AI Bibliography
AI Bibliography |
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Koh, L., Orzes, G., & Jia, F. J. (2019). The fourth industrial revolution (industry 4.0) Technologies disruption on operations and supply chain management. International Journal of Operations & Production Management. |
| Resource type: Journal Article BibTeX citation key: Koh2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Engineering, General, Innovation, Military Science Subcategories: 5G, Augmented cognition, Autonomous systems, Big data, Chaos theory, Command and control, Edge AI, Internet of things, Machine learning, Networked forces, Strategy, Systems theory Creators: Jia, Koh, Orzes Publisher: Collection: International Journal of Operations & Production Management |
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During the last five years, journals in robotics, electronics, computer science and production engineering have devoted significant attention to Industry 4.0 and related subjects, including additive manufacturing/3D printing, intelligent manufacturing and big data (Lee et al., 2014; Xi et al., 2015; Pfeiffer et al., 2016; Mosterman and Zander, 2016; Chen and Zhang, 2015; Jia et al., 2016). A systematic literature review on Industry 4.0 or on some of its specific technologies (e.g. additive manufacturing) is provided by Liao et al. (2017), Strozzi et al. (2017) and Khorram Niaki and Nonino (2017) among others. Although prominent scholars have acknowledged the relevance of Industry 4.0 for management in general, as well as for Operations and Production Management (O&PM) specifically (Brennan et al., 2015; Fawcett and Waller, 2014; Holmström and Romme, 2012; Melnyk et al., 2018), relatively little consideration has been given to these topics by mainstream O&PM journals, especially on Industry 4.0 technologies’ disruption on operations and supply chain management. A few prominent exceptions are represented by the recent attempts to shed lights on: the link between Industry 4.0 and lean manufacturing (Buer et al., 2018; Tortorella and Fettermann, 2018); the link between Internet of Things (IoT) and supply chain management (Ben-Daya et al., 2017); the impact of additive manufacturing on supply chain processes and performances (Liu et al., 2014; Oettmeier and Hofmann, 2016; Li et al., 2017); and the short-term supply chain scheduling in smart factories (Ivanov et al., 2016).While in the past there were very few pilot Industry 4.0 projects, the number of applications has significantly increased, both in terms of demonstration and “real” factories hence give rise to more empirical studies. Demonstration factories include Factory 2050 at the University of Sheffield (UK), Demonstration Factory at Aachen University (Germany), TRUMPF Group Factory in Chicago (USA) and SmartFactoryKL in Kaiserslautern (Germany), whilst “real” factories are at Audi’s Ingolstadt factory (Core77, 2016), Arla Foods (ARC, 2016), Siemens’ Amberg plant (Siemens, 2016) and Bosch’s Feuerbach plant in Stuttgart (Automotive World, 2016). A recent survey conducted by PwC on more than 2,000 companies from 26 countries showed an overall adoption rate of Industry 4.0 concepts (e.g. digitization and integration) of 33 percent, and forecasted that it will reach 72 percent by 2020 (PwC, 2015). This growth will be further fostered by the funding and innovation plans launched by several countries leading this industrial revolution, e.g., Manufacturing USA in the USA, Industrie du Futur in France, Industrie 4.0 in Germany, Industria 4.0 in Italy, Made in China 2025, Made Smarter UK. It is argued that different industrial sectors have different pace of adopting Industry 4.0. for instance, the aerospace sector has sometimes been characterized as “too low volume for extensive automation” however Industry 4.0 principles have been investigated by several aerospace companies, technologies have been developed to improve productivity where the upfront cost of automation cannot be justified, one example of this is the aerospace parts manufacturer Meggitt PLC’s project, M4. Here, the fourth industrial revolution (Industry 4.0) refers to the “confluence of technologies ranging from a variety of digital technologies (e.g. 3D printing, IoT, advanced robotics) to new materials (e.g. bio or nano-based) to new processes (e.g. data driven production, Artificial Intelligence, synthetic biology)” (OECD, 2016). These technologies have the potential to revolutionize operations and supply chain management (Brennan et al., 2015;Holmström et al., 2016; Rüßmann et al., 2015; Fawcett and Waller, 2014; Waller and Fawcett, 2013). Industry 4.0 is not merely about integrating technologies, but it is about the whole concept of how future customer demands, resources and data are shared, owned, used, regenerated, exploited, organized and recycled to make a product or deliver a service, faster, cheaper, more efficiently and more sustainably (Spath, 2013). As such, Industry 4.0 requires a rethinking and shift in mindset of how products are manufactured and services are produced, distributed/supplied, sold and used in the supply chain; thus, it will drive significant structural theoretical evolution and revolution for operations and supply chain management. Whilst classical theories such as resource based view, institutional theory, chaos theory, systems theory, stakeholder theory, transaction economic cost theory, evolutionary theory to name a few may need reshaping, the issues of trust will become prominent in such a disruptive digital environment, driving major evolvement of technological singularity in the transformation process, where blockchain may play a central role with IoT and Artificial Intelligence (AI) (Carter and Koh, 2018).
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