To remain agile and competitive, organizations must design globally while executing manufacturing, sales and services locally. Achieving this balance requires robust systems that can manage the full lifecycle of a product—from concept to retirement.

Product Lifecycle Management (PLM) systems play a pivotal role in this process. As industries face increasing complexity, these systems must evolve to become smarter and more responsive to support the modern industrial world with constant change, demanding faster innovation and greater efficiency. 

Today, AI integration with PLM has become crucial in addressing the key challenges below and enhance collaboration, improving product data quality and user efficiency in PLM system.

• Repetitive activities and are time consuming impacting user efficiency / productivity

  Manually managing large volume of data needs during the product development process in the form of product structure, entities and their relationship is a tedious task and a significant amount of time is spent performing various activities like product data creation, modification, release, etc. This is not only time consuming but is also impacts the quality of data.

• Quality of data impact decision making

  Getting access to the right and required data during different stages of product development process is an imperative, but compiling these details manually is time consuming and adversely affects accuracy.

• Lack of better collaboration causes duplication of efforts

  Engineers face delays in decision making due to data silos and scattered platforms, making it difficult to access timely, relevant insights.