Abstract
Data-driven decision-making is the lifeblood of supply chain business processes' smooth execution. Technological development is ushering in the possible improvement of supply chain data-processing architecture. The centralized server-based data-processing system has been dominating industrial supply chain management for a long time with the challenge of scalability, data integrity, and information security. In recent years, the internet of things (IoT) with service-oriented architecture (SOA) and semantic technology has eliminated the drawbacks of server-based centralized working practices. This approach enhances the information processing performance for resource-constrained supply chain operations through semantic data integration and system security enhancement. This chapter presents an IoT with semantic web service-based information system architecture to allow safe data flow along the supply chain networks. Finally, the chapter describes a semantic similarity assessment algorithm to realize the advantage of this architecture.
TopIntroduction
Wireless sensor networks (WSNs) based on the Internet of Things (IoT) technology have many potential industrial supply chain management applications. The IoT paradigm encompasses ubiquitous computing, pervasive computing, data communication protocols, sensing technologies, and embedded devices, and they merge selectively to form an information system where the physical and digital worlds meet. They can serve different categories of business services through continuous interactions. In this way, they formed an interconnected information system through the Internet, exchanging data and information to create services that bring tangible benefits to the industrial world optimize business connectivity, and its supply chain operations.
In addition, the advent of development and adoption of new technologies in supply chain operation is continuing. This continuation is driven by (i) the cumulative nature of technological change, (ii) the exponential nature of technologies such as microchips that are doubled in power every two years for more than half a century, (iii) the convergence of technologies into new combinations; (iv) drastic reduction in costs of production; (v) the emergence of digital "platforms of platforms" – most prominently the Internet; and (v) adoption of artificial intelligence (AI) techniques, the Internet of Things (IoT), and cognitive technologies have successfully been applied to various industrial applications (Zhao & Kumar, 2021). In addition, IoT has paved the way for many industrial application domains while posing several challenges as many devices, protocols, communication channels, architectures, and middleware exist. Big data generated by these devices calls for advanced machine learning (ML) and data mining techniques to understand, learn effectively, and reason with this volume of information, such as cognitive technologies. Cognitive technologies play a significant role in developing successful cognitive systems which mimic "cognitive" functions associated with human intelligence, such as "learning" and "problem-solving".
AI-based applications now help to solve real-world problems, including image recognition, problem-solving, and logical reasoning that sometimes exceed human performance. AI applications, particularly robotics, can transform production processes and business activities, specially manufacturing. Big data technologies are opening new opportunities and enabling breakthroughs related to, among others, manufacturing data analytics addressing different perspectives: (i) descriptive to answer what happened, (ii) diagnostic to answer the reason why it happened, (iii) predictive to understand what will happen and (iv) prescriptive to detect how human operators can make it happen.
Undoubtedly, the potential impact of big data technology can bring massive changes to the economy and society, boosting innovations in organizations and improving business models. Besides, today, the Internet has become ubiquitous, has influenced almost every corner of the world, and affects human life unimaginably. However, the journey is far from over. Human society is entering an era of even more pervasive connectivity where many service applications will be connected to the Web. Human society is entering an era of the IoT. Different academics and practitioners have defined this term in many ways. The IoT is a things-connected network wirelessly connected via smart sensors; IoT can interact without human intervention. Some preliminary IoT applications have already been developed in the manufacturing, transportation, and automotive industries (He et al., 2014; Pretz, 2013). The evolution of IoT involves many development issues, such as infrastructure, communications, interfaces, protocols, and standards. The IoT refers to a new world where almost all devices and appliances are connected to a network. In addition, industrial application service providers can use them collaboratively to achieve complex tasks requiring high intelligence.
Key Terms in this Chapter
Ontology: Information sharing among supply chain business partners using information systems is an important enabler for supply chain management. There are diverse types of data to be shared across the supply chain, namely – order, inventory, shipment , and customer service . Consequently, information about these issues needs to be shared to achieve efficiency and effectiveness in supply chain management. In this way, information-sharing activities require that human and / or machine agents agree on common and explicit business-related concepts (the shared conceptualization among hardware / software-agents, customers, and service providers) are known as explicit ontologies; and this help to exchange data and derived knowledge out of the data to achieve collaborative goals of business operations.
RFID Reader: An RFID transceiver, providing real and access to RFID tags information.
Web Ontology Language (OWL): The Web Ontology Language (OWL) is a semantic mark-up language for publishing and sharing ontologies on the Web. OWL is developed as a vocabulary extension of RDF (the Resource Description Framework) and is derived from the DAML + OIL Web Ontology Language.
Semantic Web service: A Semantic Web Service, like conventional web services, is the server end of a client-server system for machine-to-machine interaction via the Web. Semantic services are a component of the semantic Web because they use mark-up which makes data machine-readable in a detailed and sophisticated way (as compared with human-readable HTML which is usually not easily "understood" by computer programs).
Description Logic: Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic , but less expressive than first-order logic .
RFID Tag: An RFID tag (or transponder), typically consisting of an RF coupling element and a microchip that carries identifying data. Tag functionality may range from simple identification to being able to form an ad hoc network.
EPC: Electronic Product Code (EPC), is a low-cost RFID tag designed for consumer products as a replacement for the universal product code (UPC).
Supply Chain Management: Supply chain management encompasses the planning and management of all activities involved in sourcing, procurement, manufacturing, and distribution. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. Supply chain management integrates supply and demand management within and across companies.
Internet of Things: Internet of Things (IoT) means networks of things, software, sensors, network connectivity, and embedded ‘things or physical objects. It collects or exchanges data. IoT makes objects sensed or controlled through a network infrastructure, supports integration between physical real world and automated information systems, and brings various effects such as improved productivity or economy in manufacturing industries.