Managing production systems with machine learning: a case analysis of Suzhou GCL photovoltaic technology

References

• Ahlen, A., J. Akerberg, and M. Eriksson. 2019. “Toward Wireless Control in Industrial Process Automation: A Case Study at a Paper Mill.” IEEE Control Systems Magazine 39 (5): 36–57.
   Web of Science ®Google Scholar
• Alberto, D. O., D. S. Javier, G. Diego, and S. Basilio. 2018. “Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives Towards Industry 4.0.” Information Fusion 50: 92–111.
   Web of Science ®Google Scholar
• Amiya, K. P., P. Anand, and R. P. Mohanty. 2019. “Risk Analysis of Estimates for Cost of Quality in Supply Chain: A Case Study.” Production Planning & Control 30 (4): 299–314. doi:10.1080/09537287.2018.1541488.
   Web of Science ®Google Scholar
• Andreas, S., Z. B. Al, Z. B. Carlos, and B. Tim. 2019. “Capturing the Benefits of Industry 4.0: A Business Network Perspective.” Production Planning & Control 30 (16): 1305–1321.
   Web of Science ®Google Scholar
• Andrew, K. 2019. “Convolutional and Generative Adversarial Neural Networks in Manufacturing.” International Journal of Production Research 58 (5): 1594–1604. doi:10.1080/00207543.2019.1662133.
   Web of Science ®Google Scholar
• Arashpour, Mehrdad, Ron Wakefield, Babak Abbasi, Mohammadreza Arashpour, and Reza Hosseini. 2018. “Optimal Process Integration Architectures in Off-Site Construction: Theorizing the Use of Multi-Skilled Resources.” Architectural Engineering and Design Management 14 (1–2): 46–59. doi:10.1080/17452007.2017.1302406.
   Web of Science ®Google Scholar
• Bahria, Nadia, Anis Chelbi, Hanen Bouchriha, and Imen Harbaoui Dridi. 2019. “Integrated Production, Statistical Process Control, and Maintenance Policy for Unreliable Manufacturing Systems.” International Journal of Production Research 57 (8): 2548–2570. doi:10.1080/00207543.2018.1530472.
   Web of Science ®Google Scholar
• Bochmann, Lennart, Timo Bänziger, Andreas Kunz, and Konrad Wegener. 2017. “Human-Robot Collaboration in Decentralized Manufacturing Systems: An Approach for Simulation-Based Evaluation of Future Intelligent Production.” Procedia CIRP 62: 624–629. doi:10.1016/j.procir.2016.06.021.
   Google Scholar
• Borangiu, Theodor, Damien Trentesaux, André Thomas, Paulo Leitão, and Jose Barata. 2019. “Digital Transformation of Manufacturing Through Cloud Services and Resource Virtualization.” Computers in Industry 108: 150–162. doi:10.1016/j.compind.2019.01.006.
   Web of Science ®Google Scholar
• Choo, K. K. R., C. Esposito, and A. Castiglione. 2017. “Evidence and forensics in the cloud: challenges and future research directions.” IEEE Cloud Computing 4 (3): 14–19.
   Google Scholar
• Cohen, Yuval, Maurizio Faccio, Francesco Pilati, and Xifan Yao. 2019. “Design and Management of Digital Manufacturing and Assembly Systems in the Industry 4.0 Era.” The International Journal of Advanced Manufacturing Technology 105 (9): 3565–3577. doi:10.1007/s00170-019-04595-0.
   Web of Science ®Google Scholar
• Coleman, S. Y. 2016. “Data-Mining Opportunities for Small and Medium Enterprises with Official Statistics in the UK.” Journal of Official Statistics 32 (4): 849–865. doi:10.1515/jos-2016-0044.
   Web of Science ®Google Scholar
• Davenport, T. H., and R. Ronanki. 2018. “Artificial Intelligence for the Real World.” Harvard Business Review 96 (1): 108–116.
   Google Scholar
• Duan, Y. G., J. S. Edwards, and Y. K. Dwivedi. 2019. “Artificial Intelligence for Decision Making in the Era of Big Data- Evolution, Challenges and Research Agenda.” International Journal of Information Management 48 (8): 63–71. doi:10.1016/j.ijinfomgt.2019.01.021.
   Google Scholar
• Escobar, C. A., and R. Morales-Menendez. 2017. “Machine Learning and Pattern Recognition Techniques for Information Extraction to Improve Production Control and Design Decisions.” In Advances in DataMining. Applications and Theoretical Aspects. ICDM 2017. Lecture Notes in Computer Science, edited by Perner P, vol 10357. Springer, Cham. doi:10.1007/978‐3‐319‐62701‐4_23
   Google Scholar
• Eshghi, S. T., P. Auger, and W. R. Mathew. 2018. “Quality Assessment and Interference Detection in Targeted Mass Spectrometry Data Using Machine Learning.” Clinical Proteomics 15: 33. doi:10.1186/s12014-018-9209-x.
   PubMed Web of Science ®Google Scholar
• Fatorachian, H., and H. Kazemi. 2018. “A Critical Investigation of Industry 4.0 in Manufacturing: theoretical Operationalisation Framework.” Production Planning & Control 29 (8): 633–644. doi:10.1080/09537287.2018.1424960.
   Web of Science ®Google Scholar
• Ferreira, F., J. Faria, A. Azevedo, A. L. Marques. 2017. “Product life cycle management in knowledge intensive collaborative environments: an application to automotive industry.” International Journal of Information Management 37 (1): 1474–1487.
   Google Scholar
• Giuseppe, F., S. Imen, T. C. Maria, and S. Ludovico. 2018. “Big Data for Big Pharma: Harmonizing Business Process Management to Enhance Ambidexterity.” Business Process Management Journal 24 (5): 1110–1123.
   Web of Science ®Google Scholar
• Gupta, S., A. K. Kar, A. Baabdullah, and W. A. A. Al-Khowaiter. 2018. “Big Data with Cognitive Computing: A Review for the Future.” International Journal of Information Management 42: 78–89. doi:10.1016/j.ijinfomgt.2018.06.005.
   Web of Science ®Google Scholar
• Helo, P., and Y. Hao. 2017. “Cloud Manufacturing System for Sheet Metal Processing.” Production Planning & Control 28 (6-8): 524–537. doi:10.1080/09537287.2017.1309714.
   Web of Science ®Google Scholar
• Jarrahi, M. H. 2018. “Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making.” Business Horizons 61 (4): 577–586. doi:10.1016/j.bushor.2018.03.007.
   Web of Science ®Google Scholar
• Jia, Feng, Yaguo Lei, Jing Lin, Xin Zhou, and Na Lu. 2016. “Deep Neural Networks: A Promising Tool for Fault Characteristic Mining and Intelligent Diagnosis of Rotating Machinery with Massive Data.” Mechanical Systems and Signal Processing 72–73 (72): 303–315. doi:10.1016/j.ymssp.2015.10.025.
   Google Scholar
• Jung, Kiwook, SangSu Choi, Boonserm Kulvatunyou, Hyunbo Cho, and K. C. Morris. 2017. “A Reference Activity Model for Smart Factory Design and Improvement.” Production Planning & Control 28 (2): 108–122. doi:10.1080/09537287.2016.1237686.
   Web of Science ®Google Scholar
• Kateris, Dimitrios, Dimitrios Moshou, Xanthoula-Eirini Pantazi, Ioannis Gravalos, Nader Sawalhi, and Spiros Loutridis. 2014. “A Machine Learning Approach for the Condition Monitoring of Rotating Machinery.” Journal of Mechanical Science and Technology 28 (1): 61–71. doi:10.1007/s12206-013-1102-y.
   Web of Science ®Google Scholar
• Kousi, Niki, Spyridon Koukas, George Michalos, and Sotiris Makris. 2019. “Scheduling of Smart Intra-Factory Material Supply Operations Using Mobile Robot.” International Journal of Production Research 57 (3): 801–814. doi:10.1080/00207543.2018.1483587.
   Web of Science ®Google Scholar
• Lamon, Edoardo, Alessandro De Franco, Luka Peternel, and Arash Ajoudani. 2019. “A Capability-Aware Role Allocation Approach to Industrial Assembly Tasks.” IEEE Robotics and Automation Letters 4 (4): 3378–3385. doi:10.1109/LRA.2019.2926963.
   Web of Science ®Google Scholar
• Lee, Hoyeop, Chang Ouk Kim, Hyo Heon Ko, and Min-Kyoon Kim. 2015. “Yield Prediction through the Event Sequence Analysis of the Die Attach Process.” IEEE Transactions on Semiconductor Manufacturing 28 (4): 563–570. doi:10.1109/TSM.2015.2487540.
   Web of Science ®Google Scholar
• Lee, JuneHyuck, Sang Noh, Hyun-Jung Kim, and Yong-Shin Kang. 2018. “Implementation of Cyber-Physical Production Systems for Quality Prediction and Operation Controlling Metal Casting.” Sensors 18 (5): 1428. doi:10.3390/s18051428.
   PubMed Web of Science ®Google Scholar
• Lemaignan, S., M. Warnier, E. A. Sisbot, A. Clodic, and R. Alami. 2017. “Artificial Cognition for Social Human–Robot Interaction: An Implementation.” Artificial Intelligence. 247: 45–69. doi:10.1016/j.artint.2016.07.002.
   Web of Science ®Google Scholar
• Lim, Junseok, Moon-Jung Chae, Yongseok Yang, In-Beom Park, Jaeyong Lee, and Jonghun Park. 2016. “Fast Scheduling of Semiconductor Manufacturing Facilities Using Case-Based Reasoning.” IEEE Transactions on Semiconductor Manufacturing 29 (1): 22–32. doi:10.1109/TSM.2015.2511798.
   Web of Science ®Google Scholar
• Lolli, F., E. Balugani, A. Ishizaka, R. Gamberini, B. Rimini, and A. Regattieri. 2019. “Machine Learning for Multi-Criteria Inventory Classification Applied to Intermittent Demand.” Production Planning & Control 30 (1): 76–89. doi:10.1080/09537287.2018.1525506.
   Web of Science ®Google Scholar
• Lu, Shan, Lei Xie, Li Zhu, and Hongye Su. 2019. “Integrated Scheduling of a Hybrid Manufacturing and Recovering System in a Multi-Product Multi-Stage Environment with Carbon Emission.” Journal of Cleaner Production 222: 695–709. doi:10.1016/j.jclepro.2019.03.009.
   Web of Science ®Google Scholar
• Luthra, Sunil, Kannan Govindan, Devika Kannan, Sachin Kumar Mangla, and Chandra Prakash Garg. 2017. “An Integrated Framework for Sustainable Supplier Selection and Evaluation in Supply Chains.” Journal of Cleaner Production 140 (3): 1686–1698. doi:10.1016/j.jclepro.2016.09.078.
   Web of Science ®Google Scholar
• Majstorovic, Vidosav D. 2020. “Cloud-Based Cyber-Physical Systems in Manufacturing.” Production Planning & Control 31 (7): 611–612. doi:10.1080/09537287.2019.1655069.
   Web of Science ®Google Scholar
• Martínez-Rojas, M., M. C. Pardo-Ferreira, and J. C. Rubio-Romero. 2018. “Twitter as a Tool for the Management and Analysis of Emergency Situations: A Systematic Literature Review.” International Journal of Information Management 43: 196–208. doi:10.1016/j.ijinfomgt.2018.07.008.
   Web of Science ®Google Scholar
• Miller, S. 2018. “AI: Augmentation, More so than Automation.” Asian Management Insights 5 (1): 1–20.
   Google Scholar
• Moreira, L. C., W. D. Li, X. Lu, and M. E. Fitzpatrick. 2019. “Supervision Controller for Real-Time Surface Quality Assurance in CNC Machining Using Artificial Intelligence.” Computers & Industrial Engineering 127: 158–168. doi:10.1016/j.cie.2018.12.016.
   Web of Science ®Google Scholar
• Murthad, AL-Yoonus, Aqeel, Adel, Yaseen, & Mustafa Nayef Al-Dabagh. 2019. “Deformation detection and classification system for car parts products using image processing algorithms.” IOP Conference Series: Materials Science and Engineering 518 (4): 42006–42006.
   Google Scholar
• Ooi, J. W., L. C. Tay, and W. K. Lai. 2019. “Bottom-Hat Filtering for Defect Detection with CNN Classification on Car Wiper Arm.” In 2019 IEEE 15th International Colloquium on Signal Processing and Its Applications (CSPA), 90–95. Penang, Malaysia: IEEE.
   Google Scholar
• Pablo, F. C., T. Paolo, and H. H. Luisa. 2019. “Managing Structural and Dynamic Complexity in Supply Chains: insights from Four Case Studies.” Production Planning & Control 30 (8): 611–623.
   Web of Science ®Google Scholar
• Papananias, Moschos, Thomas E. McLeay, Mahdi Mahfouf, and Visakan Kadirkamanathan. 2019. “A Bayesian Framework to Estimate Part Quality and Associated Uncertainties in Multistage Manufacturing.” Computers in Industry 105: 35–47. doi:10.1016/j.compind.2018.10.008.
   Web of Science ®Google Scholar
• Peres, Ricardo Silva, Andre Dionisio Rocha, Paulo Leitao, and Jose Barata. 2018. “IDARTS-Towards Intelligent Data Analysis and Real-Time Supervision for Industry 4.0.” Computers in Industry 101: 138–146. doi:10.1016/j.compind.2018.07.004.
   Web of Science ®Google Scholar
• Piera, C., C. Roberto, and E. Emilio. 2019. “Efficiency and Effectiveness of Knowledge Management Systems in SMEs.” Production Planning & Control 30 (9): 779–791.
   Web of Science ®Google Scholar
• Ranjit, Manish, Harshvardhan Gazula, Simon M. Hsiang, Yang Yu, Marcus Borhani, Sonny Spahr, Leyikun Taye, Chad Stephens, and Bart Elliott. 2015. “Fault Detection Using Human–Machine Co-Construct Intelligence in Semiconductor Manufacturing Processes.” IEEE Transactions on Semiconductor Manufacturing 28 (3): 297–305. doi:10.1109/TSM.2015.2432770.
   Web of Science ®Google Scholar
• Rodrigues, N., E. Oliveira, and P. Leitao. 2018. “Decentralized and on-the-Fly Agent-Based Service Reconfiguration in Manufacturing Systems.” Computers in Industry 101: 81–90. doi:10.1016/j.compind.2018.06.003.
   Web of Science ®Google Scholar
• Romero, D., P. Bernus, and O. Noran. 2016. “The Operator 4.0: human Cyber-Physical Systems & Adaptive Automation towards  Human-Automation Symbiosis Work Systems.” In Advances in Production Management Systems. Initiatives for a Sustainable World. APMS 2016. IFIP Advances inInformation and Communication Technology, edited by Nääs I. et al, 488, 677–686. Springer, Cham. https://doi.org/10.1007/978‐3‐319‐51133‐7_80
   Google Scholar
• Sadrfaridpour, B., H. Saeidi, J. Burke, K. Madathil, and Y. Wang. 2016. “Modeling and Control of Trust in Human-Robot Collaborative Manufacturing.” In Robust Intelligence and Trust in Autonomous Systems. Boston, MA: Springer.
   Google Scholar
• Santos, G. A. V., and C. F. Alves. 2017. “The Dynamics of Power in Software Ecosystems: Insights from a Multiple Case Study.“ in43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA),66–73. IEEE.
   Google Scholar
• Shmuel, B. G. 2016. “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.” Library Journal 141 (8): 80–88.
   Web of Science ®Google Scholar
• Sorouri, M., and V. Vyatkin. 2018. “Intelligent Product and Mechatronic Software Components Enabling Mass Customization in Advanced Production Systems.” Service Oriented Computing and Applications 12 (1): 73–86. doi:10.1007/s11761-018-0230-8.
   Web of Science ®Google Scholar
• Tahir, M., N. K. Nisar, and P. Guru. 2019. “Effect of Best Management Practices on the Performance and Productivity of Small Firms.” Production Planning & Control 30 (10): 919–934.
   Web of Science ®Google Scholar
• Tamura, Yasumasa, Hiroyuki Iizuka, Masahito Yamamoto, and Masashi Furukawa. 2015. “Application of Local Clustering Organization to Reactive Job-Shop Scheduling.” Soft Computing 19 (4): 891–899. doi:10.1007/s00500-014-1416-4.
   Web of Science ®Google Scholar
• Tao, Fei, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui. 2018. “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data.” The International Journal of Advanced Manufacturing Technology 94 (9–12): 3563–3576. (9):doi:10.1007/s00170-017-0233-1.
   Web of Science ®Google Scholar
• Tao, Jialing, Kaibo Wang, Bo Li, Liang Liu, and Qi Cai. 2016. “Hierarchical Models for the Spatial–Temporal Carbon Nanotube Height Variations.” International Journal of Production Research 54 (21): 6613–6620. doi:10.1080/00207543.2016.1181809.
   Web of Science ®Google Scholar
• Terziyan, Vagan, Svitlana Gryshko, and Mariia Golovianko. 2018. “Patented Intelligence: Cloning Human Decision Models for Industry 4.0.” Journal of Manufacturing Systems 48: 204–217. doi:10.1016/j.jmsy.2018.04.019.
   Web of Science ®Google Scholar
• Theorin, Alfred, Kristofer Bengtsson, Julien Provost, Michael Lieder, Charlotta Johnsson, Thomas Lundholm, and Bengt Lennartson. 2017. “An Event-Driven Manufacturing Information System Architecture for Industry 4.0.” International Journal of Production Research 55 (5): 1297–1311.” doi:10.1080/00207543.2016.1201604.
   Web of Science ®Google Scholar
• Unal, M., Y. Sahin, M. Onat, M. Demetgul, and H. Kucuk. 2017. “Fault Diagnosis of Rolling Bearings Using Data Mining Techniques and Boosting.” Journal of Dynamic Systems, Measurement, and Control. 139 (2):021003.
   Web of Science ®Google Scholar
• Wang, Qiyue, Wenhua Jiao, Rui Yu, Michael T. Johnson, and Yu Ming Zhang. 2019. “Modeling of Human Welders’Operations in Virtual Reality Human-Robot Interaction.” IEEE Robotics and Automation Letters 4 (3): 2958–2964. doi:10.1109/LRA.2019.2921928.
   Web of Science ®Google Scholar
• Wang, X., S. K. Ong, and A. Y C. Nee. 2018a. “A Comprehensive Survey of Ubiquitous Manufacturing Research.” International Journal of Production Research 56 (1–2): 604–628. doi:10.1080/00207543.2017.1413259.
   Web of Science ®Google Scholar
• Wang, Jinjiang, Yulin Ma, Laibin Zhang, Robert X. Gao, and Dazhong Wu. 2018b. “Deep Learning for Smart Manufacturing: Methods and Applications.” Journal of Manufacturing Systems 48: 144–156. doi:10.1016/j.jmsy.2018.01.003.
   Web of Science ®Google Scholar
• Wen, Long, Xinyu Li, Liang Gao, and Yuyan Zhang. 2018. “A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method.” IEEE Transactions on Industrial Electronics 65 (7): 5990–5998. doi:10.1109/TIE.2017.2774777.
   Web of Science ®Google Scholar
• Wilson, J., and P. R. Daugherty. 2018. “Collaborative Intelligence Humans and AI Are Joining Forces.” Harvard Business Review 96 (4): 115–123.
   Web of Science ®Google Scholar
• Xu, K. 2017. “Machine Tool 4.0 for the New Era of Manufacturing.” The International Journal of Advanced Manufacturing Technology 92 (5–8): 1893–1900. doi:10.1007/s00170-017-0300-7.
   Web of Science ®Google Scholar
• Xu, A. M., J. M. Gao, and K. Chen. 2016. “Excavation of Critical Resource Node for Quality Control of Multi-Variety Mixed Production Shopfloor Based on Complex Network Property.” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 230 (1): 169–177.
   Web of Science ®Google Scholar
• Xu, X., and Q. Hua. 2017. “Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategic.” IEEE Access 5: 17543–17551.
   Web of Science ®Google Scholar
• Yin, Y. H., A. Y. C. Nee, S. K. Ong, J. Y. Zhu, P. H. Gu, and L. J. Chen. 2015. “Automating Design with Intelligent Human-Machine Integration.” CIRP Annals 64 (2): 655–677. doi:10.1016/j.cirp.2015.05.008.
   Web of Science ®Google Scholar
• Ying, C., C. Ken, S. Hemeng, Y. P. Zhang, and T. Fei. 2018. “Data and Knowledge Mining with Big Data towards Smart Production.” Journal of Industrial Information Integration 9: 1–13. doi:10.1016/j.jii.2017.08.001.
   Web of Science ®Google Scholar
• Ying, K. C., and Y. J. Tsai. 2017. “Minimising total cost for training and assigning multiskilled workers in seru production systems.” International Journal of Production Research 55 (9–10): 2978–2989.
   Google Scholar
• Zanchettin, Andrea Maria, Andrea Casalino, Luigi Piroddi, and Paolo Rocco. 2019. “Prediction of Human Activity Patterns for Human-Robot Collaborative Assembly Tasks.” IEEE Transactions on Industrial Informatics 15 (7): 3934–3942. doi:10.1109/TII.2018.2882741.
   Web of Science ®Google Scholar
• Zhang, H. 2016. “Modelling and Prediction of Tool Wear Using LS-SVM in Milling Operation.” International Journal of Computer Integrated Manufacturing 29 (1): 76–91.
   Web of Science ®Google Scholar
• Zhang, Yingfeng, Shan Ren, Yang Liu, and Shubin Si. 2017. “A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products.” Journal of Cleaner Production 142: 626–664. doi:10.1016/j.jclepro.2016.07.123.
   Web of Science ®Google Scholar
• Zheng, Hao, Yixiong Feng, Yicong Gao, and Jianrong Tan. 2018. “A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things.” Sensors 18 (9): 2871. doi:10.3390/s18092871.
   Web of Science ®Google Scholar
• Zhou, K., and P. Yao. 2019. “Overview of Recent Advances of Process Analysis and Quality Control in Resistance Spot Welding.” Mechanical Systems and Signal Processing 124: 170–198. doi:10.1016/j.ymssp.2019.01.041.
   Web of Science ®Google Scholar