Thematic Section - Present and Future of Production Engineering

A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods

Carlos Henrique dos Santos; Renan Delgado Camurça Lima; Fabiano Leal; José Antonio de Queiroz; Pedro Paulo Balestrassi; José Arnaldo Barra Montevechi

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Abstract: Paper aims: Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods.

Originality: With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry.

Research method: The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it.

Main findings: The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient.

Implications for theory and practice: The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.


Decision support system, Discrete Event Simulation, Forecasting methods, Digital Twin, Operational planning


Alam, K. M., & El Saddik, A. (2017). C2PS: a Digital Twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access: Practical Innovations, Open Solutions, 5, 2050-2062.

Arvan, M., Fahimnia, B., Reisi, M., & Siemsen, E. (2019). Integrating human judgment into quantitative forecasting methods: a review. Omega, 86, 237-252.

Balestrassi, P. P., Popova, E., Paiva, A. P., & Marangon Lima, J. W. (2009). Design of experiments on neural network’s training for nonlinear time series forecasting. Neurocomputing, 72(4), 1160-1178.

Banks, J., Carson Ii, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete event system simulation (5th ed.). New Jersey: Pearson.

Beregi, R., Szaller, Á., & Kádár, B. (2018). Snergy of multi-modelling for process control. IFAC PapersOnLine, 51(11), 1023-1028.

Beutel, A., & Minner, S. (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics, 140(2), 637-645.

Chwif, L., & Medina, A. C. (2015). Modelagem e simulação de eventos discretos (4. ed.). São Paulo: Elsevier.

Fishman, G. S. (2001). Discrete-event simulation: modeling, programming and analysis (1st ed.). New York: Springer.

Goldsman, D., Nance, R. E., & Wilson, J. R. (2010). A brief history of simulation revisited. In Proceedings of the Winter Simulation Conference. Piscataway: IEEE.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Kalchschmidt, M. (2012). Best practices in demand forecasting: tests of universalistic, contingency and configurational theories. International Journal of Production Economics, 140(2), 782-793.

Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: a categorical review and classification. IFAC-PapersOnLine, 51(11), 1016-1022.

Kunath, M., & Winkler, H. (2018). Integrating the Digital Twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP, 72, 225-231.

Law, A. M. (2014). Simulation modeling and analysis (5th ed.). Boston: McGraw-Hill Science.

Lu, Y., Min, Q., Liu, Z., & Wang, Y. (2019). An IoT-enabled simulation approach for process planning and analysis: a case from engine remanufacturing industry. International Journal of Computer Integrated Manufacturing, 32(4-5), 413-429.

Matsumoto, M., & Komatsu, S. (2015). Demand forecasting for production planning in remanufacturing. International Journal of Advanced Manufacturing Technology, 79(1-4), 161-175.

Montgomery, D. C., & Runger, G. C. (2012). Estatística aplicada e probabilidade para engenheiros (5. ed.). Rio de Janeiro: LTC.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting (1st ed.). New Jersey: John Wiley & Sons.

Moon, S., Hicks, C., & Simpson, A. (2012). The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy: a case study. International Journal of Production Economics, 140(2), 794-802.

Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949.

Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: literature review and analysis. Journal of Manufacturing Systems, 33(2), 241-261.

Poloni, F., & Sbrana, G. (2015). A note on forecasting demand using the multivariate exponential smoothing framework. International Journal of Production Economics, 162(1), 143-150.

Rego, J. R., & Mesquita, M. A. (2015). Demand forecasting and inventory control: a simulation study on automotive spare parts. International Journal of Production Economics, 161, 1-16.

Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207.

Safarishahrbijari, A. (2018). Workforce forecasting models: a systematic review. Journal of Forecasting, 37(7), 739-753.

Sargent, R. G. (2013). Verification and validation of simulation models. Journal of Simulation, 7(1), 12-24.

Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Klemp, C., Jacqueline, L., & Wang, L. (2010). DRAFT modeling, simulation, information technology & processing roadmap technology area 11 (pp. 1-27). Washington: National Aeronautics and Space Administration.

Sharma, P., Kulkarni, M. S., & Yadav, V. (2017). A simulation-based optimization approach for spare parts forecasting and selective maintenance. Reliability Engineering & System Safety, 168, 274-289.

Steringer, R., Zorrer, H., Zambal, S., & Eitzinger, C. (2019). Using discrete event simulation in multiple system life cycles to support zero-defect composite manufacturing in aerospace industry. IFAC PapersOnLine, 52(13), 1467-1472.

Tao, F., & Zhang, M. (2017). Digital Twin Shop-Floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access: Practical Innovations, Open Solutions, 5, 20418-20427.

Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576.

Terkaj, W., Gaboardi, P., Trevisan, C., Tolio, T., & Urgo, M. (2019). A digital factory platform for the design of roll shop plants. CIRP Journal of Manufacturing Science and Technology, 26, 88-93.

Teunter, R. H., Syntetos, A. A., & Zied Babai, M. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606-615.

Tratar, L. F., Mojškerc, B., & Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. International Journal of Production Economics, 181, 162-173.

Uriarte, A. G., Ng, A. H., & Moris, M. U. (2018). Supporting the lean journey with simulation and optimization in the context of Industry 4.0. Procedia Manufacturing, 25, 586-593.

Vijayakumar, K., Dhanasekaran, C., Pugazhenthi, R., & Sivaganesan, S. (2019). Digital Twin for factory system simulation. International Journal of Recent Technology and Engineering, 8, 63-68.

Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7, 13.

Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630.

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5fbe41100e8825657df5cc93 production Articles
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