Private EquityScreenerRefer

MARKET INSIGHTS

  • EventsNEWPrivate market milestones
  • NewsPrecize daily pulse
  • BlogsIn-depth articles and opinions

GUIDES

Explore Now
  • Unlisted SharesStep-by-step · KYC
  • BuyingStep-by-step · KYC
  • SellingExit in 24 - 48 hours
  • Pre-IPOReturns · risks · case studies
  • Tax GuideBudget 2026 changes
  1. Home
  2. Unlisted share news
  3. Tata Technologies’ use of AI/ML reduced power consumption by 18% on sho…

Tata Technologies’ use of AI/ML reduced power consumption by 18% on shopfloor

30 Sept 2023

Add as preferred source
Key Takeaways
  • With the use of AI & ML, the company is obtaining operational efficiency & leading to faster operations.

  • Such tools save time & effort for the company, stressing over increased process timings  & improving overall performance.

  • With the use of AI & ML, the company is obtaining operational efficiency & leading to faster operations.

  • Such tools save time & effort for the company, stressing over increased process timings  & improving overall performance.

Tata Technologies

Tata Technologies

Listed0.00%

Expert Analysis

Tata Technologies’ use of AI/ML reduced power consumption by 18% on shopfloor

Yogesh Deo, Senior VP and Delivery Head, ER&D (Engineering Research and Development), Tata Technologies’s words

“The company is putting a lot of effort and investment into building a Centre of Excellence for AI technology.”

Justification

  • The company is using Artificial Intelligence and Machine Learning (AI & ML)  to make its manufacturing processes more robust for its clients, along with reducing curtail time & pollution.

  • A/c to Deo, with the use of AI/ML, the power consumption was reduced by  18%, followed by a 10% decline in fuel consumption & 8% decrease in chemical consumption.

  • The model effectively detects anomalies (diversion from standard behavior) & lets the company take corrective actions before the actual failure.

  • To predict tooling failure in the case of dye design, it co-relates the historical  & present operational data to predict failures. 

Publisher:Autocar Professional