in the modern fisheries management advocated by business partners, an additional thrust towards a successful competitive strategy. Supply chain management in the fisheries industry has become part of the agenda of the senior management of the fishery production and the retail industry. Appropriate mechanism to optimize fisheries logistics supply chain design and effective fisheries management method is a reference to the fisheries in this article the major retail logistics supply chain management. Soft computing technology based on the proposed new program, and effectively solve the problems by a variety of dynamic segment of the logistics supply chain of the fisheries of the major retail companies.

Contributors

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. Methods: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. Results: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. Conclusions: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages.

Roberto Baldacci (“ An Exact Algorithm for the Pickup and Delivery Problem with Time Windows ”) is a researcher in operations research at the Department of Electronics, Computer Science, and Systems (DEIS) of the University of Bologna, Italy. His major research interests are in the areas of transportation planning, logistics and distribution, and the solution of vehicle routing and scheduling problems over street networks. His research activities are in the theory and applications of mathematical programming including the design of new heuristic and exact methods for solving routing and location problems. Enrico Bartolini (“ An Exact Algorithm for the Pickup and Delivery Problem with Time Windows ”) holds a postdoctoral position at the University of Bologna. His research activity concerns the study and development of heuristic and exact algorithms for solving combinatorial optimization problems with applications in logistics and distribution systems, in particular network design problems and some generalizations of the vehicle routing problem. Saif Benjaafar (“ Optimal Control of an Assembly System with Multiple Stages and Multiple Demand Classes ”) is professor of industrial and systems engineering at the University of Minnesota, where he is also founding and current director of the Industrial & Systems Engineering Program, director of the Center for Supply Chain Research, and a faculty scholar with the Center for Transportation Studies. He was a Distinguished Senior Visiting Scientist at Honeywell Laboratories and a visiting professor at universities in France, Belgium, Hong Kong, China, and Singapore. His research is in the areas of supply chain management, service and manufacturing operations, and production and inventory systems, with a current focus on sustainability and environmental modeling. He serves on the editorial board of several journals including Manufacturing & Service Operations Management, Production and Operations Management, Naval Research Logistics, and IIE Transactions. He is a Fellow of the Institute of Industrial Engineers (IIE). Dimitris Bertsimas (“ Performance Analysis of Queueing Networks via Robust Optimization ”) is the Boeing Professor of Operations Research and codirector of the Operations Research Center at the Massachusetts Institute of Technology. This research is part of his work in the last decade on robust optimization for optimization and performance analysis of stochastic systems. Atul Bhandari (“ Revenue Management with Bargaining ”) is manager of the Algorithms Team at SmartOps. He supervises the design and development of enterprise inventory optimization algorithms, supervises modeling and analysis support for sales and implementation efforts, and leads educational sessions. He earned a Ph.D. in operations research from the Carnegie Mellon University Tepper School of Business. Sushil Bikhchandani (“ An Ascending Vickrey Auction for Selling Bases of a Matroid ”) is professor of decisions, operations, and technology management at the Anderson School of Management at the University of California, Los Angeles. He is interested in the economics of incentives and its application to auctions, market institutions, and social learning. J. Paul Brooks (“ Support Vector Machines with the Ramp Loss and the Hard Margin Loss ”) is an assistant professor of operations research in the Department of Statistical Sciences and Operations Research and a fellow of the Center for Study of Biological Complexity, Virginia Commonwealth University. He is currently secretary/treasurer of the INFORMS Section on Data Mining. His research interests include the design of optimization-based algorithms for data mining and their application to biomedical data. He is also interested in applications of optimization to models of cellular metabolism and network design problems. Sungyong Choi (“ A Multiproduct Risk-Averse Newsvendor with Law-Invariant Coherent Measures of Risk ”) is an instructor in the Department of Management Science and Information Systems at Rutgers University. Dr. Choi's research interests are in the area of stochastic modeling and its application in supply chain management. Milind Dawande (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ” and “ Quantifying the Impact of Layout on Productivity: An Analysis from Robotic-Cell Manufacturing ”) is professor and area coordinator of operations management at the School of Management, University of Texas at Dallas. His research interests are in discrete optimization problems in manufacturing and operations. His papers have appeared in a number of research outlets, including Operations Research, Management Science, Manufacturing & Service Operations Management, and the INFORMS Journal on Computing. Mehmet Demirci (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ”) is a supply chain sales engineer at SmartOps. He holds a Ph.D. degree in industrial engineering from the University of Pittsburgh. His research interests include inventory optimization, operations management, large-scale combinatorial optimization, and operations research applications in health care. Sven de Vries (“ An Ascending Vickrey Auction for Selling Bases of a Matroid ”) is a professor of operations research in the Department of Mathematics at the Universität Trier. His research interests include combinatorial optimization and auctions. Xiaowei Ding (“ A Top-Down Approach to Multiname Credit ”) is an associate at Morgan Stanley's Commodity Trading Group. Mohsen ElHafsi (“ Optimal Control of an Assembly System with Multiple Stages and Multiple Demand Classes ”) is a professor at the Anderson Graduate School of Management at the University of California, Riverside, where he also serves as associate dean and graduate advisor. He holds Ph.D. and M.Sc. degrees from the Industrial and Systems Engineering Department at the University of Florida and was the Honor Graduate. He received the Qualified Engineer degree, with honors, from the Ecole Nationale d'Ingénieurs de Tunis, Tunisia. His area of research includes operations and supply chain management, manufacturing and service operations, and production and inventory systems. Amr Farahat (“ A Comparison of Bertrand and Cournot Profits in Oligopolies with Differentiated Products ”) is an assistant professor at the Johnson Graduate School of Management at Cornell University. He obtained his doctoral degree in operations research from the Massachusetts Institute of Technology. His current research focuses on differentiated product pricing, inventory management, and competition. He is interested in problems at the interface of operations management, economics, and marketing. Vivek F. Farias (“ The Irrevocable Multiarmed Bandit Problem ”) is the Robert N. Noyce Career Development Assistant Professor of Management at the Sloan School of Management and the Operations Research Center at the Massachusetts Institute of Technology. His research focuses on revenue management, dynamic optimization, and the analysis of complex stochastic systems. The paper in this issue is part of the author's research in the context of dynamic optimization. David Gamarnik (“ Performance Analysis of Queueing Networks via Robust Optimization ”) is an associate professor of operations research at the Sloan School of Management of the Massachusetts Institute of Technology. His research interests include applied probability and stochastic processes, theory of random combinatorial structures and algorithms, and various applications. He currently serves as an associate editor of Annals of Applied Probability, Operations Research, Mathematics of Operations Research, and queueing systems journals. Srinagesh Gavirneni (“ Production Planning with Patterns: A Problem from Processed Food Manufacturing ”) is an assistant professor of operations management in the Johnson Graduate School of Management at Cornell University. His research interests are in the areas of supply chain management, inventory control, production scheduling, simulation, and optimization. His papers have appeared in Management Science, Manufacturing & Service Operations Management, Operations Research, European Journal of Operational Research, Operations Research Letters, IIE Transactions, and Interfaces. Previously he was an assistant professor in the Kelley School of Business at Indiana University, the chief algorithm design engineer of SmartOps, a software architect at Maxager Technology Inc., and a research scientist with Schlumberger. His undergraduate degree from IIT-Madras is in mechanical engineering, and he received an M.Sc. from Iowa State University and a Ph.D. from Carnegie Mellon University. Kay Giesecke (“ A Top-Down Approach to Multiname Credit ”) is assistant professor of management science and engineering at Stanford University. His research and teaching interests are in financial engineering. Lisa R. Goldberg (“ A Top-Down Approach to Multiname Credit ”) is executive director of analytic initiatives at MSCI Barra with responsibility for developing and prototyping financial risk and valuation models. Randolph W. Hall (“ Discounted Robust Stochastic Games and an Application to Queueing Control ”) is vice president of research, and professor of industrial and systems engineering, at the University of Southern California. After receiving a Ph.D. in civil engineering from the University of California, Berkeley, he has held research and faculty positions at General Motors, the University of California, Berkeley, and the University of Southern California, including dir

SCM is intended to reduce uncertainty in the business environment related to the supply chain. SCM is necessary to efficiently manage and reduce costs incurred in the supply chain, such as logistics costs. SCM is meaningful in supplying products and services that can satisfy the needs of end consumers at a minimum cost. SCM refers to all the processes of organizing the connections formed to provide the desired product or service to achieve maximum performance with resources. Supply Chain refers to the process of connecting supply activities such as consumers-retailers-wholesalers-manufacturers-parts and raw material suppliers. Supply Chain Management is to reduce uncertainty in the business environment related to the supply chain. In addition, SCM is necessary to efficiently manage and reduce costs incurred in the supply chain, such as logistics costs. The business functions of SCM are as follows. First, it is a function that determines the transportation process as a supply chain function that determines the service and cost structure. Second, in SCM, planning is an important function, and it is determined in logistics transportation and warehouse management personnel allocation planning tasks. Third, the goal of SCM is to efficiently achieve quality, cost, delivery, and safety, which belong to the execution task. Fourth, SCM can continuously improve while setting and evaluating key performance indicators. This research purpose is to inspect the force of SCM activities on competence, cooperation and business performance. To this end, a survey was conducted targeting employees of domestic companies where SCM activities are taking place. In this study, SCM Activities, Competency, Cooperation, and Business Performance were selected as four variables by referring to previous studies. Based on this, four research hypotheses were composed as “SCM Activities ⇒ Competency, Activities ⇒ Cooperation, Competency ⇒ Business Performance, Cooperation ⇒ Business Performance”. The survey was conducted on a Likert 5-point scale for each item, and reliability, importance, validity, and correlation analysis were verified. In this study, SPSS 23.0 was used for this analysis. A company's SCM activities are expected to ultimately lead to business performance. As a research result, it was detected that the independent variable SCM activities had a significant effect on the parameters competency and cooperation. Competency appeared significantly in the independent variable business performance. However, Cooperation was found to be insignificant to business performance. Competency and cooperation will improve in companies with active SCM activities. As a result of the empirical analysis, the following items should be considered in order to improve SCM activities. We share costs and profits with our business partners. We jointly manage our business partners and distribution networks. We share technical information and training with business partners. We utilize the knowledge and information we obtain from trading companies in a variety of ways. In order to improve management performance, which is the purpose of this study, the following two items should be considered. Profitability is improving due to trading companies. Sales are increasing due to trading companies. And SCM activities are ultimately expected to have a favorable impact on business performance.

COVID‐19, trade and trade policy

Due to the emergence of the global economy and intensified competition, many modern firms in the fishery industry have recognized the importance of managing their fishery supply chains for fast product introduction and service innovations to the markets. For improved competitiveness, many modern firms in the fishery industry have embraced the supply chain management to increase organizational effectiveness and achieve such organizational goals as improved customer value, better utilization of resources, and increased profitability. Considering for increased efficiency in enterprise operations persists, modern management in the fishery industry advocates the collaboration among business partners and the responsiveness to client is an additional thrusts towards a successful competitive strategy. Supply chain management in the fishery industry has become part of the senior management agenda in the fishery manufacturing and retailing industries. Suitable optimized fishery logistics supply chain design mechanisms and effective fishery operating management method are modeled for the problems of the major retailing corporation logistics supply chain management in the fishery industry in this paper. Novel scheme based on soft computing technique is proposed and employed to efficiently solve problems occurring in various dynamic segments of the major retailing corporation logistics supply chain in the fishery industry.

The types of artificial intelligence, artificial intelligence integration to the food value and supply chain, other technologies embedded with artificial intelligence, artificial intelligence adoption barriers in the food value and supply chain, and solutions to overcome these barriers were analyzed by the authors. It was demonstrated by the analysis that artificial intelligence can be integrated vertically into the entire food supply and value chain, owing to its wide range of functions. Different phases of the chain are affected by developed technologies such as robotics, drones, and smart machines. Different capabilities are provided for different phases by the interaction of artificial intelligence with other technologies such as big data mining, machine learning, the Internet of services, agribots, industrial robots, sensors and drones, digital platforms, driverless vehicles and machinery, and nanotechnology, as revealed by a systematic literature analysis. However, the application of artificial intelligence is hindered by social, technological, and economic barriers. These barriers can be overcome by developing the financial and digital literacy of farmers and by disseminating good practices among the participants of the food supply and value chain.

A survey on control theory applications to operational systems, supply chain management, and Industry 4.0

КОМПЛЕМЕНТАРНІСТЬ СТРАТЕГІЙ МАРКЕТИНГУ ТА ЛОГІСТИКИ В ЛАНЦЮГУ ПОСТАВОК ТОВАРІВ ПОВСЯКДЕННОГО ПОПИТУ

In the current financial crisis, the business risk has been exacerbated. Mergers and acquisitions has become a hot issue. In knowledge management process of the mergers and acquisitions, Corporate concerns are about the risks of knowledge management in supply chain. This paper aims to develop a qualitative risk model with the data of Yangtze River Delta of China, to empirically identify the important risk factors of knowledge management for the supply chain logistics in mergers and acquisitions. Starting with the importance of knowledge management risks in supply chain logistics, then, paper proposed an optimization of the risk management process, a new paradigm for risk of knowledge management in supply chain planning and logistics control system is required in mergers based on risk management theory.

The study is an exploratory exercise to assess the transparency and quality of supply chain reporting through ESG factors. A qualitative thematic content analysis of sustainability and integrated reports was employed on a sample constituted by Romanian listed companies, following the topic of supply and value chain reporting and transparency. The textual analysis was carried out with NVivo 15 after manual data gathering of reports publicly available for 2024. Afterward, a qualitative assessment was employed on the quality of supply and value chain reporting. For both qualitative assessments, the scoring method was used. The qualitative analysis revealed that the sustainability themes regarding “Value chain policies and codes of business conduct” and “ESRS value chain reporting” are the most transparently presented, obtaining the highest scores, followed by “Supply chain impact” and “Due diligence and ESG risk management in the value chain”. The least transparent topics are “ESG criteria integrated into supply chain management and suppliers selection” and “Communication and grievance mechanisms for value chain workers”. The supply chain reporting quality was assessed through the lens of robust points and weaknesses. The results showed that three companies have a good to outstanding quality of reporting, with a clear understanding of ESRS requirements and concrete steps integrated to improve supply chain transparency through ESG reporting. The paper enriches the knowledge in the field of assessing the supply chain ESG reporting transparency by adding value through the thematic qualitative analysis and evaluation of supply chain reporting quality, following the ESRS reporting framework. The results of the study can be helpful in the process of improving supply chain reporting, integrating ESG, and increasing the transparency of information by offering managers possible options for developing appropriate reporting tools.

In a government-funded research project into the implications of digitization for book publishing in Australia, the researchers tested for the presence of global issues and trends. With a focus specifically upon book publishing to the exclusion of newspaper and journal publishing these included: revenue trends; competition; outsourcing; potential benefits of digital publishing; critical success factors for digital publishing; supply chain issues; value chain issues, business models and expectations for the future. An online survey and follow-up interviews found that technologies such the Internet and the World Wide Web, along with those for production and rights management were playing a significant role in book publishing. However, the major focus among book publishers was on business and organizational issues. There was widespread realization of the need to respond to competition from inside and outside the industry, including competition for the leisure time of users, with direct implications for value chains and business models. Key organizational changes identified included changes in structures and strategies, in human resource practices, and in cultures. The main benefits anticipated from digital technologies were in the areas of new niche markets, repackaging and repurposing of existing content, consumer-generated content and the enhancement of value chains. It is therefore, imprudent to only consider the impact of emerging technology as the fundamental in the ongoing development of digitization in book publishing, as other considerations such as demographics, social and economic factors are also essential ingredients.

Supply chain and logistics issues of bio-energy production