Intelligent Supply Chain Management Research Articles

Research on High-quality Development of Premade Food Industry from the Perspective of Supply Chain Management -- Take Freshippo as an Example

In recent years, with the acceleration of people's pace of life, the concept of premade food continues to be hot, the premade food industry has developed rapidly as well as the market scale has expanded. Supply chain is a crucial link in premade food industry chain. The proper supply chain management is of great significance to the high-quality development of the precooked dish industry. At present, there are still a series of challenges and deficiencies in the supply chain management of the prepared dish industry. Perfect food safety supervision system, sinking cold chain logistics network and intelligent supply chain management have become the focus of sustainable development of this field. This paper selects Freshippo as the research object, studying the challenges and development opportunities faced by the current premade food industry in the supply chain management through the case analysis method, and then proposes corresponding supply chain management optimization countermeasures to solve these problems, aiming to help the healthy and vigorous development of the premade food industry.

A multi-objective model for integrated supplier order allocation and supply chain network transportation planning decision-making

Effective supplier order allocation (SOA) and transportation planning (TP) are critical for the smooth operation of supply chains, especially in dynamic markets with evolving customer demands. While previous research has made significant strides in addressing these areas individually, studies that integrate both aspects while considering a comprehensive set of objectives are limited. This study introduces an innovative multi-objective model that concurrently addresses total operational costs, supplier defect rates, sustainability performance, and supply chain network disruption risks. The model applies an augmented max–min approach of fuzzy multiple objective linear programming (AMM-FMOLP), which enhances overall utility while ensuring balanced performance across all objectives. The inclusion of all five objectives in the model is essential for a holistic evaluation of supply chain performance. The model’s effectiveness and practicality are validated through a case study involving a computer numerical control (CNC) machine tool assembly company, under various scenarios. Additionally, sensitivity analysis reveals how adjustments in supply chain structure can further improve performance. Moreover, the execution process of this study does not require expert intervention, making it a unique data-driven decision model particularly suitable for intelligent supply chain management and decision-support systems. This approach enhances the flexibility and resilience of supply chains, enabling them to effectively respond to unforeseen events and minimize their impact on business operations, especially in volatile global markets.

The Role of Artificial Intelligence Technology in Promoting Socio-Economic Development

This paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.

Analysis of Business Intelligence Technology in the Big Data Era A Case Study of Alibaba Group

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibabas cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibabas success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.

Impact on Smart Supply Chain in the Context of Industry 4.0: The Case of BYD

Industry 4.0 is one of the key topics put into research. Researchers have found that Industry 4.0 has a lot of theoretical research on manufacturing and supply chain management. However, there still needs to be a research gap in enterprise management and optimization of smart supply chains thanks to the fourth information revolution. Therefore, this paper uses the actual case of a BYD enterprise to analyze its enterprise's problems and use the intelligent supply chain to improve the problems. The research methodology of this paper is as follows: firstly, collect the data on BYD's R&D investment and supply chain structure, as well as the relevant thesis research on BYD's green development and cost management. Secondly, the enterprise data, supply chain structure, and so on are analyzed. Finally, it gives the corresponding suggestions and solutions in combination with intelligent supply chain management. The study shows that big data, smart data analysis, artificial intelligence, and smart supply chain framework should be used to manage BYD's capital investment, restructure the supply chain, and balance cost and sustainable development.

A Bibliometric Literature Review of Digital Supply Chain: Trends, Insights, and Future Directions

This paper presents a comprehensive bibliometric literature review that aims to analyze and synthesize the existing research on the digital supply chain. By employing a bibliometric approach, this study provides insights into the trends, key research themes, influential authors, and relevant journals in the field of DSC. Furthermore, it explores the geographical distribution of publications, collaboration networks among researchers, and the evolution of research topics. The comprehensive bibliometric analysis of literature in DSC is performed through data visualization and content analysis. Data visualization enables the analysis of publication performance and science mapping by examining prolific journals, authors, countries, influential papers, and conducting citation, co-citation, and bibliographic coupling analyses. Content analysis is carried out by examining the occurrence and clustering of textual data. The bibliometric literature review and content analysis were carried out on 114 documents indexed in the Scopus database, which were selected using the Prisma protocol. The data visualization analysis of the literature review through VOSviewer draws the landscape of scholarly research in the digital supply chain. Based on the research landscape, the content analysis demonstrates the significant effects that the incorporation of digital technology into supply chain management has had on several important research topics. The main research trends in digital supply chain according to the content analysis are: intelligent supply chain management, technologies to drive supply chains performance, circular supply chains, information reliability in supply chains, and sustainable supply chains. The findings of this review highlight the transformative potential of digital technologies in supply chain management and its performance optimization.

Enterprise Marketing Risk Control Based on Information Technology and Supply Chain Management

The overall economic development of today’s society is showing a downward trend. In the process of operating and marketing, enterprises are affected by various factors. After the marketing risk reaches a certain level, enterprises would encounter various risks such as blocked marketing plans and failure to meet the estimated marketing goals. Enterprise marketing is affected by economic and market risks such as lack of information, decision-making errors, natural disasters, human factors, and other natural and social risks. This article combined information technology with supply chain management, applied radio frequency identification (RFID) technology to supply chain management, and controlled enterprise marketing risks through reasonable management of the supply chain. It also compared the effectiveness of enterprise marketing risk control between an information technology intelligent supply chain combined with RFID and a supply chain managed by ordinary artificial structures. The experimental results showed that the highest value of goods loss rate in the supply chain based on RFID technology and using traditional technology was 5% and 15%, respectively, according to the sample of goods loss experiments. For the information verification process of goods in supply chain management, the information identification accuracy rates of barcode and RFID technology were 89.98% and 97.7%, respectively. Therefore, applying the information technology intelligent supply chain management model combined with RFID technology to enterprise marketing risk control can save money for enterprises, ensure product quality, and effectively reduce the risks encountered in enterprise marketing by reducing information gaps, and reducing transportation and storage costs.

ЦИФРОВЫЕ ТЕХНОЛОГИИ В МЕЖДУНАРОДНОЙ ЛОГИСТИКО-ИНЖЕНЕРИНГОВОЙ МОДЕЛИ ИНТЕЛЛЕКТУАЛЬНЫХ ЦЕПОЧЕК ПОСТАВОК 4.0 (НА ПРИМЕРЕ ПРОЕКТА AGPP В АМУРСКОЙ ОБЛАСТИ)

Intelligent logistics is a comprehensive tracking and processing system supported by modern information technology to realize system perception, overall analysis, real-time processing, optimization decision- making, system support, automatic correction, timely feedback and other functions. It is also through modern high-tech means such as smart chips, World Wide Web alliance, database, AI intelligence, etc. To realize the precision, visualization and standardized management of all links in the circulation 4.0 era, improve the intelligent decision-making and modular execution and operation ability of the supply chain system, and improve the global smart logistics operation efficiency. This paper analyzes the intelligent logistics + supply chain management and risk control of AMUR Gas Processing Plant project in Russia (hereinafter referred to as -A-Ti), and expounds the international logistics + intelligent supply chain management for EPC general contract projects in Russia and CIS countries and regions from the aspects of planning and design, transportation cost and risk control nodes. How to carry out the implementation of large-scale projects, safe transportation, cargo rights control, and enhance the international influence and competitiveness of Chinese state-owned enterprises in the world, as well as the living space in Russia and CIS countries and other important theories and practices. To explore the new model development and direction of China-Russia international intelligent logistics + supply chain in the context of the world political arena.

Research on Intelligent Supply Chain Management Mode under the Background of Industry 4.0

Research on Intelligent Supply Chain Management Mode under the Background of Industry 4.0

Impact of green technology innovation based on IoT and industrial supply chain on the promotion of enterprise digital economy.

With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we study the supply chain management of enterprises in green innovation and digital economy development and complete the identification and demand prediction of warehouse goods through the Internet of Things (IoT) and artificial intelligence (AI). As the stuff meets the goods detection and storage, we employ an intelligent method to detect and classify the goods. The demand prediction analysis is carried out based on historical data on goods demand in the enterprise. The absolute error between the prediction result and the actual demand within 1 week is less than 30 goods by the particle swarm optimization-support vector machine (PSO-SVM) method used in this article. First, the goods identification task is completed based on video surveillance data using YOLOv4, and the recognition rate is as high as 98.3%. This article realises enterprises' intelligent supply chain management through the intelligent identification of goods and the demand forecasting analysis of goods in the warehouse, which provides new ideas for green innovation and digital economy development.

Guest Editorial Introduction to the Special Issue on Intelligent Supply Chain in Modern Challenges

Ever-Growing supply chain challenges and disruptions directly or indirectly impact all industries. The global pandemic and war situations have caused a significant imbalance in the demand and supply of goods and services worldwide. These horrendous situations elevated the need for more effective supply chain management strategies. Intelligent systems and tech-led solutions are noticeably transforming almost every industry worldwide, and the supply chain and logistics sectors are facing the most significant impact. Many companies worldwide are investing heavily in intelligent supply chain management solutions to address modern global challenges.

Supply Chain Intelligent Management System Based on Big Data Background

Existing researchers have confirmed the important role of big data analysis in corporate performance. Nowadays, supply chain management between enterprises is becoming increasingly important. In addition to pursuing internal performance, how to improve supply chain performance has also become a goal pursued by enterprises. This article mainly improves the efficiency of supply chain management by constructing an intelligent supply chain management system under the background of big data. This article utilizes blockchain technology to design and develop an intelligent supply chain management system, which completes the basic functions of modern supply chain platforms such as order management, warehouse management, and traceability. This article conducted functional and performance tests on the designed and developed supply chain intelligent management system. The functional test results showed that the system had 21 defects, including 1 fatal defect, 6 severe defects, and 14 general defects. The performance test results showed that the maximum concurrent number of system order modules was 150, and the TPS was 246. These results indicate that the functionality and performance of the supply chain intelligent management system fully meet daily demand standards.

Logistics Supply Chain Management Mode of Chinese E-Commerce Enterprises under the Background of Big Data and Internet of Things

With the development of information technology, the logistics supply chain of Chinese E-commerce enterprises still has the problem of backward industrial model and low product quality. It is necessary to deeply integrate the logistics supply chain with big data and Internet technology and develop a logistics intelligent supply chain management model in order to meet the needs of Chinese E-commerce enterprises for logistics supply chain. The management mode of the logistics supply chain of Chinese E-commerce enterprises will show the development trend of diversification and simplification, standardization and normalization, and intelligence and efficiency in the future.

New Business Form of Smart Supply Chain Management Based on “Internet of Things + Blockchain”

As the digital economy promotes economic growth, the Internet of Things can solve the problem of productivity, and the blockchain can solve the problem of production relations, which realizes the industrial transformation of blockchain smart contract technology and becomes a new driving force for the industry. It is becoming more and more difficult for companies to control their own raw material supply, production, and sales by relying only on their own strength, and this is the key to influencing a company to become bigger and stronger. This problem will be efficiently solved by the implementation of “Internet of Things + Blockchain” technology. As a result, research into a new sort of smart supply chain management based on “Internet of Things + Blockchain” is required. From the perspective of building a smart supply chain, this paper makes full use of literature data methods, theoretical analysis methods, case analysis methods, logic analysis methods, and other methods. To study the effectiveness of IoT and blockchain technology through case study of changes in the order quantity in the procurement link of the supply chain. By understanding the current status of related research at home and abroad, as well as the Internet of Things technology and blockchain technology, this paper analyzes China. The main problems existing in the supply chain management of enterprises are the combination of the Internet of Things technology and the blockchain with the enterprise supply chain to create a smart supply chain platform, and the feasibility and functional efficiency of the smart supply chain platform. The related check was found, its deficiencies were found, and remedial measures were taken. The research results show that this intelligent supply chain management platform based on the Internet of Things (IoT) and blockchain can make the operation of the entire supply chain clearly visible. Information and data sharing can be achieved among the various departments of the supply chain to achieve scientific management and precision of the enterprise prediction. And this model can also be used in other industries to achieve industrial upgrading.

Intelligent Supply Chain Management Modules Enabling Advanced Manufacturing for the Electric‐Mechanical Equipment Industry

Electric‐mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high‐profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high‐end equipment manufacturers using mass customization.

Research on Intelligent Supply Chain Management Under Unconventional Emergencies

The harm of unconventional emergencies is increasingly prominent, which seriously restricts the sustainable development of society. Therefore, how to effectively deal with all kinds of unconventional emergencies and improve the ability of emergency management in the face of unconventional emergencies has attracted extensive attention from all walks of life. Therefore, the establishment of a sound emergency intelligent supply chain system is an important means to speed up the development of China’s emergency industry and enhance the emergency support capacity of government. This paper mainly analyzes the problems existing in China’s logistics system under unconventional emergencies, and puts forward the necessity and specific measures of introducing intelligent supply chain under unconventional emergencies.

Intelligent supply chain performance measurement in Industry 4.0

AbstractThe concept of Industry 4.0 has been widely recognized by the world since its introduction in 2011 Hannover Industrial Expo. Industry 4.0 strategy does not only revolutionize the manufacturing system and processes but also lead to the formation of the intelligent supply chain. Currently, Industry 4.0‐based intelligent supply chain has attracted a lot of attention from academia; however, studies on performance measurement indicator of intelligent supply chain are still lacking. To fill this gap, this paper first introduces the impact of Industry 4.0 on supply chain management. After analysing key characteristics of intelligent supply chain, the paper proposes a performance measurement indicator framework consisting of seven indicators. This indicator framework enriches the theoretical knowledge of supply chain performance evaluation and provides an efficient way to improve the operational performance of intelligent supply chain management.

Fast Discrepancy Identification for RFID-Enabled IoT Networks

Radio frequency identification has become a vital technique for enabling intelligent supply chain management. Throughout the life cycle of the supply chain, one-for-one checking is necessary at all the handover points where physical shipped inventory is checked against with the receiver’s order to discover discrepancies because of missing or misplaced objects. Such operation is so universal that the improvement over its efficiency can significantly benefit the whole supply chain. Yet, there are a few challenging issues, namely, inconsistent tags, high-volume data, and high network latency. In this paper, we first carefully analyze the characteristics of EPCglobal Network. We then design two discrepant tag identification protocols with different optimization goals including minimum communication data and minimum communication round. Also, we perform thorough analysis by comparing our proposals with state-of-the-art methods and extensive experiments to validate the effectiveness and efficiency of our methods.

Application of network methods in intelligent supply chain management

A good understanding of supply chain flows is a prerequisite for intelligent supply chain management. These flows form the basis for the structure of the supply chain elements and their interconnections. Measures from network theory are proposed as viable tools for understanding and designing supply chain structures. A supply chain interpretation of the standard network measures is presented. Tuning these measures and evaluating the resulting structures for responsiveness is explained with simple examples. A methodology is proposed for network measures based design and analysis of supply chain structures and an initial foray is made into understanding the dynamic link between supply chain structures and supply chain plans. Lessons learned from these methods can be used to develop a better understanding of structural complexity, and facilitate modelling and design of the physical and informational aspects of supply chains.

Intelligent supply chain management using adaptive critic learning

A set of neural networks is employed to develop control policies that are better than fixed, theoretically optimal policies, when applied to a combined physical inventory and distribution system in a nonstationary demand environment. Specifically, we show that model-based adaptive critic approximate dynamic programming techniques can be used with systems characterized by discrete valued states and controls. The control policies embodied by the trained neural networks outperformed the best, fixed policies (found by either linear programming or genetic algorithms) in a high-penalty cost environment with time-varying demand.