How a Large Industrial Manufacturer Took Control of Its Largest Unmanaged Cost

Overview

A large industrial manufacturer runs a 24/7 operation where energy costs rank among the largest line items on the P&L. Unlike commercial facilities that can simply reduce activity during peak hours, this company's production lines needed to keep running — which meant energy felt like a fixed cost, not a manageable variable.

The company was buying power without real-time market visibility. Procurement decisions were made on past trends rather than current prices. Non-essential systems ran on fixed schedules regardless of whether power was expensive at that moment. And the company wasn't enrolled in any demand response programs, leaving a potential revenue offset completely uncaptured.

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The Problem: Unchecked Energy Costs and Market Volatility

The fundamental problem was information asymmetry. The power markets the company purchased from updated in real time. The company's decision-making didn't. Every month, the bill arrived with the same pattern: demand charges that moved unpredictably, procurement costs that could have been lower if purchases had shifted by even a few hours, and no mechanism to participate in flexibility programs that would have offset some of those costs.

Energy was treated as an unavoidable overhead category. That framing was costing the company significantly.

"We knew energy was one of our biggest expenses, but it felt like something we just had to accept as per our rate contract. We didn't know we had options."

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Solution

Working with Arcobi, the company gained real-time access to wholesale power market data through DataHub, with day-ahead and hour-ahead price forecasting from AI Forecasts layered on top.

1. Real-Time Market Visibility (DataHub)For the first time, the procurement team could see what power actually cost at each hour of the day — not what they'd paid last month, but what the market was pricing right now. That visibility alone changed how procurement decisions were made.

2. Load Shifting on Non-Essential SystemsAI Forecasts price signals were used to reschedule energy-intensive but non-critical activity — batch processing runs, HVAC cycles, auxiliary equipment — to low-cost hours. Production schedules on core lines stayed untouched. The flexibility came entirely from systems that had scheduling latitude but had never been optimized against market prices before.

3. Day-Ahead Procurement OptimizationWith AI Forecasts' day-ahead price curves, the procurement team started locking in energy at identified low-price windows rather than purchasing at spot prices reactively. Buying ahead of price events rather than through them produced a consistent reduction in average procurement cost.

4. Demand Response EnrollmentArcobi identified opportunities for the company to enroll non-essential load in demand response programs. Curtailments that were already happening for cost reasons now generated additional payments during grid stress events.

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Result

Demand charges, which had previously been treated as uncontrollable, dropped by approximately 20% as peak-hour loads were systematically shifted to lower-cost periods. The company also began generating demand response revenue — a revenue stream that didn't exist before the engagement. The shift in mindset was as significant as the dollar savings. Energy moved from a static budget line to an actively managed cost center with clear levers. The team now runs against forecasts, not against last month's invoice.