Abstract
The recent coronavirus pandemic has now unleashed a global supply chain crisis across a huge number of organizations, stemming from a lack of understanding and flexibility of the multiple layers of their global supply chains and a lack of diversification in their sourcing strategies. One of the technical options to mitigate the pandemic is to automate business processes by which heterogeneous data integration is encouraged. The convergence of Semantic Web with service-oriented computing is manifested by Semantic Web services technology. It addresses the major challenge of automated, interoperable, and meaningful coordination of web service composition in industrial applications – such as apparel business. Automatic service composition may dramatically improve the development efficiency of web service applications. This chapter proposes an approach to automatically process semantic service composition (SSC) using description logics (DLs) to provide well-defined semantics. Also, this chapter explains the role of ontologies in the architecture of the Semantic Web.
TopIntroduction
The pandemic has also created temporary “manufacturing deserts”, whereby a city, region or whole country’s output drops so substantially, they become a no-go zone to source anything apart from essential items such as food stuffs and pharmaceuticals. All manufacturing business today appreciates the value and consequence of building an effective supply chain as part of enterprise proliferation and profitability (Pal, 2017). There exist different types of industry specific supply chain (e.g. automobile, pharmaceutical, agriculture, apparel). In simple, supply chain is a system with organization, people, technology, activity, information and resource involved in, to deliver a product or service from suppliers to customers. In this way, supply chain activity transforms natural resources, raw materials and components into final products, and delivers them to customers. Therefore, a supply chain is a network of facilities and distribution options that performs the functions of material procurement, transformation of these materials into intermediate and finished products, and distribution of these finished products to customers (Pal, 2017). Supply Chain Management (SCM) aims at improving the allocation, management and control of logistical resources.
The first signs of SCM were perceptible in Toyota Motor Manufacturing’s Just-In-Time (JIT) procurement system (Shingo, 1988). Particularly, JIT was used to control suppliers to the factory just in the right quantities, to the right location, and at the right time, in order to optimize system-wide costs and customer affordability. The main goal was to reduce inventory level drastically, and to regulate the suppliers’ interaction with the production line more effectively. It consisted of two distinct flows through the supply chain organizations: material and information. The scope of the manufacturing supply chain begins with the source of supply and ends at the point of consumption. It extends much further than simply a concern with the physical movement of materials. Equal emphasis is given to supplier management, purchasing, inventory-management, manufacturing management, facilities planning, customer service, information flow, transport and physical distribution. Some of the important business processes, along manufacturing supply chain, are shown in Figure 1.
Manufacturing supply chain management tries to bring suppliers and customers together in one concurrent business process. Its main objective is to synchronize the needs of the customer with the flow of raw material from purchasers. This balances Constraint Satisfaction Problem (CSP) with appropriate customer service, minimum inventory holding cost and optimal unit cost. In this complex CSP environment, the design and operation of an effective supply chain is of fundamental importance for global manufacturing business (Taghipour, 2020) (Radhouri et al., 2018) (Cliché et al., 2020).
It is worth noting that purchasing process does not finish when the customer places an order using an existing sales channel. Customer queries, before or after order placement, are inevitable. At the same time, the seller might want to contact customers with purchase confirmation and shipping information. Customer service encompasses all points of contact between the seller and the customer and is an important output of SCM. It results from the accumulated value of all business processes along the supply chain. These business processes are responsible for offering an acceptable level of customer service. Moreover, these business processes are also interdependent, if one business function fails to provide the expected level of customer service then chain is disrupted, and the scheduled workload in other areas is destabilized. Consequently, customer satisfaction is the casualty.
Figure 1. Diagrammatic representation of supply chain business processes
Key Terms in this Chapter
Supply Chain Coordination: A supply chain consists of a network of key business processes and facilities, involving end users and suppliers that provide products, services and information. In this chain management, improving the efficiency of the overall chain is an important factor; and it needs at least four important strategic issues to be considered: supply chain network design, capacity planning, risk assessment and management, and performances monitoring and measurement. Moreover, the details break down of these issues need to consider in the level of individual business processes and sub-processes; and the combined performance of this chain. The coordination these huge business processes and their performances are of immense importance.
Ontology: Information sharing among supply chain business partners using information system is an important enabler for supply chain management. There are different types of data to be shared across supply chain, namely – order, demand, inventory, shipment, and customer service. Consequently, information about these issues needs to be shared in order 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 conceptualizations among hardware/software agents, customers, and service providers) are known as explicit ontologies; and these help to exchange data and derived knowledge out of the data to achieve collaborative goals of business operations.
Rule-Based Reasoning: In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g. IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.
Semantic Web service: The advantages of integrating and coordinating supply chain business partners’ information service applications, which are loosely distributed among participants with a wide range of hardware and software capabilities, are immensely important issue from operation of global supply chain. Web service is an information technology-based solution for system interoperability; and in this technology business services are described in a standard web service description language (WSDL). Establishing the compatibility of services is an important prerequisite to service provision in web service operation. Web service has embraced the concepts of enriching distributed information systems with machine-understandable semantic metadata (known as ontology); and these new breed of web services are known as semantic web service. In this way, semantic web service provides a common framework for web-based services, which allows data to be shared and reuse across application, enterprise, and extended community boundaries.
Description Logic: Knowledge-based software system relies on its stored knowledge and decision-making mechanisms. At the time of knowledge-based system design and development stages, software engineers use different knowledge representation techniques; and one of the techniques is symbolic logic-based representation. Different symbolic logic representation is used for knowledge presentation purpose. Description Logics (DLs) are a family of knowledge representation languages that can be used to represent the knowledge of an application domain in a structured way.
Case-Based Reasoning: Case-based reasoning (CBR) is one of the useful mechanisms for both modeling human reasoning and building intelligent software application systems. The basic principle of case-based reasoning systems is that of solving problems by adapting the solution of similar problems solved in the past. A CBR system consists of a case base , which is the set of all cases that are known to the system. The case base can be thought of as a specific kind of knowledge base that contains only cases. When a new case is presented to the system, it checks the case base for similar cases that are most relevant to the case in hand, in a selection process . If a similar case is found, then the system retrieves that particular case and attempts to modify it (if necessary) to produce a potential solution for the new case. The process is known as adaption .