A quick peek into some research on … the use of business intelligence in PLM provides insight on taking advantage of the tremendous amount of product data accumulating in today’s PLM systems. The research discusses how the maturation of manufacturers’ PLM implementations has created a tremendous volume of untapped information that can be leveraged to improve product innovation, product development, and engineering performance. As it has in previous enterprise applications (ERP, CRM, SCM, others), the time has come for manufacturers to tap into their growing information goldmines through the use of business intelligence (BI) tools.
The Research Findings
The research points out two parallel trends in PLM implementations today:
- Manufacturers have moved forward along the PLM implementation maturity curve – meaning they now have stable implementations and clean data
- PLM has evolved and expanded to incorporate more valuable, business-focused data in addition to technical information – meaning the data to be mined covers a broader spectrum of the product lifecycle, including cost, projects, sourcing, service, and more in addition to purely technical engineering data
The result of these two trends is that there is now a lot more usable business data in PLM. The report points out a number of areas of value that can be mined from the data, including savings on new product development timelines, closing the loop from service to engineering, improving product quality, analyzing sourcing, and reducing cost. In short, it shows that value can be extracted by improving pretty much any part of the product innovation, product development, and engineering processes. Please read the report for more details and examples.
Implications for Manufacturers
The message for manufacturers is that “there is gold in them hills” …. errrrrr, in those databases. The research also points out some special considerations for business intelligence in a PLM environment. For most manufacturers, applying a BI tool is not the difficult part. In fact, they probably have (at least) one tool available in their IT toolkit. But before diving in from a technical perspective, manufacturers need to be very careful to consider security, IP protection, and regulatory requirements that surround this very sensitive data. Manufacturers should also look for ways to leverage PLM vendor offerings or partnerships that give them a ready-made view into the PLM data and security model, to avoid spending time recreating the wheel and potentially making mistakes that provide misleading “facts” that people will trust. As the report says, “Developing an effective BI in PLM strategy also requires knowledge of the engineering and product development domains and the specific software applications being mined.”
So that was a quick peek into some recent research on the maturation of PLM implementations and the opportunity it provides for data mining in PLM, I hope you found it interesting. Does the research reflect your experiences? Do you see it differently? Let us know what it looks like from your perspective.
Please feel free to review more free research and white papers about PLM and other enterprise software for manufacturers from Tech-Clarity.