PLM Success – Knowledge within the operational data that you produce

This blog continues a discussion which has been the topic of two other blogs; PLM – Vision or micro-ambition? and  PLM Success - Think Inside the Box. Previous blogs was about the importance of embracing changes and input along the way of implementing PLM and not only working blindly towards a set goal.  And the usage of your employees and cross-fertilization as a source for input.

Today we will deal with the usage of operational data as input for process improvements. 

I’m talking about the data generated while executing the company’s processes, and not “product data”. I believe that this is one of the “untapped” or at least underestimated sources for input we have, as it’s already collected to a large extent, to manage the company’s day-to-day business.

IT is traditionally a service and infrastructure provider for the business-side of the company; giving them the tools to execute their work and make well founded decisions. But, what if we could use IT to also provide the means to turn the “eye” inwards in terms of methods? Providing that a PLM system doesn’t always force a process and way of working upon you (read: not controlled in detail by the system), there is potential work to be done to see how the stipulated methods are followed. What if we analyzed how work is done and thereby receive feedback on current working methods and potential areas for improvements or alignment to reality (does it look shiny enough?).

Data warehouse analysis related to PLM processes, that I’ve seen, usually has their focus in time, money, quality and risk. Sometimes you’ll find this type of analysis in dashboard in today’s PLM systems and other enterprise systems. But they are almost always used to support the operational aspect of the business. What if we looked at the list but with method improvement focus?

• Time – How many iterations does it take to get to a certain status in the development and what are the underlying reasons for it (communication, education, etc)?
• Money – are we selling to the right price in the different markets? Are we able to get our “raw material” to the right price? How is our sourcing affecting transportation cost?
• Quality – sustainable sourcing and material statistics, scrap- and recall-statistics
• Risk – are we placing our orders “right” to get the right risk exposure? How is our in-stock volume? Could we use more low fair transportation alternatives? How good are we at forecasting and thereby being able to book, buy and commit so that we get better in-price?

With the information that we get from the data; processes and practices could be followed up to see how they are adopted and applied by business. We could also analyze good performing teams, and thereby improve company methods and best practices.

We could even take this one step further – with tools such as dynaTrace we could analyze on application level what functions are being used and how to optimize the flow and usability within the specific application in a prioritized way.

The important point here is that we could get input for the PLM journey by looking at how the company is performing and acting in its current processes. Changes doesn’t always have to be revolutionary and ground breaking, in this case they are evolutionary. Derived from the knowledge we get by analyzing data.

This is definitely not science fiction. All technology is there to be used; it’s more a question of maturity and determination to use the data with long term strategic focus and not only making the coming quarter look good.

Robert Wallerblad
www.infuseit.com