A recommender system applied to the indirect materials selection process (RS-IMSP) for producing automobile spare parts

In this work, the development of a recommender system that aims to facilitate the indirect materials selection task for the creation of spare parts is proposed. In the industrial sector there are spare parts manufacturing companies, where there is a high rotation of staff and this leads to loss of knowledge as new users do not know what indirect materials they should select in the warehouse to create certain parts. The proposed system aims to integrate an indirect materials recommender system to assist this warehouse task. The proposed system is based on the non-personalized approach and similar order circumstances, to perform the recommendation process. From the evaluation of the proposed system, we could conclude that the indirect materials selection process for producing auto parts was improved.