If you’re long enough time in tech, you probably remember Knowledge Management (KM). KM emerged as a scientific discipline in the early 1990s. It has long history of research, development, debates, forums, corporate libraries and indexes and mentoring applications (expert systems). With an increased use of software and data management, specific applications such as knowledge bases, decision support systems and others joined the effort.
In enterprise software and specifically in product data management, collection of data records and meta data about CAD files, recognized the importance of knowledge management as a discipline, but usually failed to deliver something meaningful beyond data records, process control and measurement. Many systems tried to go beyond that with knowledge creation, cognitive, social and organizational learning. But for most of PDM and PLM applications, it mostly ended up with blunt marketing and use of “knowledge” word instead of organizing action oriented software.
Things have changed for the last few years and you probably hear word “Artificial Intelligence” and “Machine learning” more often. I shared some of my thoughts about these topics in my earlier blogs – Future trajectories of PLM and AI platforms and AI opportunity for product lifecycle management.
Forbes article – How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017 is a good reminder that AI is coming to enterprise space and we better get prepared how not to miss that opportunity.
81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.
So, what are top recognized opportunities for AI/ML. The following list can give you an idea.
Although, PLM is not listed explicitly, pay attention to #2 in the list – data analytics. It is a goldmine for all PLM companies and the thing that most of PLM vendors have missed for many years while the focus was about how to manage CAD files and optimizing check-in/check-out process. Companies are sitting on a piles of product data and related information, which is yet to be discovered, analyzed and connected.
According to the same article, data classification, tagging and predictive maintenance will be top major revenue generators to AI/ML. In such connection information about product changes and impact on physical product in design and operation can change the way people think about design and product lifecycle. You probably had a chance to see some of examples of machine learning from my earlier blog about Digital Factory 2017 event last week and keynote by Carl Bass. Navigate here in case you missed my earlier blogs. Also check my reports from LiveWorx 17 earlier this month here and here. Product Lifecycle Management has a good chance to move from a discipline to control data and processes into a discipline to provide an insight on data and actionable information to designers, product and manufacturing planners and service managements. In other words, product data will become a platform to build a future of PLM intelligence.
What is my conclusion? PLM is changing and AI technologies combined with huge piles of data accumulated by manufacturing companies are potential to change the trajectory of PLM value proposition. From single point of truth controlling behavior PLM systems will move into intelligent backbones, crunching, analyzing and delivering actionable information for 21st century manufacturing networks. Just my thoughts…
Best, Oleg
Want to learn more about PLM? Check out my new PLM Book website.
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.
