Use The Edge To Get Sustainable

Using edge analytics to improve machine efficiency will play a major role in manufacturers realizing increases in operational efficiency from IIoT investments.

By Johan Jonzon, Crosser

Leveraging the industrial edge could help the manufacturing industry improve its sustainability credentials.

In the wake of landmark climate event COP26, manufacturers are striving to align their activities with the global net-zero targets. But the sector has a long way to go. It currently produces more than 16 gigatons of CO2 each year. The damage that industry has been doing to our planet isn’t new knowledge. But the window of opportunity to act is starting to close. According to the Global System for Mobile Communications Association, London (gsma.com), to reach net zero by 2050, manufacturing needs to halve its emissions in the next decade. The urgency of the situation is clear.

Manufacturers should strive for better sustainability for the future of the planet and for the future of their facilities. The PricewaterhouseCoopers, New York City (pwc.com) 2021 Global Investor Survey revealed that reducing scope one and two emissions—direct emissions from owned or controlled sources and indirect emissions from purchased electricity generation—is the top priority for 65% of investors. How can leveraging the industrial edge make it happen?

Monitoring performance

As smart manufacturing facilities grow in number, the quantity of data produced by the industry is growing exponentially. This data is typically stored in data centers which, according to the International Energy Agency (IEA), Paris (iea.org), consumed between 200 and 250 terawatt hours (TWh) of electricity in 2020, which is 1% of total global demand. With vast quantities of data available, it’s important for manufacturers to learn how to use it to improve their facility’s sustainability.

Edge analytics allows businesses to process data close to its source. After collecting data from various machines on a factory floor, manufacturers can employ edge-analytics tools to process it. They can adopt smart filtering to remove any irrelevant, invalid, or “dirty” data and only send relevant information to the data center.

Accessing and using this data in the correct way allows manufacturers to unlock its power and monitor environmental key performance indicators (KPIs). Environmental KPIs typically relate to emissions, such as CO2, and resource use such as water or material waste. Sensors on each machine can calculate individual emissions and resource usage, which can then be standardized to create one uniform data set, generating KPIs in real time. 

Once machine data has been obtained, filtered, and standardized, manufacturers are left with deep insight into asset performance, making it easier to identify key contributors to emissions or a particular resource drain. Further actions can then be taken to improve the facility’s sustainability credentials, particularly through improved energy efficiency and reduced waste.

Edge-enabled efficiency

Once the machine’s data is available, it can be used to reduce energy consumption. Industry accounts for 39% of the world’s final energy use according to the IEA. Making small improvements to individual asset performance can result in incremental energy savings that build to a big impact.

Manufacturers can streamline machine-to-machine (M2M) communication by using the data from one machine to gain an actionable insight into its performance and generate an output that determines the configuration of the next machine.

Using edge analytics to improve machine efficiency will be a major reason behind the technology’s adoption in 2022. According to Deloitte, 45% of manufacturers expect increases in operational efficiency from IIoT investments. Although the economic benefits of improved efficiency are clear, embracing the edge also transforms a facility’s sustainability status.

Identifying energy inefficiencies in real time presents manufacturers with an opportunity to act quickly. For example, edge analytics can be used to monitor the speed at which a machine is running or the quantity of a material it dispenses. In response to the data collected, each machine’s configuration can be modified immediately according to a predetermined algorithm for streamlined operations. By optimizing several individual machine processes that are significant energy consumers, edge analytics controls and reduces a facility’s overall energy consumption.

Decreased defects

Industrial waste is another huge element of manufacturing’s sustainability problem. The World Bank has estimated that, globally, industrial waste generation is almost 18 times more than municipal solid waste. The two greatest contributors to industrial-waste generation are overproduction and defective output.

If a machine begins to malfunction, its output will be affected, meaning all of the defective parts it produces will ultimately end up as industrial waste. By analyzing machine data immediately and continuously, manufacturers can identify performance anomalies before they become a problem. Once an anomaly has been identified, it can trigger a warning to inform machine operators that direct action must be taken. Production can be temporarily paused, the machine repaired, and output remains as intended.

If manufacturing is to reach net zero and align its activities with international climate goals, it must act now. Forrester Research, Cambridge, MA (forrester.com), has predicted sustainability-related services powered by edge technology will grow in the near future. With the urgency of the crisis clear, there’s no time to waste in adopting environmental, social, and governance goals. Embracing the edge is crucial to making it happen. EP

Johan Jonzon is CMO and Co-Founder of Crosser, Stockholm, Sweden (crosser.io), producer of the Crosser Flow Studio edge-analytics platform. Jonzon bio continues here and we will share his expertise, education, and experience in the manufacturing industry as he works to improve manufacturing sustainability and overall efficiency.