A Case for Understanding Standard Deviation
Plant managers, production supervisors, and engineers in the food processing industry know that profitability depends on efficient operations. In order to be profitable, organizations must understand their standard deviation — but that’s only the beginning. The key then is in taking control of it – with process control and in-line analysis.
Standard deviation is the costly variance between your targeted setpoint and actual performance. You may be able to gain instant efficiency by asking yourself a critical question. Do you know what the standard deviation is from your target setpoint for each product on your production line? If you don’t, you could be missing out on product quality, profitability, or both. You are not alone if you don’t spend your time thinking about this. Yet, standard deviation isn’t something you can ignore any longer. With every point your products deviate, profit is leaking from your organization. Even a fraction of a point could add up to significant costs in your operation. Whether you produce solids, liquids, slurries, or powders, process control and in-line analysis will help you get your setpoints back under control and avoid costly deviations. With a focus on monitoring your process and then controlling that process you will improve both your profitability and customer satisfaction.
The importance of knowing your standard deviation
All production processes have targeted setpoints for each of the critical product components, generally referred to as product “constituents.” These targets might be set by the customer’s own specifications or by required legal limits. In either case, variations from the target setpoint can be costly. For instance, let’s presume that you are in a butter production process that requires an 80% butterfat value into the product and the target setpoint is 80%. Using in-line analysis, you will monitor what is actually happening in the process and within seconds see that the butterfat content is ranging from 78% on the low side up to 82% on the high side.
- When your production run deviates to the high side of your target you are giving product away unnecessarily, resulting in a higher raw material cost and a loss of profit.
- When it deviates to the low side – in this example down to 78% - you may have customer complaints due to quality or, in some products, you may not be meeting legal specifications. This may require additional rework or the potential for costly discounts to help sell an inferior product.
You may look at this graphic and think that the average of the four point fluctuation in performance isn’t too far off the target. The problem with this thought is that “average” isn’t what you actually produce for the customer – you produce the actual highs and lows – either of which comes with a cost. That is why it is more accurate to consider your standard deviation. How far away from the targeted setpoint (+/-) is the actual? That difference is your standard deviation. A number you should know, but may not.
How to monitor standard deviation
In graph 2, we are looking at a processed cheese production plant that legally requires no more than 39% moisture into the product, so the target setpoint is 38.8%. Again, using in-line analysis, you will monitor what is actually happening in the process and within seconds see that the product moisture content is ranging from 38% on the low side up to 39% on the high side. Contrary to the first example, where both highs and low variances are concerning, in this example where we are producing a product that has a significant amount of a low-cost raw material, such as water, the lows are as concerning as the highs. Why? Because when you run low you are missing the opportunity to produce the product with more of a low-cost component while still commanding a higher price for it, thus losing out on a higher profit margin.
- When your production run deviates to the high side of your target you are adding more moisture than desired. The raw material expense is not the concern in this example because moisture (water added) is a relatively low cost constituent. What is of concern is that too much moisture may cause product quality issues. And, when running too high you may risk exceeding the legal limit for processed cheese production which is 39%.
- When the process deviates to the low side with content such as water you may not be adding enough moisture to your product. This can cause two issues. One is a quality issue – your product may not be the right consistency without the proper moisture levels which may result in quality, flavor, and texture issues.
- The second issue is a lost profit opportunity. If you had the opportunity to add more moisture – a low cost component of your product – you could garner the same price per weight as if you used a more expensive constituent. Adding a less costly component such as water at your target setpoint yields a higher profit without sacrificing quality.
How to control standard deviation
So now that you know what to look for in standard deviation, what do you do to control it? One option is in-line analysis, which uses near infrared transmission technology to constantly test and monitor your product and its constituents for deviation from your target setpoint. Near Infrared transmission technology (NIR) uses relative light absorption to quantitatively predict a constituent’s content.
Using NIR technology, reports may show that the actual content is running higher than your setpoint; if so, you are able to put controls into the process which can ultimately lower that standard deviation - or increase it, if it is running too low. Lowering the highs and the raising the lows will result in a much narrower production run – producing tighter control on raw material cost, labor and rework, and ultimately on profitability. Keep in mind that even with this technology you will continue to see fluctuations from your target setpoints and averages. However, you will see marked improvement —a narrowing of the range — when you focus on your standard deviation.
In-line analysis technology can report back setpoint deviations as quickly as within a few seconds and can flex to meet your operational requirements – whether you need the reporting done in-line, at-line, or in a lab environment. Such an approach produces quality enhancements, costs savings, and more profit driven to the bottom line. With standard deviation data in hand, you can make immediate changes to product components or constituents. Some in-line analysis systems also display the data on a control panel or human machine interface (HMI) strategically located at various points throughout your operation. Others operate in a wireless environment where your control panel can be as mobile as you are.
When you consider the standard deviation of your process, take into account the amount of product you may be giving away or the product inconsistency you may be experiencing because the deviation range is too high and out of your control in today’s operation. Your company could be paying a heavy price in raw material, labor and rework waste and your customers may be experiencing inconsistent product quality. Getting control of the deviation will enable you to instantly show an ROI, in potentially as few as 1.4 months, while also improving customer satisfaction.
Standard deviation yields quick ROI
Once you understand your standard deviation, and what it does to your operation, it quickly becomes obvious that using in-line analysis to control it will yield a considerable return on investment. Let’s look at the case of a plant manager whose goal was to increase the moisture percentage by .30% thus increasing the yield. The plant was currently running at 38.5% moisture with a setpoint target of 38.8%. Using in-line analysis the plant was able to conduct constituent value testing much faster than with a traditional production process, resulting in the ability to change moisture content quickly and accurately. They increased their moisture by .30% to 38.80 resulting in a higher yield per batch without sacrificing quality and without any additional expense. To calculate their return on investment, the plant considered the following:
Volume x Frequency x Standard Deviation Improvement x Price of Constituent (or product) = Savings or Profit Gained
30,000 lbs per batch x 20 batches per day x .0030 (.30) improvement x $1.80 per lb. = $3240.00 daily savings or profit
$3240.00 x 6 days per week run x 50 weeks per year run = $972,000 annual savings or profit
The plant calculated the increased profitability resulting from a higher yield per batch at $3,240 per day or $972,000 per year. The increase in moisture content by just .30% resulted in a payback on the initial solution investment in only 1.4 months. So obvious was the success that the plant easily justified and implemented the in-line analysis solution at two additional plants almost immediately.
As this case illustrates, the key to increasing profitability begins with understanding your standard deviation. Improving it and managing it requires a smart investment in process control and in-line analysis.
For more information about standard deviation, contact Greg Sommer at firstname.lastname@example.org.
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