Yield analysis and management is a complicated topic that most wouldn’t consider a potential source of competitive advantage. But there is some compelling research that suggests that a robust, evidence-based approach to analyzing and optimizing yield can have an enormous impact on a company’s bottom line.
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There is some discussion of regression analysis and modeling. Some familiarity with those concepts is helpful but not required.
Every company performs yield analysis to some degree, examining where unnecessary waste happens between raw materials and finished goods and making efforts to reduce that waste. Some companies have a more formal approach than others in seeking out these incremental improvements, but most act on the assumption that if improvements occur on a regular basis, management is doing a good job.
The logical flaw in that assumption, and the danger of using your own past performance as your only benchmark, is that it’s possible to improve frequently and still not be very good.
While it’s true that any yield improvement is desirable, management needs more information to know if they’re doing a good job. Specifically, they need to know what the best possible yield is and how close they are to it. Then they’ll have an objective standard to compare themselves to, rather than comparing themselves to their own past performance which may or may not be a good standard.
With so many steps and variables involved in transforming raw ingredients into finished products, it’s very hard to know the maximum possible yield. When you consider that some waste is necessary for the transformation process and is therefore wanted, and that some processing steps (e.g. rinsing) can add rather than subtract weight, it’s clear that simply comparing the weight of the raw material to the weight of the finished goods will not give you a good idea of your yield efficiency and where your best opportunities for improvement are. You need a more sophisticated approach.
The solution to the problem of quantifying maximum yield and getting the associated benefits is solved in the research of Derk Somsen and Anthony Capelle. Their research proves that, when food companies know the theoretical maximum possible yield, they can improve profits significantly by taking more systematic and effective steps to reduce unwanted waste.
Their approach uses a new concept they call the yield index. The yield index is a ratio.
YIELD INDEX = ACTUAL YIELD / HIGHEST POSSIBLE YIELD
A yield index of 1.00 means the actual yield and the highest possible yield are the same; there is no unwanted waste. A yield index of 0.92 would indicate 92% raw material efficiency — a little room for improvement. A yield index of 0.50 would tell you half of your lost mass is unnecessary and preventable.
Somsen and Capelle developed a 10-step process for determining a company’s yield index and building a program of continual improvement around it. It works because it’s rooted in the scientific method and objective standards of performance rather than just historical ones.
Regression analysis helps you find the formula that best describes your data points, which means you can use historical information to make predictions about the future. It will, for example, tell you precisely what the relationship is between the number of eyes on your potatoes and production yield. With that information, you can build a spreadsheet model that uses your raw material parameters as inputs and gives you the best possible yield as its output, making it possible to calculate your yield index. Ideally, those parameters will be recorded when the raw materials are received. For help with this, Somsen and Capelle suggest Applied Linear Statistical Models by Neter, Kutner, Nachtsheim and Wasserman.
Take special note: yields greater than 1 are possible in practice and often indicate inefficient dissection when dealing with protein. Look to see if the yield index of less valuable parts greatly exceeds the yield index for more valuable parts. If it does, dissection is probably not cutoff efficient – too much yield is being attached to the less valuable parts. This is bad for profitability, so treat very high yield indexes with suspicion.
This is an involved process that can’t be entered into lightly. Somsen and Capelle estimate that following their process in a typical production facility will take 1.5 full-time employees about 1.5 years to complete, so the investment in time and money is significant. But their work proves the value of the effort. The French fry production facility used as the test subject in their research saw their yield index improve from 0.77 to 0.90 over a ten year period, with the entire improvement going directly to the bottom line.
With the right commitment, skills and investment, you can approach yield analysis and optimization in a way your competitors likely aren’t, improving your margins and profitability and turning your new mastery of production yield into a sustainable competitive advantage. There are few parts of your business where so much stands to be gained with so little risk.
Production Yield Analysis in Food Processing (2004) by Prof. A. Capelle and Prof. D Somsen
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