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      To address this, the team at Tredence developed an analytically robust approach with the following specifications:

      • Identified primary drivers among the selected machine variables using ML variable reduction techniques
      • Driver models to understand key influential variables and determine the energy consumption profile
      • Identified the right combination of drivers under the given production constraints – time, quantity and quality
      • Optimization engine to provide the machine settings for a given production plan


      • The learnings will be used across similar machines to create operational guidelines for reducing energy consumption


      • We were able to achieve a ~5% reduction in energy consumption across major machines

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