Data analytics help food company spot bottlenecks and defects

Data analytics help food company spot bottlenecks and defects Berner Food and Beverage deploys predictive analytics to increase operational efficiency and manage product defects

The Project: Deploy predictive analytics to increase operational efficiency and manage product defects at a contract manufacturer of private-label and store-brand food and beverages.

The Business Case: For Berner Food and Beverage, controlling costs while maintaining tight control over inventory is key, because 90 percent of its products are made to order. The company needed a low-cost data analytics tool to help it improve plant utilization, forecast demand and address problems. “We were looking for predictive analytics to help us get a grasp on how to be more efficient and improve product quality,” says Berner CIO Troy Grove.

First Steps: For years, Microsoft Access and Excel had been Berner’s data reporting tools. Grove wanted a system that could extract and analyze structured and unstructured data from various sources, including the company’s ERP and CRM systems, but he held off on the investment until he found a cost-effective approach. In 2014, Berner’s ERP vendor, Aptean, added data connections between its ERP and factory systems, paving the way for Grove to implement an analytics layer from QlikView on top of those systems. Grove started by feeding financial data into the system to replace daily operational reports, such as days outstanding for accounts receivable, and do some financial forecasting. “The technology itself wasn’t an issue; data accuracy was,” he says. “We had to make sure our daily transactions were being recorded accurately.”

Once managers were comfortable with the data and the analytics tool’s predictive capabilities, the company began applying them to other operational issues. For example, when a customer has a complaint about a product–say, a can of aerosol cheese won’t spray–managers can use Aptean to see where on the factory floor the specific product was made. Factory personnel and distributors are alerted to possible problems with products, and the analysis is fed in to a monthly report detailing the troubles so process improvements can be made. “Before, it was a time-consuming, manual process,” Grove says. “Now we can combine post-fail data with operational data to tell us if and where we have a problem. Once we get a complaint in, we can figure that out within an hour.”

Advanced analytics have also enabled Berner to predict customer demand with 95 percent accuracy. “Before, we were lucky if we were in the 80 percent range,” says Grove. “Being able to forecast that is a big plus. That frees up millions of dollars in inventory that we don’t need to have, helping with cash flow.”

What They Discovered: Aptean “takes us through every point of our operation that gives us data and helps us move things along,” says Grove. “We can continue to slice and dice to see where we can make improvements.” The intelligence has enabled Berner to uncover operational weaknesses that previously would have taken weeks to find. “That’s way too long after the fact,” says Grove. For example, the company used analytics to uncover the source of a bottleneck that was slowing down production to less than 200 pieces per minute–an unacceptable rate–so they added more equipment for filling containers with food and added more cookers. The continuous improvement helps Berner get its products to stores faster, improving freshness and shelf life. And company leaders are glad they waited for a tool within their budget. “We’ve been able to enter the modern era of analytics and reporting without spending hundreds of thousands of dollars to do it,” says Grove.