One area that Castrip has been working on for the last two years is increasing the use of machine intelligence to increase process efficiency in the yield. “This is quite affected by the skill of the operator, which sets the points for automation, so we are using reinforcement learning-based neural networks to increase the precision of that setting to create a self-driving casting machine. This is certainly going to create more energy-efficiency gains—nothing like the earlier big-step changes, but they’re still measurable.”
Reuse, recycle, remanufacture: design for circular manufacturing
Growth in the use of digital technologies to automate machinery and monitor and analyze manufacturing processes—a suite of capabilities commonly referred to as Industry 4.0—is primarily driven by needs to increase efficiency and reduce waste. Firms are extending the productive capabilities of tools and machinery in manufacturing processes through the use of monitoring and management technologies that can assess performance and proactively predict optimum repair and refurbishment cycles. Such operational strategy, known as condition-based maintenance, can extend the lifespan of manufacturing assets and reduce failure and downtime, all of which not only creates greater operational efficiency, but also directly improves energy-efficiency and optimizes material usage, which helps decrease a production facility’s carbon footprint.
The use of such tools can also set a firm on the first steps of a journey toward a business defined by “circular economy” principles, whereby a firm not only produces goods in a carbon-neutral fashion, but relies on refurbished or recycled inputs to manufacture them. Circularity is a progressive journey of many steps. Each step requires a viable long-term business plan for managing materials and energy in the short term, and “design-for-sustainability” manufacturing in the future.
IoT monitoring and measurement sensors deployed on manufacturing assets, and in production and assembly lines, represent a critical element of a firm’s efforts to implement circularity. Through condition-based maintenance initiatives, a company is able to reduce its energy expenditure and increase the lifespan and efficiency of its machinery and other production assets. “Performance and condition data gathered by IoT sensors and analyzed by management systems provides a ‘next level’ of real-time, factory-floor insight, which allows much greater precision in maintenance assessments and condition-refurbishment schedules,” notes Pierre Sgrafna, circularity program leader at Schneider Electric’s energy management business.
Global food manufacturer Nestle is undergoing digital transformation through its Connected Worker initiative, which focuses on improving operations by increasing paperless information flow to facilitate better decision-making. José Luis Buela Salazar, Nestle’s eurozone maintenance manager, oversees an effort to increase process-control capabilities and maintenance performance for the company’s 120 factories in Europe.
“Condition monitoring is a long journey,” he says. “We used to rely on a lengthy ‘Level One’ process: knowledge experts on the shop floor reviewing performance and writing reports to establish alarm system settings and maintenance schedules. We are now coming onto a ‘4.0’ process, where data sensors are online and our maintenance scheduling processes are predictive, using artificial intelligence to predict failures based on historical data that is gathered from hundreds of sensors often on an hourly basis.” About 80% of Nestle’s global facilities use advanced condition and process-parameter monitoring, which Buela Salazar estimates has cut maintenance costs by 5% and raised equipment performance by 5% to 7%.
Buela Salazar says much of this improvement is due to an increasingly dense array of IoT-based sensors (each factory has between 150 and 300), “which collect more and more reliable data, allowing us to detect even slight deteriorations at early stages, giving us more time to react, and reducing our need for external maintenance solutions.” Currently, Buela Salazar explains, the carbon-reduction benefits of condition-based maintenance are implicit, but this is fast changing.
“We have a major energy-intensive equipment initiative to install IoT sensors for all such machines in 500 facilities globally to monitor water, gas, and energy consumption for each, and make correlations with its respective process performance data,” he says. This will help Nestle lower manufacturing energy consumption by 5% in 2023. In the future, such correlation analysis will help Nestle conduct “big data analysis to carbon-optimize production-line configurations at an integrated level” by combining insights on material usage measurements, energy efficiency of machines, rotation schedules for motors and gearboxes, and as many as 100 other parameters in a complex food-production facility, adds Buela Salazar. “Integrating all this data with IoT and machine learning will allow us to see what we have not been able to see to date.”
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