“Energy would be free in another few years. The State of California paid neighboring states to receive excess solar power for 17 days in 2017. So why should we worry about energy bills?” was the comment from a CXO. We were sitting in the conference room of a venerated CPG multinational and working on the ROI of an energy analytics project. Hurdle rate of 13% IRR becomes difficult with each unit of captive wind energy available at INR 3.00. But like many innovative approaches, energy analytics can pack much larger benefits.
Energy analytics is not only about reducing energy consumption. Savings from energy consumption is the lowest hanging fruit — larger benefits lie elsewhere. Energy data provides a reliable and always-on signature of operating equipment — making it compelling even when energy is free and on tap.
Energy data can provide cost-effective condition monitoring and predictive maintenance (PdM) of diverse equipment. Most factories would have built its equipment landscape over many years and with competing vendors. Implementing a (PdM)system there without significant disruption is difficult in conventional approaches like lubricant programs, sonar/ultrasound/vibration analysis at all. Hence many factories take the practical approach of covering only the most critical assets under predictive maintenance(PdM); leaving the rest to wasteful preventive plans. Cheap, ubiquitous and continuous energy data can provide the missing link — quickly bringing 100% of assets in the folds of predictive maintenance (PdM)programs.
How can a more effective predictive maintenance (PdM)program impact profitability of production processes? One approach to calculate this impact uses the principles of Operating Equipment Effectiveness (OEE). Imagine a current day factory — automated CAM/CAE environment with specialized machines, guided vehicles, robotic operators and inspectors. In this context of inter-related complex equipment, impact of planned and unplanned downtime can be significant (~30–35% of total available hours). An effective predictive maintenance (PdM)program can cut it by one third — reducing scheduled hours or increasing production yield to that extent.
Energy analytics provide quick savings from energy consumption in critical equipment like chillers, motors or transformers. But there can be much larger benefits from a comprehensive framework — that enables predictive maintenance (PdM)across equipment. Energy data, processed through analytical models, can present the first dashboard of equipment condition across the facility — at a fraction of the cost of ultrasonic or lubricant analysis programs. Let us not throw away the energy meters yet — energy might become free, but energy data could be invaluable for reducing OEE losses.
The essential aspects of energy in predictive maintenance (PdM) involve Energy analytics, energy data, condition monitoring, OEE and OEE insights.
Energy Analytics | Energy | Energy Data | Condition Monitoring | Predictive Maintenance | PdM | OEE | OEE Insights
– Sugato Guha,
– Sales Head