How Continuous Learning Fuels PLM Knowledge, and Ultimately Product Development Success
It is commonly acknowledged, if I may say, that “knowledge gives every organization a competitive edge”(Prusak, 1996). The intellectual capital of an organization includes key assets, covering explicit and tacit knowledge. Similarly, there is a body of PLM knowledge in every industrial or manufacturing organization, in the form of written documentation, coupled with less tangible information and beliefs as part of an operating culture.
People typically learn during their formal education time, and pretty much throughout their entire life as they “learn by doing” things that matter to them or as part of their job. Through people, organizations learn about their ability to develop products and implement change; learning includes failure to deliver. When it comes to PLM, there is a lot of knowledge that is not static—as technology and processes advances, and even as the PLM scope evolves or as organizations mature. Therefore, PLM knowledge needs to be continuously refreshed based on the level of maturity of an organization, also learning from one industry to another; and obviously, from one individual to another.
In this post, I elaborate on how to learn about PLM, the type of information and insight that relate to the discipline (not just the tool); but also, what can be perceived as peripheral but critical knowledge such as organizational and business change management.
Back in early 2015, I referred to how organizations can be positively impacted and make the most from their “PLM practice”:
“Some organizations make more of their PLM [practice] because they don’t just see it as a toolset, but a social + knowledge sharing + innovation platform from which people can create, innovate with their products and processes, share ideas, learn, …” (virtual+digital blog, 2015)
PLM as a social platform
Learning how to collaborate goes beyond PLM; however, putting the PLM practice in context of an organization helps set boundaries about how to enable and guide its operations; building on from core principles such as having common data and converging process knowledge.
In a given operating context, PLM practices contribute to foster social interactions across product development and support teams, based on the value added from their contributions and interactions. Learning how to exchange data, prepare information for others to consume (make use of), present data changes and implications, contribute to learning about a new products and sometimes learning about a new processes or practices.
PLM as a knowledge sharing platform
Beyond social interactions, tangible knowledge can be formally created and “stored” in knowledge platforms and other repositories—to be shared across functions, but also for future reference or compliance purposes. Understanding how to search, retrieve, re-use, and share such data, and improve how it is done is part of the PLM learning journey—across the wider product development learning journey. Better informed people make better products.
Learning about new data, new processes and new applications contribute to accessing and building new PLM knowledge; this can be seen as a virtuous circle, in simple terms:
Sharing data (and be more willing to collaborate).
Creating better data.
Enabling more focused, faster product (and process) innovation.
Creating better customer engagement
Back to point 1 above
Closing the loop on information sharing is what translates into valuable information, and in turn, insights to foster creativity and innovation.
PLM as an innovation platform
Everyone refers to PLM and an “innovation framework” (or platform) because, like the data it refers to, it links to ongoing interactions and learning. This exact cycle is what brings together products, data, models and of course people.
Linking learning to basic continuous improvement principles like Plan-Do-Check-Act, and putting it in context of PLM knowledge:
What are the business objectives in adopting PLM practices, and how will these be achieved?
How are key functions and teams interacting, what is manual vs automated, effective vs ineffective?
What is the core data, how is data quality governed, how to detect data issues early and operational issues linking to data?
How do teams and people learn from the above and organize their work differently so that they can improve the next time, in being more effective and efficient at what they do, putting in practice what they have learned from the product data and the data lifecycle?