A recommender system applied to the indirect materials selection process (RS-IMSP) for producing automobile spare parts |
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| Affiliation: | 1. Instituto de Telecomunicações, University of Beira Interior, Polytechnic Institute of Castelo Branco, Rua Marquês D’Ávila e Bolama, 6201-001 Covilhã, Portugal;2. Instituto de Telecomunicações, University of Beira Interior, Rua Marquês D’Ávila e Bolama, 6201-001 Covilhã, Portugal;3. University ITMO, Kronverkskiy pr, 49, 197101 St. Petersburg, Russia;4. University of Haute Alsace – IUT, 34 rue du Grillenbreit, 68008 Colmar, France;5. CISTER Research Unit, ISEP/IPP, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal;1. Jožef Stefan Institute, Ljubljana, Slovenia;2. University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia;1. Department of Industrial and System Engineering/Engineering Research Institute, Gyeongsang National University, South Korea;2. Department of Industrial Engineering, Changwon National University, South Korea;1. Tsinghua National Laboratory for Information Science and Technology, Research Institute of Information Technology, Tsinghua University, No. 1 Tsinghua Yuan, Beijing 10084, China;2. Fuwai Hospital, Chinese Academy of Medical Science, North Lishi Road, Xicheng District, Beijing 100037, China;3. Beijing University of Posts and Telecommunications, Beijing 100876, China;4. IBISC Laboratory, University of Evry, 23 Bd de France, 91034 Évry, France |
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| Abstract: | 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. |
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| Keywords: | Recommender system based on the non-personalized approach Selection of indirect materials in warehouse |
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