We live in a very interesting time. The manufacturing environment is changing rapidly. It is driven by multiple factors such as global description because of COVID and supply chain, the introduction of new products, amazing technological innovations, and digital transformation that is happening everywhere these days. It leads to challenges and amazing opportunities in the industry.
The amount of the data we create on an everyday basis is skyrocketing and on the other hand, the amount of the data that companies already hold in their existing and legacy environment is huge. At the same time, PLM technologies are still lagging behind, companies live in data chaos, and engineers and manufacturing companies are experiencing substantial challenges with their PLM implementations.
According to Peter Bilello, CEO, and president of CIMdata, a leading analytical and service outfit, which is specializing in PLM, here is a list of PLM persistent challenges.
– Too many implementations still focus on product data management (PDM, critical core of your typical PLM environment)
-Too many installed PLM solutions have become “legacies,” nearing the end of life—while resources to reimplement are scarce
-Widespread management disconnects despite agreements to focus on faster, better, and cheaper
-Confusion about the roles of enterprise-level systems
– Conflicts among business cultures, practices, and priorities
– Resistance to change
If you’ve been following my blog for some time, you won’t be surprised – these problems are for a long time and live in manufacturing and engineering teams that cannot break from the reality and legacies. Which made me think about how PLM vendors can shift their products to unlock a bigger value that can lead to successful replacement of legacy systems and bringing new distinct value to industrial companies.
Artificial Intelligence (AI) is a buzzword that absorbed many technologies, but in a nutshell, focuses on how to extract more value from data in a variety of forms and ways. Data exploration can bring huge value similar to how it happened with other industries and services. . We can see how similar technologies made a difference in the field of e-commerce, driving navigations, and information search. Here are some of my previous articles about PLM and AI
What PLM can learn from 20 years of Amazon AI
AI Opportunity for PLM
From single siloed PLM to knowledge graph and AI
So, the technology is here. What can be low-hanging fruit for these technologies to make a showcase for industrial companies about the intersection between PLM and AI? I don’t have a crystal ball, but here are three possible options where I think AI can really shine.
1- Design Options
It is a very rare thing when the products are built from scratch. Usually, engineers are reusing designs, making improvements, and maybe developing a few unique components. Which brings the opportunity for intelligent design re-use. It might sound like a search, but it actually should go much more forward. The opportunity for AI-drive design is to intelligently recognize all similar design use cases, customer types, design options, and many others.
2- Cost estimation
Moving next from the design, the opportunity is around costing. I never saw a single manufacturing company that was not interested in cost and how the cost is impacted by anything they do. So, what is the opportunity? Cost management is a multi-faceted approach that can be also very specific for one component (eg. how to 3D print, CNC or use some specific providers and suppliers to build a product. But on a much bigger scale., the AI option is to provide a complete 360 view on cost factors and what can impact product cost.
3- Supplier management
Last, but not least is everything related to supply chain management. I know, a supply chain is a hot topic these days as many companies are experiencing big challenges and component shortages. One way to think about it is to provide a way to search for other suppliers. That can be an interesting option, but it is not all. Another, more promising option is to get AI to work proactively, using product information such as design, bill of materials, and others to spot potential issues in the supply chain.
What is my conclusion?
An opportunity to turn engineering and manufacturing data into intelligence is huge in the manufacturing industry and PLM vendors are probably in the front lines to make it happen. Recombining and intertwining multiple data silos, bringing data from CAD, engineering, production, suppliers, contractors can be interesting opportunities. While the technology is here, getting the data from some manufacturing companies can be a challenge. PLM vendors will have to develop special go-to-market options to get data accessed in secured environments to show the value of AI in the manufacturing and supply chain. Just my thoughts…
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital network-based platform that manages product data and connects manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.