For Industry 4.0, sustainability is more than just a lofty ideal. It’s a legal and financial responsibility—one that needs to be measured as precisely as any KPI. On the regulatory side, manufacturers are under increasing pressure to meet carbon emissions targets set by governments. Investors are also paying greater attention to companies’ environmental, social, and governance (ESG) practices, of which sustainable energy management is an integral part.
In an era marked by growing concern over climate change, this trend has broad support. But it has caused serious issues for the industry. For one thing, it’s hard to find new ways to increase energy efficiency, especially since sustainability goals are often quite aggressive. And beyond this, capturing and reporting on sustainability data—both to regulators and to shareholders—often entails a tremendous amount of work.
“For smaller manufacturers, basic compliance presents a problem,” says Julia Chih, Product Manager for Advantech, a Taiwan-based manufacturer of smart products. “Larger businesses have more resources, but they often lack the organizational knowledge to automate data collection and reporting—and paying a high-priced consultant to do the work isn’t an attractive option.”
It’s a difficult situation for manufacturers. But AI-enabled smart factory solutions may provide the answer: a system that offers rapid ROI and is flexible enough to be future-proof against changes in the regulatory and reporting landscape.
IoT, Edge AI, and Cloud: a multilayered solution
The power of smart factory systems comes from their design. There are multiple layers of technology, each one playing a specialized role in the solution as a whole. On the factory floor, IoT sensors and edge AI handle data acquisition and real-time process optimization.
Sensors and smart meters are deployed as needed to collect data from industrial machinery. They report on performance, power consumption, temperature, water usage, and so on. This provides visibility into what is actually happening in the factory, which is the essential first step in identifying waste and gathering the raw data required for reporting. Edge AI is used to improve efficiency by processing the sensor data in real time and automatically optimizing the production line through the factory’s SCADA systems.
Behind the scenes, data is sent to the cloud for further processing. At this level, business operators are able to use the collected data to generate regulatory and ESG reports. The mass of information produced by a smart factory can also be used here by big data and AI applications to extract additional insights and develop longer-term optimization strategies.
This modular, layered architecture means that smart factory solutions are inherently flexible. Manufacturers can configure such solutions to meet their operational and reporting needs. They can also adjust them as needed if sustainability targets or reporting requirements change in the future. Henry Chen, Advantech’s Business Development Manager, says that Intel® technology has been particularly helpful in this regard:
“Intel processors excel at both edge AI applications and heavier server workloads. The capabilities are well defined and documented, which makes it easy for us to choose the correct processor to meet the customer’s specifications no matter the scenario.”
A Smart Manufacturing case study in Mexico
Advantech’s deployment at a Foxconn facility in Mexico shows how smart factory systems can bring about dramatic improvements in sustainability with minimal capital investment. Foxconn needed to comply with the local environmental regulations. It also wanted to meet an internal goal of reducing carbon emissions across their manufacturing sites worldwide.
As a global manufacturer, Foxconn was looking for technology that would both deliver the results it needed in Mexico and also be used at factories in other countries. Ideally, it wanted a solution that could be managed via a standardized, centralized system. Advantech worked with Foxconn to install smart sensors and power meters throughout their Mexico facility. The companies worked together to set up an always-on data collection system and connected it to a back-end that could be monitored remotely from a central location. They also implemented an energy management optimization strategy.
The results were striking. There was an immediate improvement in energy efficiency, representing an 8-13% cost savings on average. In addition, Foxconn discovered that the new visibility into energy consumption could be used to develop a capacity forecasting plan, helping the company avoid overage penalties with their utility and reap additional savings in the long term.
The future of sustainability
The short-term results that a smart factory solution can produce are impressive, and the promise of rapid efficiency improvements will no doubt drive adoption in the manufacturing sector. But in the future, the ability to capture data from the factory and mine it for business insight may open up even greater opportunities.
For instance, the IoT and AI in smart factory settings can be used for prognostics and health management (PHM). PHM, the practice of monitoring the health of machinery and performing proactive maintenance to prevent unplanned shutdowns, has a clear business case. “If you can repair a machine before it fails,” says Chen, “you reduce downtime—something that is extremely costly for manufacturers.”
And PHM is just one example of the wider potential of smart factories. As the digital transformation of industry accelerates, manufacturers will continue to find innovative uses for the technology. For this reason, Advantech has decided to open-source their AI platform.
“The future is going to require flexibility and openness,” says Chen, “because companies will want to build their own applications to take advantage of the opportunities offered by IoT and AI in the factory.”
In the coming decade, this should lead to a richer, more comprehensive model of sustainability—one that delivers long-term value for both manufacturers and communities alike.