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    Topic review

    Smart Manufacturing and Industry 4.0

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    Submitted by: syed radzi
    (This entry belongs to Entry Collection "Society 5.0 ")

    Definition

    Industry 4.0 is an advanced strategy introduced by the German government to promote the integration of diverse technologies that make manufacturing process smarter. For instance, the technologies such as the Internet of Things (IoT), Internet of Service (IoS), social product development, Enterprise Resource Planning (ERP), and radio frequency identification (RFID) have been widely implemented to achieve smarter production system. The combination of this technology will foster mass customization, inexpensive product planning, accurate control of the manufacturing process, condition base maintenance, as well as the automated manufacturing process.
    These technological trends are designed to facilitate machine to machine (M2M) communication using minimal to null dependence on human force. Industry 4.0 transform processes, operations, machinery, supply chain management, and the entire energy footprint of manufacturing enterprises. It also enhances and monitors the after-purchase performance such as maintenance and servicing. At the global front, manufacturing enterprises are already exploring the limitless possibilities of Industry 4.0 and are reimagining the future. The traditional manufacturing processes and machinery are undergoing digitization and technological transformation to accelerate efficiency, flexibility, and speed to survive the fierce market competition.

    1. Digitalization Transformation towards the Industrial Revolution

    Digital technology is perceived as a mixed match of information, computing, communications, and connectivity technologies [1]. Digitalization will ensure that the manufacturing business runs efficiently, responsively, and cost-effectively [2]. In a holistic approach, digitalization is an enabler for a vertical (within the company) and horizontal (between companies) integration. Monostori et al. [3] and Nick et al. [4] reiterated that the vision of Industry 4.0 should have these four goals:
    • Vertical integration: In a smart factory system, a digital model communicates via the cyber-physical system, which comprises people, machines, and resources, is mapped.
    • Horizontal integration: The smart factory adapts itself to the surrounding system as it can optimize the entire production process in real-time.
    • Smart product: The ability to retrieve product lifecycle information that will add to the digital modeling of the smart factory and offer a better product-based service.
    • Human Being: The focal point and the driver of valued addition to the entire chain.
    With the relationship between the service provider, supplier, and customer becoming integrated, a stand-alone automation manufacturing system is becoming an isolated choice [4]. Such a system has its limitation within the organization [5][2]. The disapproval of the stand-alone automation system is also due to the rising concern on interoperability between machines. The inability to communicate vertically and horizontally in Industry 4.0 offers the ideal answer for the misalignment between automation technologies. With the ability to interact with each other, the Cyber-Physical System (CPS) and the Internet of Things (IoT) are the missing puzzle pieces for the stand-alone automation solution [6]. Thus, integrating the philosophy of Industry 4.0 will help manufacturing businesses leap forward, and this involves strategical decisions due to the enormous investments that need to be made.
    Many success stories have been attained from the significance of the digital transformation of an organization [1][7]. Digital technologies are viewed as a tool that profoundly eases the transformation of business strategies, processes, capabilities, the way their customers are served, and their product offerings. Digitalization is in line with Industry 4.0 [8][9]. Nevertheless, embracing digitalization is not an easy task and can become complicated at times. A survey done by the Harvard Business Review [10] encompasses that corporate and people’s culture, process, and technologies of the organization are directly influencing the transformation process. Henceforth, the manufacturing enterprises need to emphasize further the assimilation of inclusive knowledge and technology transfer [11].

    2. Industry 4.0 Pillars in Crafting Smart Manufacturing (SM)

    In line with Industry 4.0, Smart Manufacturing (SM) employs computer-integrated manufacturing, high levels of adaptability and rapid design changes, digital information technology, and more flexible technical workforce training [9]. Throughout the first industrial revolution to this date, other industrial revolutions have been driven by automation and digital transformation. Following the revolutions’ trends, a smarter manufacturing system that is integrated with robots and sensors is expected to play a pivotal role in next-generation manufacturing.
    With the birth of 5G technology, machines can interact, visualize the production chain, and make crucial decisions accurately and timely. Industry 4.0 is being backed by the combination of several new technology enablers as well as some existing technological apparatuses. Visibly, SM is a repackage of the combination of capabilities and technologies that serve as the pillar in making SM happen. The IoT, robotics, cloud computing, big data analytics, virtual reality, system integration, additive manufacturing, cyber security, and cyber-physical system are the pillars of the Industrial Revolution 4.0 [12][13][14][15][16][17].

    3. Smart Manufacturing

    Today’s manufacturing is getting more complicated [18][19]. The term SM and Industry 4.0 expound the same reference meaning. In the United States of America, “smart manufacturing” is referred to as “Industry 4.0”, and the Germans have officiated it in the Hannover Fair in the year 2011. Similarly, the Koreans refer to the term “Industry 4.0” as a “Smart Factory”. Regardless of the terminology, these three terms signify the key goal of improving businesses and their manufacturing environments in diverse countries to connect and embrace the narrative of technological advancement in information and operation technologies. This noble aim is expected to facilitate steady income flows with the associated cost reduction and efficacy gains.
    Lasi et al. [8] explained that to reduce operating costs, the prospect of future production is no longer dependent on batch order. This phenomenon has changed, and the manufacturer needs to think about smarter alternatives to meet customer-driven batch sizes while retaining mass production economies. As a driving force, intelligent sensors, intelligent solutions, innovative technologies, and the Internet and the Cloud are proven to promote digitalization and automation. Such disruptive developments of the technology enable product development time to be fostered and customization and versatility to be encouraged.
    A smart factory consists of integrated systems from various components within a factory that makes the entire factory system more flexible and reconfigurable Wang et al. [20]. The smart factory system integrates and connects the industrial network via the cloud and supervisory control terminals with smart shop floor object that leads to autonomous decision making. This was primarily to develop smarter and higher-efficiency factory systems. Without cloud services, big data analysis, and networks, an intelligent self-organized multi-agent system cannot be established. Kang et al. [9] stressed that cutting-edge ICT technologies are enablers and drivers of manufacturing’s fourth revolution. Wan et al. [21] and Zhong et al. [22] agree that a myriad of useful data is needed in making a smart factory. Davis et al. [23] explained that SM will lead and respond to dramatic and fundamental business transformations to demand dynamic economies with IT-enabled smart factories. Ultimately, Smart Factory focuses on the shift on task-connected changes focusing not only on profit but also on flexibility and product output, together with further declining cost, reducing resource utilization, as well as a decrease in ecological impact.
    Despite the hype towards Industry 4.0, smart manufacturing, and the Smart Factory concept, Canetta, Barni, and Montini [24] have mentioned that numerous businesses have encountered challenges in implementing Industry 4.0. Rajnai and Kocsis [25] and Sony and Naik [5] indicated that some business owners are not clear on the current trend of industrial digitalization, and some leaders are clueless on how to implement it. Many manufacturing companies are still struggling to employ the SM concept [19][26][27][28]. With regards to the highlighted problems, readiness and maturity assessments have become an integral tool for the manufacturing enterprises, specifically for SMEs. A readiness model promotes the initialization of the development process. Since the business owner is uncertain about the consequence of Industry 4.0 technology [29], the readiness assessment is the right fit to diminish uncertainties on the invested technologies [30]. Readiness assessments are typically conducted on a self-assessment basis that can be performed online or offline. In this self-assessment, the likely information gathered includes understanding, awareness, perception, current practice, as well as attitude of the organization.
    To this date, many manufacturers are still exploring, working, and planning to boost their potentials so that they can stay relevant and face the market competition in time to come. In improving competitiveness, global manufacturing needs to empower themselves with technological breakthrough [9]. Rajnai and Kocsis [25] have commented that there is no rigid or standard model and commonly accepted methodology for measuring the adopters′ Industry 4.0 readiness. Some readiness assessments are found to only focus on organizational Infrastructure Technology (IT) readiness [31][32][33][34]. Furthermore, the assessment outcome is often measured using various dimensions. Shifting to a smarter way of manufacturing practice involves high investment and changes in the organization’s vision-mission at the strategic level, as well as the modification of infrastructure and a new normal for the worker to adapt to. It does impact the Management, Machine, Method, and Man dimension in an organization. Choi, Jung, and Lee [35] have also reported that a maturity assessment could enhance manufacturing companies’ competitiveness.
    Since moving into Industry 4.0 is directly correlated to the amount of investment an organization must make, this is risky, as failure is costly for the SME’s. Prior to the implementation of SM, it is crucial for SMEs to assess the readiness of their manufacturing company’s practice [36][37]. For instance, most SMEs in Malaysia is still in the adopting stage of modern technology under Industry 3.0 [38]. The adoptions of the Industry 4.0 concept are still at the infancy level among the small and medium enterprises (SMEs) [39]. Herewith, this paper is constructed to propose a readiness assessment model to help SMEs stimulate their journey in their implementation of SM. Schumacher et al. [30], in their industry-wide interview, have spotted problems in implementing Industry 4.0 in a manufacturing business, as follows:
    • Failure of assessing Industry 4.0 readiness and capability is costly.
    • Failure to understand the uncertainty of the technology implementation will directly influence its benefits and cost.
    • Lack of strategic assistance and prior knowledge of the Industry 4.0 concept will tarnish the organization’s development.

    The entry is from 10.3390/su13179793

    References

    1. Lin, T.C.; Wang, K.J.; Sheng, M.L. To assess smart manufacturing readiness by maturity model: A case study on Taiwan enterprises. Int. J. Comput. Integr. Manuf. 2020, 33, 102–115.
    2. Fatorachian, H.; Kazemi, H. The Management of Operations A critical investigation of Industry 4.0 in manufacturing: Theoretical operationalisation framework. Prod. Plan. Control. 2018, 7287, 1–12.
    3. Monostori, L.; Kádár, B.; Bauernhansl, T.; Kondoh, S.; Kumara, S.; Reinhart, G.; Sauer, O.; Schuh, G.; Sihn, W.; Ueda, K. Cyber-physical systems in manufacturing. CIRP Ann. 2016, 65, 621–641.
    4. Nick, G.; Szaller, Á.; Bergmann, J.; Várgedo, T. Industry 4.0 readiness in Hungary: Model, and the first results in connection to data application. IFAC Pap. OnLine 2019, 52, 289–294.
    5. Sony, M.; Naik, S. Key ingredients for evaluating Industry 4.0 readiness for organizations: A literature review. Benchmarking Int. J. 2019, 27, 2213–2232.
    6. Öberg, C.; Graham, L. How Smart Cities will Change Supply Chain Management: A Technical Viewpoint. Prod. Plan. Control. 2016, 27, 529–538.
    7. Heavin, C.; Power, D.J. Challenges for digital transformation—Towards a conceptual decision support guide for managers. J. Decis. Syst. 2018, 27, 1–14.
    8. Lasi, H.; Kemper, H.G.; Feltke, P.; Feld, T.; Hoffmann, M. Indusrtry 4.0 in Business & Information System Engeineering. Bus. Inf. Syst. Eng. 2014, 6, 239–242.
    9. Kang, H.S.; Lee, J.Y.; Choi, S.; Kim, H.; Park, J.H.; Son, J.Y.; Kim, B.H.; Noh, S.D. Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manuf. Green Technol. 2016, 3, 111–128.
    10. Walker, M. Reassessing Digital: The Culture and Process Change Imperative. Harv. Bus. School Publ. 2018, 14. Available online: https://www.redhat.com/cms/managed-files/cm-harvard-business-review-digital-transformation-pulse-survey-f14828-201811-en.pdf (accessed on 25 March 2020).
    11. Kagermann, H.; Wahlster, W.; Helbig, J. Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUS-TRIE 4.0. Final Rep. Ind. 2013, 4. Available online: https://docplayer.net/254711-Securing-the-future-of-german-manufacturing-industry-recommendations-for-implementing-the-strategic-initiative-industrie-4-0.html (accessed on 15 January 2020).
    12. Erboz, G. How to Define Industry 40: The Main Pillars of Industry 4.0. In Proceedings of the Conference: Managerial Trends in the Development of Enterprises in Globalization Era, Nitra, Slovakia, 1–2 June 2017; pp. 761–767.
    13. Jones, M.; Zarzycki, L.; Murray, G. Does industry 4.0 pose a challenge for the SME machine builder? A case study and reflection of readiness for a UK SME. IFIP Adv. Inf. Commun. Technol. 2019, 530, 183–197.
    14. Machado, C.G.; Winroth, M.P.; Ribeiro da Silva, E.H.D. Sustainable manufacturing in Industry 4.0: An emerging research agenda. Int. J. Prod. Res. 2019, 1–23.
    15. Piccarozzi, M.; Aquilani, B.; Gatti, C. Industry 4.0 in management studies: A systematic literature review. Sustainability 2018, 10, 3821.
    16. Rüßmann, M.; Lorenz, M.; Gerbert, P.; Waldner, M.; Justus, J.; Engel, P.; Harnisch, M. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Bost. Consult. Group 2015, 9, 54–89.
    17. Vaidya, S.; Ambad, P.; Bhosle, S. Industry 4.0—A Glimpse. Procedia Manuf. 2018, 20, 233–238.
    18. Esmaeilian, B.; Behdad, S.; Wang, B. The evolution and future of manufacturing: A review. J. Manuf. Syst. 2016, 39, 79–100.
    19. Mittal, S.; Khan, M.A.; Romero, D.; Wuest, T. A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). J. Manuf. Syst. 2018, 49, 194–214.
    20. Wang, S.; Wan, J.; Zhang, S.; Li, D.; Zhang, C. Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 2016, 101, 158–168.
    21. Wan, J.; Yi, M.; Li, D.; Zhang, C.; Wang, S.; Zhou, K. Mobile services for customization manufacturing systems: An example of industry 4.0. IEEE Access 2016, 4, 8977–8986.
    22. Zhong, R.Y.; Xu, C.; Chen, C.; Huang, G.Q. Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors. Int. J. Prod. Res. 2017, 55, 2610–2621.
    23. Davis, J.; Edgar, T.; Porter, J.; Bernaden, J.; Sarli, M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 2012, 47, 145–156.
    24. Canetta, L.; Barni, A.; Montini, E. Development of a Digitalization Maturity Model for the Manufacturing Sector. In Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, Germany, 17–20 June 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–7.
    25. Rajnai, Z.; Kocsis, I. Assessing the Industry 4.0 Readiness of A Certain Industry. In Proceedings of the 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics, Košice, Slovakia, 7–10 February 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 225–230.
    26. Sevinç, A.; Gür, Ş.; Eren, T. Analysis of the difficulties of SMEs in industry 4.0 applications by analytical hierarchy process and analytical network process. Processes 2018, 6, 264.
    27. Wuest, T.; Schmid, P.; Lego, B.; Bowen, E. Overview of Smart Manufacturing in West Virginia; Bureau of Business & Economic Research and Industrial and Management Systems Engineering; College of Business and Economics, West Virginia University: Morgantown, WV, USA, 2018.
    28. Masood, T.; Sonntag, P. Industry 4.0: Adoption challenges and benefits for SMEs. Comput. Ind. 2020, 121, 103261.
    29. Akdil, K.Y.; Ustundag, A.; Cevikcan, E. Maturity and Readiness Model. for Industry 4.0 Strategy. In Industry 4.0: Managing the Digital Transformation; Springer Series in Advanced Manufacturing; Springer: Cham, Switzerland, 2018.
    30. Schumacher, A.; Erol, S.; Sihn, W. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP 2016, 52, 161–166.
    31. Gill, M.; VanBoskirk, S.; Evans, P.F.; Nail, J.; Causey, L.G. The Digital Maturity Model 4.0. Forrester Res. 2016. Available online: https://dixital.cec.es/wp-content/uploads/presentacions/presentacion06.pdf (accessed on 14 January 2020).
    32. Gökalp, E.; Sener, U.; Eren, E. Towards the Development of a Testing in Automotive SPICE and TestSPICE: Synergies and Benefits. Int. Conf. 2017, 1, 30–42.
    33. Leyh, C.; Martin, S.; Schaffer, T. Industry 4.0 and Lean Production-A matching relationship? An analysis of selected Industry 4.0 models. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 3–6 September 2017; Volume 11, pp. 989–993.
    34. Menon, K.; Kärkkäinen, H.; Lasrado, L.A. Towards a maturity modeling approach for the implementation of industrial internet. In Proceedings of the 20th Pacific Asia Conference on Information Systems (PACIS 2016), Chiayi, Taiwan, 27 June–1 July 2016; Association for Information Systems: Atlanta, GA, USA, 2016.
    35. Choi, S.; Jung, K.; Lee, J.Y. Development of an assessment system based on manufacturing readiness level for smart manufacturing and supplier selection. Int. J. Comput. Appl. Technol. 2017, 56, 87–98.
    36. Mittal, S.; Romero, D.; Wuest, T. Towards a Smart Manufacturing Toolkit for SMEs Towards a Smart Manufacturing Toolkit for SMEs. In Proceedings of the 15th International Conference on Product Lifecycle Management, Turin, Italy, 2–4 July 2018; Volume IFIP, AICT 540. Springer: Cham, Switzerland; pp. 476–487.
    37. Basl, J.; Doucek, P. A Metamodel for Evaluating Enterprise Readiness in the Context of Industry 4.0. Information 2019, 10, 89.
    38. Abod, S.G. Industry 4.0: Are Malaysian SMEs Ready? BizPulse 2016, 17, 1–3.
    39. Matt, D.; Modrák, V.; Zsifkovits, H. Industry 4.0 for SMEs Challenges, Opportunities and Requirements: Challenges, Opportunities and Requiremen; Palgrave Macmillan: London, UK, 2020.
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