A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods
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...
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Engenharia de Produção (ABEPRO)
2020-11-01
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| Series: | Production |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100708&tlng=en |
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| author | Carlos Henrique dos Santos Renan Delgado Camurça Lima Fabiano Leal José Antonio de Queiroz Pedro Paulo Balestrassi José Arnaldo Barra Montevechi |
| author_facet | Carlos Henrique dos Santos Renan Delgado Camurça Lima Fabiano Leal José Antonio de Queiroz Pedro Paulo Balestrassi José Arnaldo Barra Montevechi |
| author_sort | Carlos Henrique dos Santos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-144bda230557414ea88c3ebca482dafc |
| institution | OA Journals |
| issn | 1980-5411 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Associação Brasileira de Engenharia de Produção (ABEPRO) |
| record_format | Article |
| series | Production |
| spelling | doaj-art-144bda230557414ea88c3ebca482dafc2025-08-20T02:04:52ZengAssociação Brasileira de Engenharia de Produção (ABEPRO)Production1980-54112020-11-013010.1590/0103-6513.20200018A decision support tool for operational planning: a Digital Twin using simulation and forecasting methodsCarlos Henrique dos Santoshttps://orcid.org/0000-0002-8847-8951Renan Delgado Camurça Limahttps://orcid.org/0000-0003-4690-5238Fabiano Lealhttps://orcid.org/0000-0001-9814-5352José Antonio de Queirozhttps://orcid.org/0000-0002-1658-3525Pedro Paulo Balestrassihttps://orcid.org/0000-0003-2772-0043José Arnaldo Barra Montevechihttps://orcid.org/0000-0002-6443-5113Abstract 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100708&tlng=enDecision support systemDiscrete Event SimulationForecasting methodsDigital TwinOperational planning |
| spellingShingle | Carlos Henrique dos Santos Renan Delgado Camurça Lima Fabiano Leal José Antonio de Queiroz Pedro Paulo Balestrassi José Arnaldo Barra Montevechi A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods Production Decision support system Discrete Event Simulation Forecasting methods Digital Twin Operational planning |
| title | A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods |
| title_full | A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods |
| title_fullStr | A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods |
| title_full_unstemmed | A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods |
| title_short | A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods |
| title_sort | decision support tool for operational planning a digital twin using simulation and forecasting methods |
| topic | Decision support system Discrete Event Simulation Forecasting methods Digital Twin Operational planning |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100708&tlng=en |
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