Smart Factory: History
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Contributor:
Juil Kim
,
Hye-ryun Jeong
,
Hyesu Park

In the manufacturing sector, industrial, academic, and research entities have been making joint efforts to move beyond process automation and help the industry build intelligent factories that autonomously recognize and control the work situation by incorporating technologies such as artificial intelligence (AI), cloud, big data, and 5G with existing manufacturing processes. 

  • smart manufacturing adoption
  • digital transformation adoption
  • smart factory
  • digital transformation in manufacturing
  • industry 4.0
  • smart manufacturing
  • automation

1. Smart Factory

The concept of smart factories emerged in the late 1990s in the wake of the rapid growth of information technology (IT) presenting the basis for further discourse by naming the entire variable hardware, software, and structure that can rapidly reconfigure manufacturing capabilities and functions in response to sudden changes in the market and the regulatory environment [1]. In the 2010s, the paradigm expanded to intelligent smart factories with the advancement of technologies such as artificial intelligence (AI), sensor networks, big data, and cloud. When Internet of Things (IoT) technology, which connects all objects at the manufacturing site to a network, was applied to intelligent technology, which can make autonomous judgments and controls, the concept of “factory-of-things” was proposed [2]. Furthermore, by applying the idea of ubiquitous access to a network anytime anywhere, the concept of ubiquitous factory was proposed [3]. Here, ubiquitous factories have certain key requirements, such as transparency that can collect, exchange, and monitor a wide range of data in real time, autonomy that can judge and control itself, and sustainability that can manage energy in real time.
Recently, a smart factory referred to a factory that extends the concept of factory automation and is modularized for each individual process to enable active production of customized products. The public–private joint smart manufacturing innovation team, Korea Smart Manufacturing Office (KOSMO), which oversees policies related to the dissemination and advancement of smart factories, defines smart factories as “people-centered intelligent factories that produce customized products at minimum cost and time by integrating all production processes from product planning to sales with information communication technology (ICT)” [4]. Here, “smartization” can be applied to all areas, including premanufacturing stages such as planning, design, production, distribution, and sales. Furthermore, it is applied to application systems, control automation, and field automation. In terms of the level of “smartization,” the scope of smart factories includes the basic stage of process logistics management or performance aggregation automation to the advanced stage of IoT and Internet of Services-based Cyber-Physical System (CPS) and big-data-based diagnosis and operation. Nevertheless, there are minimum requirements to form a smart factory, and the Smart Manufacturing Innovation Promotion Team considers digitalization of 4M + 1E (Man, Machine, Material, Method, and Energy), algorithm or AI-based intelligence, horizontal and vertical integration, creation of data-based engineering knowledge, and connection with smart systems. These smart factory concepts and standards are constructed by public–private experts based on domestic and foreign manufacturing status and previous studies, so these concepts and standards are widely accepted.
In Korea, smart factories are divided into five stages according to the level of manufacturing innovation capabilities [4]. Stage 1 is the nonapplied stage in which all manufacturing processes are performed manually; Stage 2 is the basic stage with the application of point of production (POP), individually operated corporate resource management systems, etc.; Stage 3 is the first intermediate stage that applies a real-time decision-making operation system, automatic facility data aggregation system, etc.; Stage 4 is the second intermediate stage with the application of a facility control automation system, real-time factory control system, integrated operation system, etc.; and Stage 5 is an advanced stage that enables Internet of Things (IoT)- and Internet of Service (IoS)-based big data diagnosis and operation. It is reasonable that the adoption of smart factories should be implemented gradually depending on the level of a company’s manufacturing abilities. However, companies’ decision making on smart factory adoption by companies can be influenced by various other factors, such as management and environmental situations. In addition, it will be possible to attract more companies considering the transition to smart factories only when the feasibility of smart factory adoption is increased by confirming the high performances of using smart factories. Therefore, it is more necessary to identify factors that affect the decision making on adoption and effects caused by the adoption of smart factories to induce their adoption.
From a management perspective, research related to smart factories is focused not only on factors that affect decision making, but also the management performance due to adoption of smart factories. 

2. Factors of Smart Factory Adoption

Internal and external factors can affect the adoption of smart factories and attempts have been made to identify the factors through various empirical analyses and case studies. These studies generally identified the internal and external motives of the analysis unit based on the Technology Acceptance Model (TAM) proposed by Davis [5][6] and its subsequent models. The usefulness and ease of specific technologies are at the core of TAM. The degree to which a specific technology is found useful or easy to use is a decisive factor in the acceptance of technology by a specific entity. This model has been supplemented by TAM2 [7] and TAM3 [8] and has recently been developed as an integrated technology acceptance model (UTAUT) by combining and extending eight prominent models, including motivational models, planned behavioral theory, and social cognition theory. In this model, performance expectancy, effort expectancy, social influence, and facilitating conditions are considered major factors in technology acceptance, and demographic factors, such as gender and age, and experience and voluntariness of use intervene as control variables in the technology acceptance process. Recently, UTAUT2 with the addition of hedonic motivation, price value, and habit has been proposed for the existing UTAUT model [9]. Studies that applied TAM to organizational units, such as companies, cited competition and industrial environment [10][11] as external factors and the pursuit of efficiency and competitive advantages as internal factors [12][13].
According to Stocker et al. [14], who qualitatively reviewed previous studies on conditions for successful introduction of technologies related to the Fourth Industrial Revolution on a wider scale than smart factories, social factors, such as culture and working environment, and organizational factors, such as management and process, are believed to be important for the successful adoption of technologies. In terms of culture, communication, openness, and innovation-friendly culture are important and, with regard to the working environment, digitalization capabilities should be built and developed. Regarding management, efforts should be made to establish and disseminate digital strategies and establish a company-wide digital culture. As for the process, incorporating digital processes into the working environment is important. Touriki et al. [15], who analyzed the integration trend of smart, eco-friendly, resilient, and lean production, analyzed that the rise in regulatory and environmental issues, innovation of business models, changes in the labor market, and efforts to enhance corporate image are some of the factors that encourage the integration of smart factories. Expectations of efficiency and competitive advantages in accepting technologies related to the Fourth Industrial Revolution have a positive effect, while competitor and supplier factors have no statistically significant effect [16]. In Japan, relative advantages as technological factors, support from top executives as organizational factors, and market uncertainty as environmental factors were found to be the main factors in SMEs’ acceptance of technologies for the Fourth Industrial Revolution [17].
Empirical studies on factors affecting the adoption and continuous use of smart factories in Korea were actively conducted with various variables. In many studies, among the factors considered in UTAUT, performance expectancy and social influence were positive variables in the decision-making process for introducing smart factories [18][19][20][21]. It was confirmed that the expectation of effort was generally not statistically significant, except in some studies [22][23]. Other variables that UTAUT include are the relative advantages of smart manufacturing technology [24], technology readiness [25], and supplier technology [23], while entrepreneurs’ willingness and support level were also confirmed as elements for introducing smart factories [18][23]. Other external factors, such as government support [18][24][25][26] and co-operation with external entities [27][28], were also confirmed as components for the adoption of smart factories. These analysis results show that various factors within and outside the organization are closely involved in the introduction of smart factories by SMEs. 

3. Performances of Smart Factory Adoption

For companies, the ultimate purpose of converting manufacturing processes into ICT-based smart factories, despite budget and time constraints, is to maximize productivity and streamline costs to generate profits. There are various ripple effects at the mid to long term and it may impact a firm’s financial performance. Consequently, smart factory performance can be broadly defined as achieving production efficiency or creating and propagating social value. According to Kamble et al. (2020) [29], who developed a Smart Manufacturing Performance Measurement System (SMPMS) for small- and medium-sized enterprises by combining 98 previous studies and two surveys at industrial sites, smart factory performance can be redefined in 10 big-picture ways: cost, real-time diagnosis, production optimization, quality, integration, flexibility, computing, time, social performance, and ecological performance. By applying 59 detailed performance indicators, performance can be diagnosed in multifaceted ways according to the perspective and purpose that is desired to be measured.
Previous empirical studies in Korea also reported that smart factories at manufacturing SMEs had a statistically significant effect on multidimensional management performance. In general, many studies have confirmed that the promotion of smart factories had a positive effect on management performance by integrating financial and nonfinancial performance [22][30][31]. Some studies have integrated productivity performance, such as cost reduction, automation facility introduction, quality improvement, and visibility, into management performance [32]. Furthermore, it presented environmental performance as an indicator, considering that sustainability and social responsibility had become significant topics of interest at manufacturing sites. In addition, there were studies that presented net benefits [24] or corporate competitiveness [33] as performance indicators, which signify the degree to which a company’s productivity and flexibility have improved, driven by smart factories.

This entry is adapted from the peer-reviewed paper 10.3390/su15086496

References

  1. Koren, Y.; Heisel, U.; Jovane, F.; Moriwaki, T.; Pritschow, G.; Ulsoy, G.; Van Brussel, H. Reconfigurable manufacturing systems. CIRP Ann. 1999, 48, 527–540.
  2. Zühlke, D. Smart Factory-towards a factory-of-things. Annu. Rev. Control 2010, 34, 129–138.
  3. Yoon, J.S.; Shin, S.J.; Suh, S.H. A conceptual framework for the ubiquitous factory. Int. J. Prod. Res. 2012, 50, 2174–2189.
  4. Korea Smart Manufacturing Office (KOSMO). Available online: https://www.smart-factory.kr/smartFactoryIntro (accessed on 10 June 2022).
  5. Davis, F.D. A Technology Acceptance Model for Empirical Testing New End-User Information System: Theory and Results; Massachusetts Institute of Technology: Cambridge, MA, USA, 1986.
  6. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–339.
  7. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204.
  8. Venkatesh, V.; Bala, H. Technology acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci. 2008, 39, 273–315.
  9. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012, 36, 157–178.
  10. Gatignon, H.; Robertson, T.S. Technology diffusion: An empirical test of competitive effects. J. Mark. 1989, 53, 35–49.
  11. Chwelos, P.; Benbasat, I.; Dexter, A.S. Research report: Empirical test of an EDI adoption model. Inform. Syst. Res. 2001, 12, 304–321.
  12. Obal, M. What drives post-adoption usage? Investigating the negative and positive antecedents of disruptive technology continuous adoption intentions. Ind. Mark. Manag. 2017, 63, 42–52.
  13. Müller, J.M.; Kiel, D.; Voigt, K.-I. What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability 2018, 10, 247.
  14. Stocker, A.; Rosenberger, M.; Schmeja, M.; Schneider, G. Key success factors for the implementation of digital technologies in the context of Industry 4.0. In Proceedings of the International Federation for Information Processing/IEEE IM 2021—International Symposium on Integrated Network Management, Bordeaux, France, 17–21 May 2021.
  15. Touriki, F.E.; Benkhati, I.; Kamble, S.S.; Belhadi, A.; El Fezazi, S. An integrated SMART, Green, resilient, and lean manufacturing framework: A literature review and future research directions. J. Clean. Prod. 2021, 319, 128691.
  16. Čater, T.; Čater, B.; Černe, M.; Koman, M.; Redek, T. Industry 4.0 technologies usage: Motives and enablers. J. Manuf. Technol. Manag. 2021, 32, 323–345.
  17. Prause, M. Challenges of Industry 4.0 technology adoption for SMEs: The case of Japan. Sustainability 2019, 11, 5807.
  18. Kim, S. A Study on the Factors Influencing the Introduction of Smart Factories: Focusing on the Moderating Effect of Innovation Resistance. Ph.D. Thesis, Incheon National University, Incheon, Republic of Korea, 2021.
  19. Kim, J.; Lee, S. A study on the factors influencing smart factory technology acceptance. J. Korean Stat. Soc. 2020, 27, 75–95.
  20. Lee, Y.; Park, C. Study on the policy direction for the acceptance and promotion of smart factories by small and medium businesses in Korea. Small Medium Bus. Res. 2020, 42, 251–283.
  21. Lee, Y.; Park, C.; Seo, Y. A study on the determinants of smart factory acceptance by small and medium enterprises. Bus. Manag. Res. 2020, 27, 157–182.
  22. Lee, J.; Gil, J. An empirical study on the effect of integrated technology acceptance theory (UTAUT) on the intention to introduce smart factories and management performance: The moderating effect of CEO’s. Korean Manag. Consult. Rev. 2021, 21, 61–84.
  23. Chung, S.; Park, H. The factors influencing small and medium businesses’ intention to accept smart factory advancement. J. Digit. Converg. 2021, 19, 199–211.
  24. Gil, H. Empirical Study on the Analysis of Smart Factory Acceptance Factors and Performance: Focusing on TOE and IS Success Models. Ph.D. Thesis, Hansung University, Seoul, Republic of Korea, 2019.
  25. Park, H. An Empirical Study on the Factors Affecting the Introduction of Smart Factories by SMEs: Focusing on the Effect of Controlling Innovation Characteristics. Ph.D. Thesis, Incheon National University, Incheon, Republic of Korea, 2021.
  26. Kim, S. Empirical Study on Big Data-Based Small and Medium-Sized Manufacturing Companies’ Intention to Accept Smart Factgories and Business Performance of Smart Factories. Ph.D. Thesis, Hansung University, Seoul, Republic of Korea, 2019.
  27. Lee, J.; Kim, J. An empirical study on the degree of use of smart factories by small and medium-sized domestic metal processing companies. Humanit. Soc. 21 2020, 11, 269–284.
  28. Lee, C. Study on the Introduction of Smart Manufacturing According to Corporate Assets and Activities. Master’s Thesis, Kyungpook National University, Daegu, Republic of Korea, 2021.
  29. Kamble, S.S.; Gunasekaran, A.; Ghadge, A.; Raut, R. A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs—A review and empirical investigation. Int. J. Prod. Econ. 2020, 229, 107853.
  30. Lee, R.; Kim, C. The impact of application of smart factory technology by small and medium venture companies on quality and innovation performance. Venture Start-Up Res. 2020, 15, 59–71.
  31. Han, S. The Effect of Managerial Competence and Organizational Characteristics of Companies Introducing Smart Factories on Management Performance: Mediating Effect of Product Innovation Performance and Moderating Effect of R&D Competency. Master’s Thesis, Hansung University, Seoul, Republic of Korea, 2021.
  32. Jang, S. A Study on the Effect of Smart Factory Manufacturing Data on Small and Medium Business Management Performance: Focusing on Small and Medium Manufacturing Industry. Master’s Thesis, Kyung Hee University, Seoul, Republic of Korea, 2021.
  33. Choo, Y.; Lee, D.H. A study on factors for introducing smart factory to improve competitiveness of small and medium-sized metal processing companies. J. Korean Inst. Ind. Eng. 2019, 45, 70–80.
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