The recent global energy price shocks impacted multiple sectors across our economy, directly and indirectly. In the EU alone, the spikes in gas prices caused by the war between Russia and Ukraine reduced industrial production by 2% between September 2021 and 2022.

Even with more stable prices today, the tendency is that volatility of energy prices is here to stay for a longer period. The first driver behind this trend is the impact of more renewable energy connected to the grid. While crucial for a sustainable future, the rapid integration of intermittent energy sources like solar and wind power introduces an element of unpredictability for when energy generation will be available that traditional fossil fuel-based systems did not have to deal with. 

A second driver of these energy shocks lies in geopolitical conflicts and tensions that further compound the uncertainty and increase the pressure on energy markets. Disruptions in supply chains, trade disputes, and political instability have the potential to ripple through global energy networks, causing sudden price fluctuations and disrupting the balance of supply and demand. 

Extreme energy price events impact not only those energy-intensive industries like aviation, chemical, metal processing, or cement and concrete. Even industries where energy is not a core input of their production or service provision suffer from cost increases in their supply chain. Food & beverages, travel agencies, and rubber & plastic products are great examples of the negative impact on their procurement costs from very energy-intensive sectors.    

An alternative for companies, especially manufacturers, to protect their operations from these spikes is to step up their basic energy monitoring capabilities into a data-driven energy management strategy. On the back of Industry 4.0 and the digital transformation of equipment and processes, companies have the possibility to leverage multiple internal data sources and enrich them with external information like weather data to go beyond only understanding their energy consumption. And this can transform how they consume, procure energy, and manage their production processes. Three macro maturity stages allow all companies to step up their energy behaviour through a data-driven strategy, either starting with their consumption optimisation or integrating advanced energy procurement and flexibility with their operations. Let’s see how these three areas impact companies’ efficiency:

1. Energy consumption

2. Energy procurement

Gone are the days when energy procurement was an annual task, a passive exercise of locking in fixed contracts with utilities. In today's volatile energy landscape, organizations are increasingly embracing an active, data-driven approach to energy trading, empowering them to navigate the market's uncertainties and implement more precise cost monitoring of their production. With a clearer understanding of their energy consumption, energy cost control can come from two strategies or a mix of both: building generation assets and/or implementing more robust energy price hedging strategies.  

By building their own renewable generation portfolios, these organizations are breaking free from the constraints of traditional energy procurement models. Asset ownership allows long-term visibility of both price and energy generation, especially when distributed across multiple sites, with the possibility of sizing the asset optimally to factory demand. Alternatively, they may choose to employ advanced hedging strategies, utilizing sophisticated analytics to anticipate market fluctuations and execute trades that mitigate risk and optimize costs. 

At the heart of these strategies lies a deep commitment to data-driven decision-making, where historical consumption patterns, real-time market data, and predictive models converge to inform every energy procurement decision. In this new paradigm, organizations are no longer consumers but active participants in the energy ecosystem, shaping their futures with precision and agility.

3. Energy flexibility

Conclusion

These examples showcase the power of data-driven energy management, where organizations harness the insights hidden within their data to make smarter, more informed decisions. By embracing advanced analytics and AI, they can navigate the uncertainties of the energy landscape more confidently, optimizing their operations, reducing costs, and contributing to a more sustainable future.