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07/12/2021

Can AI Create Smarter CG Manufacturing Production Teams?

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Manufacturing or production line machine operators are the heart of most consumer goods manufacturing operations, handling everything from equipment operations to monitoring, troubleshooting and performing preventive maintenance. They help ensure that the goods that are produced meet the expectations of the brand by keeping the production facility humming along at the proper pace, and with high quality outputs.

Often, manufacturing production volume and efficiency come down to how well operators perform their jobs. But not all employees perform at the same level due to different types of skills, experience, training or other factors. Identifying high performers who can act as models for other employees in similar roles can significantly improve overall production performance, ROI, output and even employee job satisfaction.

The challenge most CG brands face is how to measure individual performance. With hundreds or thousands of employees spread across multiple facilities, gathering, and analyzing data on performance in order to make operational improvements can be nearly impossible. Even in organizations that gather some data, the insights are usually outdated by the time all the data is analyzed.

Smarter Data Leads to Smarter Employees

The more data an organization collects, the more difficult it can be to identify the most critical data points needed to help solve a particular problem, like uncovering opportunities for operator training. But new tools exist that help collect and analyze data very quickly, without the need for complicated or expensive software, long implementation times or data specialists, like data scientists.  

The idea that we can automatically analyze and make changes to operations based on data behavior is called automated business analysis (ABA). It’s a strategy of using ongoing curated data analysis that elevates and proactively reports on unexpected changes in data, or data points that fall outside the set KPIs. This is critical for CG brands that want to get ahead of changes in consumer behavior, competitive tactics, and other business trends. But it’s also increasingly valuable for identifying efficient (or inefficient) behaviors among employees working the production floor.

With ABA, it’s possible to show the relationship between machine operators, rank them based on how they compare against others and determine which employees are producing consistently high-quality results. The key is using ABA to aggregate data and leverage artificial intelligence to identify performance data trends quickly and easily. These trends can then be tied to individual employees, allowing an organization to identify high performers and build programs around training and best practices for other employees.

Using Performance Data to Create Efficiencies

For example, one global CG food and dessert brand has hundreds of machine operators responsible for production roles in areas like mixing, frying and wrapping, using equipment that may vary per facility. Not only are physical tasks required, but operators must also have strong organizational skills, be able to accurately read and follow formulations, calculate production times, monitor multiple machines concurrently, adjust formulations and more. While many of the employees are experienced, they all perform slightly differently.

The company was aggregating data from equipment and processes on the production floor. By integrating this process with an ABA platform, production managers were able to quickly see the day-over-day performance metrics across individual operators. Immediately, they could identify which operators were generating higher levels of daily tasks. This insight was valuable because it showed how operator productivity changed, relative to other operators, and which operators or shifts were more productive and efficient based on their assigned tasks. 

With this information, the brand was able to identify the high performers, reward them for their behavior and develop best practices for other operators to create greater efficiency across all tasks.

This, in turn, led to a 4% efficiency gain in just the first week of using an ABA platform. Without the ability to analyze performance data, the brand would have continued to operate with varying degrees of performance and inefficiencies, which eat into revenue and profit. Now, high performing employees can be recognized and rewarded, leading to greater job satisfaction. And training can be adjusted to elevate other employees to the same performance standards.

By looking at daily ranking reports, managers can work with employees to maintain consistently high performance, and quickly adjust if data behaviors indicate drops in performance.

With an ABA platform in place, CG production managers can better understand key trends, see, and act on unexpected changes in predetermined performance KPIs across the whole production process. This might lead to changes in staffing, new training, or adjustments to processes. And, just as AI can be applied to employee performance, it can be applied to machine performance.

ABA can serve as the connection across fragmented manufacturing, production, and supply chain data sets to deliver a complete view of disruptions or improvements. By uncovering and addressing these issues quickly, CG brands can boost yield and improve both human and machine efficiency across the entire supply chain.

Sean Byrnes is cofounder and CEO of Outlier.

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