18 May 2021


Top 6 Data Analytics Use Cases for the Food & Beverage Industry

Here at RapidMiner, beer is important. We were founded in Germany and our office in Dortmund consists largely of researchers who work on grant projects with organizations that include the German government.

Given our German roots, it’s probably not surprising that our research team has been keen to explore ways that machine learning can support breweries. In this blog post, we’ll explore some of the most impactful machine learning use cases that have been developed to assist with complex beer creation and distribution processes, and how they can be applied to food and beverage manufacturing in general.

Six Key Use Cases for Data Science in Beverage and Food Manufacturing

The research team has discussed over 40 ways that machine learning can help brewers. In this post, we’ll focus on six initiatives that have come out of the work on a project called data-driven process optimization in the beverage industry based on machine learning (DaPro). Although these projects were developed specifically for breweries, as you will see, they address a wide range of food and beverage—and general manufacturing—challenges.

(If you prefer video to reading, you can also check out Excuse me, bartender, there’s AI in my beer, a presentation of these same projects at the DATAcated Conference 2021 by our own Martin Schmitz.)

1. Predicting truck arrivals and wait times

You might think that breweries have a system in place to schedule shipments to distributors—but you’d be wrong, at least in Germany. It’s not uncommon for a distributor to show up at a brewery without any notice and ask for a whole truckload of beer. In addition to creating a bottleneck for the brewery’s workers and creating long wait times if multiple trucks show up at the same time, these unannounced arrivals also make it difficult for the brewery to plan how much beer to have ready at any given time.

By creating a model that predicts when trucks are likely to arrive, breweries can make sure they have the right amount of beer on hand. Plus, they can give logistics companies information about the best time for trucks to arrive and even provide estimated wait times.

As you can imagine, the benefits of a system like this go far beyond breweries. Any industry that needs to make sure they have enough stock on hand—without having too much—and efficiently distribute that stock to buyers can benefit from a system like this one.

2. Energy management

The beer-making process consumes a lot of energy. For example, water has to be heated up and then cooled down again, and energy needs to be used to refrigerate the beer after it’s been produced.

The cooling processes are managed by energy-hungry compressors. These machines turn on if more “coolness” is needed, but often there’s just a small peak in demand and it isn’t necessary to turn the machines on; the temperature will stabilize before it creates a problem with the product. However, these machines aren’t smart enough to realize that they don’t need to power up.

But with a model that predicts how temperatures are going to change in the coming minutes and hours, activation of the cooling devices can be delayed or even canceled unless it’s absolutely necessary, saving on energy costs. Although the specifics of this use case are for beer brewing, the energy cost of any food and beverage manufacturing process is potentially significant. In the brewery use case, reducing energy usage by 1% saves tens of thousands of euros per year.

3. Predict and maximize filter lifetime

Beer comes in both filtered and unfiltered varieties. Filtration is popular not just because people like clear beer—it also makes the beer last longer, improving its stability for storage and shipping.

Beer is typically filtered using diatomaceous earth, a chalky substance made up of fossilized shells. However, the filtration process is prone to failure, based on a variety of factors including the various ingredients and types of beers being produced in a brewery. Because of the large number of variables that affect the filters, they sometimes clog unexpectedly. When the filter clogs, you have to stop the filtration process, clean everything, possibly replace the filter, and then start the process over again.

Because there’s an expected timing of the flow of beer through the entire brewing process, these clogs impact subsequent steps. This is especially problematic in the peak summer season when breweries need to produce significantly more beer than at other times and can’t afford unscheduled downtime. With machine learning, though, possible filtration problems can be detected early so that appropriate action can be taken before filters are clogged and subsequent steps are impacted.

Beer production—like any other complex, multi-step manufacturing process—is prone to slowdowns in certain areas that affect the rest of the process. By having an early indicator that there’s something wrong, steps can be taken to make sure things run smoothly and production goals are met.

4. Predictive maintenance

Predictive maintenance is an extremely common use case in manufacturing, and beer brewing is no exception. In the beverage industry, one of the most frequent failure points is rotating motors in bottling facilities. These machines take in dirty bottles and crates, clean them, fill the bottles with beer, and then crate them so they’re ready to go. Because the machines are always under a heavy workload, they’re particularly prone to breakdowns.

Increased friction in the machine—often an early indicator of an impending failure—can be detected by observing a slight increase in power consumption of the electric engines driving the rotating motors. Hence, a time series analysis of power consumption can be used to predict failures before they occur and allow proactive maintenance to be carried out.

If you’re curious about some of the other ways that predictive maintenance can be used in manufacturing, check out our Using Machine Learning for Predictive Maintenance blog post.

5. Faster handling of filling machine failures

Although predictive maintenance can reduce the frequency with which machines break down, they can’t completely eliminate failures—at least not when it comes to bottle-filling machines in breweries. These machines consist of seven complex sub-machines that often experience breakdowns multiple times per day. However, these failures aren’t the kind of thing that can be predicted ahead of time, so predictive maintenance can’t help.

There’s an obvious cost associated with having your filling machines out of commission, which makes it critical to quickly and correctly identify what’s wrong and repair the problem. Typically, mechanics have to manually check multiple parts of the machine until they find the issue and then fix it, which can take a significant amount of time. With machine learning tools, however, error messages can be refined with the addition of a prediction about what the problem is and where it occurred, speeding triage and repair.

In any manufacturing scenario, having machines go offline unexpectedly is going to result in delays and downstream problems. But even if machine learning can’t predict problems ahead of time, it can still provide information to help you address breakdowns quickly and effectively.

6. Malt yield prediction and optimization

Malt is one of the most important beer ingredients—and also one of the most expensive. That’s why brewers try to maximize the amount of flavor and sugar extracted from a given amount of malt. However, how much can be extracted from a given malt depends on a number of factors, including things like the region it’s from, the weather in that region, and the season it was harvested. Additionally, malts are often mixed together, with makes it even more difficult for a brewer to know what they can expect to get from a particular batch.

With the use of a machine learning model, however, brewers can predict the best mixture of malts to use, along with the ideal brewing time, in order to maximize malt yield. This reduces the brewery’s overall resource consumption and thus its costs.

If you work in any kind of manufacturing industry, you can probably relate to this example. Inconsistent inputs can make it difficult to keep your processes running smoothly. But with a machine learning model like this one, adjustments and optimizations can be made early on to support the overall workflow.

Summing Up

Machine learning and data science have a lot to offer breweries and, by extension, the entire food and beverage industry. From helping to quickly fix machinery—and even prevent breakdowns in the first place—to managing the logistics of getting your products out of factories and into customers’ hands, machine learning can optimize your workflows from beginning to end.

If you’d like to learn more about how you can assess the value of a machine learning project in manufacturing, take a look at Talking Value: Impactful Machine Learning Models for Industry 4.0. It will walk you through how to make sure you’re considering the right variables so that you can understand the impact of machine learning in financial terms.

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