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Like it did many industries, the pandemic transformed industrial operations. Skyrocketing demand in some sectors affected others, creating wild swings in labor and raw material sourcing. Workers and businesses learned—on the fly—to adjust to new conditions. In addition to increasing throughput to meet the growing demand, worker safety took a more prominent seat at the table. Companies sought to protect their workers’ health while keeping their businesses running. The solution? Advanced technology. Advanced technology including robotics and data science led the movement to automate manufacturing during a time of uncertainty and dire need. Robots performed the work consistently and quickly, while AI helped improve the process speed and efficiency passively, without action by a human. This blog explores the two leading technologies making it all possible.
With workers getting sick or requiring physical distancing, robots—many of which require AI and advanced software—have been a go-to solution to ensure manufacturing lines continue to produce. Industrial robots can arc and spot weld, handle materials, load and unload raw materials, assemble products, pick, package, and palletize. They can also paint, glue, seal, and transfer to cutting machines. As a result, robots were implemented to handle most automated tasks while minimizing human interaction to ensure proper physical distance.
Companies are intentionally viewing AI and software as technologies that can reduce costs and improve sustainability. This shift has fueled spending on automation. As a result, robotics manufacturers are surging with the transition toward increased automation. This trend opens new markets in the future through improved process consistency.
Despite the improved efficiency, the advancement in technology leads to a shifting of roles in the facility. Companies can and should automate repetitive steps, which can make workers redundant. However, this can be mitigated by reskilling workers, as businesses will need experienced analysts and technicians to help assess data and diagnose failures. Kearney's report, “Robots vs. COVID-19: how the pandemic is accelerating automation,”1 indicates that companies should strive to balance robotics/automation and reskilling workers, as experience is always an asset when issues arise.
One example of a reskilled application is factory video. Factory video is a tool that is aiding the move toward automation. Recording manufacturing processes can capture the moment a failure occurs or highlight repeated patterns of inefficiency in a process. It is then critical that engineers and operators review and diagnose takeaways from the video feedback, implementing corrective actions in the process. Reskilling comes from converting years of experience with a continuous improvement mindset.
From leveraging videos on factory processing to machine learning, data is the currency of Industry 4.0, and analytics is the investment strategy. The power of the massive amount of data hinges on AI entirely, as AI can process and interpret the data for real-time adjustments rapidly—a stark transformation from current traditional approaches.
This movement could enable more digital executive positions and uncover substantial amounts of underutilized data sets to transform industrial operations. For example, companies generate and retain terabytes of data but cannot produce their products due to a lack of labor availability. AI solves this, immediately capitalizing on improved processes throughout the company and applying them to the limiting manufacturing step. This improvement could have a significant impact on the profitability of a business. Given this proposition, it is not surprising a majority of companies would welcome AI improvements in their automation processes.
As businesses learn of this opportunity, fast adopters will separate from slow ones. The operational efficiency improvements widen the competitive gap and create increased opportunities for early AI-adopters to win. Furthermore, as the number of companies that use AI increases, industrial processes will need to be reimagined due to the optimization procedures identified by AI that data engineers had not analyzed previously. These improvements could accelerate worker transition from assembler to operator or lead. AI will pull manufacturing to develop innovative ways to access and evaluate all the data, no longer bound by human capacity.
Robotics and AI do not signal the end of human activity in manufacturing. Instead, they represent a shift in the industry toward more automated repetitive steps and an increase of skills required to assess and correct a failure. While AI passively works on increasing efficiency, humans will still need to audit the process and improve and repair the machinery to ensure all continues as intended. Humans can record and diagnose systemic issues through factory video, and AI can increase process efficiency during operation. The intersection of AI, data science, video, and software gives manufacturing leaders the tools they need for optimal industrial automation. Upskilled workers ensure the data produced and failures encountered are addressed urgently with experience-based insight.
1. Peterson, Erik R., Terence Toland, and Gabriella Huddart. “Robots vs. COVID-19: How the Pandemic Is Accelerating Automation.” www.kearney.com. Kearney. Accessed March 23, 2022. www.kearney.com.
Adam Kimmel has nearly 20 years as a practicing engineer, R&D manager, and engineering content writer. He creates white papers, website copy, case studies, and blog posts in vertical markets including automotive, industrial/manufacturing, technology, and electronics. Adam has degrees in chemical and mechanical engineering and is the founder and principal at ASK Consulting Solutions, LLC, an engineering and technology content writing firm.
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