PLM Recommendation Engine
As an enterprise-wide data management platform, one of the main PLM goal is to provide the users with the right data at the right moment. The digital thread should provide a...
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As an enterprise-wide data management platform, one of the main PLM goal is to provide the users with the right data at the right moment. The digital thread should provide a...
Posted by Yoann Maingon
There is a debate on cloud PLM stack. Some would argue that cloud PLM = SaaS PLM = Multi-tenant. Any discussion on this topic becomes quickly technical and looses 80% of the...
Posted by Yoann Maingon
As I’m now in the business of editing PLM software with Ganister. I have been involved in defining what would be a correct way of dealing with configuration management (CM)...
Posted by Yoann Maingon
Now that we have seen the most simple change in my previous blog post, let’s talk about the revision mechanical engineers will hate : THE NON-INTERCHANGEABLE REVISION !!!...
Posted by Yoann Maingon
On my french blog I wrote a blog post about some suggestions for a flexible configuration management solution. I’ll reproduce the article in English soon for a broader...
Posted by Yoann Maingon
Wow, this blog took a dramatic 180° turn. Don’t worry, this is still talking about PLM and more precisely about the Fit, Form, Function (and Safety for some companies)...
Posted by Yoann Maingon
When it comes to PLM, it is difficult to have a clear opinion on business models. These last few days I have seen multiple business models and pricing configuration from PLM...
Posted by Yoann Maingon
I wish you all a happy new year. My blog colleagues all came up with predictions for either the coming years or like oleg and Jos, for 2030. There are some very interesting...
Posted by Yoann Maingon
I just stumbled upon a video from CNBC just yesterday about the rise of Open Source. It gave me a strange reaction at first. Open source is getting bigger every day. NPM packages...
Posted by Yoann Maingon
What is the language your PLM solution has been built with? It is something that barely comes up in PLM evaluation. Does it matter? I think so, but in order to know why it matters...
Posted by Yoann Maingon
As an enterprise-wide data management platform, one of the main PLM goal is to provide the users with the right data at the right moment. The digital thread should provide a continuity to easily reach the right data. But the more data you store, the more data will come for a similar need. Recommendation Engine have been built for this purpose in the retail industry. Once you get too many options for buying an item, the recommendation engine guides to the right one.
It is not yet the time to replace designers, although we are “sadly” getting closer. Like someone could claim that some people just buy stuff because the recommendation engine tells them to. If we stick to the good values that it could bring in an automated way, we could imagine that it helps for standardizing product design in a company. When you get to select a screw, the recommendation engine could show you which screws have been mostly used for similar designs.
It doesn’t have to be just for Parts. What about requirements? A recommendation engine could suggest requirements based on other projects that were done under similar regulations or for a similar client.
Same thing for Manufacturing options, you could accelerate the industrialization process by suggesting manufacturing operations others have used for similar products.
Until we get back to retail recommendations… At some point you will cover the sales configuration of your product. That’s where you will meet again with the retail sales configurator, but your configurator engine may be the same for all these purposes if you selected the right stack.
The first time I got into a graph database (maybe around 2013) conference, it seemed like everyone was coming for the same purpose: producing personalized emails for retails website. These IT guys could not produce the amount of email they had to send with the recommendations they wanted to display, on a daily basis. They had document databases and SQL databases. The first type could not handle much traversal queries. The SQL database required larger infrastructure and highly skilled query builders to get some results.
It is just a technical fact. The concept of a graph database makes it much more scalable for traversal queries. You will not be able to support PLM-wide recommendation with anything else than a graph database.
At Ganister we are actually working on it. We will make it easier for anyone to configure a recommendation engine at any stage of your product lifecycle.
Hera are some videos from Neo4j on how to build a recommendation engine:
Once you get the concept, you may question if you have the right recommendation algorithms in place. It could be then smaller step to allow the recommendation engine to adapt more granularly with each specific context with the help of some artificial intelligence processes. Let’s take it one step at a time !
I am connected with hundreds of people on LinkedIn who have “PLM” in their job description. In terms of education PLM is very new. I don’t think there was any...
Data is the essence of most applications and this is particularly true for PLM. How you store the data is a key aspect of your PLM application. It will define how much data you...
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