Exploring PLI: An Evolution of PLM

A must-have for various companies, Product Lifecycle Management, also known as PLM, has been around for about two decades. Described as a backbone technology, PLM can drive growth in the global marketplace while streamlining operations, reducing costs, and improving time to market. 

If we were to zoom out, we would see that PLM software has traveled a long way to reach the maturity levels and best practices available today. Nick Wei, Centric Software’s Regional Sales Director, defines the PLM System as “a tool that supports the entire product development process at every level, from planning, ideation, development, sampling, sourcing, and production.” 

Used as a strategic enabler, PLM has been going through dramatic shifts that include Artificial Intelligence (AI). Playing a pivotal role in PLM’s evolution, AI helps automate low-value tasks by making the PLM process much more efficient.

The Digitalization of PLM

AI has a reputation for setting new standards when it comes to efficiency, quality, and functionality. So much so that some PLM vendors have been leveraging its power to drive the evolution of PLM processes and systems into a new digital transformation era. Those who haven’t jumped in with both feet are instead starting to take small steps towards understanding the bigger picture of how AI partnered with PLM could potentially improve the fashion industry.

Taking PLM to a higher level, AI makes it possible for PLM systems to do more than collect vast amounts of data. AI injects additional insight into the product lifecycle. When embedded into PLM functionality or integrated into the PLM solution, AI can bring to the table various advantages like a higher-quality design experience and better decision management. Also, AI x PLM makes it possible to introduce alternative suppliers’ recommendations at competing costs, in a geographical location that won’t increase costs or cause further delays. 

The Importance of Product Lifecycle Intelligence

Product Lifecycle Intelligence (PLI) is when PLM applies artificial intelligence and automation to help PLM users. Bridging the gap in PLM analytics, PLI supports PLM users to extract meaningful insights from product data, formulate predictions, recommend improvements, and automate actions within systems and processes. You no longer need to sit on months of untapped R&D product data; PLI makes it possible to work through data mining and analytics.

According to Kalypso, a Rockwell Automation company, PLI can address PLM user’s core business needs. PLI can also make evidence-based decisions and reliable planning and forecasting. Capable of continuously improving business results, PLI leverages technologies to maximize the value of PLM data. It takes the guesswork out by using the rich dataset in PLM to drive valuable insights and recommendations. PLI does this by using advanced analytics to bring more meaningful and actionable insights to the table. 

Kalypso believes that PLI helps innovators “explore current and historical product development performance metrics, and explain performance trends with multivariate statistics, identify important patterns, correlations, and root causes”. They also maintain that PLI can help “forecast future performance based on predictive analytics and machine learning techniques.” This visibility makes it possible for companies to make reliable expectations on cycle time, cost, quality, and manufacturability. Lastly, according to Kalypso, PLI makes it possible to improve future outcomes thanks to PLM’s automated actions. Still, it is worth noting that, as great as PLI can be for your business, it is only as good as your PLM data quality.

Learn how the Suuchi GRID’s PLM module can improve reactivity across the supply chain

Written by Muchaneta Kapfunde


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