The Internet of Things (IoT) brought in an unprecedented tsunami of structured and unstructured data. Information, in conjunction with the easily available inexpensive computing systems, paved the way for the next level of manufacturing. AI, being the flagbearer of the transition, plays a vital role.

A village cobbler performs all shoe manufacturing-related tasks on her own – from design, manufacturing, and repair to reuse. Ameri and Dutta cited this crisp model as an ideal example of an integrated and competence-centered enterprise.

The all-efficient cobbler’s model, however, becomes un-pragmatic in scaled-up enterprises. In heterogeneous decentralized organizations, getting information and ideas on the same page is a major challenge. Aside from operational impediments, it greatly hampers innovation.

This bottleneck plagued enterprises for centuries. They worked assiduously in silos to arrive at a holistic solution.

In their own limitations, manufacturing giants like Land Rover, P&G, Johnson & Johnson, Unilever, and Rolls-Royce unearthed the panacea for this puzzle by introducing PLM in their systems.

As Industry 4.0 deepens its roots, now the PLM model is increasingly getting replaced by AI-led PLI. In this article we try to decode the how and why of it.

A Brief Backdrop

Somewhere around the 1980s, engineers recognized the need for data discernment. They conceptualized an umbrella of independent process supplements: Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). They had a name for this cohort – they called it Product Data Management (PDM).

But soon they realized that data alone was incapable of delivering holistic insights, so they extended their efforts to the entire product lifecycle, marking the birth of Product Lifecycle Management (PLM).

https://www.artiba.org/blog/ai-led-manufacturing-in-industry-4