Community of Practice for Product Innovation

Community of Practice for Product Innovation Towards the Establishment of Industry 4.0 Mohammad Maqbool Waris1 ✉ , Cesar Sanin1, and Edward Szczerbicki2 (

)

1

The University of Newcastle, Callaghan, NSW, Australia [email protected], [email protected] 2 Gdansk University of Technology, Gdansk, Poland [email protected]

Abstract. The aim of this paper is to present the necessity of formulating the Community of Practice for Product Innovation process based on Cyber-Physical Production Systems towards the establishment of Industry 4.0. At this developing phase of Industry 4.0, there is a need to define a clear and more realistic approach for implementation process of Cyber-Physical Production Systems in manufac‐ turing industries. Today Knowledge Management is considered as the next arena of global competition. One of the most promising areas where Knowledge Management is studied and applied is product innovation. This paper explains the efficient and systematic methodology for Knowledge Management through Community of Practice for product innovation, thus connecting manufacturing units at global level. Keywords: Smart Innovation Engineering · Product innovation Cyber-Physical Production Systems · Set of experience Community of Practice · Industry 4.0

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Introduction

The process of Product Innovation is a series of knowledge activities, such as collecting, assimilating, creating, storing, and reusing knowledge at different stages. Knowledge Management (KM) has become the only sustainable competitive advantage for manu‐ facturing organizations in the current turbulent context [1], also product innovation is a continuous learning process rather that a sporadic event. These organizations are also facing continuous market changes and need for short product life cycles [2]. Proper knowledge management therefore plays an important role in product innovation. The fourth industrial revolution, originally initiated in Germany as Industry 4.0, has attracted much attention in recent times. It is closely related with Cyber-Physical Production System (CPPS), Internet of Things, Information and Communication Technology and Enterprise Integration. Knowledge Engineering (KE) and KM are important role players in CPPS. The concept of Smart Innovation Engineering (SIE) system proposed by Waris et al. [3, 4] is a semi-automatic tool for facilitating product innovation process. The SIE system uses a collective, team-like knowledge developed by past experiences of the © Springer International Publishing AG, part of Springer Nature 2018 N. T. Nguyen et al. (Eds.): ACIIDS 2018, LNAI 10752, pp. 651–660, 2018. https://doi.org/10.1007/978-3-319-75420-8_61

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innovation-related formal decisional events. This knowledge is stored in the form of a combination of Variables, Functions, Constraints and Rules that comprehensively repre‐ sents the product’s information including its properties, manufacturing, requirements, structure and changes that were made in the past, for more details see [4]. Whenever any innovation-related query is presented to SIE system, it uses this experience-based knowledge to find the top similar experiences. These past experiences help the user to take proper decisions. It is same like seeking advice from the experts before taking the final decision. The goal here is to show how SIE system can be used by the common Community of Practice formed by global group of manufacturing organizations in dealing with the issues of product innovation process and that will indeed be a substantial step towards the establishment of Industry 4.0. In the context of manufactured products, product innovation can be defined as the process of making required changes to the already established product by introducing something new that adds value to users and also providing expertise knowledge that can be stored in the organization [5]. Strategy for implementation of product innovation includes the use of better components, new materials, advanced technologies, and new product features/functions. Moreover, the establishment of cross-functional, multidis‐ ciplinary teams was found to be vital to the success of the innovation project [6] within the organization. The SIE system can initially be used within a manufacturing organi‐ zation. However, it can certainly be extended for connecting a network of manufacturing units around the globe.

2

Background

To have a clear idea about the integration of different fields viz. KE, CPPS, Industry 4.0 and CoP, a brief discussion about them is presented in this section. 2.1 Knowledge Engineering (KE) Due to the fact that manufacturing organizations need to perform innovation process frequently and knowledge plays a critical role in it, they need to manage knowledge effectively within and across their organizational borders [1]. Feigenbaum [7] defines KE as an engineering discipline that aims to solve complex problems, normally requiring a high level of human expertise, by integrating knowledge into computer systems. Members of Communities of Practice (CoPs) are interacting on an ongoing basis to deepen their knowledge and expertise in their concerned area. Although CoPs follow the human-oriented KM approach, the use of technology, particularly knowledge management systems (KMS), is however important [8]. It is essentially required in large CoPs where proper and effective interaction is not possible without the support of KMS applications. Moreover, human-oriented CoPs have some drawbacks such as trust, lack of openness, power conflicts among members, emotional effects and so on which are critical aspects that may result in failure. One of the main external or environmental elements that can be thus considered is the knowledge strategy pursued by the organization aiming for the best use of the

Community of Practice for Product Innovation

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knowledge-based resources in the view of the organization’s competitive advantage [9]. The knowledge strategy defines aims and tools of KM programs and, thus, of CoPs. Moreover it is associated strictly with the competitive strategy of the organizations [10]. 2.2 Cyber-Physical Production Systems (CPPS) Cyber Physical Systems (CPSs) can be described as the transformative technologies for managing interconnected systems between its physical assets and computational capa‐ bilities with the possibility of human machine interaction [11]. CPSs has drawn a great deal of attention from academia, industry, and the government due to its potential bene‐ fits to society, economy, and the environment. Some of the practical examples in the present world are autonomous cars, robotic surgery, smart manufacturing, smart electric grid, implanted medical devises, and intelligent buildings. Application of CPS in the manufacturing industry leads to CPPS and hence the ability for continuous viewing of product, production equipment and production system under consideration. The intro‐ duction of CPPS in any production system promises social, economic and even ecolog‐ ical benefits. Due to the competitive nature of today’s industry and recent development resulting in higher availability and affordability of computer networks, sensors, and data acquis‐ ition more and more industrial organizations are forced to move toward implementation high-tech methodologies. Consequently, the ever growing use of networked machines and sensors has resulted in the continuous generation of high volume data which is known as Big Data [12]. In modern manufacturing organizations, especially high-tech industries, CPPS can be further developed for managing knowledge and experience in the form of Big Data and leveraging the interconnectivity of machines to reach the goal of intelligent factories. Furthermore by integrating CPPS with logistics and services in the current industrial practices would transform today’s factories into an Industry 4.0 factory with significant economic potential [13, 14]. 2.3 Industry 4.0 Systematic infusion of newest developments in computer science, information and communication technology, and manufacturing science and technology into CPPS may lead to the fourth Industrial Revolution. The term “Industry 4.0” is used for the fourth industrial revolution which is about to take place right now. According to the Federal Ministry of Education and Research, Germany (BMBF): “Industry is on the threshold of the fourth industrial revolution”. Driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial produc‐ tion of the future will be characterized by the strong customization of products under the conditions of highly flexible production as the manufacturing units of Industry 4.0 will have to deal with so called radical innovations frequently. This transformation is based on the extensive integration of customers (by collecting continuous feedback) and stake-holders along with value-added processes, and the linking of production and highquality services leading to so-called hybrid products.

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The concept of Industry 4.0 came into existence in 2011, when an association of representatives from academia, business, and politics promoted the idea as an approach for strengthening the competitiveness of the German manufacturing industry [15]. Industry 4.0 is a collective term for technologies and concepts of value chain organiza‐ tion. Industry 4.0 makes factories more intelligent, flexible, and dynamic by equipping manufacturing with autonomous systems, actors, and sensors [16]. Consequently, indus‐ tries including smart products, machines and equipment will achieve high levels of automation and self-optimization. In addition, production of complex and customized products with high standards can be manufactured as per expectations [16]. Thus, intel‐ ligent factories and smart manufacturing are the major goals of Industry 4.0 [17]. Nowadays, product customization resulting from the preferences and demands of consumers tends to be one more variable in the manufacturing process, and smart facto‐ ries will have to be able to innovate the manufactured products adapting to the prefer‐ ences of the users [18]. Thus, Use of experience-based knowledge, is one of the main characteristics of Industry 4.0 to support the integration and virtualization of product design/innovation and production process using KBS to create smart products. This will lead to early launch of innovated products with the objectives to meet the growing needs and expectations of the users. The use of SIE system in manufacturing organizations seems to be promising in this scenario. It will also help in handling the continued flow of new product innovation projects in the industry resulting from shorter lifecycle of products. Therefore implementation of SIE system in Industry 4.0 is a concept that will transform current industries into Smart Factories having a well-defined network of intelligent machines, smart products, systems and processes creating real world virtu‐ alization into a huge information system. Potential benefits of Industry 4.0 are flexibility, reduced lead times, reduced costs and customization of products. 2.4 Community of Practice (CoP) The most popular definition of CoP is that of Wenger et al. [19] who defines CoP as a group of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise in this area by interacting on an ongoing basis. From the above definition it is clear that CoP focuses on a specific domain, and its members interact around a common pool of knowledge and develop their practice to find the possible solutions to problems. Most of the CoPs are developed among large organizations, in various sectors, such as: Automotive industry: Ford, DaimlerCrysler, Caterpillar. Oil industry: Shell, ChevronTexaco, ENI. Computer: HP, IBM and Consulting: Accenture, CAP Gemini. In all these cases, the CoPs are created for sharing and diffusion of knowledge for improving the problem solving capability and innovative potential of their employees. For example, engineers of DaimlerChrysler working at different plants participate in the “Tech Clubs” CoPs for knowledge sharing about similar problems and solutions. CoPs by Shell, Turbodude, were formed to establish a platform for sharing knowledge of different teams involved in deep water exploration. Virtual CoPs at ANI were created in order to provide guidance and support to exploration managers from more experienced colleagues working elsewhere. The CoPs are considered key components of systematic


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and deliberate KM strategies [20]. So, the primary function of CoP is to promote knowl‐ edge sharing in order to improve the overall performance of the organization [21]. Secondly to provide knowledge database and builds norms, trust and assessment in favor of knowledge sharing. The performance can be improved at the individual, group, and organizational level by improving employees’ working experience, reducing the learning curve, accumulating professional talents for the organization, and avoiding overlapping investment on new products and services [22, 23].

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Smart Innovation Engineering (SIE) System

The importance of the aforementioned aspects creates the necessity for systems that collectively work together for Industry 4.0. One such system is SIE system developed by Waris et al. [4]. This system is a prominent tool to support the innovation processes in a quick and efficient way. The SIE System is based on the Set of Experience Knowl‐ edge Structure (SOE) and Decisional DNA (DDNA), which were first presented by Sanin and Szczerbicki [24]. It is a Smart Knowledge Management System (SKMS) capable of storing formal decision events explicitly [24, 25]. The architecture of SIE system consists of three main modules: Systems, Usability and SIE_Experience (see [4] for more details). Sets of experience are created for each module individually that allows the experienced-based knowledge to be stored more systematically for a wide range of similar manufactured products. Set of experience is a unique combination of Variables, Functions, Constraints and Rules. Combination of all the individual Sets of experience are combined under the SIE system that represents complete knowledge and experience necessary for supporting innovation process of manufactured products. Graphical User Interphase (GUI) for the SIE System is shown in Fig. 1. This GUI will allow the user to interact with SIE System in a user-friendly language. The user can select the set of values from the drop-down menu and is also able to define the required Constraints and preferences in the form of set of variables with selected values. The query based on innovation objectives is converted into SOE and compared with the similar Sets of Experience that were generated by the SIE System from Comma Separated (CSV) files for each module containing complete information about the product in the form of Variables, Functions, Constraints and Rules. The CSV files contains data in standard format so that the parser collects information as required. The SIE System provides a list of proposed solutions (say five) that is displaced in the GUI. At this time, the user/entrepreneur/innovator has the privilege to select the best possible solution from that list. This selected final solution is stored in the SIE System as SOE that can be used in future for similar query. In this way, the SIE System is a semiautomatic system that facilitates the process of Product Innovation. The SIE System gains experience with each decision taken that increases its expertise and behaves as an expert in its domain. SIE is an expert system that can facilitate CPPS and it can play a vital role towards the establishment of Industry 4.0 and has the potential to be used by large enterprises or group of SMEs, manufacturing similar products and sharing data among themselves.

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Fig. 1. Graphical User Interphase (GUI) for SIE system

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In this section, we will try to explain how the SIE system itself can be used as a CoP for PI by the means of virtual connection of manufacturing organizations at a common platform (i.e. SIE system). The importance of the aforementioned aspects proves that the cognitive role of a CoP is focused on the knowledge transfer process. Some central elements must be considered here, for instance, what kind of knowledge is to be exchanged, i.e. the nature of the shared knowledge, what procedure and tools are to be used, and more importantly for what purpose. The value of CoPs is more significant in a long-term perspective. “Tech Clubs” communities in DaimlerChrysler help to solve problems on a daily basis, which means benefits in a short-term perspective, but, simul‐ taneously developing the expertise of members, which means benefits in a long-term perspective.


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Similarly, SIE system is used to facilitate PI process frequently to meet the demands for customized products and short product life cycles. But, at the same time, it is gath‐ ering knowledge by storing all the experiences of the decisional events that can be used for decision making when a similar query is presented in future. Thus presenting longterm benefits. As already proved that SIE system behaves like a group of experts and the experience-based knowledge is stored in different modules (chromosomescontaining experiences of certain category) in the form of particular SOEs. And these chromosomes are grouped together to form what is called as a Decisional DNA (DDNA) of the manufacturing organization. Connecting DDNAs of various organizations through the SIE system will bring the experience-based knowledge of various groups of experts at the same platform. This will help these smart factories in PI process as they can perform innovation process systematically and quickly due to fast computational capabilities of SIE system. For the successful transformation of current manufacturing organizations into smart factories of Industry 4.0, the knowledge possessed by various actors needs to be sought, elaborated, and mixed to boost innovation and flexibility [8]. This task can be accom‐ plished by using a smart knowledge management system as a CoP where experiencebased knowledge can be stored, shared and reused among the groups of manufacturing organizations. For this purpose, SIE system can be implemented in each organization and interconnected with each other to form a group of expert systems, calling it Community of Practice for Product Innovation. The proposed architecture of the CoP for PI is shown in Fig. 2.

Fig. 2. Architecture of community of practice for product innovation.

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According to Ji et al. [26], the innovation sources of enterprises mainly include three aspects under the mode of communities of practice: Internal CoP of the enterprises, CoP outside the enterprise, and the interaction among diverse CoPs. We used the same meth‐ odology in defining the proposed CoP for PI (see Fig. 2). At the root level, the CoP for PI starts by implementing the SIE system in different PI projects inside the same organ‐ ization. This will bring experience-based knowledge of all PI projects at a single plat‐ form, thus building a cognitive connection between them. This will allow them to share and reuse the knowledge from the different units thus increasing the overall expertise of the organization. Similarly, different organizations manufacturing the same or similar products join hands together to form a CoP for PI. These organizations now share and reuse experiences among themselves. Thus expanding their expertise at a broader level. One of the main advantage of this system is that the expert/innovator can take quick and systematic decisions that are based on real experiences. There is no need for scheduling the group of experts’ meeting or any other formal procedure. Here, it should be made clear that the SIE system and the CoP for PI are basically the same. In fact, CoP for PI is interconnected extension of various Production units inside and outside the organi‐ zations. Further extension of CoP with cross-border integration of industries in different fields will lead to the establishment of highly expert CoP at a global level. The member smart factories will communicate with those of other fields. User preferences in other products, demographic and economic factors will be used to create new ideas leading to innova‐ tion. These CoPs are very beneficial for high-tech start-ups as they can manufacture prod‐ ucts confidently with a highly successful probability. At the same time, CoP for PI is also beneficial for already successful organizations as they can regularly update their knowledge system, can create new ideas and modify regular practice. For example, a manufacturing organization who plans to establish another unit in another country can use this system to modify the product according to the user preferences, demographic factors, economic conditions, resources and technologies available in that country, government norms, economic factors and other such conditions. Obviously they cannot manufacture exactly the same product for users of other countries having different taste and economic conditions. The role of government is also very crucial in forming such CoPs. It should be committed to create and provide favorable environment for proper and legal communi‐ cation among its members at global level. Making and implementing effective industrial policies and providing incentives for new members will be fruitful both for industries and nation. The importance or contribution of SIE system can be justified from the potential benefits that it offers towards the establishment of Industry 4.0 that itself is a complex system. Some of them are more flexibility, quick and systematic, customization and Reduction in costs.


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Conclusion

SIE system is a semi-automatic tool to facilitate the process of product innovation. This paper presented the concept of Community of Practice for product innovation, its meth‐ odology for implementation towards the establishment of Industry 4.0, and its advan‐ tages for manufacturing organizations and nations as a whole. It was further explained that how the Smart Innovation Engineering (SIE) itself behaves as an expert Community of Practice. Manufacturing organizations around the globe can store, share and reuses the experience-based knowledge among themselves at a common platform. Imple‐ menting this system in manufacturing organizations will allow them to take quick and systematic innovation-related decisions. The user get the advice from the SIE system similar to that of group of experts from the click of a mouse making the innovation process quick and systematic. The analysis of basic concepts and implementation method proves that SIE is an expert system that can facilitate Cyber Physical Systems (CPS) and it can play a vital role towards the establishment of Industry 4.0 and has the potential to be used further for lean innovation and sustainable innovation in future. The SIE system has the potential to be used by large enterprises or group of SMEs manu‐ facturing similar products or even by new high-tech start-ups.

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