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Introduction – THE Routledge Companion to Technology Management
Published in Tugrul Daim, Marina Dabić, Yu-Shan Su, The Routledge Companion to Technology Management, 2023
Tugrul Daim, Marina Dabić, Yu-Shan Su
In Chapter 31, Su and Lin conducted a study on “Smart Manufacturing: An Evaluation Model for Taiwan’s Innovation Applications.” Smart manufacturing makes production more efficient and sustainable. An evaluation model for smart manufacturing was built upon five constructs and 29 criteria. The AHP and Decision-Making Trial and Evaluation Laboratory (DEMATEL) were used to analyze Taiwan’s data. The results showed that the Industrial Internet of Things (IIoT) is the first priority for developing smart manufacturing based upon the five constructs. For each of the five constructs of smart manufacturing: (1) IIoT: sensors are prioritized in hardware development; (2) smart production: developing advanced process control is prioritized; (3) smart factory: developing digital transformations is prioritized; (4) smart designs; (5) smart services. Smart design and smart service are the last concerns in smart manufacturing.
Real-Time Management-Based Production Scheduling for Sustainability
Published in Vijaya Kumar Manupati, Goran D. Putnik, Maria Leonilde Rocha Varela, Smart and Sustainable Manufacturing Systems for Industry 4.0, 2023
Smart manufacturing has been promoted as a new manufacturing paradigm with the development of advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI) and cyber-physical systems, among others (Qu, Ming, Liu, Zhang, & Hou, 2019; Mittal, Khan, Romero, &Wuest, 2019). Research in advanced ICT technologies, under the 4th Industrial Revolution (so-called Industry 4.0), can create a competitive, digital, low-carbon and circular industry (European Commission, 2021) and should be aligned with the Sustainable Development Goals (SDGs) from the United Nations. Industry 4.0 is seen as a powerful instrument for achieving sustainability goals (Varela, Araújo, Ávila, Castro, & Putnik, 2019). Advanced information and communication technology (ICT), in the scenario of Industry 4.0, allows data collection, processing, and decision-making in real time to control (in real time) manufacturing (Alves & Putnik, 2019).
Augmented Reality in Supply Chain Management
Published in Turan Paksoy, Çiğdem Koçhan, Sadia Samar Ali, Logistics 4.0, 2020
Sercan Demir, Ibrahim Yilmaz, Turan Paksoy
The new generation of technologies such as robotics, artificial intelligence, big data, and augmented reality assist supply chains to improve and become more sustainable against growing environmental challenges. These newly emerging technologies help companies to make optimized decisions, administer automation devices, forecast demand, and plan the vital processes (Merlino and Sproge 2017). Smart manufacturing (a.k.a. intelligent manufacturing) aims to optimize production by using advanced information and manufacturing technologies. The entire life cycle of a product can be facilitated with the integration of smart technologies into the manufacturing process. Smart sensors, adaptive decision-making models, advanced materials, intelligent devices, and data analytics are some of these smart technologies that increase production efficiency, overall product quality, and customer service level. Physical processes can be easily monitored by smart manufacturing systems, and real-time optimized decision can be made by the intelligent systems that enable the interaction and cooperation between humans, machines, sensors and smart devices (Zhong et al. 2017).
Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework
Published in International Journal of Production Research, 2023
Jingwei Guo, Ying Cheng, Dongxu Wang, Fei Tao, Stefan Pickl
Nowadays, smart manufacturing is a popular method for industrial enterprises to achieve manufacturing tasks better, decrease cost or consumption, and improve production efficiency (Mittal et al. 2020). Current technical focuses for smart manufacturing could be categorised into two aspects, data and decision, whose goals are management and processing of data, and decision result with better accuracy. As the data collected by enterprises become enormous and more data processing tools are investigated, data-driven approaches for smart manufacturing are drawing more attention (Kuo and Kusiak 2019; Zheng et al. 2019). Two different directions of research on data-driven method exist, centralised or distributed, neglecting the mixture of both. Traditional data management or processing usually take the default that data is stored in centralised server, bringing the advantage that some technical tools are easy to be implemented, and responses of data processing and utilisation within the server are fast. But the defect in centralised approach exists too, such as high cost of maintenance, reluctance from industrial enterprises to make adaptation, and inconvenience for centralised operations.
Decision-making in smart manufacturing: A framework for performance measurement
Published in International Journal of Computer Integrated Manufacturing, 2023
Shreyanshu Parhi, Kanchan Joshi, Milind Akarte
The literature on smart manufacturing is proliferating, making the researchers and practitioners believe it to be one of the preferred areas of focus (Ghobakhloo 2018). Smart manufacturing, an emerging domain, is creating new business avenues and opportunities for the firm’s performance enhancement (Hoeppe 2018; Schroeder et al. 2019). It is reported that smart manufacturing enhances productivity by 8% – 20%, revenue growth by 1%, employment by 6%, and an average reduction in downtime as well as cost by 20% & 3.6%, respectively (Rüßmann et al. 2015). A Gartner survey estimated that by 2022 more than 50% of the organizations will prefer to use smart manufacturing (Hoeppe et al. 2018). Further, IBM found that 72% of the industrial executives surveyed believe in smart manufacturing initiatives to withstand a competitive environment (IBM Institute of Business Value 2020). Given these perspectives, the market of smart manufacturing is expected to grow at 12.4% by 2025 and can reach up to US$220.4 billion by 2025 (MarketsandMarkets 2020; Lin, Wang, and Sheng 2020). The recent black swan event of COVID 19 forced many companies to transform their business models by focusing on digitalization, pushing smart manufacturing (Tratz-Ryan et al. 2020; Ivanov and Dolgui 2020). Therefore, it is apparent that smart manufacturing is a potential hit rather than being just a hype (Tao et al. 2018a; Ghobakhloo 2018).
The individual and integrated impact of Blockchain and IoT on sustainable supply chains:a systematic review
Published in Supply Chain Forum: An International Journal, 2023
Pankaj Dutta, Rahul Chavhan, Pogala Gowtham, Amrinder Singh
Industry 4.0 technologies can benefit the corporate world by digitising SCs, reverse logistics, services, and customer relations (Manavalan and Jayakrishna 2019). Blockchain, IoT, cloud computing, data science, cyber-physical systems would enable the next industrial revolution with smart manufacturing as an integral part (Kusiak 2018). Poor data quality jeopardises the company’s operation and leads to faulty strategy formulation (Mahyuni et al. 2020). Industries prefer more to wait than entire investment now because of complications in SC (D. Choi et al. 2020). Blockchain plays an essential part to rectify this situation leading companies towards optimised and sustainable operations (Choi and Luo 2019). Blockchain applications make the SC system cost-effective, energy-efficient, and high performance-oriented (Kim and Shin 2019; Yadav et al. 2020). Each transaction is permanently documented to significantly reduce time delays, additional costs, sustainability problems and the human fallacy (Öztürk and Yildizbaşi 2020). Traceability and Transparency are the advantages of using blockchain technology (Bai and Sarkis 2020). Electronic industries are more concerned with integrating blockchain and robotics to bring visibility to SC (Gupta et al. 2020). The main innovative technological features of blockchain are safe transactions without intermediation, robustness, resilience, durability, open structure, pseudonymity, and process reliability (Astarita et al. 2020).