RESEARCH PAPER
Artificial intelligence-based hybrid forecasting models for manufacturing systems
1
Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Centre for Advanced Manufacturing Technologies, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):263-277
HIGHLIGHTS
- In the paper 4 new hybrid forecasting artificial intelligence-based models are proposed.
- A problem of defining explanatory variables when access to data is limited is addressed.
- Study fills in literature gap of hybrid forecasting application in manufacturing systems.
- The presented case studies cover production planning, maintenance and quality control.
- Algorithm for forecasting accuracy assessment and optimal method selection is presented.
KEYWORDS
artificial neural networksupport vector machineextreme learning machinehybrid forecastingproduction planningmaintenancequality control
ABSTRACT
The paper addresses the problem of forecasting in manufacturing systems. The main aim of
the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model
and Hybrid extreme learning machine model. The study focuses on solving the problem
of limited access to independent variables. Empirical verification of the proposed models
is built upon real data from the three manufacturing system areas – production planning,
maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting
accuracy assessment and optimal method selection for industrial companies is introduced. It
can serve not only as an efficient and costless tool for advanced manufacturing companies
willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0
implementation.
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