The ‘E’ in ESG refers to environmental concerns, and in the context of industrial manufacturers, the optimization of energy usage.
ESG is a hot topic today among industrial manufacturers. The ‘E’ in ESG refers to environmental concerns, and in the context of industrial manufacturers, the optimization of energy usage. E includes energy consumption, energy waste, carbon emissions and the resulting impact on climate change. Additionally, 2022 has seen unprecedented increases in the cost of energy, to the point where some manufactured products are no longer profitable to make. This combination of environmental concerns and rapidly rising energy costs has led to manufacturers establishing stretch goals around energy reduction in their manufacturing operations. We’ve seen energy reduction targets among our customers of 25%, 30%, 40% and higher. Those energy reduction targets are passed down from the executive suite to the business units and plant operations teams to execute. The question is: how does a manufacturer go about achieving these lofty objectives?
No E in a Vacuum Perhaps a better question is: what are the implications associated with energy reduction initiatives? As my colleague Sheila Kester pointed out in her recent blog (www.twinthread.com/resources/why- tackling-energy-reduction-in-isolation-doesnt-make-sense-and-a-better-way-to-go), why not just shut the plant down and achieve 100% energy savings? This was stated tongue in cheek, but the point is there are implications to any energy reduction initiative. When energy consumption is reduced, other performance indicators within the plant can be impacted. Those may include critical items such as throughput and product quality. Energy cannot be reduced in a vacuum. The interrelationships between a wide range of performance metrics must be understood and optimized to enable a successful initiative, and an energy initiative may often have competing goals. Energy reduction is relative. To state the obvious, energy reduction reduces costs, and if manufactured product key performance indicators around waste, rework, and throughput are improved, energy costs will go down.
Collaborative Communication Manufacturers require a data and artificial intelligence (AI) infrastructure, not only to optimize energy in the context of other performance metrics such as throughput and quality, but also to enable and digitize a collaborative process and communication mechanism across the entire organization focused on energy driven opportunities and tradeoffs. If energy reduction goals can be achieved, but product quality is compromised, what is the impact on the business? Should the business sacrifice on quality to reduce energy consumption? If reduced throughput is a result of an aggressive energy reduction initiative, is that acceptable to the business? What processes are impacted by change and how is innovation driven around those processes? Manufacturers require an AI-enabled digital twin platform that will allow them to measure, communicate, optimize, and predict the various tradeoffs and opportunities based on what is best for their business.
The AI-enabled digital twin platform from TwinThread enables industrial manufacturers to optimize energy, quality, throughput, asset reliability, and other key performance indicators while simultaneously identifying, prioritizing, and executing on a range of other continuous improvement and innovation initiatives.