Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model


assembly cycle, machine assembly, forecasting, Make-to-Order, artificial neural networks, input signals, output signals, MatLab

How to Cite

Brzozowska, J., Gola, A., & Kulisz, M. (2023). Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model. Technologia I Automatyzacja Montażu (Assembly Techniques and Technologies), 121(3), 13-20.


This article presents the problem of forecasting the length of machine assembly cycles in make-to-order production (Make-to-Order). The model of Make-to-Order production and the technological process of manufacturing the finished product are presented. The possibility of developing a novel method, using artificial intelligence solutions, to estimate machine assembly times based on historical company data on manufacturing times for structurally similar components, is described. It is assumed that the result of the developed method will be an intelligent system supporting efficient and accurate estimation of machine assembly time, ready for implementation in production conditions. Such data as part availability, human resource availability and novelty factor will be used as input data for learning the neural network, while the output variable during learning the neural network will be the actual machine assembly time.


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