Rediscovering scientific management. The evolution from industrial engineering to industrial data science




Scientific Management, Industrial Engineering, Industrial Data Science, Data Science, Data Analytics, Process Chain


Industrial Engineering, through its role as design, planning and organizational body of the industrial production, has been crucial for the success of manufacturing companies for decades. The potential, expected over the course of Industry 4.0 and through the application of Data Analytic tools and methods, requires a coupling to established methods. This creates the necessity to extend the traditional job description of Industrial Engineering by new tools from the field of Data Analytics, namely Industrial Data Science. Originating from the historic pioneers of Industrial Engineering, it is evident that the basic principles will remain valuable. However, further development in view of the data analytic possibilities is already taking place. This paper reviews the origins of Industrial Engineering with reference to four pioneers, draws a connection to current day usage, and considers possibilities for future applications of Industrial Data Science.


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Author Biographies

Jochen Deuse, University of Technology Sydney; Technical University Dortmund

Prof. Dr.-Ing. Jochen Deuse, born 1967, is head of the Institute of Production Systems (IPS) at the TU Dortmund and professor at the University of Technology Sydney (UTS), Faculty of Engineering and Information Technology, School of Mechanical and Mechatronic Engineering, Centre for Advanced Manufacturing (CAM).

Nikolai West, Technical University Dortmund

Institute of Production Systems

Marius Syberg, Technical University Dortmund

Institute of Production Systems


Awad, M., & Khanna, R. (2015). Efficient Learning Machines: Theories, Concepts, and Applications for Engi-neers and System Designers. Apress Media.

Bohnen, F., Buhl, M., & Deuse, J. (2013). Systematic procedure for leveling of low volume and high mix pro-duction. CIRP Journal of Manufacturing Science and Technology, 6(1), 53-58.

Bortolini, M., Faccio, M., Gamberi, M., & Pilati, F. (2020). Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes. Computers & Industrial Engineering, 139(1-4).

Burbidge, J. L. (1963). Production flow analysis. Production Engineer, 42(12), 742.

Burbidge, J. L. (1975). The Introduction of Group Technology. W. H.

Burbidge, J. L. (1991). Production flow analysis for planning group technology. Journal of Operations Manage-ment, 10(1), 5-27.

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0. SPSS Inc.

Corallo, A., Lazoi, M., & Striani, F. (2020). Process mining and industrial applications: A systematic literature review. Knowledge and Process Management, 27(3), 225-233.

Deming, W. E. (1950). Elementary Principles of the Statistical Control of Quality: A Series of Lectures. Nippon Kagaku Gijutsu Remmei.

Deuse, J., Dombrowski, U., Nöhring, F., Mazarov, J., & Dix, Y. (2020). Systematic combination of Lean Man-agement with digitalization to improve production systems on the example of Jidoka 4.0. International Journal of Engineering Business Management, 12(3), 184797902095135.

Deuse, J., Lenze, D., Klenner, F., & Friedrich, T. (2016). Manufacturing data analytics to identify dynamic bot-tlenecks in production systems with high value-added variability (in German). In C. Schlick (Ed.), Me-gatrend Digitalisierung (pp. 11-26). GITO.

Deuse, J., Stankiewicz, L., Zwinkau, R., & Weichert, F. (2019). Automatic Generation of Methods-Time Meas-urement Analyses for Assembly Tasks from Motion Capture Data Using Convolutional Neuronal Net-works. International Conference on Applied Human Factors and Ergonomics, 141-150.

Deuse, J., Wischniewski, S., & Fischer, H. (2006). Rediscovering Industrial Engineering - Methods for applying lean production principles (deutsch: Renaissance des Industrial Engineering - Methoden für die Um-setzung Ganzheitlicher Produktionssysteme.). Werkstattstechnik Online, 96(1/2), 57-60.

Dold, L., & Speck, C. (2021). Resolving the productivity paradox of digitalised production. International Jour-nal of Production Management and Engineering, 9(2), 65.

Eller, R., Alford, P., Kallmünzer, A., & Peters, M. (2020). Antecedents, consequences, and challenges of small and medium-sized enterprise digitalization. Journal of Business Research, 112, 119-127.

ER, M., Arsad, N., Astuti, H. M., Kusumawardani, R. P., & Utami, R. A. (2018). Analysis of production planning in a global manufacturing company with process mining. Journal of Enterprise Information Manage-ment, 31(2), 317-337.

Eversheim, W., & Deuse, J. (1997). Formation of Part Families based on Product Model Data. Production Engi-neering (2), 97-100.

Fayyad, U., Piatetsky-Shapiro, & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. International Conference on Knowledge Discovery and Data Mining, 2(1), 82-88.

Feng, Z., & Hua, X. (2020). Pattern Recognition and Its Application in Image Processing. Journal of Physics: Conference Series, 1518, 12071.

Gallina, V., Lingitz, L., Breitschopf, J., Zudor, E., & Sihn, W. (2021). Work in Progress Level Prediction with Long Short-Term Memory Recurrent Neural Network. Procedia Manufacturing, 54, 136-141.

Gilbreth, F. B. (1912). Primer of scientific management. D. Van Nostrand C.

Goldratt, E. M., & Cox, J. (1984). The Goal. North River Press.

Goldratt, E. M., & Fox, R. E. (1986). The Race. North River Press.

Gorobets, V., Holzwarth, V., Hirt, C., Jufer, N., & Kunz, A. (2021). A VR-based approach in conducting MTM for manual workplaces. The International Journal of Advanced Manufacturing Technology, 117(7), 2501-2510.

Huang, Z., Kim, J., Sadri, A., Dowey, S., & Dargusch, M. S. (2019). Industry 4.0: Development of a multi-agent system for dynamic value stream mapping in SMEs. Journal of Manufacturing Systems, 52, 1-12.

IISE. (2021). What is industrial and systems engineering? (IISE official definition). Institute of Industrial & Sys-tems Engineers.

Johnson, S. B. (1997). Three Approaches to Big Technology: Operations Research, Systems Engineering, and Project Management. Technology and Culture, 38(4), 891.

Johnson, S. B. (2013). Technical and institutional factors in the emergence of project management. International Journal of Project Management, 31(5), 670-681.

Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902-904.

Knoll, D., Waldmann, J., & Reinhart, G. (2019). Developing an internal logistics ontology for process mining. Procedia CIRP, 79(1), 427-432.

Kusiak, A., & Dagli, C. H. (1994). Artificial Neural Networks for Intelligent Manufacturing. Springer Netherlands.

Lee, E. (2006). Cyber-Physical Systems - Are Computing Foundations Adequate?

Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality Prediction in Interlinked Manufactur-ing Processes based on Supervised & Unsupervised Machine Learning. CIRP Conference on Manufac-turing Systems, 46, 193-198.

Little, J. D. C. (1961). A Proof for the Queuing Formula: L = λ W. Operations Research, 9(3), 383-387.

Lödding, H. (2013). Handbook of Manufacturing Control: Fundamentals, description, configuration. Springer-Link Bücher. Springer.

Maschek, T., Heuser, C., Hasselmann, V.-R., Deuse, J., & Willats, P. (2014). Variability-based classification of production systems.: Basis for individual design and management concepts. Zeitschrift Für Wirtschaft-lichen Fabrikbetrieb, 109(9), 591-594 ((in German)).

Maury, M. F. (1963). The Physical Geography of the Sea, and Its Meteorology. Harvard University Press.

Maynard, H. B., & Zandin, K. B. (2001). Maynard's industrial engineering handbook (5th ed.). McGraw-Hill.

Mazarov, J., Wolf, P., Schallow, J., Nöhring, F., Deuse, J., & Richter, R. (2019). Industrial Data Science in Value Creation Networks (in German). Zeitschrift Für Wirtschaftlichen Fabrikbetrieb, 114(12), 874-877 (Concept of a Service Platform for Data Integration and Analysis, Competence Development and Novel Business Models).

Merkle, J. A. (1980). Management and Ideology: The Legacy of the International Scientific Management Move-ment. UC Press.

Mitrofanov, S. P. (1946). Scientific Principles of Group Technology.

MTM ASSOCIATION e. V. (2021). Brand History. MTM ASSOCIATION e. V.

Richter, R., & Deuse, J. (2011). Industrial Engineering in Modern Production (in German). Zeitschrift F. Angew. Arbeitswissenschaft, 207(1), 6-13.

Roser, C., Lorentzen, K., Lenze, D., Deuse, J., Klenner, F., Richter, R., Schmitt, J., & Willats, P. (2017). Bottle-neck Prediction Using the Active Period Method in Combination with Buffer Inventories. IFIP-APMS, 374-381.

Roser, C., Nakano, M., & Tanaka, M. (2002). Shifting Bottleneck Detection. Proc. Of the Winter Simulation Conference(1), 1079-1086.

Schmitt, J., Bönig, J., Borggräfe, T., Beitinger, G., & Deuse, J. (2020). Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing. Advanced Engineering Informatics, 45, 1-10.

Schmitt, J., Hahn, F., & Deuse, J. (2019). Practical Framework for Advanced Quality-based Process Control in Interlinked Manufacturing Processes. IEEE-IEEM, 511-515.

Schulte, L., Schmitt, J., Meierhofer, F., & Deuse, J. (2020). Optimizing Inspection Process Severity by Machine Learning Under Label Uncertainty. Advances in Human Factors and Systems Interaction, 3-9.

Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Martino Publishing.

Sokolowski, A. P. (1938). Problems of Typification of Technological Processes. Lenitomasch.

Strauß, P., Schmitz, M., Wöstmann, R., & Deuse, J. (2018). Enabling of Predictive Maintenance in the Brown-field through Low-Cost Sensors, an IIoT-Architecture and Machine Learning. In 2018 IEEE Interna-tional Conference on Big Data (Big Data).

Taylor, F. W. (1911). The Principles of Scientific Management. Harper & Brothers Publishers.

Valamede, L. S., & Akkari, A. C. S. (2020). Lean 4.0: A New Holistic Approach for the Integration of Lean Manufacturing Tools and Digital Technologies. International Journal of Mathematical, Engineering and Management Sciences, 5(5), 851-868.

van der Aalst, W., Adriansyah, A., de Medeiros, Ana Karla Alves, Arcieri, F., Baier, T., Blickle, T., Bose, J. C., van den Brand, P., Brandtjen, R., Buijs, J., Burattin, A., Carmona, J., Castellanos, M., Claes, J., Cook, J., Costantini, N., Curbera, F., Damiani, E., Leoni, M. de, . . . Wynn, M. (2012). Process Mining Mani-festo. In F. Daniel, K. Barkaoui, & S. Dustdar (Eds.), Business Process Management Workshops (pp. 169-194). Springer Berlin Heidelberg.

Wang, K.-S. (2013). Towards zero-defect manufacturing (ZDM)-a data mining approach. Advances in Manu-facturing, 1(1), 62-74.

Wang, L., Zhao, G., Cheng, L., & Pietikäinen, M. (2011). Machine Learning for Vision-Based Motion Analysis. Springer London.

Wang, T., Wang, X., Ma, R., Li, X., Hu, X., Chan, F. T. S., & Ruan, J. (2020). Random Forest-Bayesian Optimi-zation for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber-Physical Systems. IEEE Internet of Things Journal, 7(9), 8641-8653.

Wedel, M., Hacht, M., Hieber, R., Metternich, J., & Abele, E. (2015). Real-time Bottleneck Detection and Predic-tion to Prioritize Fault Repair in Interlinked Production Lines. Procedia CIRP, 37, 140-145.

West, N., Schlegl, T., & Deuse, J. (2021). Feature extraction for time series classification using univariate de-scriptive statistics and dynamic time warping in a manufacturing environment. IEEE-ICBAIE, 2(1), 762-768.

West, N., Syberg, M., & Deuse, J. (2022). A Holistic Methodology for Successive Bottleneck Analysis in Dy-namic Value Streams of Manufacturing Companies. In A.-L. Andersen, R. Andersen, T. D. Brunoe, M. S. S. Larsen, K. Nielsen, A. Napoleone, & S. Kjeldgaard (Eds.), Lecture Notes in Mechanical Engineer-ing. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems (pp. 612-619). Springer International Publishing.

Wierse, A., & Riedel, T. (2017). Smart Data Analytics : Mit Hilfe Von Big Data Zusammenhänge Erkennen und Potentiale Nutzen. Walter de Gruyter GmbH.




How to Cite

Deuse, J., West, N., & Syberg, M. (2022). Rediscovering scientific management. The evolution from industrial engineering to industrial data science. International Journal of Production Management and Engineering, 10(1), 1–12.