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Seeing the light in actionable data collection

Taking action on data can deliver big returns for food and beverage processors.

Pfw 3845 Blue Eye

The early results are in: Investing in data capture and analytics can lead to big payoffs for food and beverage manufacturers who take action on the information they collect. Sample results tell the story, from a global processor that achieved sustained OEE ratings above 85 percent to a baking company that leveraged data to increase production by 5 percent and reduce cycle time by more than 20 percent. 

Processors learning their way to better running efficiency, product quality and productivity call it a journey that often requires overcoming both behavioral and physical hurdles. 

Yogurt maker Chobani began the process five years ago, when Hugh Roddy, VP of global engineering and project management, joined the company. As a native of Ireland with extensive experience in the European approach to manufacturing engineering, the idea of using data to continuously improve processes came naturally. 

“People want instantaneous data at their fingertips. We have projects and changes that are complete in six months that take other companies a year or more to do,” says Roddy. “Collecting data is useless unless you act on it; you have to collect it, display it and then be able to analyze it. Our engineering, operations and continuous improvement teams work in tandem, using the data we collect to learn, change and get better.” 

According to Roddy, not every change is perfect, “but we stay focused on finding out where we still need to improve, making investments based on the potential income to be gained. We started at the packaging/filling level, so we’ve probably touched on only 5 percent of what we can do.” 

Among the most important metrics for Chobani are KPI improvements, OEE monitoring and targeting goals, as well as throughput measures and input materials to output yields. “We employed all the tools from Rockwell Automation that we have embedded, but without using the Ignition platform from Inductive Automation, which allows unlimited clients without paying additional licensing fees, collecting all this data would be prohibitively expensive. We’ve also utilized mobile clients extensively to drive the data to the plant floor,” Roddy adds.

“We’re most interested in driving change from the plant floor and empowering our people,” he says. “Our systems allow operators to record what’s happened on the line and take real-time action to correct problems. At the same time, someone has to be accountable for taking action, so you need a team focused on change.”

Roddy can look at data from all of the company’s plants from his smartphone. “We noticed, for example, that the second and third shifts at our Australian plant were routinely producing less than the first shift. It turns out that operators on those shifts sometimes weren’t at their stations when actions were required. It’s one of the reasons we’ve put live dashboards in front of operators at all our plants. It makes people accountable and motivates friendly competition to achieve goals. As a result, we’ve seen huge changes in operator performance, productivity and efficiency.”

Walk before you run

It is common practice for companies to begin extensive data collection and analysis with one process or at one plant, and then extend their efforts to other areas as they gain confidence and see the benefits.

For Campbell’s Soup Company, the process began at its Pepperidge Farm facilities. 

 “While data gathering helped us with the initial formation of our strategies for what data to collect and how to collect it, at the beginning the effort was very disparate and paper-based, as well as very tactically driven,” explains Mike Hughes, senior director of global supply chain process excellence. “We often collected the same data multiple times for different projects.” 

The company has been measuring line performance, OEE, downtime, yield and other factors for the past 10 years, but Jim Prunesti, VP of global engineering, says digitizing the information has made it much more usable for decision-making at all levels. “We act on the data by using it for daily direction-setting and shift overlap meetings, as well as rolling it up to the broader leadership. Summarizing our findings across plants has been very helpful, since many of them deal with common issues. We’ve already seen at least a 10 percentage point improvement in line performance.”

Prunesti cautions that, despite these benefits, “the reality is that implementing this kind of program is a significant challenge. There’s no solution out of the box, especially when you’re dealing with both new and legacy equipment. You have to find creative tools to achieve your goals and it takes years to implement,
especially when you’re rolling it out over 20-plus plants worldwide.”

Hughes says data collection needs to start at the lowest level in the plant. “If you don’t have data detail, you can’t make changes. We start with micro data sets and then roll it up.” But more data often creates more problems to solve. “We’ve had to retool our database architecture on the reporting side to support data analysis and make our findings useful,” he states.

“We’re trying to understand our process from the back-end to the front-end and identify where the opportunities lie,” states Hughes, “but we’re already seeing this as an investment with real financial returns for the company. More workers are also engaged and embracing data because it’s a lot easier for them to see it digitally. Decision-making has gotten easier as well, because now it’s based on facts rather than emotions.”

A platform to integrate

One of the advantages of digital data collection over manual, paper-based efforts is that it can help identify and calculate minor process disruptions that are normally overlooked. 

“Micro-stoppages, which typically last five minutes or less, are a major source of downtime,” says Reid Paquin, industry analyst for food and beverage manufacturing and consumer product goods for GE Digital. “It’s too difficult to record the data on this manually, but these hidden losses contribute to a significant amount of lost production.”

Paquin says that only a few companies, typically larger ones, are using advanced analysis of their collected data to drive change. “Approximately 80-90 percent are still focused on real-time data collection. Many companies are still collecting data manually with spreadsheets, often in silos and for a single purpose.

“You need a platform to integrate and automate data collection and analysis,” he explains, while admitting that it can be a massive task. “One of our customers has over 5,000 manufacturing applications in its production systems.”

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