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Artificial intelligence-based method for forecasting flowtime in job shops

Paulo Modesti (Postgraduate Programme in Mechanical and Materials Engineering, UTFPR, Curitiba, Brazil)
Jhonatan Kobylarz Ribeiro (Department of Electrical Engineering, Universidade Federal do Paraná, Curitiba, Brazil)
Milton Borsato (Postgraduate Programme in Mechanical and Materials Engineering, Federal University of Technology – Paraná/Brazil (UTFPR), Curitiba, Brazil)

VINE Journal of Information and Knowledge Management Systems

ISSN: 2059-5891

Article publication date: 25 February 2022

Issue publication date: 19 January 2024




This paper aims to develop a method based on artificial intelligence capable of predicting the due date (DD) of job shops in real-time, aiming to assist in the decision-making process of industries.


This paper chooses to use the methodological approach Design Science Research (DSR). The DSR aims to build solutions based on technology to solve relevant issues, where its research results from precise methods in the evaluation and construction of the model. The steps of the DSR are identification of the problem and motivation, definition of the solution’s objectives, design and development, demonstration, evaluation of the solution and the communication of results.


Along with this work, it is possible to verify that the proposed method allows greater accuracy in the DD definition forecasts when compared to conventional calculations.

Research limitations/implications

Some limitations of this study can be pointed. It is possible to mention questions related to the tasks to be informed by users, as they could lead to problems in the performance of the artifact as the input data may not be correctly posted due to the misunderstanding of the question by part of the users.


The proposed artifact is a method capable of contributing to the development of the manufacturing industry to improve the forecast of manufacturing dates, assisting in making decisions related to production planning. The use of real production data contributed to creating, demonstrating and evaluating the artifact. This approach was important for developing the method allowing more reliability.



This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.


Modesti, P., Ribeiro, J.K. and Borsato, M. (2024), "Artificial intelligence-based method for forecasting flowtime in job shops", VINE Journal of Information and Knowledge Management Systems, Vol. 54 No. 2, pp. 452-472.



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