One of the trending topic in PLM these days is model driven enterprise discussion. Here are few articles to start with – PLM’s Tough Journey: From Managing Documents to the Model-Based Enterprise – TV Report by Engineering.com, Digital PLM requires a model based enterprise , PLM software future – Model driven vs Model based.
The terminology is so confusing that I honestly have hard time to bring a consistent set of 2-3 references without realizing that these references are conflicting. The situation is so bad that even CIMdata, a leading analytic and research company in PLM, failed to bring a set of definitions without creating of duplicated acronyms – 2xMBD, 2xMBE and 1xMBSE. I summarized it my article – Model-based confusion in CAD and PLM, which generated great debates and follow up blog discussions.
Jos Voskuil published his Model Based – The Confusion article in which he didn’t find a better way to argument than calling everyone who honestly confused – a generation that doesn’t feel and understand new approach in digital technologies. Here is the passage:
Oleg’s post unleashed several reactions of people who shared his opinion (read the comments here). They are all confused, it is all about marketing / let’s not change / too complex. Responses you usually hear from a generation that does not feel and understand the new approaches of a digital enterprise. If you are in the field working with multiple customers trying to understand the benefits of model-based definition, you would not worry about terminology – you would try to understand it and make it work.
You can find this funny picture in Jos article. Do you like simple clear answers? You’re obvious wrong says that picture. However, no doubt that complex is right.
The challenge of modern businesses is that too often we conclude too fast on complex issues or we frame new developments because they do not fit our purpose. You know it from politics. Be aware it is also valid in the world of PLM. Innovation and a path to a modern digital enterprise do not come easy – you need to invest and learn all the aspects. To be continued (and I do not have all the answers either)
The political association and conclusion from Jos Voskuil reminded me my childhood in Soviet Union long time ago when Communist Party of Soviet Union was trying to convenience everyone that empty shelves in the supermarkets is a temporarily condition while country is moving towards bright future of communism and new type of supermarkets without money will soon become available. So, everyone is expected TERPET’ and make it work. (TERPET’ is Russian emotionally charged word that can express impatiently waiting process combined with long-suffering). And, of course, while you’re impatiently waiting for bright model driven enterprise future (think about it as digital communism), you should not complain about you suffering paying for overpriced food from grey market.
My attention was caught by recent Jos’ article – Moving to a model-based enterprise (the business model), in which Jos proposed to build a “digital twin” of an organization. I love to see how these two trends (digital twin and model-based enterprise) are intertwined, btw. Article speaks about business modeling and presents a picture of ontology created by a startup company Clearvision.
I liked the example of ontology. In my view, it is a great opportunity to learn with the example of semantic web and ontology development for the last 10-15 years. Maybe you’ve heard about semantic web and web 3.0 development. If not, check these links – Semantic Web. The technological stack behind Semantic Web was brilliant – RDF, OWL, etc and provided an excellent way to build sophisticated knowledge driven models. However, the implementation and usage was really hard. Complexity of semantic web technologies was skyrocketing and it was picked up by companies that succeeded to simplify and make them easier (but maybe be less sophisticated).
Here is a passage from Wikipedia about challenges of Semantic web and knowledge representation models:
While learning the basics of HTML is relatively straightforward, learning a knowledge representation language or tool requires the author to learn about the representation’s methods of abstraction and their effect on reasoning. For example, understanding the class-instance relationship, or the superclass-subclass relationship, is more than understanding that one concept is a “type of” another concept. […] These abstractions are taught to computer scientists generally and knowledge engineers specifically but do not match the similar natural language meaning of being a “type of” something. Effective use of such a formal representation requires the author to become a skilled knowledge engineer in addition to any other skills required by the domain. […] Once one has learned a formal representation language, it is still often much more effort to express ideas in that representation than in a less formal representation […]. Indeed, this is a form of programming based on the declaration of semantic data and requires an understanding of how reasoning algorithms will interpret the authored structures.
One of my favorite examples is to see a trajectory of semantic web vs graph database such as Neo4j. The last one is clearly a simplified and much easier to adopt approach to build system of relationships and data representations. It was clearly easier than using some products from pure semantic web tech stack.
As you can see from the Google trend, simplicity wins for a long run and adoption of Neo4j these days is much higher compared to RDF/OWL semantic web development.
What is my conclusion? Ideas of model based enterprise are great. How to take the rational of model-based approach and create a simple implementation model that can be easy adopted? This is a real challenge. Complex can be expensive, but doesn’t automatically means right. On the other hand, simplicity is really hard, but can win much higher adoption. Just my thoughts…
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
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