Smart Supply Chain Research Articles

Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America.

The emerging information and communication technologies (ICT) related to Industry 4.0 play a critical role to enhance supply chain performance. Employing the smart technologies has led to so-called smart supply chains. Understanding how Industry 4.0 and related ICT affect smart supply chains and how smart supply chains evolve with the support of the advanced technologies are vital to practical and academic communities. Existing review works on smart supply chains with ICT mainly rely on the academic literature alone. This paper presents an integrated approach to explore the effects of Industry 4.0 and related ICT on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well.

Implementation Of Active RFIDs In Inventory Systems

The commoditization of integrated circuits has revolutionized the world of IoT based networks. Supply chain management relies heavily on the application of passive RFID tags for inventory management. However, this technology is outdated and does not provide real-time monitoring for warehouse administration. Moreover, due to the high error rate of traditional passive RFIDs, active RFIDs are graded higher in terms of operational efficiency. The biggest challenge in the deployment of active RFIDs in inventory systems is high power consumption. The passive RFIDs were favored due to their lack of dependency on an internal power supply. The inception of low power long range radio modules has fully transformed the range of applications for active RFIDs. The long range low power active RFIDs are used to develop IoT ecosystems for the purpose of creating a smart supply chain management system. In this research, the hierarchical wireless mesh network consists of three levels; the end node, the concentrator and the gateway. The model takes advantage of low power long range radios to design a system that can monitor and track inventory in a warehouse. The unique circuit has small and compact properties that has the potential to be deployed in real life warehouses.

Investigation of Industry 4.0 in the Robotized Millennium

. Industry 4.0 Wireless Supported by connectivity and sensors Factories has machines. These sensors Display the entire production line and monitor and can make its own decisions are integrated into one system. Industry 4.0 Smart production Uses processes to produce essential waste products to meet the COVID-19 epidemic shortage. Patients need themselves Essential medical supplies to get on time, during this crisis Medical disposable items and Smart supply chain of equipment it provides Industry is 4.0 as a flexible product line Smart system used. Intelligence (I) by RTC, real-time information on Internet of Things (Yoda) and almost all other digital technologies for the entire production process. Any medical area Design and upgrade pre-designed Using software is done quickly and to print the required components Digital production such as 3D printing Uses technologies. Industrial 4.0 Technologies COVID19Diagnose symptoms of, It is related to this disease Helps to avoid the chances of getting sick Can predict. Possible health problems and expected recovery Helps keep track of opportunities. Govt for 19 explosions Assistive Industry 4.0 Notable Technologies. Cross New Business Opportunities Is open. However, flexibility, Performance and product customization the requirements of Real to handle value creation Procedures no longer apply. Industrial 4.0, (Fourth Industrial Revolution) is a term coined in Germany; more important ideas were first published in 2011.It is the internet of future technologies Physical systems (CPS), Integration in the Internet and production systems as defined. Control computers in real time, Plan and predict Helps. In the field of technology, in the production of this concept causing revolution and related services have been discussed in research for many years. One of the basic principles for creating an industrial 4.0 environment is to connect organizations and Working value chain individually and Control each other arbitrarily.

The Impact of Blockchain Technology and The Internet of Things on the Agile and Sustainable Supply Chain

In addition to being a revolutionary technology for all industries, the Internet of Things; has also shown its potential in processes such as supply chain. With the advancement of technology and the emergence of the new era, the challenges of the traditional supply chain that follows a path from top to bottom (suppliers of raw materials to final consumers) are revealed. Also, due to environmental issues and the increasing speed of the changes in the needs of customers in various industries, including the retail industry, which has undergone many changes with the emergence of innovation, the entire path of the supply chain requires the implementation of strategies such as agile and green supply chains and achieving smartness. Therefore, emerging technologies such as The Internet of Things play a significant role in the agility and greenness of the entire retail supply chain. The presence of blockchain technology with decentralized data archiving in supply chain processes based on the Internet of Things guarantees the security of big data and helps in optimal data performance. This helps the agility of supply chain processes. In this chapter, dimensions, components, and key indicators effective in the implementation of an agile smart supply chain based on transformative technologies are examined.

A combined forecasting method for intermittent demand using the automotive aftermarket data

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation, and accurate demand forecasting can reduce costs and increase efficiency for enterprises. This study proposes an intermittent demand combination forecasting method based on internal and external data, builds intermittent demand feature engineering from the perspective of machine learning, predicts the occurrence of demand by classification model, and predicts non-zero demand quantity by regression model. Based on the strategy selection on the inventory side and the stocking needs on the replenishment side, this study focuses on the optimization of the classification problem, incorporates the internal and external data of the enterprise, and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning, respectively. Based on the real data of auto after-sales business, these methods are evaluated and validated in multiple dimensions. Compared with other intermittent forecasting methods, the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision, which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice. The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.

Analysis of the Challenges of Artificial Intelligence of Things (AIoT) for the Smart Supply Chain (Case Study: FMCG Industries).

In today’s competitive world, supply chain management is one of the fundamental issues facing businesses that affects all an organization’s activities to produce products and provide services needed by customers. The technological revolution in supply chain logistics is experiencing a significant wave of new innovations and challenges. Despite the current fast digital technologies, customers expect the ordering and delivery process to be faster, and as a result, this has made it easier and more efficient for organizations looking to implement new technologies. “Artificial Intelligence of Things (AIoT)”, which means using the Internet of Things to perform intelligent tasks with the help of artificial intelligence integration, is one of these expected innovations that can turn a complex supply chain into an integrated process. AIoT innovations such as data sensors and RFID (radio detection technology), with the power of artificial intelligence analysis, provide information to implement features such as tracking and instant alerts to improve decision making. Such data can become vital information to help improve operations and tasks. However, the same evolving technology with the presence of the Internet and the huge amount of data can pose many challenges for the supply chain and the factors involved. In this study, by conducting a literature review and interviewing experts active in FMCG industries as an available case study, the most important challenges facing the AIoT-powered supply chain were extracted. By examining these challenges using nonlinear quantitative analysis, the importance of these challenges was examined and their causal relationships were identified. The results showed that cybersecurity and a lack of proper infrastructure are the most important challenges facing the AIoT-based supply chain.

Problem identification model of agricultural precision management based on smart supply chains: An exploratory study from China

Nowadays, agricultural precision management (APM) has become an important way to transform the agricultural industry and realize the sustainable development of the agricultural supply chain. However, there is no relevant research based on the agricultural supply chain to explore the problems in the process of APM currently. There is also no literature to construct a problem identification model to provide guidance for corporate practice and provide suggestions for the government to issue encouraging policies. In order to fill the gaps, this paper adopts grounded theory method, interviews 24 business managers who carry out APM through a combination of personal in-depth interviews and focus group interviews, uses continuous comparative analysis ideas, and continuously refines and revises theories, and innovatively proposes an identification model of APM based on supply chain. It found that the four main categories of supply chain coordination, production resource support, scientific and technological equipment and structural system construction are the problems that hinder APM. Based on the problem identification model, this paper puts forward suggestions for the better development of APM from two aspects: corporate practice and government policy.

Impact of LoRA and 5G on Smart Manufacturing from Automation Perspective

The Industrial Internet of Things (IIoT), also known as Industry 4.0, is a forward-thinking industrial era in which numerous developing technologies are combined to produce digital solutions. In industrial enterprises, Industry 4.0 technologies are separated into base and front-end technologies. Cloud computing, IoT, analytics, and big data are all essential technologies. In this article, we evaluate the current landscape of factory automation and the influence of 5G and LoRA communication on smart manufacturing automation, while front-end technologies are split into four categories: smart goods, smart working, smart manufacturing, and smart supply chain. First, we looked at the idea of Industry 4.0, as well as the four front-end dimensions and four basic technological components. Second, we spoke about smart industrial automation and the influence of 5G and LoRA communication. The broad use of front-end technology, of which Smart Manufacturing is a crucial component, is required for Industry 4.0 to succeed. However, the implementation of the foundation technologies is difficult. Current automation standards will serve as a solid foundation for future smart manufacturing systems. The distribution, adoption, and development of relevant morality in a requirement-driven process may result in faster, deeper, and bigger implementation of smart industrial automation.

Readiness and Maturity of Smart and Sustainable Supply Chains: A Model Proposal

Many companies embrace Industry 4.0 technologies to enable operational sustainability against increasing climate change effects, decreasing natural resources, and raising consumer awareness of environmental issues. Even though readiness and maturity assessment of smartness and sustainability concepts are nested, no study simultaneously focuses on these concepts. As pioneering research, we propose a novel model titled “Smart and Sustainable Supply chain Readiness and Maturity model (S3RM)” and validate it by conducting a case study in the automotive industry. We design our model upon the triple-bottom-line (TBL) approach consisting of smartness and sustainability dimensions. Our study introduces the TBL of smartness covering availability, integrity, and adaptability sub-dimension. TBL of sustainability includes social, environmental, and economic sub-dimensions. The proposed model calculates the Smart and Sustainable Readiness and Maturity Index by averaging sustainability scores’ summation and smartness scores’ multiplication. Each sub-dimension consists of items measured by a readiness and maturity scale. The findings suggest how smartness and sustainability items create strengths, weaknesses, opportunities, and threats for the supply chain operations. Our model provides managerial implications in assessing the readiness and maturity of Industry 4.0 tools and sustainability indicators. This study offers a road map to managers on smart and sustainable supply chains’ defined target areas.

AIoT-based Sustainable Smart Supply Chain Framework

Purpose: Supply chains in today's global environment operate in a market that is increasingly complex and dynamic in nature. In such an environment, a stable supply chain to respond to drastic changes in customer needs becomes inevitable. Based on these studies, it is obvious that organizations operating in the field of supply chain should accelerate their focus on sustainability and use technologies such as "Internet of Things" (IoT) and artificial intelligence to achieve the organization's goal of creating sustainable processes. Methodology: The presence of the Internet of Things, along with artificial intelligence technology, has created Artificial intelligence of things (AIoT) technology, which gives the big data from the Internet of Things great power. In this research, an attempt has been made to study and analyze the key dimensions, components and indicators of the AIoT-based sustainable supply chain. Also, a conceptual framework for Padidar intelligent supply chain based on these evolving technologies is presented, which can help to understand the elements of this intelligent supply chain in order to optimize. Findings: A study of the literature shows that investing in this technology to achieve sustainable benefits is inevitable. In addition, the use of this technology due to networking and the presence of the Internet requires appropriate security solutions for information technology, a workforce with the required set of skills, sharing information in an integrated environment with business partners. Originality/Value: The generalities of AIoT-based sustainable supply chain components in general, and things can be added to different industries and behaviors. Understanding the dimensions of this framework can always help to implement it effectively.

Research and implementation of high priority scheduling algorithm based on intelligent storage of power materials

The Smart Supply Chain technology has been gradually established and improved as a popular power system over different application fields, which raises the development of informatization and intelligence in intelligent warehouse management. Intelligent scheduling systems are eagerly required to standardize warehouse operation management and intelligently schedule inbound and outbound tasks, maximizing the rational use of intelligent devices and realizing the automation unmanned operations in actual scheduling situations. Since the scheduling algorithm is the key component of the intelligent scheduling system that acts as the most powerful tool adding automation and intelligence to electric power material storage, this paper studies the principle of the high-priority based scheduling algorithm carefully and proposes a novel data model based on the algorithm to improve the execution efficiency and performance of intelligent scheduling system. The superiority of the proposed algorithm is verified by its principle analysis and comparing with other state-of-the-art methods, ensuring that our algorithm is perfectly applicable in actual scheduling systems. Additionally, we design an efficiency scheduling system satisfying the needs of intelligent warehouse management, and further demonstrate our algorithm works well in realizing the automatic loading and unloading operation in the storage of electric power materials under unmanned condition with simulation experiments.

Green Supply Chain Performance Analysis Under Industry 4.0 Using Fuzzy Intellectual Approach

SThe industry 4.0 possesses the intelligent network and interrelation with components of production systems and provides the feedback digitally across the industrial supply chain. In last decade, the growth of industry 4.0 research is expanded across industries i.e. steel, oil, electricity pulp and paper industry. Supply Chain 4.0 is the system of the Internet of Things (IoT), which explores the advanced flexible operations, smart supply chain network, and the enhances the utility of smart analytics of big data in SCM i.e., introducing the sensors, wiles less network throughout supply chain, build the networks, and automate operations to considerably improve the performance and customer’s satisfaction. In this research paper, a green supply chain 4.0 multi level hierarchical model is established by considering the eleven green practices. The model is assisted by in centre of Centroid Method for ranking industry 4.0 industrial practices. The results are exhibited in conclusion section.

Rotary Kiln Thermal Simulation Model and Smart Supply Chain Logistics Transportation Monitoring Management

Rotary kiln is a large-scale instrument for industrial firing of cement. Due to its thermal insulation characteristics, this article studies the application of rotary kiln in supply chain logistics transportation. The main research focus of this paper is the thermal simulation model of rotary kiln and intelligent supply chain logistics transportation monitoring management. This paper analyzes the rotary kiln and its parameters and then designs a thermal simulation model of the rotary kiln. Then this article also combines the relationship between logistics and supply chain, studies the characteristics of supply chain, summarizes and designs a new type of smart supply chain logistics transportation method, and then applies the rotary kiln thermal simulation model to this new type of transportation method. In order to optimize its transportation efficiency and thermal insulation degree, this paper designs the supply chain optimization experiment and the rotary kiln simulation thermal numerical optimization experiment. This article also carries out the overall efficiency analysis of logistics based on DEA and analyzes the results of the experiment and applies it to the intelligent supply chain logistics transportation method of the thermal simulation model of the rotary kiln and compares this new type of transportation method with the traditional transportation method. The experimental results show that the intelligent supply chain transportation method based on the thermal simulation model of the rotary kiln improves the insulation effect by 5%–9% compared with the traditional transportation method. Compared with the traditional transportation method, the transportation efficiency of the smart supply chain transportation method based on the thermal simulation model of the rotary kiln has increased by 4%–8%.

Design and Develop the B2C Farming Information System Solution using Mobile and Web Geospatial Development Technologies for Smart Supply Chain of Fruit Crops of UP, India

The main objective of writing this paper is to use innovative blend of open source geospatial and software development technologies, for maximizing benefits to both exporter/buyer and grower/farmer, by establishing smart supply chain of the fruit crops of UP, India The striving endeavour drone to build and/or utilize develop GIS, web GIS Portal, and Mobile Application by synergizing computer . In this, this anomalous problem can be solved by creating a web GIS portal and mobile application by coordinating computer science, GIS, remote sensing and mobile technology. After analyzing the satellite remote sensing data, the information of various Orchards are extracted and converted to GIS feature geodatabase. The format is converted and imported into the mobile application, with the help of Geographical Coordinates, the fruit related information of their field be filled by grower/farmer through handy user-friendly vernacular Mobile Application. The submitted information pulled on created user-friendly GIS Portal. After filling it, the web will be updated automatically on the GIS portal. With the help of latitude and longitude, all the information of crop information of the farmer such as which variety of fruit is available in the field and how much quantity can be filled in the mobile application. After filling it, the web will be updated automatically on the GIS portal. All buyer's list will already be available on the web GIS portal, this will end the problems of both the farmer and the buyer.

Blockchain technologies for interoperation of business processes in smart supply chains

The Smart Manufacturing paradigm requires support by smart supply chains with capabilities of self-organizing and self-optimizing capabilities to accommodate uncertainties. Conventional supply chains face challenges in optimizing logistic processes and reducing overall costs of products and services. The emerging Internet of Things, Cyber-Physical Systems, and blockchain have laid the foundation to support interoperation in satisfying scalability, security, flexibility, and responsiveness of supply chains. Blockchain has shown potential in improving scalability and security. We are highly motivated to scrutinize technical obstacles of integrating blockchain into smart supply chains in the industrial Internet of Things environment. Our attempt is to integrate blockchain into Service-oriented Architecture for seamless interoperations. The system architecture and framework are presented and prototyped to enable Blockchain for the cooperation of business processes. The simulation has shown the effectiveness in improving the scalability, robustness, and adaptability of business processes in a smart supply chain.

Optimal subsidy strategies in a smart supply chain driven by dual innovation

Due to the deep integration of modern information technology, supply chain management has moved into a new stage of a smart supply chain. Considering the dual smart innovation of the manufacturer's production and retailer’s service, the manufacturer-led Stackelberg game model is constructed in the smart supply chain. Under the single and coordinated government subsidy strategies, the optimal decisions of the smart supply chain are researched, and the impacts of manufacturers' risk aversion on the government subsidy strategies and supply chain decisions are analysed. In addition, the efficiencies of different government subsidy strategies are compared and analysed by numerical simulation. Finally, the results show that: (i) The moderate risk aversion by the manufacturer can improve social welfare and help provide consumers with more affordable products. (ii) The government expenditure and product prices are highest under the coordinated subsidy strategy. (iii) Subsidising manufacturers is more beneficial than subsidising retailers among the two single government subsidy strategies. (iv) In general, the coordinated government subsidy strategy is more effective than the single subsidy strategy for the innovative development of a smart supply chain. In conclusion, the research provides a significant practical reference for jointly building the smart supply chain.

How to apply the ERP model for Smart Mining?

For a long time, and especially today, the energy crisis has been a limiting factor for the growth and development of the world economy. On the other hand, improving the reliability and readiness of energy production systems is becoming a first class priority for research and development institutions around the world. Therefore, the process of production, transport, distribution and usage of energy is increasingly becoming a very important part of smart systems, whose basic framework is Industry 4.0. Thus, starting from the analogies between industrial manufacturing and mining (i.e. “ore production”), the concept of smart mining is developed. This model has three dimensions: (i) application of advanced digital technologies (Cloud Computing and Internet of Things) with automated Cyber-Physical Systems (CPS), Adaptive Manufacturing Processes (depending on working conditions) and Control of Manufacturing Processes (with optimal resource usage); (ii) Smart Maintenance of CPS (for machinery and equipment); and (iii) Smart Supply Chains (procurement of materials and spare parts / delivery of final products). Deeper analyses have shown that most of the Industry 4.0 elements could be applied with some modifications in mining (there are 45 in total, and analyses have shown that 32 of them can be successfully applied in smart mining) – which was the starting point for the ERP model presented in this paper. The developed ERP model has three main parts: a virtual part based on the Cloud Computing model (SaaS model) and usage of Internet of Things to connect different business processes (procurement, sales, management, finance, warehousing, downtime monitoring etc.), the manufacturing part (coal production in open-pit mine) and the technology process part (monitoring and maintenance of auxiliary machinery). This paper presents the developed and partially implemented ERP model for Industry 4.0 in smart mining at one surface coal mine in the Republic of Serbia.

Machine Learning For the Future Integration of the Circular Economy in Waste Transportation and Treatment Supply Chain

Machine-learning technologies are key enablers to enhance the circularity in smart supply chain distributions. The digital and sustainable supply chain is a tangible representation of circular economy future growth that takes environmental challenges, by reducing materials consumption for manufacturing and reducing transportation emissions. Yet, machine learning, circular economy and digital supply chain are novel disciplines. There is limited research to fully acknowledge the promise of circular approaches in the context for waste treatment and transportation. To fill in this gap, this study investigate the integration of circularity using machine-learning techniques for waste treatment supply chain. Resulting in a circular economy framework that integrate several tools and concepts, which will assists manufacturers in implementing circular solutions for waste and leachate treatment and transportation. The aim of the study is to present the idea of the approachal application of the opportunities offered by digital technologies and the circularity for waste processing organizations in term of their smart supply chain management. The proposed framework may be handy for practitioners to develop coordinating operations throughout waste treatment supply chain, with the circular economy concepts and digital technology opportunities.

A Quality Information Transmission Framework Based on Smart Supply Chain with the Integration of M-Qfd and Sentiment Analysis

A Quality Information Transmission Framework Based on Smart Supply Chain with the Integration of M-Qfd and Sentiment Analysis

Fundamental Models for Enterprise Collaborations in Smart Supply Chains

Fundamental Models for Enterprise Collaborations in Smart Supply Chains