Big Data in Food Plants: Short-term and Long-term Investments that Yield ROI

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Big Data in Food Plants: Short-term and Long-term Investments that Yield ROI

Today’s big data tools and technology can create significant cost savings in modern food and beverage plants — and the return on investment (ROI) can come in the form of reducing losses or improving production.

Every company has a different outlook when it comes to capital spending, though, with some focused on short-term investing and others taking a long-term approach. Let’s look at some short-term and long-term options when it comes to big data and analytics tools.

Short-term capital improvements

In general, short-term capital improvements are realized through fixing or optimizing existing processes. If you can improve overall equipment effectiveness (OEE) on a production line, that produces an immediate return on investment. Oftentimes, low-hanging-fruit opportunities on a decent production line can yield a 5-10% boost in OEE.

Sometimes improving supporting systems such as compressed air, steam, refrigeration, water, lighting or waste management can offer the best near-term ROI. One example would be upgrading an old water-cooled process to glycol or ammonia in order to improve thermal efficiency and reduce energy usage. Stellar offers “smart” refrigeration systems and controls that allow compressors and evaporators to modulate to the demand.


With IIoT sensors monitoring energy consumption (electricity, natural gas, compressed air) throughout a facility, you can pinpoint areas of waste and make critical decisions that can reduce waste or increase efficiency. For example, compressed air is used throughout a facility for various processes and all of those use points pose a potential for leaks. By strategically monitoring the cubic feet per minute (CFM) through the branch lines or even individual drops, and correlating the time this air is being used when the process is down, you can locate leaks.

Another source of ROI could come from reducing material usage variances (MUVs), which amounts to process waste. This involves analyzing where product is being lost and discovering how it can be recovered. Big data tools can help analyze the yield of each step in a complex process to identify the best savings potentials.

Long-term capital improvements

In general, long-term investments usually have a better potential for future capital gains, but they typically require higher upfront capital and more time to implement. For example, these investments might include an expansion of a plant or acquiring new brands or facilities altogether.

However, there are other long-term investments when it comes to automation and big data tools. For example, it may seem difficult to justify investing in a manufacturing execution system (MES) or SCADA system for a facility without this technology, but the production data they provide can help identify major areas for improvement. This can lead to a small (or large) OEE gain throughout the facility on all processes.

A processor must answer the question: “What does 1% of OEE cost us annually?” Every percentage point you gain from optimizing processes is a return on that investment.

Leveraging big data technology, like a SCADA system, can also yield indirect ROI in that it can help identify where additional capital spending should be focused. Incorporating big data and IIoT sensors into a predictive maintenance system can mean less unscheduled downtime and lower maintenance costs.

Take pumps, for instance. A process could have upwards of 100 pumps, and it’s difficult to keep them all running at peak performance with traditional maintenance. But some companies, such as Grundfos, now have “smart” pumps with connected sensors that monitor factors like temperature, vibration and cavitation. With that data and OEM algorithms, you can predict if one of your many pumps is about to have a failure so you can schedule the required downtime.

Perhaps the most obvious capital projects with a return on investment are ones that eliminate human jobs, however these require real validation. What looks ideal on paper may actually be a flawed picture of reality when you factor in downstream effects such as maintenance costs, changes in downtime, production losses through startup, etc.

In some cases, a project like roboticizing a certain process may take longer to yield ROI than expected. That shouldn’t necessarily deter a company from making that investment, but it’s a consideration when assessing the allocation of resources and the timeline for that return.

In the end, investing in automation will improve pretty much any process over time and help a processor meet market demand and grow.

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