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

Abstract

de Itajubá, Itajubá, MG, Brasil *chenrique.santoss@gmail.com 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 Implications for theory and proposed method be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the The real process records the materials consumed directly in the operation’s ERP system. Such registration occurs at the moment of consumption by the production lines, supplying the process database. The dashboard automatically accesses the standard ERP system reports and analyzes the data to transform it into information, allowing the forecasting methods to be automatically executed. The dashboard compares the results obtained through the forecasting techniques to choose the result with the lowest error, based on three metrics: Mean In which y is the observed time series, m is the length of seasonality, t l represents the level of the series, t b denotes growth, t s is the seasonal component and  | t ht y + is the forecast for h periods ahead of t based on all data up to time t. The , , αβ and γ are the smoothing parameters of the