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The Role of Data Analytics in Unlocking Hidden Energy Savings

4 December 2015
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Mike Charles, Operational Intelligence Product Manager at Wonderware UK, looks at the role of automation software and data analytics in unlocking hidden energy savings.

UK manufacturers have recently found themselves under increasing pressure to get their energy usage under control following the National Grid’s stark warning of the possibility of black outs if the industry does not act quickly.

Anyone able to remember the events of the 1970s, when rolling power cuts led to the three-day working week, will know how important it is to keep the power on. Since then, we’ve become even greater users of energy, but the clock is ticking and by December 2015 all UK companies with 250 or more employees, or an annual turnover in excess of £42m, must have completed a comprehensive audit of their energy use.

The need for energy efficiency is driven by both regulatory and commercial considerations. With both raw materials and energy prices continuing to rise, ensuring that unnecessary usage is eliminated is imperative. In short, effective energy management is no longer an option; it’s a strategic business necessity.

Most manufacturers are making efforts to reduce their energy costs and have made substantial inroads into reducing their usage by using various well established methods, such as implementing more energy efficient equipment and ensuring staff switch off non-essential equipment. But is it enough?

Energy consumption in manufacturing facilities can be reduced by using more efficient technologies and equipment but also through improved monitoring and control of energy used in infrastructure. This software and data based approach is increasingly popular with UK manufacturers who have either exhausted other methods or find the cost to modernise their entire infrastructure prohibitive. For many manufacturers, the prospect of a complete infrastructure upgrade is impractical, but through the implementation of Corporate Energy Management solutions, businesses can develop a stronger and more insightful understanding of their operations, and are able to visualise their energy use and see any inefficiencies. Used effectively, such solutions can allow plant operatives to monitor real-time energy usage and automatically notify operators, supervisors and management of energy inefficiencies and waste.

Recently, a manufacturer in the automotive industry wanted to improve the way they monitored their energy usage. The company already had smart meters in place on most key pieces of plant equipment, but this data was in an isolated database, in data silos and only ever reviewed on an ad hoc basis, so of limited use. The company implemented Wonderware Intelligence, an Operational Intelligence (OI) product that creates an information framework to simultaneously connect to industrial data sources and to automate the calculation, contextualisation and storage of operational Key Performance Indicator (KPI) metrics.

“Effectively ‘slice and dice’ a diverse range of data”

This allowed the manufacturer to effectively ‘slice and dice’ a diverse range of data, including energy metering data, manufacturing performance statistics, machinery rates and even shift patterns. As a result, the company immediately noticed that energy usage on the production line peaked significantly one day per week, with no apparent pattern. Previously, the company’s access to only smart metering information would have left them unable to identify the root cause, but cross referencing smart meter data with information about shift patterns allowed the company to identify that the surge in energy use correlated with the start of a particular team leader who was rebooting and recalibrating machinery far more often than any other team. Identifying this allowed the company to retrain the individual and make an immediate energy saving as well as improve Overall Equipment Effectiveness (OEE).

“Spot patterns that would have normally gone unnoticed”

Operational Intelligence technologies can be used to monitor manufacturers’ energy usage down to each machine’s component, which is then compared with other variables such as shift pattern, day of the week or even product. This allows manufacturers to treat energy like any other ingredient, enabling them to calculate a Cost Per Unit (CPU) of energy for each product. Crucially, these tools can also be used to compare energy metering data. Currently, many manufacturers just accept their tariff, but with this technology it is easy to spot patterns that would have normally gone unnoticed, allowing companies to identify areas where energy could be saved and, more importantly, the factors leading to this.

The way more companies are beginning to understand energy data is indicative of a broader trend towards using operational data in innovative ways. It is predicted that Operational Intelligence will continue to develop at pace as senior decision makers become aware of what is now possible with the latest tools and how valuable they are to their businesses. It will become common practice that CEOs and the like will expect to be able to get high level insight into operational Key Performance Indicators in the same way they do for systems such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP).

The next step for the industry will see data from various niche manufacturing systems such as Manufacturing Execution Systems, Supervisory Control and Data Acquisitions, Maintenance Management Systems and Laboratory Information Management Systems aggregated together. This can then be manipulated and analysed using accessible browser based, slice-and-dice enabled technologies such as Wonderware Intelligence and Wonderware Corporate Energy Management. This will become common place and allow those running businesses to see energy usage across the production process and how that data tracks back to the bottom line.

Manufacturers need to realise the value that is hidden in their operational data and find out more about the tools available to help them extract and analyse it. The answers to questions such as: ‘why is my energy usage higher on line A rather than line B?’ can now be found, allowing businesses to strip out costs, be more competitive and maximise their investments.

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