“If you can’t measure it, you can’t manage it.” This oft-quoted adage has universal relevance, and manufacturing is no exception. The rising importance of sustainability initiatives is making manufacturers rethink their energy optimization strategies. Many are turning to IoT and advanced data analytics for achieving their green manufacturing goals. According to a global study of 30,000 consumers, 73% of millennials are ready to pay more for sustainable goods. Another report noted that environmental sustainability is an integral part of many businesses, thanks to the positive impact on their corporate image.
Resource Optimization Through Data Utilization
Most of the green initiatives are driven by cost pressures. So, most companies remain focused on increasing output and minimizing supply chain costs to raise profitability. Energy saved is energy earned – that’s a no-brainer. But it’s still considered ‘unmanageable’ due to the technical complexities linked to product variations. Additionally, the environmental regulations are getting more stringent.
Resource intensive organizations face more challenges related to optimization of plant efficiency, utilization of assets, identification of underperforming assets, and assessment of real-time financial implications of energy loss.
The various control systems and ERP applications are usually designed to focus on operations, and not on computing resource usage at the plant, equipment and process level. Also, the plants handle a humongous amount of disaggregated data on a regular basis. This too adds to the complexity of computation. Basic common energy consumption metrics (like kWh/ton of product) are of little use, since they don’t present the accurate picture of today’s dynamic production environments.
Essentials of an Efficient Energy Analytics Solution
An advanced solution framework is based on a resource-use optimization model supported by a real-time dashboard that’s customized for the plant’s requirement. It should provide an intelligent analysis (based on meaningful KPIs like energy savings) and simple visualization of energy use.
The dynamic benchmark models have to be created by resource use efficiency for each plant. Advanced analysis of historical data must be performed to identify patterns affecting efficiency. Then the new efficiency benchmarks are created and the performance gaps identified, so corrective actions can be taken. A single IoT and dashboard platform can enable users to visualize losses across production lines and geos. This ensures better control and higher performance. Six Sigma and other quality management systems need to be adopted for better efficiency and control. Last, but not least, the plug-and-play solution must have a user-friendly architecture that makes the task easy for all functions.
The Visible Benefits of a Real-Time Analytics Solution
The noticeable savings in energy costs is one of the biggest factors that determine the decision to adopt an energy analytics solution. Efficiency enhancements can be undertaken with minimal capex investment. There’s also a better accountability of energy use across functions due to precise clarity on performance. Bad maintenance practices is usually one of the biggest reasons for underperformance.
The provision for precise root-cause analysis and real-time dashboards of the solution help address this issue by showing all the deviations. The implementation of a solution like this is usually performed by a technology provider with a background in manufacturing, energy management & audits and advanced analytics.