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After a product lifecycle management (PLM) system has been implemented and used for a while, the accumulated data within the system becomes valuable. This data not only supports daily operations but it also has the potential to help companies to better understand historical performance and predict the future, if it can be interpreted properly. In fact, reporting and analytics has been a part of some PLM offerings for a while. However, because most PLM adopters are still focusing on improving product design and development productivity, analytics remains a relatively quiet area.
Although the PLM industry has not reached a consensus on the definition of PLM analytics, it doesn't prevent discussing what insights PLM users should expect after mining through available data within a PLM system. The main purpose of this article is to propose a framework that may help you comb through possible areas that PLM analytics may apply to.
Different Data Sets Within a PLM System
Data is the raw material that needs to be processed in order to produce the final output of PLM analytics. Hence, before heading for analytics, taking a look at the different orientations of PLM data may help conceptualize what PLM analytics can provide.
This is the most prominent group of data within the PLM system. Product data (i.e., product definition information) is the backbone of the entire PLM data set. Other data exists and is organized around product data.
• Product requirements
• Product structure data
• Product document data
• Product document metadata
Project-oriented data is used to define and help execute product development projects and processes. This group of data exists for the purposes of facilitating the creation of product definition information, but it is not categorized as product data.
• Work breakdown structure (WBS)
• Resource information
• Work progression data
• Project risk data
This group of data refers to PLM users' specific business processes. In general, there are overlaps between this group and the previous group (project data). Process data refers to the daily operational activities that are not managed as projects.
• Routing and approval activities
• Problem-solving activities
• Collaboration records
• Transactional data associated with business processes
User information (with regard to PLM systems) may be associated with all the previous categories. However, it is necessary to treat the user-oriented data as the fourth data set since the "people" element is an important part of a PLM system.
• System user information
• Roles and groups
• User login data
• User participation records
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