Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31097
Systematic Mapping Study of Digitization and Analysis of Manufacturing Data

Authors: R. Clancy, M. Ahern, D. O’Sullivan, K. Bruton


The manufacturing industry is currently undergoing a digital transformation as part of the mega-trend Industry 4.0. As part of this phase of the industrial revolution, traditional manufacturing processes are being combined with digital technologies to achieve smarter and more efficient production. To successfully digitally transform a manufacturing facility, the processes must first be digitized. This is the conversion of information from an analogue format to a digital format. The objective of this study was to explore the research area of digitizing manufacturing data as part of the worldwide paradigm, Industry 4.0. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Specific research questions were defined to assess the key benefits and limitations associated with the digitization of manufacturing data. Research papers were classified according to the type of research and type of contribution to the research area. Upon analyzing 54 papers identified in this area, it was noted that 23 of the papers originated in Germany. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. The literature suggested future research directions and highlighted one specific gap in the area. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. The data analytics expertise is not useful unless the manufacturing process information is utilized. A legitimate understanding of the data is crucial to perform accurate analytics and gain true, valuable insights into the manufacturing process. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. To test the concept of this gap existing, the researcher initiated an industrial case study in which they embedded themselves between the subject matter expert of the manufacturing process and the data scientist. Of the papers resulting from the systematic mapping study, 12 of the papers contributed a framework, another 12 of the papers were based on a case study, and 11 of the papers focused on theory. However, there were only three papers that contributed a methodology. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research.

Keywords: Manufacturing, Analytics, Digitization, Industry 4.0

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 226


[1] I. Castelo-Branco, F. Cruz-Jesus, and T. Oliveira, “Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union,” Comput. Ind., vol. 107, pp. 22–32, May 2019.
[2] D. T. Monitor, “Germany: Industrie 4.0 Fact box for Germany’s Industrie 4.0 policy initiative,” no. January, 2017.
[3] EFFRA, “European Factories of the Future Research Association,” 20019.
[4] C. O. Klingenberg, M. A. V. Borges, and J. A. V. Antunes, “Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies,” J. Manuf. Technol. Manag., 2019.
[5] C. Leyh, K. Bley, T. Schaffer, and S. Forstenhausler, “SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, 2016, vol. 8, pp. 1297–1302.
[6] A. Haleem and M. Javaid, “Industry 5.0 and its applications in orthopaedics,” Journal of Clinical Orthopaedics and Trauma, vol. 10, no. 4. Elsevier B.V., pp. 807–808, 01-Jul-2019.
[7] R. Geissbauer, J. Vedso, and S. Schrauf, “Industry 4.0: Building the digital enterprise,” 2016.
[8] UNIDO, “Emerging trends in global advanced manufacturing,” Cambridge Inst. Manuf. Univ. Cambridge, p. 80, 2015.
[9] S. Shaiholla, A. Bekov, and I. A. Ukaegbu, “Industry 4.0: Redefining Manufacturing in Kazakhstan,” in International Conference on Advanced Communication Technology, ICACT, 2019, vol. 2019-Febru, pp. 606–609.
[10] A. Pankajakshan, C. Waldron, M. Quaglio, A. Gavriilidis, and F. Galvanin, “A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model-Identification Platforms,” Engineering, Oct. 2019.
[11] J. Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, “Smart manufacturing, manufacturing intelligence and demand-dynamic performance,” Comput. Chem. Eng., vol. 47, pp. 145–156, 2012.
[12] G. Schneider, S. Keil, and G. Luhn, “Opportunities, Challenges and Use Cases of Digitization within the Semiconductor Industry,” in 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 2018.
[13] B. Jia, M. Heng, A. K. Ng, R. Kong, and H. Tay, Digitization of Work Instructions and Checklists for Improved Data Management and Work Productivity. 2019.
[14] C. Hegedus, A. Franko, and P. Varga, “Asset and Production Tracking through Value Chains for Indsutry 4.0 using the Arrowhead Framework,” in 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 2019, p. 832.
[15] V. Shivajee, R. K. Singh, and S. Rastogi, “Manufacturing conversion cost reduction using quality control tools and digitization of real-time data,” J. Clean. Prod., vol. 237, Nov. 2019.
[16] McKinsey Global Institute, “Twenty-Five Years of Digitization - Ten Insights into how to Play it Right,” McKinsey & Company^, pp. 1–11, 2019.
[17] P. Weill and S. L. Woerner, “Thriving in an Increasingly Digital Ecosystem,” MITSloan Manag. Rev., vol. 56, no. 4, 2015.
[18] R. Bose, “Advanced analytics: opportunities and challenges,” Ind. Manag. Data Syst., vol. 109, no. 2, pp. 155–172, Mar. 2009.
[19] L. Kok Kiang, F. Pin Fen, A. Chin Tah, X. Yinghui, B. Ong, and C. Foo, “The Singapore Smart Industry Readiness Index,” 2017.
[20] K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” 12th Int. Conf. Eval. Assess. Softw. Eng. EASE 2008, no. June, 2008.
[21] P. O’Donovan, K. Leahy, K. Bruton, and D. T. J. D. T. J. O’Sullivan, “Big data in manufacturing: a systematic mapping study,” J. Big Data, vol. 2, no. 1, 2015.
[22] H. Lu, L. Guo, M. Azimi, and K. Huang, “Oil and Gas 4.0 era: A systematic review and outlook,” Computers in Industry, vol. 111. Elsevier B.V., pp. 68–90, 01-Oct-2019.
[23] C. Izurieta and J. M. Bieman, “How software designs decay: A pilot study of pattern evolution,” Proc. - 1st Int. Symp. Empir. Softw. Eng. Meas. ESEM 2007, pp. 449–451, 2007.
[24] A. Brem, M. M. Adrita, D. T. J. O’Sullivan, and K. Bruton, “Industrial smart and micro grid systems – A systematic mapping study,” J. Clean. Prod., vol. 244, p. 118828, 2020.
[25] G. Joós-Kovács, B. Vecsei, S. Körmendi, V. A. Gyarmathy, J. Borbély, and P. Hermann, “Trueness of CAD/CAM digitization with a desktop scanner-an in vitro study.”
[26] R. Wieringa, N. Maiden, N. Mead, and C. Rolland, “Requirements engineering paper classification and evaluation criteria: A proposal and a discussion,” Requir. Eng., vol. 11, no. 1, pp. 102–107, 2006.
[27] I. Grangel-Gonzalez, L. Halilaj, G. Coskun, S. Auer, D. Collarana, and M. Hoffmeister, “Towards a Semantic Administrative Shell for Industry 4.0 Components,” in Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016, 2016, pp. 230–237.
[28] M. Ghobakhloo and M. Fathi, “Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing,” J. Manuf. Technol. Manag., 2019.
[29] S. Mittal, M. A. Khan, D. Romero, and T. Wuest, “A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs),” Journal of Manufacturing Systems, vol. 49. Elsevier B.V., pp. 194–214, 01-Oct-2018.
[30] J. M. Müller, “Sustainable Industrial Value Creation in SMEs : A Comparison between Industry 4 . 0 and Made in China 2025,” vol. 5, no. 5, pp. 659–670, 2018.
[31] N. Petersen, L. Halilaj, I. Grangel-González, S. Lohmann, C. Lange, and S. Auer, “Realizing an RDF-Based Information Model for a Manufacturing Company – A Case Study,” in International Semantic Web Conference, 2017, vol. 10588.
[32] M. Ghobakhloo, “The future of manufacturing industry: a strategic roadmap toward Industry 4.0,” J. Manuf. Technol. Manag., vol. 29, no. 6, pp. 910–936, Oct. 2018.
[33] A. Lipsmeier, A. Kühn, R. Joppen, and R. Dumitrescu, “Process for the development of a digital strategy,” Procedia CIRP, vol. 88, pp. 173–178, 2020.
[34] R. Joppen, S. Von Enzberg, A. Kuhn, and R. Dumitrescu, “A practical Framework for the Optimization of Production Management Processes,” Procedia Manuf., vol. 33, pp. 406–413, 2019.
[35] F. Wortmann, R. Joppen, M. Drewel, A. Kühn, and R. Dumitrescu, Developing and evaluating concepts for a digital platform, no. June. 2019.
[36] R. Joppen, S. von Enzberg, J. Gundlach, A. Kühn, and R. Dumitrescu, “Key performance indicators in the production of the future,” Procedia CIRP, vol. 81, pp. 759–764, 2019.
[37] R. Joppen, S. Enzberg, A. Kühn, and R. Dumitrescu, “Data map - Method for the specification of data flows within production,” Procedia CIRP, vol. 79, pp. 461–465, 2019.
[38] R. Joppen, A. Lipsmeier, C. Tewes, A. Kühn, and R. Dumitrescu, “Evaluation of investments in the digitalization of a production,” in Procedia CIRP, 2019, vol. 81, pp. 411–416.
[39] T. D. Oesterreich and F. Teuteberg, “Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry,” Computers in Industry, vol. 83. Elsevier B.V., pp. 121–139, 01-Dec-2016.
[40] B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges,” IEEE Access, vol. 6, pp. 6505–6519, Dec. 2017.
[41] C. Schöder, “The Challenges of Industry 4 . 0 for Small and Medium-sized Enterprises,” no. August, 2016.
[42] M. Eibl and M. Gaedke, “The potential value of digitization for Business - Insights from German-speaking experts,” Lect. Notes Informatics, p. 1647, 2017.
[43] H. Kagermann, “Change through digitization—value creation in the age of industry 4.0,” in Management of Permanent Change, Springer Science+Business Media, 2015, pp. 23–45.
[44] F. Müller, D. Jaeger, and M. Hanewinkel, “Digitization in wood supply – A review on how Industry 4.0 will change the forest value chain,” Computers and Electronics in Agriculture, vol. 162. Elsevier B.V., pp. 206–218, 01-Jul-2019.
[45] H. Hirsch-Kreinsen, “Digitization of Industrial Work: developments paths and prospects,” J. Labour Mark. Res., vol. 49, no. 1, Jul. 2016.
[46] R. Joppen, A. Kühn, D. Hupach, and R. Dumitrescu, “Collecting data in the assessment of investments within production,” in Procedia CIRP, 2019, vol. 79, pp. 466–471.
[47] V. Roblek, M. Meško, and A. Krapež, “A Complex View of Industry 4.0,” SAGE Open, vol. 6, no. 2, Jun. 2016.
[48] P. Pal and K. K. Ghosh, “Estimating digitization efforts of complex product realization processes,” Int. J. Adv. Manuf. Technol., vol. 95, no. 9–12, pp. 3717–3730, Apr. 2018.
[49] P. E. C. Johansson, L. Malmsköld, Å. Fast-Berglund, and L. Moestam, “Challenges of handling assembly information in global manufacturing companies,” J. Manuf. Technol. Manag., vol. ahead-of-print, no. ahead-of-print, Nov. 2019.