@ARTICLE{Bruno_Giulia_An_2020, author={Bruno, Giulia and Faveto, Alberto and Traini, Emiliano}, volume={vol. 11}, number={No 2}, 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={Today, the changes in market requirements and the technological advancements are influencing the product development process. Customers demand a product of high quality and fast delivery at a low price, while simultaneously expecting that the product meet their individual needs and requirements. For companies characterized by a highly customized production, it is essential to reduce the trial-and-errors cycles to design new products and process. In such situation most of the company’s knowledge relies on the lessons learnt by operators in years of work experience, and their ability to reuse this knowledge to face new problems. In order to develop unique product and complex processes in short time, it is mandatory to reuse the acquired information in the most efficient way. Several commercial software applications are already available for product lifecycle management (PLM) and manufacturing execution system (MES). However, these two applications are scarcely integrated, thus preventing an efficient and pervasive collection of data and the consequent creation of useful information. The aim of this paper is to develop a framework able to structure and relate information from design and execution of processes, especially the ones related to anomalies and critical situations occurring at the shop floor, in order to reduce the time for finalizing a new product. The framework has been developed by exploiting open source systems, such as ARAS PLM and PostgreSQL. A case study has been developed for a car prototyping company to illustrate the potentiality of the proposed solution.}, title={An open source framework for the storage and reuse of industrial knowledge through the integrationof PLM and MES}, URL={http://journals.pan.pl/Content/116852/PDF/514-kolor.pdf}, doi={10.24425/mper.2020.133729}, keywords={industry 4.0, PLM, MES, knowledge management, ARAS, PostgreSQL}, }