<bdo id="u9ldg"></bdo>
        <tbody id="u9ldg"></tbody>

        <bdo id="u9ldg"><optgroup id="u9ldg"></optgroup></bdo>
          <menuitem id="u9ldg"><dfn id="u9ldg"></dfn></menuitem>
          <track id="u9ldg"></track>
        1. <tbody id="u9ldg"><span id="u9ldg"></span></tbody>


          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

          KEY BENEFITS

          • 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