How to transform PLM system into ‘Insight engine’

A key insight, I would say, is that the same piece of data may or may not be relevant depending on in which context it appears. So, for example, the color of a car may be highly relevant in marketing but less relevant in manufacturing. The context may be called different things – domain, function, business unit, silos or whatever – but contextualization is a fact of life that doesn’t go away because we want it to. Silos are not problems, they are inevitable. Another key insight, then, is how to maintain local ‘silo’ integrity while at the same time secure the necessary connection between them, i.e. not making them into isolated islands. Future data management platforms need to acknowledge this, above all that contextualization drives data interpretation. Thus, there can be no “truth” regarding data; only relevance or not for taking actions. It’s difficult to see from the article exactly how Unilever has done, but it seems to me that they did a good job in accessing and collecting such data that have some kind of relevance in a particular context. Just some additional thoughts….

Hi Oleg,

As always, I really enjoyed this post, and you raised some really great issues that I bet the vast majority of people interested in PLM will not realize until 5 – 20 years from now! On the topic of PLM, I see so far too much focus on system architecture and implementation at the expense of understanding the organization’s current state, setting a compelling vision for the future that isn’t just the PLM system vendors’ marketing material repackaged with the company lexicon, and making a strong effort to really understand the change impacts necessary to go from current state to ideal state.

Although there is no doubt that system architecture and implementation are critical aspects of a PLM journey, without an obsessive focus on organizational structure and its attendant impact on data quality, the resultant value of any PLM implementation is going to be greatly diminished. Additionally, the ROIs that are frequently promised, such as a year 1-3 productivity gain of between 1% and 5% of raw materials costs, will not be realized. Years of neglecting data quality cannot be erased simply by bringing in a new software system.

In the PLM space, I see far too much constrained thinking. Companies who view PLM simply as replacing paper specifications stored in metal filing cabinets with specifications stored as PDFs in document management systems are akin to people who insisted on buggy-whip holders in their first automobile. Sears-Roebuck used to spend all year working on their catalog, and then, once it was printed, it was static for the entire year. Contrast that model with the Amazon model of today. The Amazon catalog changes thousands of times per hour based on customer demand, supplier availability, etc. I’ve even had some people say to me, “BUT OUR PLANTS STILL REQUIRE PAPER!,” to which I reply, “Do they not have computers and printers in those plants?”

Some companies are a little more sophisticated, but they have unrealistically high expectations for what their IT groups can accomplish with master data management systems, as well us unrealistically low expectations for the amount of effort required by the various non-IT business units for data governance and data quality. Data quality assurance should be just as (if not even more) important than physical product quality, but in my experience, it’s an afterthought. Imagine where companies like Netflix, Facebook, or Google would be if they were not obsessive about harvesting, cleansing, assessing, and processing data.

Even if an R&D organization within a very large company is able to standardize their workflows and business processes globally, the challenge of R&D successfully linking with all of the various operations groups around the globe is daunting. I see far too many organizations ignoring that scale factor.

When the issue is raised in meetings, someone inevitably produces a Power Point slide with various business units in different geographic regions represented as SmartArt connected by arrows. Next, they proudly proclaim, “All of our company’s ERPs have web-services, and so does our MDM. Integration is trivial!”.

If only real life were that simple. R&D groups are frequently organized very differently than the business units they serve. Too many R&D people do not appreciate the complexity that results from that misalignment. Even factors such as the number of time zones and languages within a business unit can be a stumbling block to roll-outs that is completely ignored (until the roll-out is compromised), especially by US based personnel who are accustomed to three time zones and one language in a very large market.

For any enterprise software project involving R&D at a large company, one of my first actions is to pull the last three – five years of the company’s annual report and look at its business units with an eye towards geographical distribution of research sites, manufacturing plants, fraction of company-owned vs non-company owned manufacturing, scale of revenue/earnings, and rate of change of revenue/earnings. I also look for signs that the company hasn’t stabilized their organizational structures, which is most easily observed in the accounting statements. Obscuring such dysfunction within R&D is much easier. Next, I compare and contrast the finance-driven structure of the organization with the stated R&D structure.

The really sharp R&D leaders see value in this approach, but I’ve had more R&D leaders than I care to count ask me why such an analysis is important, since R&D is a centralized function. How do I really feel when I get that question? Well, let’s just say that when that happens, I try hard not to express what I am feeling.

You and I will be in Orange County week after next at the PI OC meeting. At that meeting, I’m chairing a focus-group in the PLM2.0 session on Thursday. Prior to reading this post by you, I was worried that nobody in the audience would grasp the perspectives I plan for us to discuss, including modern data science concepts and why business leaders need to understand them for a successful PLM implementation. After reading your post, I can rest assured that at least one person at the meeting will understand the points that I am trying to make!