Managing Manufacturing Knowledge in Europe in the Era of Industry 4.0

Justyna Patalas-Maliszewska

• Editorial: Routledge
• Año: 2022
• ISBN:
  9781000619027
• Edición: 1
• Categoría: Business
• Categoría: Business generale

Manufacturing companies need to adapt to the requirements of functioning in the era of Industry 4.0 and major technological disruptions. The use of knowledge-based decision support tools has also become necessary in order for enterprises to survive in a competitive environment. This book offers a new approach to designing the knowledge management process and integrating it with the implementation of Industry 4.0 technology.

The book presents the methods used in a customer-oriented organisation for management of manufacturing knowledge. More specifically, methods for defining and collecting customer requirements are presented and methods on how to receive manufacturing knowledge, as well as how to formalise the acquired knowledge using key technologies of Industry 4.0, are discussed. The author also presents real case studies from Western and Central Europe and offers recommendations for the production manager. The instrumentation of methods and tools to support knowledge management, in the production of individualised products presented therein, will allow the manufacturing company to be transformed digitally into a customer-oriented organisation operating in accordance with the assumptions of Industry 4.0.

This book will be a valuable read for production researchers, academicians, PhD students and postgraduate-level students of industrial engineering and industrial management. The practical case studies will also make the book a useful resource for managers of manufacturing enterprises.

1 Manufacturing knowledge in the era of Industry 4.0

DOI: 10.4324/9781003263708-2

1.1 Customer-oriented organisation

The pressure to cooperate closely with customers is of especially great significance for manufacturing enterprises functioning under the new conditions of the market. Developing new business models during the continuous digitalisation of work activities within supply, production, service and delivery functions, not to mention customer communications and service processes, requires changes at the management level. Moreover, the need to reduce employee turnover is paramount in order to guarantee that at least some key activities in an enterprise can continue, automatically, especially in terms of long-term, severe disruptions. There is even something to be said for introducing automated technologies by replacing employees with robots, thus relieving them from repetitive tasks; this is a huge challenge for management boards.

Introducing changes to the management model in an enterprise requires changes both to the structure of an organisation or to the organisation of the workflow, and to the development strategy of the enterprise. The management board is aware of the new trends in the development of manufacturing and of the need to be more adaptable and knowledge-based, better connected, integrated and networked and also more intelligent, autonomous and self-optimising (ElMaraghy et al., 2021). It seems that designing a new approach to managing manufacturing knowledge (the so-called architecture for manufacturing knowledge), which will be applied to the structure of a customer-oriented organisation in the context of Industry 4.0 (I4.0), may become a reference pattern, the implementation of which in manufacturing will enable it to provide the changes according to the four defined paradigms of the evolution of manufacturing systems: products, technology, business strategies and production (ElMaraghy et al., 2021).

The reference pattern proposed is based on the following elements in the context of the evolution of manufacturing systems:

• Product evolution – mass customisation and personalisation.
• Technological evolution – I4.0 technologies.
• Business strategy evolution – sociotechnical strategy.
• Production evolution – adaptive cognitive manufacturing systems.

Mass customisation (MC) is treated as the model in which manufacturing offers a mass-customised product/service at a reasonably low price, balancing the requirements both of the consumer and of the company. MC is considered an important strategy in ensuring higher profitability and better relations with clients (Franke et al., 2009; Zhang and Zheng, 2021). Personalisation can be understood as the level at which a product or service is tailored to meet the requirements of the individual customer (Bilgihan et al., 2016). Personalised production means offering unique products or services through close integration with customers, not only during the design process but also during production, servicing and when introducing changes to products and services.

I4.0 is known as the integration of people and digitally controlled machines with the internet and information technologies. Making a strategic decision on the implementation of selected I4.0 technologies may be helpful in transforming an enterprise into a customer-oriented organisation, not only for larger enterprises but also for small and medium-sized enterprises (SMEs) (Müller et al., 2021).

In order to design the architecture reference model for the management of manufacturing knowledge, it is necessary to build a catalogue of I4.0 technologies, from which the appropriate technology can be selected in order to support the implementation of a given stage of the manufacturing knowledge management (MKM) process. I4.0 includes five main approaches: the Internet of Things (IoT), the cyber-physical system (CPS), information and communications technology (ICT), enterprise architecture (EA) and enterprise integration (EI) (Yli-Ojanperä et al., 2019).

IoT is treated as an infrastructure of physical and virtual objects in communication with each other using information and communication technologies (https://www.itu.int/ITU-T/recommendations/rec.aspx?rec=y.2060). The implementation of IoT-based solutions within manufacturing can significantly improve productivity (Jagtap et al., 2021) by providing real-time monitoring of products (Cui et al., 2021). In that context, therefore, IoT helps to conduct closer and valuable interactions between workers and customers. IoT is connection technology to today’s low-power wide-area network (LPWAN) technology, from traditional, cellular communication and local IoT technology (Lakshmanaprabu et al., 2019). IoT for manufacturing can be defined as the infrastructure of sensors, actuators and edge nodes and the IoT platform to process, analyse and integrate the data, information and knowledge included in information and communications technologies adopted within a company (Figure 1.1). In the context of Industry 4.0, IoT has been extended to the Internet of Things and Services, the Industrial Internet of Things (IIoT), the Internet of Everything and Internet 4.0 (Gilchrist, 2016). The infrastructure of IIoT is similar to IoT for manufacturing. The Industrial Internet supports business strategy development and realisation, integrates the digital and real worlds across the entire value chain, and enables horizontal and vertical integration within manufacturing (Pivoto et al., 2021).

Figure 1.1 Infrastructure IoT for manufacturing. Based on Garg et al. (2021).

The CPS enables the simplification of the physical system and uses IoT to collect and exchange data and information between workers and external partners. Nowadays, it is important to provide real-time communication, not only between machines and products, but also between clients, service providers and stocks, most interactions of which should be autonomous. (Hermann et al., 2015). The 5C architecture of CPS (Figure 1.2) is proposed for manufacturing and allows the factors and resources of processes to be coordinated thanks to information and communications technologies and unlimited access to distributed industrial data (Pivoto et al., 2021).

Figure 1.2 Architecture of CPS. Based on Pivoto et al. (2021).

ICT, which is applied in manufacturing, can be treated as information systems in three layers: the business layer, the operational layer and the control layer.

The information systems and models of an organisation build the EA. In the context of customers and Industry 4.0, EA represents the main manufacturing elements at three managerial levels, namely operational, tactical and managerial, along the lines of digital transformation (Petrasch and Hentschke, 2016; Alcácer and Machado, 2019). It also can be treated as a conceptual framework for the development of smart manufacturing or for the transformation of enterprises into smart manufacturing. In that context, EI can be understood as enterprise application integration (EAI), application integration (AI), systems integration (SI), value chain integration (VCI), extended business integration (e-BI) and e-business integration (Fazlollahi and Franke, 2018).

Introducing technological changes to an enterprise results in changes to other areas of the manufacturing system. The implementation of I4.0 technologies in manufacturing should lead to a rethink of relationships between human and machine. It is indicated that in complex systems human and machine should be treated as a dynamic unit or a cooperating team (Hoc, 2000). The literature on the subject presents a number of definitions of human–machine interaction (HMI), including (1) the behaviour between human and machine (Mithun and Bakar, 2017); and (2) the interaction between people and computers (Gautam and Singh, 2015). It should be emphasised that successful HMI implementation requires an understanding of the organisational aspects of an organisation (Lodgaard and Dransfeld, 2020) and that HMI is considered a key factor for managing flexible production (Hernandez et al., 2020). Recent research in the field of HMI shows that the increasing complexity of production processes exposes operators to high cognitive loads that may affect their performance and emotions (Lušić et al., 2016). Adaptive systems enable the scope in which the activities between human and machine can be varied and enlarged. The two aspects of the evaluation of a manufacturing system should be more balanced, that is, between the cognitive abilities of the workers (learning, adaptability and problem-solving) (AlMulhim, 2020), and the collaborating machines or robots (Burggräf et al., 2021).

Smart manufacturing can meet the dynamic requirements of the customer, thanks to industrial assets, well-connected by IIoT, collaborative work and a well-designed process of MKM. This MKM process should be integrated by implementing I4.0 technologies and should be adapted to the structure of the customer-oriented organisation in the I4.0 concept. Regarding the customer as integrating “actors of the processes of things, data, people and services” (Sanchez et al., 2020) enables t...