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Creative Commons licence CC BY-NC (Attribution-NonCommercial) Logforum. 2022. 18(1), article 4, 35-50; DOI: https://doi.org/10.17270/J.LOG.2022.637


Mariusz Kmiecik, Hawre Zangana

Silesian University of Technology, Zabrze, Poland


Background: Enterprises’ decision-making could be facilitated by properly creating or choosing and implementing demand forecasting systems. Currently, there are more and more advanced forecasting algorithms based on sophisticated technologies such as artificial neural networks and machine learning. The following research paper focuses on a case study of an automotive manufacturer. The main research aim is to propose the proper demand forecasting tool and show the prospects for implementing the mentioned solution.

Methods: The research paper contains the statistical analysis of a chosen time series referring to the demanded quantity of the manufactured products. To create forecasts, models based on the following forecasting algorithms were created: ARIMA, ELM (Extreme Learning Machine), and NNAR (Neural Network Autoregressive). All algorithms are based on the R programming language. All algorithms are run in the same time series where the training and testing periods were established.

Results: According to the forecasts ex-post errors and FVA (forecasts value-added) analysis, the best fitting algorithm is the algorithm based on ELM. It yields the most accurate predictions. All other models fail to add value to the forecast. Specifically, the ARIMA models damage the forecast dramatically. Such significant magnitudes of negative FVA values indicate that choosing not to forecast and plan based on the sales of the same period of the previous year is a better choice. However, in the case of the ELM model, the forecasts can be worth the time, finance, and human resources put into preparing them.

Conclusion: The increased accuracy of ELM forecasts can contribute to optimizing the process of reaching consensus forecasts. While unconstrained statistical forecasts tend to be overridden, not only to produce constrained forecasts incorporating various variables such as calendar events, promotional activities, supply capacity, and operational abilities, they are also overridden by planners to reflect their foreseeing of demand. The proposed solution could also be easily implemented in the resource planning process to improve it. The proposition of the resource planning process supported by the proposed forecasting system is also shown in the following paper using a BPMN 2.0 (Business Process Modelling Notation 2.0) map.

Keywords: demand forecasting, R Studio, ARIMA model, Neural Network model, Machine learning model, manufacturing system
Full text available in in english in format:
artykuł nr 4 - pdfAdobe Acrobat
For citation:

MLA Kmiecik, Mariusz, and Hawre Zangana. "Supporting of manufacturing system based on demand forecasting tool." Logforum 18.1 (2022): 4. DOI: https://doi.org/10.17270/J.LOG.2022.637
APA Mariusz Kmiecik, Hawre Zangana (2022). Supporting of manufacturing system based on demand forecasting tool. Logforum 18 (1), 4. DOI: https://doi.org/10.17270/J.LOG.2022.637
ISO 690 KMIECIK, Mariusz, ZANGANA, Hawre. Supporting of manufacturing system based on demand forecasting tool. Logforum, 2022, 18.1: 4. DOI: https://doi.org/10.17270/J.LOG.2022.637