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filler@godaddy.com
$10M Annual Savings Through Predictive Energy Modeling
Context
A Fortune 500 industrial gas company operated a large production network with energy-intensive equipment and significant exposure to power costs and operational variabililty.
Constraint
The organization lacked a simple, actionable way to understand when equipment was underperforming, how operational decisions affected energy efficiency, and how production schedules could be oprimized against time-varying electricity prices.
Approach
Developed a generic algebraic modeling framework to analyze equipment performance, and specific power across multiple facilities. The models revealed ineffiencies, highlighted periods of degraded equipment performance, and identified opportunities to shift production to lower-cost power windows.
Outcome
Enabled data-driven operational decisions that improved energy efficiency, optimized production timing, and supported corrective action on underperforming assets - resulting in approximately $10M per year in recurring cost savings.