The paper looks at how businesses can reduce their production costs using artificial intelligence to increase the energy efficiency of production processes and management approaches. It finds that while businesses are already facing rising volatility in electricity prices, energy procurement is often disconnected from production planning, leaving significant savings unrealised.
While many businesses already use demand response (DR) to react to peak price events, this is only a starting point. When combined with advanced forecasting, AI optimisation, and industrial controls, DR evolves into proactive production scheduling. Instead of merely avoiding high prices, manufacturers can plan production around favourable market conditions, lowering total production costs.
By embedding energy intelligence directly into day-to-day operations, companies can reduce costs without sacrificing output.
By using tools such as strike prices, market forecasts, and digital twins, businesses can simulate scenarios, anticipate price spikes, and align production accordingly. This enables them to shift energy-intensive processes to low-price periods, build inventory ahead of high-cost windows, and temporarily curtail production during peaks while maintaining customer commitments.
These technology-driven approaches allow businesses to better manage price volatility, improve resilience, and unlock significant cost savings in an increasingly renewable-driven energy system. Ultimately, the report finds that energy-aware production scheduling transforms energy from a volatile cost risk into a source of resilience, predictability, and competitive advantage.
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