For food and beverage companies, data already sitting inside your machines can help you improve operations and meet ever-changing industry demands.
By Cory Garlick, CPG, regional industry manager, Rockwell Automation
When it comes to food and beverage options, the choices are numerous. Do you want organic, locally sourced, craft made, single serving, gluten free? These endless choices make consumers happy, but producers frustrated.
In addition to meeting the various consumer demands, food manufacturers also face production, supply chain and resource challenges. The rising cost of raw material, and the increasing cost of maintaining aging infrastructure, diminishes profitability. And, as more changeovers are needed to keep up with the constant change in consumer tastes, food and beverage companies also struggle with inefficiencies.
So, what’s the best way to address these competing challenges? By using your own data. Here’s how data used by machine learning and predictive analytics can help you make informed decisions to increase efficiency.
Use Your Machine Data – Wisely
The great news is the machine data you need already exists. While new equipment can be designed “smart” to deliver data, you can also get data from older equipment. It’s available through technology implemented over the past decades, including sensors, components, programmable logic controllers (PLCs), drives, historians, databases, human-machine interfaces (HMIs), and more.
But here’s the reality. Most facilities are more than 20 years old, made up of individual cells or lines acquired over time. While under the same roof, the lack of connectivity between disparate islands of technology makes it nearly impossible to assess the overall productivity story. Instead of machine learning, operators rely on personal experience to make decisions — an increasingly dangerous formula as more and more workers retire.
Having the data isn’t enough, of course. Accessing it takes the right infrastructure, and using it to drive improvement takes advanced technology like predictive analytics. Of the companies accessing data, only 25% are using it for proactive purposes. This is equivalent to driving a car based on what you see in the rearview mirror.
Moving from where you are today to an integrated, data-driven operation will not happen overnight. Many companies have teams exploring Industry 4.0, smart manufacturing and other factory-of-the-future concepts, looking for ways to apply technology for greater productivity.
Transform Your Infrastructure
Successful food and beverage producers are identifying use cases, conducting pilots, implementing technology on a single cell, process or line to prove out the return on investment (ROI), and then determining how to scale across the operation and enterprise. And they’re seeing significant results.
For instance, Quebec, Canada-based Agropur Dairy Cooperative started with one of its milk processing plants, looking to create new access to disparate machine data, so facility operators could make informed decisions. With advanced technology, they not only eliminated 2,500 hours of manual data collection each year, but quickly saw a 30% reduction in lube consumption and a 25% efficiency gain.
Similarly, Chicago-based Kraft Heinz started small to prove out the concept on a line at its Ore-Ida plant in Ontario, Oregon. This decades-old potato processing line had aging controls and relied on the knowledge of operators to optimize settings and troubleshoot. Through the use of technology upgrades that included predictive analytics, they realized a 10% capacity increase on the pilot line and an ROI in under 12 months.
More importantly, the data to make this improvement was always there, it was just inaccessible nor being leveraged without the right machine learning infrastructure.
Finally, we worked with Clermont, Kentucky-based Jim Beam to increase bourbon production at one of its distilleries by one liter per minute. Again, the opportunity was always there, hidden in the data. The use of analytics allowed them to understand where they could drive production capacity in the proofing stage. They were able to drive a 60% reduction in variability that lead to this significant increase in capacity.
Data Helps You See
Avoid analysis paralysis and getting swamped by all the data. You probably already have a sense of where your inefficiencies lie. Look at what part of the process has the highest input cost, greatest impact on profitability or most recurring issues.
That’s where to start. If you can improve yield from the highest cost components into your finished products, that’s pure profitability. What needs to happen within an identified part of the process is where predictive analytics can help you focus, and where technology can drive real-time productivity.
Pilot projects can be done with minimal disruption to operations. This approach typically leads to a rapid and positive ROI, creating the use case you need to scale throughout your food and beverage operations, and begin to realize the productivity improvements lying just beneath the surface.
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