@ARTICLE{Sajko_Nika_Manufacturing_2020, author={Sajko, Nika and Kovacic, Simon and Ficko, Mirko and Palcic, Iztok and Klancnik, Simon}, volume={vol. 11}, number={No 3}, journal={Management and Production Engineering Review}, howpublished={online}, year={2020}, publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management}, abstract={Due to fast-paced technical development, companies are forced to modernise and update their equipment, as well as production planning methods. In the ordering process, the customer is interested not only in product specifications, but also in the manufacturing lead time by which the product will be completed. Therefore, companies strive towards setting an appealing but attainable manufacturing lead date. Manufacturing lead time depends on many different factors; therefore, it is difficult to predict. Estimation of manufacturing lead time is usually based on previous experience. In the following research, manufacturing lead time for tools for aluminium extrusion was estimated with Artificial Intelligence, more precisely, with Neural Networks. The research is based on the following input data; number of cavities, tool type, tool category, order type, number of orders in the last 3 days and tool diameter; while the only output data are the number of working days that are needed to manufacture the tool. An Artificial Neural Network (feed-forward neural network) was noted as a sufficiently accurate method and, therefore, appropriate for implementation in the company.}, title={Manufacturing lead time prediction for extrusion tools with the use of neural networks}, URL={http://www.czasopisma.pan.pl/Content/117938/PDF/5-539-K.pdf}, doi={10.24425/mper.2020.134931}, keywords={Manufacturing lead time, neural network, artificial intelligence, extrusion}, }