Computational modeling study demonstrates AI-driven order completion time forecasting in textile fiber manufacturing, highlighting opportunities for production process optimization.
The production processes for creating textile fibers are extremely complex. In the general case, the creation of the final product - spools of thread, goes through many different stages and different production machines and workshops. Depending on the item, different types of raw materials are used in the production process, different additional materials are added, specific activities are performed. Predicting the time required to complete an order is extremely important, because it allows to optimize the production process. It depends on many factors - type of item, planned quantity of production, specifics of the production process, number of used machines and the time for their adjustment, available quantities of the necessary raw materials in stock, free machines, etc. The paper presents the work on creating a software application for forecasting the time for execution of customer orders. The model of the application and the conducted experiments are presented. Artificial intelligence algorithms have been used for forecasting.
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Trankov et al. (2024) studied this question.
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