Extended Product lifecycle management with knowledge management : Research Proposal

Anything that is produced and offered to the markets can be a product. Each product has a lifecycle, whether the product is a car or a software program or a service. Product lifecycle has different phases from the time that it is raised as business idea at the first phase to disposal as the last stage. Product lifecycle management is a business approach for management and the use of product, process and resource related data, information and knowledge. With this management, enterprises try to use their company’s intellectual capitals. The initial PLM systems had been developed to store and manage Computer Aided Design (CAD) files and giving access to these data in different stage of the product lifecycle for users. Afterward, PLM systems, more developed to cover the management of process and resource data, and later on, managing of product related data, information and knowledge on all phases of product lifecycle. Each enterprise, according to its needs and competencies, implementing and using different capabilities of PLM systems, but still Bill of Material (BoM), Bill of Process (BoP) and Bill of resource (BoR) are forming the core of PLM systems. PLM systems try to manage data by integration with other engineering software programs to import data from them and manage those data for exporting to other software programs or makes them accessible for users. These integrations cause the managing of virtual data and information by the PLM systems, which are generated by different engineers such as designers or manufacturing engineers. CAD files and simulations are two types of virtual data. These data consist of some knowledges that had been generated by different engineers, which can be called virtual knowledge.Real World Knowledge is another type of knowledge that are exist in the enterprises. This knowledge can be captured from the happenings in the real environment such as failure reports, quality and audit reports, product performance, production data and operator’s experiences. Sometimes capturing these knowledges is very easy for example production throughput, but sometimes it is very hard, because they are unwritten and uncodified.Capturing and managing these real world knowledges, can help manufacturers to reduce their costs by making a better decisions and reusing of virtual models.Firstly these knowledges can clarify consequences and of previous decisions. They can also clarify some hidden and unconsidered issues about decision cases. The Real World Knowledge covers different types of knowledge, such as production reports, maintenance reports or operator experience.Secondly, the real world knowledge, can support to determine the level of virtual confidence (Oscarsson et al., 2015). Virtual models as one kind of virtual knowledge which had been explained before, have been designed to reduce costs by simulating the reality. The correctness and accuracy of a virtual model, clarify the level of confidence for that model and its results, for reusing that model to solve another problem. With comparing of the real world knowledge and virtual models expectations, the accuracy of the model can be evaluated, and the reliability of the model can be measured.There are lots of knowledge management systems have been developed, but most of them are trying to manage the organizational knowledge. The focus of this research is collecting the real world knowledge, in an automotive industry and converting them to the usable and classified format. Afterward, those knowledges should be stored and managed in the extended PLM platform.Research proposal, PhD programme, University of Skövde