Crystal Gaze 2021: Will 2020 Be the Tipping Point for AI Adoption in Industrial Operations?

AI adoption has been the biggest trend-setter in 2020. It will remain so in 2021 as well.

I think we can all agree that 2020 will go down in the record books as ‘unprecedented’ on all fronts. Despite the challenges, 2020 put the spotlight on how agile manufactures can be. From seeing auto manufacturers pivot their production lines to fulfill the global need for ventilators to chemical companies adjusting their manufacturing operations in response to urgent calls for hand sanitizer, PPE, protective screens, and other products.

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These pandemic pivots really epitomized a situation where necessity was the mother of invention.

The pandemic also forced manufacturers to look within. In a survey of 100 manufacturers Canvass conducted in May, 42% of respondents said that operational efficiency decreased and 56% said that operational costs increased or significantly increased due to COVID-19 operating restrictions. Our survey found that for the majority, workforce readiness and digital skills created one of the biggest challenges to their ability to adapt to COVID-19 restrictions. As we wait for news of a successful COVID-19 vaccine, unfortunately for industrial manufacturers, the disruption of the pandemic is not the only challenge that has put the sustainability of their business at risk as we head into 2021.

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Some top challenges include the:

• Pressure to reduce waste: The Natural Resources Defense Council (NRDC) estimates that food manufacturers lose between 16 -36% of their raw materials during manufacturing. Waste in the manufacturing sector can come in scrap, defective products as well as excess water and energy consumption.
• Pressure to reduce costs: Energy continues to be one of the industrial sector’s highest operating costs. For most plants, energy costs make up approximately 20% of total plant costs. For one of our customers, this is upwards of $10M per year at just one plant showing the extent and opportunity that energy efficiency programs can impact an industrial sector’s bottom line.
• Pressure to improve operational efficiency: ensuing processes and assets are operating at their optimal is critical to ensuring batch-on-batch consistency and maintaining their profit margin. To demonstrate the effect of unplanned downtime, an unlucky North American food processing plant experienced three unplanned power outages in just four weeks, which was estimated to have cost the company more than $10 million.
• The pressure to decarbonize their operations: There has been a marked increase in how Industrial companies are prioritizing their sustainability goals. However, some are facing increasing pressure than others. For example, McKinsey estimates it in the steel industry, approximately 14 percent of steel companies’ potential value is at risk if they are unable to decrease their environmental impact due to carbon offset requirements. While the steel industry is at the extreme end of the spectrum, as it accounts for about 8 percent of global carbon dioxide emissions, sustainability is an industry-wide challenge that is becoming more urgent as strives to meet targets set in the Paris Agreement.
• Pressure to address a skills shortage: Under a backdrop of all these challenges, nearly one-quarter of the sector’s workforce are age 55 or older. This combined with the increasing digitization of the plant floor, industrial companies are facing a skills shortage whereby MAPI found that 47% of industrial workforces lacked the digital skills needed to derive value from their digital investments.

AI is shepherding in this new era where industrial operations can move from situational/post-production awareness to utilizing predictive analytics to control current production conditions and anticipate and respond to upcoming changes in the operating environment. And most importantly – insights related to AI adoption come in near real-time so that the appropriate action is taken before production is impacted, and operators are empowered to continuously improve their performance. Having the benefit of a prediction that shows a process falling out of spec and having the time to make the required adjustments to prevent it contributes to less waste, fewer defects, consistent quality, and reduced costs.

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Unfortunately, AI isn’t a black box that can magically conjure up an unexplainable solution out of thin air, but instead combines math and statistics with theory and human domain expertise to refine the model before the true value of AI can be achieved.

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However, it should be considered as a powerful and proven tool for industrial operators as they look to address their operational challenges as we ring in a new ‘normal’ year.