The perceived value of product lifecycle management (PLM) is to help companies to boost innovation and improve product development, operation and service processes. It is indeed very important, hence there are so many discussions in PLM industry about how to improve processes in general. However, most of these discussions are stuck in PLM implementations. Existing PLM systems reached their limits and analog PLM implementations are demanding PLM hero to make it through.
I’ve been reading an excellent HBR article Building an Insight Engine (https://hbr.org/2016/09/building-an-insights-engine) over the weekend. The authors – execs from Unilever and Kantar Vermeer describe the elements of the insights engine and show how it works at consumer goods giant Unilever.
One of the most interesting things that caught my attention was about data synthesis.
What matters now is not so much the quantity of data a firm can amass but its ability to connect the dots and extract value from the information. This capability differentiates successful organizations from less successful ones: According to the i2020 research, 67% of the executives at overperforming firms (those that outpaced competitors in revenue growth) said that their company was skilled at linking disparate data sources, whereas only 34% of the executives at underperformers made the same claim.
Another CMI program, PeopleWorld, addresses the problem “If only Unilever knew what Unilever knows.” Often the answer to a marketing question already exists in the firm’s historical research; finding it is the challenge. But using an artificial intelligence platform, anyone within Unilever can mine PeopleWorld’s 70,000 consumer research documents and quantities of social media data for answers to specific natural-language questions.
Data intelligence, analysis, big data, customer information made me think about data integration in manufacturing.
PLM integrations are not easy
For a long time, integration challenge is one of the biggest challenges in manufacturing. Especially when it came to PLM implementations. I can see 3 main problems standing in front of any PLM vendors that will try to improve “data integration and intelligence” of PLM implementations.
1. Create a common set of data elements.
This is so important and it is often missed in many implementations. The challenge of last generation of PLM systems is to come with some out-of-the-box best practices that can serve as a starting point in any implementation. So, businesses are starting from some ready templates and often stuck with changes. At the same time, company should have a full flexibility to define descriptive data models that can help to conduct business insight and decision making processes.
2. Create one “version of truth”
How to form a single trustful data representation? This is the most critical question. Data is duplicated in many manufacturing and enterprise systems. For many years, companies were creating islands of data. This activity was driven by operation excellence and interest of department to divide and concur in everything that was related to enterprise software. So to create “version of truth” that is crossing IT and department boundaries is not a simple task.
3. Integrate disparate datasets
System must onboard any new data quickly, clean up, index, create relationships and process it to the form that can allow support business programs and initiatives. Existing PLM data is too focused on engineering and PLM vendors have hard time to acquire data sets outside of engineering departments.
These are critical requirements I can see standing in front of any organization thinking how to move from old fashion enterprise data control mechanisms into insight engine.
What is my conclusion? For many years, PLM platforms were focusing on how to control data in a single company. It started from CAD files and related engineering data. These days it is expanding into related domains of manufacturing, services, etc. However, to solve data management for a single department or even a whole company will only perpetuate the “data silos” problem. The future is belonging to new type of data management platforms capable to connect data across multiple domains and companies; expanding into cloud, connected products and big data. Just my thoughts…
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Disclaimer: I’m co-founder and CEO of openBoM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.