Stuck in the dark? Why the energy crisis in manufacturing needs real-time data to keep the lights on and compete

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The ability to predict and adapt to the waves of change will determine the success or failure of manufacturing, writes Alessandro Chimera, Director Digitalisation Strategy, TIBCO.

It may be an understatement to suggest that soaring energy prices and rising inflation are doing nothing to ease the fears of a looming recession for the European manufacturing industry. The war in Ukraine has thrown a big spanner into the sector’s post-COVID recovery plans, but no one said it was ever going to be easy. No matter where you look, weak economic growth is forecast for 2023 and manufacturing, a traditional bellwether for prosperity, is forecast to contract.

The immediate challenge facing most European manufacturers is how to contend with rising energy costs. For energy-intensive industries like manufacturing, these drastic price increases are inflating production costs, which have risen by almost 50% in some sectors. Eurostat reported that between March 2021 and this year, energy prices increased 101% across the EU, but other production costs are also rising. Eurostat added that all other industrial producer price increases totalled 14.1% in the same period.

Despite some governments trying to cap surging energy prices, the reality is that most businesses will be faced with some difficult choices in the coming months. With 68% of the industry’s energy usage tied to fossil fuels, the dramatic fluctuations in pricing will continue to have an impact and could even lead to factory closures and insolvencies. You only need to look at the struggles of Murano glass making in Venice or the frozen food industry to realise the scale of the issue. 

Energy management

In addition to costs, manufacturers also have to consider Environmental, Social, and Governance (ESG) compliance. It’s a big challenge marrying the increasing demands for transparency and information about ESG goals, with the need to manage energy costs and deliver competitively priced products.

If anything, ESG is going to take a hit. The complexity and scale of managing emissions data, especially across supply chains where fossil-fuel dependence remains high, is not helping. Rightly or wrongly, it is seen as an added burden at a time when the focus should be on keeping the lights on, and yet compliance is only going to get more stringent.

What is clear is that manufacturers must make plans now to ensure compliance with ESG, to avoid the fines, while also finding ways to adapt to the fluctuations of energy and raw material pricing. There really is only one way to do this and although it comes with a number of challenges, it will deliver cost savings and, more importantly, an understanding of the energy demands of each production line.

In 2021, Deloitte predicted that “converging trends will likely accelerate industrial companies’ adoption of energy management solutions and potentially boost their interaction with electric utilities and the grid.”

It’s an interesting point. Almost certainly energy management is at the heart of the solution and a well-designed energy management strategy rests on the collection and analysis of quality energy data. This in itself is no easy task. It demands a smart, connected organisation empowered with an intelligent digital platform that offers a wide range of on-demand capabilities to manage any aspect of data.

Data-driven wins

However, it’s not just a matter of collecting and aggregating energy data. For many manufacturers, the real gap in their use of data comes, not from a lack of data, but from a lack of ability to turn it into useful, actionable intelligence. Manufacturers must have clear and consistent data governance policies, the ability to integrate disparate data while maintaining data integrity, and the ability to democratise that data, so that stakeholders throughout the organisation can easily access and analyse the data for specific business uses.

Only then can organisations start to use tools that can connect data across processes, equipment, and IoT devices, and apply predictive and prescriptive analytics to find areas for optimisation of energy consumption. This is also where digital twins come in, ultimately delivering a real-time vision of the organisation that can not just optimise energy consumption, but also enable the trial and testing of processes to continually drive optimal change of the entire organisation.

We’ve already seen this in operation. Our work with Hemlock Semiconductor (HSC), a market-leading producer of polysilicon, has seen the business focus on understanding how every facet of the organisation operates. The ultimate aim was to reduce costs but also improve its quality, while taking advantage of new business models and optimising its energy consumption.

HSC uses our platform that connects data across processes, equipment, and IoT devices. This intelligently unifies the data and applies AI predictive analytics, which recommends which production lines are profitable, which energy sources are required, where production costs can be cut and how the business can meet its ESG goals.

As a vehicle to having a sophisticated understanding of site processes and energy use, this has proved invaluable in the business’s drive to find efficiencies but also opportunities. HSC admits it is a major user of electricity, so it is imperative that it finds a way to optimise its performance to avoid waste and incur unnecessary costs. The ability to visualise the data was key to the organisation initiating a peak power management programme, which helped to optimise its overall performance.

This meant the company could run more of its assets during off-peak hours, lowering demand on the electric grid when consumption is at its highest (between 11am and 7pm on weekdays). While this enabled its electricity utility to better manage its total demand, it also saved HSC approximately $300K a month.

There are so many facets to this. With more granular detail about specific energy use, manufacturers can accurately track reductions and impacts. With predictive and prescriptive analytics, manufacturers can proactively address equipment issues before they cause spikes in energy consumption. With predictive maintenance, manufacturers can lower operational costs and prevent unscheduled downtime.

Given the challenges ahead, the idea that any manufacturer would survive ‘flying blind’ is unimaginable. More than ever before, manufacturers need visibility. They need operational intelligence and they need it urgently. Real-time energy management is just the start.

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