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1
Luo, Haobo. "The Impact of Value Chain Embedding on Industrial Structure Optimization." Journal of Education, Humanities and Social Sciences 35 (July 4, 2024): 156–61. http://dx.doi.org/10.54097/rm5g2375.
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Value chain embedding refers to the participation of enterprises in the activities of the global value chain and the benefits derived from it. Existing studies have shown that value chain embedding can bring multiple benefits, including improving production efficiency, reducing costs, enhancing product quality, and innovation capabilities, among others. However, the specific impact of value chain embedding on industrial structure optimization is still controversial in current literature. This article analyzes the impact of value chain embedding from the perspectives of manufacturing industry, service industry, industrial sector, and developing countries. By using a literature review approach combined with existing findings, this article summarizes the impacts of value chain embedding and proposes relevant suggestions to promote sustainable economic development. This study has important practical significance for enterprises to effectively embed value chains in their business processes and achieve efficient development.
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Alomar, Madani Abdu. "Performance Optimization of Industrial Supply Chain Using Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (July 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/9306265.
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Nowadays, organized retailing has witnessed a newer trend in the upcoming generations. Globally, these changes are attributed to growing family income, increased female participation, the transformation from joint to nuclear family structure, and technological advancements. Moreover, other variables such as lower supply chain costs, growing sales, rising consumer demands, changing market structure, and increasing competition also influenced supply chain networks. It is observed that the organizational nonlivestock supply chain performance is affected by strategic, operational, and environmental aspects. AI is helping to deliver powerful optimization capabilities, which are required for more accurate capacity planning, improved productivity, high quality, lower costs, and greater output, all while fostering safer working conditions. These benefits are all made possible thanks to the introduction of AI in supply chains. By conducting a comprehensive analysis of the relevant previous research, the purpose of this work is to determine the specific contributions that artificial intelligence (AI) has made to supply chain management. This research attempted to discover the present as well as possible AI strategies that may increase both the study of Supply Chain Management as well as the practice of it. This was done in order to solve the current scientific gap of AI in Supply Chain Management. It was also found that there are holes in the existing study that need to be filled by more scientific investigation. To be more exact, the following four facets were discussed: (1) the AI approaches that are most often used in Supply Chain Management; (2) the AI techniques that have the potential to be used in Supply Chain Management; (3) the Supply Chain Management subfields that have benefited from the application of AI so far; and (4) the subfields that have a high potential to be improved by AI. Identifying and evaluating articles from the four supply chain management domains of logistics, marketing, supply chain management, and manufacturing require the use of a predetermined set of inclusion and exclusion criteria. In this study, insights are provided via the use of methodical analysis and synthesis. A better understanding of these parameters not only improves the nonlivestock supply chain processes but also ensures competitive advantage. The present research aims to test the following elements that including supply chain speed, customer retention, supply chain management integration, and various management. The proposed work categorizes the performance of the supply chain using the Improved Feed Forward Network with Particle Swarm Optimization technique. Results indicate that inventory management, customer happiness, profitability, and client base identification are listed as competitive advantage elements. On the other hand, stakeholder satisfaction, innovation and learning, market performance, customer satisfaction, and financial success are the six recognized organizational performance criteria. Resultantly, the overall performance metrics of the proposed work is 94.12%, while accuracy rate, specificity, and sensitivity rate are found to be 94.12%, 92.15%, and 89.14%, respectively. The research can be helpful for industrial managers to optimize the performance of supply chain systems using artificial intelligence.
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Rybina, I. A., М. А. Goncharova, and S. S. Lomakin. "INDUSTRIAL SUPPLY CHAIN OPTIMIZATION TOOLS UNDER SANCTIONS PRESSURE." Вестник Алтайской академии экономики и права 2, no. 7 2024 (2024): 336–43. http://dx.doi.org/10.17513/vaael.3603.
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Zhou, Hang, Rui Qian Li, and Yue Yu. "Investigation of the Datamation of Manufacturing Industrial Chain in the Big Data Era." Applied Mechanics and Materials 670-671 (October 2014): 1629–32. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1629.
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The big data era enable us to carry out data operations based on the extremely large data volumes to realize the datamation of manufacturing industrial chain. This provides us a good opportunity to optimize the manufacturing industrial chain and upgrade the development of manufacturing industry. In order to provide beneficial references for the optimization of manufacturing industrial chain and the development of manufacturing industry, in this paper, we explore new paths to realize the datamation of manufacturing industrial chain based on the big data era background. We also analyze the advantages, as well as problems remained to be solved of promoting the datamation of manufacturing industrial chain.
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Bányai Tóth, Ágota. "Real-time supplier selection using digital twin technology: an Analytic Hierarchy Process-based optimization approach." Advanced Logistic Systems - Theory and Practice 18, no. 2 (2024): 97–107. http://dx.doi.org/10.32971/als.2024.021.
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Today, the selection of the optimal suppliers plays an increasingly important role in the efficient operation of supply chains. One of the main reasons for this is that the fourth industrial revolution has seen the emergence of increasingly complex supply chains, involving a growing number of suppliers to meet increasingly diversified customer needs. While manufacturing companies use framework contracts to secure the parts needed to meet uncertain customer needs, framework contracts often fix requirements for a fixed future period in the light of past supplier performance. The Fourth Industrial Revolution is enabling the use of a number of new methods and tools to collect real-time data on supply chain operations and to define key performance indicators (KPIs) that show the performance of individual players in the value chain in real time. In this paper, the author proposes a supplier selection method based on analytic hierarchy process (AHP) that is able to determine the optimal suppliers for current component requirements in real time based on real-time information about the state of each player in the supply chain.
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Amol, Shenkar*1 &. Hredeya Mishra2. "METHODOLOGY CONCEPTS FLOW FOR DESIGN, MODELING AND ANALYSIS OF ROLLER CHAIN CONVEYOR SYSTEM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2018): 239–43. https://doi.org/10.5281/zenodo.1313535.
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The basic aim of this Paper has been conducted on the most of the time conveyor chain is under tension which causes failure of chain assembly which is the major problem for industrial sector. Causes of this failure are improper design. It is important to study the influence of these parameters. All these parameters can be considered simultaneously and chain link design optimally. Optimization is the process of obtaining the best result under given circumstances in design of system. In optimization process we can find the conditions that give the maximum and minimum value of function. In this study a shape optimization process is used for the design of roller chain link for minimization of failure modes. This process various design variables, such as wall thickness of link, breaking area of link and shape of the link. While deciding the shape optimization of roller chain link raw material plays important role, so it is necessary to decide new material and Design the new chain with Suitable Design Process.
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Makarova, O. V. "Predictive method of inventory optimization in industrial manufacturer performance." E3S Web of Conferences 247 (2021): 01066. http://dx.doi.org/10.1051/e3sconf/202124701066.
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Strong collaboration in supply chain at B2B market may improve operational and financial performance of both suppliers and clients. Need for collaboration benefits realization is especially actual for supplying manufacturers with huge investments in inventory as well as high ordering and carrying costs. Traditional stock level optimization models rely on historic data which makes them inefficient when supply if influenced by changing customized demand of key B2B clients. Strong collaboration with key clients, synchronization of planning process through the whole supply chain up to the end user and a forward-looking demand adjustment (ΔD, %) to the forecasting model are suggested to improve efficiency of planning. The model is validated at the B2B industrial manufacturer with positive effect. Application of the demand adjustment reorients the whole inventory planning practices towards a proactive approach that lead to a higher operational and financial efficiency.
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Hu, Xuhua, and Linyu Zhang. "Research on the integration level measurement and optimization path of industrial chain, innovation chain and service chain." Journal of Innovation & Knowledge 8, no. 3 (2023): 100368. http://dx.doi.org/10.1016/j.jik.2023.100368.
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Lin, Xuze. "Artificial Intelligence in the Industrial Engineering." Advances in Operation Research and Production Management 1, no. 1 (2024): 1–6. http://dx.doi.org/10.54254/3006-1210/direct/0106.
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The integration of Artificial Intelligence (AI) into industrial engineering, epitomized by the advent of Industry 4.0, has reshaped manufacturing landscapes. This article explores the profound impact of AI over the past decade, focusing on predictive maintenance, operational optimization, robotics, quality control, and supply chain management. Predictive maintenance, facilitated by machine learning algorithms, minimizes downtime and optimizes resource allocation. Operational optimization, achieved through AI's real-time data analysis, enhances decision-making, resource utilization, and overall efficiency. The infusion of AI into robotics elevates manufacturing capabilities, while quality control processes benefit from advanced image recognition and machine learning, ensuring higher standards. In supply chain management, AI predicts demand, optimizes inventory, and streamlines routes, fostering resilience. Human-machine collaboration, highlighted by collaborative robots and AI-driven workforce empowerment, underlines the transformative synergy. The article concludes with a reflection on the past decade's developments, emphasizing the ongoing evolution of AI in industrial engineering, promising smarter, more adaptable, and globally competitive operations in the future.
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Lisienkova, Liubov. "A method for optimisation of enterprise logistic supply chain." MATEC Web of Conferences 239 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201823903009.
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The aim of the study was to develop an optimisation method for management of current assets of an industrial enterprise by the concept of logistics supply chains. The ways of acceleration of working capital turnover at the stages of supply, production and marketing of goods and services have been identified. The necessity of applying the concept of supply chain management to working capital management has been justified. This allowed defining the management principles in the supply chain of an industrial enterprise. As a result, a method for optimization of enterprise current assets of an industrial enterprise as a central element of a supply chain has been offered. It has been proved that the presented method makes it possible to determine the effect of working capital acceleration both in the material and financial subsystems. A tool for realisation of this effect is the structuring of working capital by title deeds.
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Feilong, Liu. "Optimization and Development of Country Smoke-Free Homestay Industry Chain." Tobacco Regulatory Science 7, no. 6 (2021): 5220–29. http://dx.doi.org/10.18001/trs.7.6.14.
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Objectives: The rapid development of the global tourism industry has brought about a boom in the development of the homestay industry, and the development of homestays in China is no exception. With the advent of the era of large accommodation, the country smoke-free homestay industry can not only meet people's basic needs for travel and accommodation, but also a place to experience culture and social consumption, which is an increasing need for people's better life. From the perspective of the business model of homestays, the new types of homestays that incorporate the upstream and downstream industrial chain of homestays are more in line with future development trends. In order to solve the problems of "weak", "scattered" and "small" in the form of the country homestay industry chain, and to continuously optimize the coordinated development of the country homestay industry chain, it is very necessary to adopt the following paths and measures: The first is to strengthen the coordination and integration of country homestay with other industrial chains; the second is to strengthen the agglomeration of the homestay industry and create a branded management road for country homestay; the third is to strengthen government guidance, coordinate the distribution of benefits, and create beautiful country homestay; the fourth is to increase policy support Make efforts to promote the flow of homestay talents and achieve high-quality development of the homestay industry.
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Jia, Peiyao, Jiankun Chu, Wenjia Niu, Puyao Jia, and Ling Zhang. "Development and Comprehensive Evaluation of Ecological Agriculture Industry Chain System Based on Genetic Algorithm." Advances in Multimedia 2022 (September 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/3768943.
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Due to the constraints of traditional agricultural development factors, the existing agricultural industrial chain structure was unreasonable, which can not only promote the development of related industries but also affect the sustainable development of the whole agriculture. Therefore, it was of great significance to build a scientific and orderly ecological agriculture industry chain system and optimize it on the existing basis for improving the industrial benefits of modern agriculture and enhancing the vitality of agricultural development. From the perspective of the construction of an ecological agriculture industry chain system, this study put forward the development and evaluation system of the agricultural industry chain system based on genetic algorithm, in order to provide support for better promoting the development of modern agriculture. First, based on summarizing the related concepts of ecological agriculture and industrial structure, the definition and organization of agricultural industrial chain were given. Second, it expounded the basic theory of genetic algorithm and its application in the optimization of agricultural industrial structure, and put forward the optimization model of ecological agricultural industrial structure combined with the objectives of agricultural industry development. Finally, by constructing the evaluation index system of ecological agriculture industry chain system, a comprehensive evaluation model of ecological agriculture industry chain system based on genetic algorithm was designed. Through the case verification and comparative analysis, the results showed that the ecological agriculture industry chain system evaluation system and evaluation model based on genetic algorithm proposed in this study had certain feasibility and effectiveness, and can better evaluate the changing trend of various industries in the process of agricultural industry development.
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Zagurskiy, O. M., and T. S. Zhurakovska. "Optimization of transport processes in supply chains of epicenter hypermarket network." Naukovij žurnal «Tehnìka ta energetika» 11, no. 3 (2020): 55–60. http://dx.doi.org/10.31548/machenergy2020.03.055.
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Negative changes in the environment, leading to public pressure and environmental legislation require fundamental changes in the industrial practice of business. Survey of the environment in 22 countries found that: in half of the countries surveyed, the environment is considered to be one of the three most serious problems of concern to society. In most countries, the overwhelming number of citizens surveyed indicated that the state of the environment had an impact on their health, and an even larger proportion stated that the environment had an impact on the health of their children. The article deals with the problem of increasing the level of environmental friendliness of transportation in supply chains. With the modern requirements in the integrated green supply chain, the reduction of the harmful impact of production and logistics activities on nature should be considered at all stages of the technological cycle of product development and its promotion through the supply chain. It is determined that the key technologies for reducing the anthropogenic impact on the environment are the optimization of transport processes due to the reduction of distance during transportation at all stages of the supply chain; the increase in the use of local resources (reducing fuel costs and harmful emissions); the use of modern environmentally friendly energy-efficient vehicles. The basic principles and approaches of the consolidated cargo transportation model have been substantiated, which balances environmental and economic problems and their testing has been carried out in the supply chain of the Epicenter hypermarket chain
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Hum, Sin-Hoon, and Mahmut Parlar. "Measurement and optimization of supply chain responsiveness." IIE Transactions 46, no. 1 (2013): 1–22. http://dx.doi.org/10.1080/0740817x.2013.783251.
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Chen, Yong Xiang, Jin Biao Liu, and Nan Zhang. "Research on Supply Chain Network System Based on Industrial Cluster." Applied Mechanics and Materials 587-589 (July 2014): 1907–11. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.1907.
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The reliability of supply chain network system design and optimization based on the industrial cluster was discussed, that is, the operational platform of multi-objective supply chain network was moved to industrial cluster, so the platform has the effects of both regional radiation and scale of economy. Instead of some static research methods such as former traditional mathematic planning, systematic and dynamic analysis, this paper put forward a coordinative mechenism construction, this mechenism used dynamic simulation method which combined the supply chain system with industrial cluster multi-objective weighing in the region, the mechenism should be cost efficient and satisfy the application in the industrial cluster radiation region, the mechenism should integrate all possible resources in the industrial cluster and consider heterogeneity, layout, nonlinear, limitation and dynamics of the supply chain network system.
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van der Sman, R. G. M. "Impact of Processing Factors on Quality of Frozen Vegetables and Fruits." Food Engineering Reviews 12, no. 4 (2020): 399–420. http://dx.doi.org/10.1007/s12393-020-09216-1.
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Abstract In this paper I review the production of frozen vegetables and fruits from a chain perspective. I argue that the final quality of the frozen product still can be improved via (a) optimization of the complete existing production chain towards quality, and/or (b) introduction of some promising novel processing technology. For this optimization, knowledge is required how all processing steps impact the final quality. Hence, first I review physicochemical and biochemical processes underlying the final quality, such as water holding capacity, ice crystal growth and mechanical damage. Subsequently, I review how each individual processing step impacts the final quality via these fundamental physicochemical and biochemical processes. In this review of processing steps, I also review the potential of novel processing technologies. The results of our literature review are summarized via a causal network, linking processing steps, fundamental physicochemical and biochemical processes, and their correlation with final product quality. I conclude that there is room for optimization of the current production chains via matching processing times with time scales of the fundamental physicochemical and biochemical processes. Regarding novel processing technology, it is concluded in general that they are difficult to implement in the context of existing production chains. I do see the potential for novel processing technology combined with process intensification, incorporating the blanching pretreatment—but which involves quite a change of the production chain.
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Lu, Yuechen. "The Current Situation and Optimization Path of Enterprise Supply Chain Management Construction in the Context of Digitalization." Advances in Economics, Management and Political Sciences 115, no. 1 (2024): 24–34. http://dx.doi.org/10.54254/2754-1169/115/2024bj0214.
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Abstract: The advancement of information technology has resulted in a rapid rise of a new phase of technical and industrial revolution, propelling the global shift from an industrial economy to a digital economy at an accelerated rate. Digital transformation has become an irreversible and pervasive tendency in contemporary society. Despite the significant growth of China's economy, the industrial supply chain is not operating at its highest efficiency. To achieve sustainable cost reduction and efficiency, enterprises need to promote digital transformation and supply chain upgrading. By doing so, enterprises can optimize resource allocation among different links in the supply chain and enhance the quality of their development. This study conducted an investigation of the value benefits, problems, and difficulties of enterprise supply chain digital transformation, using Jingdong as an example. The following article aims to provide a credible reference for organizations by discussing the successful paths and measures of digitizing the supply chain, using Jingdong's experience and activities in supply chain digital transformation as a reference.
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Li, Lin, Huaming Wu, Yuan Yuan, and Liyun Zhou. "Industrial Supply Chain Optimization Based on 5G Network and Markov Model." Microprocessors and Microsystems 80 (February 2021): 103559. http://dx.doi.org/10.1016/j.micpro.2020.103559.
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Zhang, Yuzhou. "Collaborative Optimization of Supply Chain Intelligent Management and Industrial Artificial Intelligence." Frontiers in Computing and Intelligent Systems 6, no. 2 (2023): 15–17. http://dx.doi.org/10.54097/fcis.v6i2.04.
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It is urgent for the manufacturing industry to transform its development mode and achieve intelligent transformation. With the increasingly fierce global market competition, relying solely on first-class product quality can no longer guarantee a long-term competitive advantage for enterprises. Therefore, this article conducts research on the collaborative optimization of supply chain intelligent management and industrial AI(Artificial Intelligence). By timely displaying the quality status of each link, intelligent management of goods is achieved, strengthening control and tracking of product quality, greatly improving the efficiency of the quality management system, and ensuring that enterprises can provide high-quality products as much as possible. Incorporate supplier production flexibility, continuous research and development capabilities, and information technology into the criteria for selecting suppliers, seek higher quality suppliers, and establish strategic partnerships with suppliers. The research in this article is beneficial for improving the production and manufacturing efficiency of enterprises, and is an important theoretical exploration in the development process of intelligent manufacturing.
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Kota, László, and Károly Jármai. "Application of a Multilevel Firefly Algorithm on a Large Variable Number Logistic Problem." Advanced Logistic Systems - Theory and Practice 13, no. 2 (2019): 21–28. http://dx.doi.org/10.32971/als.2020.002.
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During our research and industrial projects, we often meet difficult optimization problems, a lot of variables, a lot of constraints, nonlinear and mostly discrete problems, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In the most cases in the logistic industry the strongest constraint is the time. The optimizations are running on a usual office configuration and the company accepts the suboptimal solution what the optimization method gives in the appropriate time limit. In this article we will investigate a multilevel method on supply chain problem, to increase the effectivity, improve the solution in a strict time condition.
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Pang, Yongshi, Yanqing Xiao, and Jingkuang Liu. "A Dynamic Model for Simulation of the Scale of Industrial Chain of Construction Waste Reclamation in China." Open Construction and Building Technology Journal 11, no. 1 (2017): 164–81. http://dx.doi.org/10.2174/1874836801711010164.
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The scale of the industrial chain for construction waste reclamation in China is still at a small magnitude with a relatively slow development rate, yet to form an industrial chain that is mature. With the industrial chain theory, this paper takes Guangzhou as an example to construct a simulation model of the scale of industrial chain of construction waste reclamation based on system dynamics. As the results show: (1) though the subsidy policy carried out by the government has a significant effect on the development of the industrial chain of construction waste reclamation, a greater amount of compensation will not necessarily lead to a larger scale of industrial chain of construction waste.(2) The inventory optimization management is conducive to promote the development of industrial chain of construction waste. (3) The governmental compensation amount of RMB 50,000 per 10,000 tons in the next 5 to 10 years will be the optimal to ensure the rapid development of the industrial chain of construction waste. (4) The scale of the industrial chain of construction waste reclamation, the inventory of the dealers and the inventory of resource-oriented enterprises interact and mutually constrain, implying that with the expansion of the production scale of resource-oriented enterprises, the oscillation amplitude of the inventory of the dealers will become greater while the frequency will be smaller .The conclusion of the study provides reference value and theoretical basis for the development of industrial waste resource industry chain.
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Al-Duleimy, Haidar Hamza Jody, and Shatha Abdul Hussein Jebur. "Working Capital Optimization: A Supply Chain Perspective." Webology 18, Special Issue 04 (2021): 581–90. http://dx.doi.org/10.14704/web/v18si04/web18150.
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The reason for this study is to examine the effect of working capital optimization on the efficiency of Iraqi companies across various industries. The study uses least square model and panel data used for four industries. The analysis gives empirical confirmation that cash conversion cycle used as a proxy of working capital and impact significantly and negatively impact on profitability of the firm and also firm level control factor age and industry influence on the firm performance. Such results indicate that administration can improve organizational profitability by reducing its working capital. The findings of this study can be examined in various socio-economic and industrial contexts. This research is limited to the Iraqi context. This research applies the existing body of information to investigate a panel data set in the context of a developing economy by using the regression equation. This innovative research explores empirically the effect of working capital management on the performance.
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Tao, Min, and Shaoting Yuan. "Research on the Optimization and Upgrading of the Industrial Chain of Zhanjiang Diving Enterprises from the Perspective of Cultural and Tourism Integration." Economics & Business Management 2, no. 1 (2025): 85. https://doi.org/10.63313/ebm.9067.
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Against the backdrop of the cultural and tourism integration emerging as a cru-cial trend in the tourism industry's development, this paper focuses on the op-timization and upgrading of the industrial chain of diving enterprises in Zhan-jiang. By integrating theories such as industrial clusters, green development, and ecological industrial chains, and drawing on the practical experiences of cultural and tourism integration in multiple regions across the country, a re-search framework of "current situation analysis - problem diagnosis - path op-timization" is constructed. The study reveals that although the industrial chain of diving enterprises in Zhanjiang has advantages in resource endowment and policy support, it also has issues such as an imperfect structure and insufficient innovation capabilities. By introducing the coupling coordination degree model and the industrial ecosystem theory, specific strategies centered around the "diving +" multi - integration concept are proposed, with an emphasis on strengthening policy support, technological empowerment, and talent cultiva-tion, aiming to provide theoretical and practical references for the high - quality development of the regional diving industry.
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Yin, Guocheng. "Information Asymmetry Problems and Solutions in Supply Chain Management of Industrial Enterprises." Journal of Economic Theory and Business Management 1, no. 1 (2024): 14–16. https://doi.org/10.5281/zenodo.10576455.
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This paper will explore the causes and effects of information asymmetry in supply chain management of industrial enterprises, as well as its impact on supply chain efficiency and cost. In response to the problem, some solutions to the problem of information asymmetry in supply chain management of industrial enterprises are proposed, including information sharing, contract design and supply chain coordination, etc., and through case analysis, solutions to the problem of information asymmetry in supply chain management of industrial enterprises are discussed. practical applications and effects.
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Meng, Ai Ying. "Optimization on the Bottleneck Process of 63PF2 Conveying Chain Assembly." Advanced Materials Research 308-310 (August 2011): 734–38. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.734.
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Aiming at the problem of more difficult and low efficiency to assembly 63PF2 conveying chain, the work measurement, action research and production line balancing method are studied, and the data of 63PF2 conveying chain assembly lines is obtained. Taking 63PF2 conveying chain assembly lines for example and using industrial engineering foundation of stopwatch time study, two-handed operation analysis, process flow analysis and quality management of fishbone diagram, the problems existing in 63PF2 conveying chain assembly process are analyzed. And then the work site preliminary optimization is carried out. The bottleneck process of assembly process is optimized and improved. And the balance of assembly line is optimized, so as to improve the transmission chain assembly efficiency.
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Mohammadi, Tahereh, Seyed Mojtaba Sajadi, Seyed Esmaeil Najafi, and Mohammadreza Taghizadeh-Yazdi. "Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies." Mathematics 12, no. 5 (2024): 679. http://dx.doi.org/10.3390/math12050679.
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With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.
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Naidu, D. Shashank, and Dr Smitha Kurian. "Deep Learning Models for Specific Industrial Problems using Predictive Maintenance." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02838.
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Deep learning has revolutionized various industries by enabling intelligent automation, predictive analytics, and enhanced decision-making. This paper explores the application of deep learning models in solving specific industrial problems across diverse domains such as manufacturing, healthcare, finance, and supply chain management. We analyse the effectiveness of convolutional neural networks (CNNs) in quality control, recurrent neural networks (RNNs) in predictive maintenance, and transformer-based models in financial forecasting. Additionally, we discuss challenges such as data scarcity, model interpretability, and computational costs, providing potential solutions and future research directions. The findings highlight the transformative impact of deep learning in industrial problem-solving and emphasize the need for industry-specific model optimization to achieve higher efficiency and accuracy. Keywords: Deep Learning, Industrial Applications, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer Models, Predictive Maintenance, Quality Control, Financial Forecasting, Supply Chain Optimization, Artificial Intelligence (AI).
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Natalia, N. Trofimova, and I. Afanasiev Vladislav. "The Role of Digital Twin Technology in Transforming and Managing Industrial Supply Chain Risks." Economic consultant, no. 4 (December 1, 2022): 33–41. https://doi.org/10.46224/ecoc.2022.4.4.
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<strong>Introduction.</strong> Investigating the impact of digital twins (DT) on supply chain resilience in economic crises or unexpected changes such as a pandemic is a current research challenge. <em>The paper aims</em> to analyze the potential applications of DT in supply chain formation. <strong>Materials and Methods.</strong> The study materials were research articles from peer-reviewed journals covering the latest developments in digital twins. The article uses a case method (the example of the Siemens Company). <strong>Research Findings.</strong> With the help of DT technologies, today’s industry is empowered to solve a wide range of online problems covering all interrelated aspects of the enterprise: research and development, manufacturing and assembly, marketing and sales. As awareness of the economic benefits of DT technology grows, its application will increase in various fields to drive industrial restructuring and modernization. <strong>Conclusion. </strong>Using DT in supply chain optimization has several advantages. In particular, manufacturing companies can use DT to optimize processes, reduce costs, and improve efficiency. Real-time route optimization and inventory management can minimize delays and congestion. The role of DT in supply chain optimization is becoming increasingly important. They can significantly reduce risk, improve efficiency, and promote flexibility. Implementing such technologies will undoubtedly be a hot topic for research and practical initiatives in the coming years.
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Gao, Haiwei, Xiaomin Zhu, Binghui Guo, et al. "Synchronization Optimization Model Based on Enhanced Connectivity of New Energy Vehicle Supply Chain Network." Mathematics 13, no. 4 (2025): 632. https://doi.org/10.3390/math13040632.
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The synchronization of the new energy vehicle (NEV) supply chain network is crucial for enhancing industrial integration, building intelligent supply chain systems, and promoting sustainable development. This study proposes a novel synchronization model for the NEV supply chain network, incorporating a technical method for measuring synchronization intervals. The research makes three key contributions: (1) development of a dynamic synchronization model capturing the complex interactions within NEV supply chains; (2) introduction of a quantitative method for assessing synchronization intervals; and (3) identification of critical parameters influencing network synchronization. Methodologically, we employ a combination of complex network theory and nonlinear dynamic systems to construct the synchronization model. The study utilizes real-world data from two major NEV companies (X and T) to validate the model’s effectiveness. Through network topology analysis and parameter optimization, we demonstrate significant improvements in supply chain efficiency and resilience. The practical application of this research lies in its ability to provide actionable insights for supply chain management. By optimizing network structure, coupling strength, and information delay, companies can enhance synchronization, reduce the bullwhip effect, and improve overall supply chain performance. The findings offer valuable guidance for NEV manufacturers and policymakers in building more resilient and efficient supply chain networks in the rapidly evolving automotive industry.
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Wang, Minxing, Zhenming Chen, Jiajun Li, et al. "Analysis of the Photovoltaic Market in China: Optimization of Industrial Chain and Prospect Forecast Under the ‘Double-Carbon’ Background." BCP Business & Management 33 (November 20, 2022): 250–61. http://dx.doi.org/10.54691/bcpbm.v33i.2756.
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Since the Paris Agreement was signed in 2016, the development of renewable energy has become a strategic consensus of all countries in the world. Since 2020, China has officially put forward the goals of “peak carbon dioxide emissions” in 2030 and “carbon neutrality” in 2060, and actively responded to the global proposition of sustainable development goals and carbon emission reduction in Paris Agreement. According to the statistics of the National Energy Administration, fossil energy such as coal, oil and natural gas accounts for more than 80% in China’s current energy consumption structure, while clean energy such as hydro power, wind power and natural gas accounts for only 25.5%. At the same time, China’s energy consumption is increasing year by year, with the total consumption reaching 5.24 billion tons of standard coal equivalent in 2021, and the reform of energy consumption structure is imminent. The new energy represented by photovoltaic is expected to become the main industry to achieve the goal of carbon neutrality in 2030. Based on the investigation of national and local statistical data, combined with the current development of clean energy and photovoltaic industry, this paper analyzes the operation status of leading photovoltaic enterprises, deconstructs the photovoltaic industry chain, extracts data, grasps the future development direction of photovoltaic industry, and reveals the shortcomings and loopholes in the development of photovoltaic industry. At present, the photovoltaic industry is subject to many industrial chain structures, and the market fluctuation between upstream and downstream industrial chains changes periodically. However, in the long run, the photovoltaic industry is on the rise. If the specific links in the industrial chain can be optimized and cost reduced, the coordination capacity within the industrial chain can be increased, the integration of the industrial chain can be realized as soon as possible, meanwhile, the coverage area of photovoltaic power stations can be promoted, the high-quality sunshine conditions in the western region can be fully utilized, and the regional economy can be driven by the development of photovoltaic industry, so that the promotion of green energy industry can be realized, the economic vitality of the western villages and towns can be developed, and the double cycle of domestic and international economy can be promoted.
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Mu, Wangang. "Research on the Construction of Virtual Reality Technology Major Course System." Education Reform and Development 6, no. 12 (2024): 130–35. https://doi.org/10.26689/erd.v6i12.9245.
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Along with virtual reality technology is widely used in all walks of life, China’s social and economic development urgently needs high-quality innovative, and compound virtual reality technical personnel. In this context, as the main front of training virtual technical talents, universities are urgently required to promote the innovative construction and deepening reform of the virtual reality technology curriculum system, and on this basis to promote the deep integration of the industrial chain, innovation chain, discipline chain and talent chain. Based on this, combined with the background of the optimization of the curriculum system of virtual reality technology major in colleges and universities under the new situation, this paper discusses the specific optimization path by elaborating the relevant optimization ideas to improve the quality of talent training for virtual reality technology majors.
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Liu, Ziyuan, Yingzhao Wu, Tianle Liu, et al. "Double Path Optimization of Transport of Industrial Hazardous Waste Based on Green Supply Chain Management." Sustainability 13, no. 9 (2021): 5215. http://dx.doi.org/10.3390/su13095215.
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With the deepening of the concepts of “sustainable development”, green supply chain management has gradually been attached great importance by the government and enterprises. Based on the green supply chain management method, this paper studies the path optimization of industrial hazardous waste treatment transportation in environmental protection enterprises, aiming at the green purchasing link, in order to realize the management of the green purchasing of environmental enterprises linked to green production under the green supply chain management which integrates green purchasing, hazardous waste storage and green disposal.
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Huang, Fang, and Wenting Lan. "RESEARCH ON THE CONSTRUCTION OF TRAINING BASES IN LOCAL APPLIED COLLEGES AND UNIVERSITIES EMPOWERED BY THREE-CHAIN SYNERGY." International Journal of Advanced Research 13, no. 03 (2025): 61–67. https://doi.org/10.21474/ijar01/20536.
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In the context of production-education integration, joint construction of practical training bases by schools and enterprises is crucial for improving the quality of talent training in local universities and supporting local economic and social development. Research on the existing training bases at Hezhou University reveals issues such as limited specialty coverage, low alignment between the industrial chain and talent development,weak industry-university-research collaboration, and the need for improved support for disciplinary competitions. To address these issues, an optimization strategy based on the synergy of the industry chain, practice chain, and competition chain is proposed. This includes expanding industry-education integration, deepening practical training integration, leveraging the industry chains benefits, creating a multi-dimensional practice system, and promoting the transformation of competition results to enhance high-quality talent development.
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Wei, Xiao Bing, Jie Luo, and Fei Sun. "Study on Innovation Strategies of China Petroleum Supply Chain Optimization." Applied Mechanics and Materials 733 (February 2015): 955–59. http://dx.doi.org/10.4028/www.scientific.net/amm.733.955.
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With China's sustained and rapid development of economy and industrial modernization and urbanization process continues to advance, the greater demand for oil and other energy and foundation. Due to lack of oil production, China's oil imports rose steadily in recent years, the import dependence continues to expand, coupled with the frequent fluctuation of the oil price, the oil supply chain in China has huge potential safety problems. Therefore, the optimization of existing oil supply chain network system, and give full play to the role of market allocation of resources, is one of the effective ways to solve the shortage of petroleum resources. This article first elaborated the petroleum status of supply chain optimization problem, points out the problems in present research. Further research on the strategic petroleum reserve value, and constructed and put forward suggestions for perfecting China's petroleum reserve system. The purpose is to enrich the petroleum supply chain optimization strategy, further improve the supply chain management of our country actual oil.
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Alghieth, Manal. "Sustain AI: A Multi-Modal Deep Learning Framework for Carbon Footprint Reduction in Industrial Manufacturing." Sustainability 17, no. 9 (2025): 4134. https://doi.org/10.3390/su17094134.
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The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural Networks (RNNs) for predictive energy consumption modeling, and Reinforcement Learning (RL) for dynamic energy optimization to enhance industrial sustainability. The framework employs IoT-based real-time monitoring and AI-driven supply chain optimization to optimize energy use. Experimental results demonstrate that Sustain AI achieves an 18.75% reduction in industrial energy consumption and a 20% decrease in CO2 emissions through AI-driven processes and scheduling optimizations. Additionally, waste heat recovery efficiency improved by 25%, and smart HVAC systems reduced energy waste by 18%. The CNN-based defect detection model enhanced material efficiency by increasing defect identification accuracy by 42.8%, leading to lower material waste and improved production efficiency. The proposed framework also ensures economic feasibility, with a 17.2% reduction in operational costs. Sustain AI is scalable, adaptable, and fully compatible with Industry 4.0 requirements, making it a viable solution for sustainable industrial practices. Future extensions include enhancing adaptive decision-making with deep RL techniques and incorporating blockchain-based traceability for secure and transparent energy management. These findings indicate that AI-powered industrial ecosystems can achieve carbon neutrality and enhanced energy efficiency through intelligent optimization strategies.
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Jay, Patel. "Supply Chain Optimization Software: Market Distribution, Leading Companies, and Challenges." Journal of Advances in Developmental Research 16, no. 1 (2025): 1–8. https://doi.org/10.5281/zenodo.14979976.
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Supply chain optimization software has become essential for businesses aiming to enhance efficiency, reduce costs, and improve decision-making. These solutions leverage various mathematical and computational techniques; however, they often lack the customization required to align with specific operational needs. This article explores the market distribution of supply chain optimization software, identifies leading companies and users, examines the core methods employed, and highlights existing shortcomings. Finally, it advocates for the adoption of cloud-based platforms utilizing linear and mixed-integer linear programming (MILP) methods and emphasizes the role of industrial engineers in this evolution.
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Chen, Weinberg Jiang, Griffin Schworm Marcus, and D'Souza Leesburg. "Quantum computing for manufacturing and supply chain optimization: enhancing efficiency, reducing costs, and improving product quality." International Journal of Enterprise Modelling 15, no. 3 (2021): 130–47. http://dx.doi.org/10.35335/emod.v15i3.48.
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The research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.
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Wang, Qiankun, and Qiao Shi. "The incentive mechanism of knowledge sharing in the industrial construction supply chain based on a supervisory mechanism." Engineering, Construction and Architectural Management 26, no. 6 (2019): 989–1003. http://dx.doi.org/10.1108/ecam-05-2018-0218.
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Purpose Knowledge sharing is an important way to improve the knowledge system of industrial construction, and the supervision mechanism is an important way to improve the efficiency of knowledge sharing. However, some research works and practices indicate that the effects of applying the supervision mechanism are not obvious. Therefore, the purpose of this paper is to propose an incentive method of knowledge sharing based on the supervision mechanism for promoting knowledge sharing among member enterprises in the industrial construction supply chain. Design/methodology/approach A basic incentive model and an optimization model of knowledge sharing in the industrial construction supply chain based on the supervision mechanism were developed via the principal–agent theory. Weighted coefficients of explicit and implicit knowledge sharing were introduced into the basic model, while the supervision reward was added into the basic model of the optimization model. The effect of these two models was compared and analyzed via numerical simulation. Findings The optimal incentive coefficient and effort level of knowledge sharing can be obtained by solving the two aforementioned models. The results of the comparison between the two models indicate that the introduction of a supervisory reward improved the effort level and expected earnings produced by knowledge sharing, but reduced the confirmed equal earnings of member enterprises in the industrial construction supply chain. Research limitations/implications Mutual transformation between tacit and explicit knowledge was not considered, and supervisory costs were also not considered, in the estimation of the output of knowledge sharing. Practical implications The new models proposed by this study provide theoretical guidance for the design of knowledge sharing incentive measures in the industrial construction supply chain based on the supervision mechanism. The findings suggest that member enterprises should pay attention to the costs of knowledge sharing, in order to obtain more benefits. Originality/value This study introduced the weight coefficients of explicit and implicit knowledge sharing into a previous incentive model, proposed an incentive optimization model of knowledge sharing in the industrial construction supply chain based on a supervisory mechanism, and revealed the change rules of related variables that affect the model with the change in weight coefficients. The findings verify the effectiveness of introducing supervisory reward measures and extend the range of theoretical application.
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Tong, Dong. "The Influence of Technological Innovation Diffusion on the Optimization of Beijing’s Cultural Industrial Structure." SHS Web of Conferences 168 (2023): 03017. http://dx.doi.org/10.1051/shsconf/202316803017.
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Technological innovation diffusion is an important direction of industrial structure upgrading research. In the context of intelligent technology enabling the cultural industry to improve the efficiency of the whole industrial chain, it is critical to examine its impact on the optimization and upgrading of the cultural industry structure from the perspective of technological innovation diffusion. This paper constructs an empirical model based on the impact of the per capita output of regional cultural industry, the capital labor ratio of regional cultural industry, and the capital labor ratio of cultural industry segmentation on the optimization of cultural industrial structure.
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HUANG, Z. Y. "DYNAMIC PROGRAMMING PATH OF MOBILE ROBOT BASED ON EVENT-DRIVEN PROCESS." Latin American Applied Research - An international journal 48, no. 4 (2018): 317–22. http://dx.doi.org/10.52292/j.laar.2018.247.
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Event-driven process chain is an important method for path flow and system optimization in the process of robot dynamic planning path research. Path planning is an important part of robot research. The path planning problem and the path optimization planning problem of the robot in the dynamic environment are explored deeply in this paper based on the event-driven process chain perspective and the actual needs of the research project. Therefore, it uses the event-driven process chain method to optimize the path according to the original process and existing requirements based on the concept of event-driven process chain. A dynamic path planning model under event-driven process chain is constructed, and then the function, user and permission, code, input and output, and security of industrial robot system are optimized.
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Mahesh, G. Uma, Atti Mangadevi, and B. Kalyan Chakravarthy. "A Novel Advanced IoT System for Predictive Maintenance Systems and Supply Chain Optimization Solutions for Manufacturing Industries." Advancement of IoT in Blockchain Technology and its Applications 3, no. 1 (2024): 31–42. http://dx.doi.org/10.46610/aibtia.2024.v03i01.005.
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This paper presents a pioneering approach to revolutionize manufacturing industries through the development of a novel advanced Internet of Things (IoT) system tailored for predictive maintenance and supply chain optimization. Leveraging the vast array of data generated within manufacturing environments, the proposed system integrates cutting-edge IoT technologies with advanced predictive analytics to enable proactive identification of equipment failures and optimization of supply chain operations. The research encompasses a comprehensive dataset comprising sensor data, maintenance records, and supply chain information sourced from industrial pumps, facilitating the creation of robust predictive maintenance models and insightful supply chain optimization strategies. Experimental procedures involve data pre-processing, feature engineering, and model development, followed by rigorous evaluation against ground truth data. The results showcase significant enhancements in equipment up-time; maintenance cost reduction, and supply chain responsiveness, demonstrating the efficacy and scalability of the IoT-based solutions. Through this innovative approach, manufacturing industries can achieve unprecedented levels of operational efficiency, cost savings, and competitive advantage, paving the way for transformative advancements in predictive maintenance and supply chain management paradigms.
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Mohite, Rohit, Ravi Chourasiya, Sandeep Sharma, and Sandesh Akre. "Enhancing the Competitiveness of MSMEs Through Industrial Engineering Innovations in Supply Chain Management." International Journal of Engineering Management 9, no. 1 (2025): 30–38. https://doi.org/10.11648/j.ijem.20250901.14.
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MSME&apos;s are the foundation of many economies, making substantial contributions to industrial output, job creation, and economic expansion. However, because of their limited infrastructure, resources, and access to cutting-edge technologies, MSME&apos;s frequently struggle to manage supply chain and logistics operations. This research focuses on developing an innovative mathematical model that enhances MSME’s competitiveness by optimizing supply chain and logistics operations using industrial engineering principles. The proposed model adopts a multi-objective optimization framework, addressing critical aspects of supply chain efficiency, including cost minimization, service level enhancement, and resource utilization. By integrating supplier, facility, customer, transportation, and inventory data, the model provides actionable insights for MSME’s to streamline operations, reduce costs, and improve service delivery. This research focuses on developing a mathematical model to optimize supply chain and logistics operations for MSME’s, addressing challenges related to limited resources and infrastructure. The model employs a multi-objective optimization framework, aiming to minimize costs, enhance service levels, and improve resource utilization. It integrates data on suppliers, facilities, customers, transportation, and inventory, providing actionable insights for MSME’s to streamline operations and reduce costs. A hypothetical dataset, representing suppliers, facilities, and customers across different locations with varying demands, is used to demonstrate the model&apos;s applicability. Decision variables in the model represent transportation flows, facility operations, and inventory levels, while constraints ensure demand fulfilment and operational feasibility. The objectives of the model include minimizing procurement, transportation, and inventory holding costs, while maximizing service levels and resource utilization. The optimization process considers factors like transportation lead times, operational costs, and stockout penalties, making it highly relevant to MSME’s contexts. A realistic dataset simulates MSME’s supply chain scenarios, including supplier capacities, facility costs, customer demands, and transportation dynamics, reflecting logistical challenges in India. Transportation data captures cost-per-unit distance, lead times, and distances between nodes, while resource data provides insights into labor availability and equipment utilization. Preliminary results show that the model significantly reduces supply chain costs while maintaining high service levels and improving resource efficiency. It identifies optimal transportation routes, balances inventory levels at facilities, and suggests ways to enhance labor and equipment utilization. Overall, the model contributes to operational efficiency and competitiveness for MSME’s by optimizing logistics and supply chain operations.
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Weng, Wei Bing, Guang Jun Yang, Yun Zhang, and Jian Wu. "Case Study: Supply Chain View of Plant Location Decision Making with the Use of AHP Methodology." Advanced Materials Research 271-273 (July 2011): 719–24. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.719.
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As a node of a supply chain, plant plays a key role in the network, which has been a strategic topic in the study of supply chain management model. Plant location decision is one of the crucial problems in the optimization and design of supply chain. The converte of competitions between single companies to competitions between different supply chains urges the extension of plant location decision from the view of single plant to the entire supply chain. This paper presents the application of AHP methodology in decision making of plant location considering the roles of plant in an entire supply chain. The different levels of criteria such as cost, cycle time of supply chain, and quality of plant locations, are proposed to be considered in the decision model. The case presented in this paper concerns plant location decision of a British group, who selected an optimal plant location from six alternative industrial parks in China.
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Wang, Dong Hui. "Countermeasure Research on New Energy Automobile Industry Chain Optimization." Advanced Materials Research 986-987 (July 2014): 267–70. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.267.
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As the international energy supply remains tense, international oil price continues rising and calls for global environmental protection becomes higher, people attach more and more importance to the research and development of new energy automotive technology, and the industrialization development. Rapid industrialization led to heavy pollution, increasing greenhouse gas emission, which makes the development of new energy automobile have practical significance. Automobile industry is an important mainstay in national economy, its industrial chain is long, correlation degree is high and the consumption pursuing is big. New energy automobile based on the gradual development of low carbon economy is a systemic revolution to the transportation that our society uses and the way of using energy, which will bring to the technical revolution of automobile industry.
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Zambrzhitskaya, Evgeniya. "Models for Assessing the Strategic Effectiveness of Production Cooperation." Baikal Research Journal 14, no. 4 (2023): 1418–26. http://dx.doi.org/10.17150/2411-6262.2023.14(4).1418-1426.
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The production capacity management strategy based on industrial cooperation is currently one of the most effective. At the same time, the issue of assessing the effectiveness of industrial cooperation at the level of strategic decisions regarding the management of production capacities currently has a number of unresolved issues, namely, there is no practical toolkit for its assessment that takes into account the peculiarities of the functioning of modern Russian industrial enterprises. To solve the problem, the author proposed an optimization model formed on the basis of a graph representation of a production system, including production units of industrial enterprises included in production cooperation according to a certain technological chain. The proposed optimization model includes two optimization criteria written in matrix form: production system load factor and total profit. The use of the proposed tools for assessing the strategic effectiveness of industrial cooperation will significantly improve the quality of management decisions.
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Trofimova, Natalia N., and Vladislav I. Afanasiev. "The Role of Digital Twin Technology in Transforming and Managing Industrial Supply Chain Risks." Economic Consultant, no. 4 (December 1, 2022): 33–41. https://doi.org/10.46224/ecoc.2022.4.4.
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Introduction. Investigating the impact of digital twins (DT) on supply chain resilience in economic crises or unexpected changes such as a pandemic is a current research challenge. The paper aims to analyze the potential applications of DT in supply chain formation. Materials and Methods. The study materials were research articles from peer-reviewed journals covering the latest developments in digital twins. The article uses a case method (the example of the Siemens Company). Research Findings. With the help of DT technologies, today’s industry is empowered to solve a wide range of online problems covering all interrelated aspects of the enterprise: research and development, manufacturing and assembly, marketing and sales. As awareness of the economic benefits of DT technology grows, its application will increase in various fields to drive industrial restructuring and modernization. Conclusion. Using DT in supply chain optimization has several advantages. In particular, manufacturing companies can use DT to optimize processes, reduce costs, and improve efficiency. Real-time route optimization and inventory management can minimize delays and congestion.
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KLESHCHOV, Anton. "Logistics supply of the eco-industrial parks’ residents." Economics. Finances. Law 9/2024, no. - (2024): 14–16. http://dx.doi.org/10.37634/efp.2024.9.3.
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This paper provides a comprehensive analysis of the logistics supply for residents of eco-industrial parks and examines its influence on the competitiveness and sustainability of these parks. Eco-industrial parks, as integral components of circular economy systems, facilitate the efficient use of resources, reduction of environmental impact, and optimization of supply chain operations. The purpose of the paper is to assess the logistics systems within eco-industrial parks, identify the challenges and opportunities for enhancing operational efficiency, and explore how these strategies can be adapted to the Ukrainian context, particularly during the ongoing war. The research draws upon international case studies, focusing on the integration of advanced technologies and green logistics practices in China, Europe, and other regions. The methodology used in this paper combines inductive analysis of literature, comparative analysis, and empirical research based on existing models of logistics management within EIPs. A key part of the analysis involves the evaluation of institutional pressures and their role in promoting sustainable supply chain management. The paper examines how these practices, when applied in a coordinated and efficient manner, help to reduce costs and carbon emissions, while also improving resource-sharing between companies located within eco-industrial parks. The results show that implementing circular supply chain management significantly enhances the performance of companies operating within EIPs. Firms that adopt renewable energy sources and optimize their transportation systems using electric vehicles report a 13-14% reduction in operational costs and carbon emissions. The integration of technologies such as Geographic Information Systems and Multi-Criteria Decision-Making tools has also proven beneficial in the site selection and planning of eco-industrial parks, allowing for the optimization of logistics and resource allocation. In Ukraine, which is currently facing critical challenges due to war, adopting these strategies can strengthen industrial clusters, reduce dependence on external resources, and enhance resilience against economic shocks. The scientific novelty of this research lies in the proposed structure of the logistics supply for eco-industrial parks’ residents. The practical implications of this paper include the application of green logistics technologies, the development of self-sustaining energy networks, and the implementation of closed-loop supply chains that promote resource efficiency. Future research directions should focus on the development of logistics optimization models for Ukrainian eco-industrial parks, particularly in war-torn areas, and the assessment of their long-term economic and environmental impact. Additionally, the paper advocates for the expanded use of renewable energy sources and Geographic Information Systems-based planning tools to further improve logistics processes and reduce the ecological footprint of industrial activities. The results of this paper underscore the importance of inter-firm cooperation and the strategic use of logistics to foster both economic and environmental resilience in eco-industrial settings.
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Huang, Zhiping, Tianran Wang, and Na Li. "Reciprocal and Symbiotic: Family Farms’ Operational Performance and Long-Term Cooperation of Entities in the Agricultural Industrial Chain—From the Evidence of Xinjiang in China." Sustainability 15, no. 1 (2022): 349. http://dx.doi.org/10.3390/su15010349.
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The family farm is an important entity in the modern agricultural industrial chain. It is of great significance to empirically study its operational performance improvement and sustainable development. This paper introduces symbiosis theory to establish a symbiosis system framework of the family farm industrial chain and analyzes family farms’ operational performance from the view of industrial symbiosis cooperation. We selected 552 agricultural planting family farms in China’s Xinjiang Production and Construction Corps as samples to measure the operational environment and performance of family farms using factor analysis and examining the effects of long-term cooperation among the industrial chain entities on family farms’ operational performance using the ordered probit model. The results show that the long-term cooperation of the family farms with other entities has a significant positive impact on the family farms’ operational performance, which can be enhanced by the improvement of cooperation and moderated by the external environment. Therefore, it is suggested to promote the long-term cooperation between family farms and other industrial chain entities, as well as the industrial environment optimization, to accelerate the healthy and sustainable development of family farms with a continuous, symmetrical, and reciprocal symbiotic model.
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Ye, Ruiqi. "Revolutionizing industrial efficiency through generative AI: Case studies and impacts on supply chain operations." SHS Web of Conferences 207 (2024): 03015. https://doi.org/10.1051/shsconf/202420703015.
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With the advancement of Industry 4.0, the manufacturing industry is working to create a new smart industrial world through computerization, digitization and intelligence enhancement. Gen AI is primarily characterized by its ability to generate novel data patterns and solutions rather than merely analyzing predefined data inputs. This paper explores the transformative impact of Gen AI on supply chain efficiency in industrial engineering and logistics. Key applications include inventory optimization, predictive maintenance, fraud detection, risk management, logistics optimization, and demand forecasting. The study shows that Gen AI significantly improves operational efficiency and reduces stress for industrial workers by providing dynamic data-driven solutions. Through real-world case studies, including companies, this study demonstrates how Gen AI can revolutionize supply chain management and increase productivity. Despite its significant benefits, Gen AI still faces several challenges due to its cutting-edge nature. Further, in-depth research is needed in the future as the number of relevant cases and literature increases.
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Ran, Haojie, Dichen He, and Huajun Tang. "Network Optimization of Fresh Products Cold Chain Considering Supply Disruption and Demand Fluctuation Under the Dual-Carbon Policy." Mathematics 13, no. 9 (2025): 1539. https://doi.org/10.3390/math13091539.
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Against global industrial upgrading and China’s “dual-carbon” policy, the cold chain for fresh products faces numerous challenges such as supply disruptions, demand fluctuations, and low-carbon transformation. This study focuses on introducing the key optimization goals of the cold chain network for fresh products: maximizing the service level while minimizing operating costs and carbon emissions. To this end, this study proposes a high-dimensional multi-objective optimization model for the cold chain network of fresh products and designs four resilience strategies to address supply disruption and demand fluctuation scenarios. To solve this model, this study innovatively designs a hybrid algorithm combining neighborhood search and swarm intelligence, integrating the advantages of local exploration and global optimization to balance the relationships among multiple objectives efficiently. In addition, this study conducts a real-world case analysis to verify the effectiveness of the proposed model and the algorithm. Furthermore, by deeply exploring the comprehensive impacts of supply disruptions and demand fluctuations on the cold chain network for fresh products, the mechanism of action of resilience strategies in dealing with supply chain risks is highlighted. The research results provide valuable decision-making support for fresh cold chain enterprises to develop resilient and low-carbon network optimization strategies for cost reduction, efficiency improvement, and sustainable development.