Disciplined Benchmarking

Knowing the common mistakes may help you avoid them.

By John Deen and Sukumaran S Anil

In the literature, benchmarking is viewed as a valuable but often abused business practice. It also takes more work and discipline than we sometimes plan to invest. To review the opportunities and threats of benchmarking, we have enlisted the guidance of the author Anne Evans, whose article, Avoid These Ten Benchmarking Mistakes, can be found at www. swine.farms.com

These 10 mistakes are common across different industries, and it is good to apply these guidelines to our exercises as well.

Mistake #1:

Confusing benchmarking with participating in a survey
Surveys have many different biases. The most common survey deficiency is that it is not based upon common calculated methodologies and, in many cases, is based upon a biased recollection of past events. That bias is often compromised by commonly held expectations in the industry. In this issue of Benchmark, we have used actual PigCHAMP production records and not a survey to provide estimates for benchmarking.

Mistake #2:

Thinking there are pre-existing "benchmarks" to be found
Off-the-shelf benchmarks are often historic in nature and may not reflect technological trends. Benchmarks must consistently be updated and contextualized within their areas.

Mistake #3:

Forgetting about service delivery and customer satisfaction
Not all production and economic parameters can be benchmarked. Some qualities - particularly those of concern to customers of the product - need to be considered in benchmarking also. Though not always true, a common syndrome is that benchmarks are overemphasized to the detriment of other issues. Many in business circumstances speak of a "balanced scorecard," to allow monitoring of all concerns of the firm.

Mistake #4:

The process is too large and complex to be manageable
The summary numbers of the system are often the "most attractive" to benchmark. Reporting and providing comparison is simple, but this does not allow one to get down to the basics of the opportunities in the production system. Breaking down benchmarks to the level of tasks or management areas is very useful.

Mistake #5:

Confusing benchmarking with research
Benchmarking looks at the process that is already in place. Innovation in any area, including the utility of genetics, nutrition, or treatment protocols should be assessed through a more formal method than benchmarking.

Mistake #6:

Misalignment occurs when the benchmarking focus is something that is not within the overall strategy of the unit. This is less common in pig production, as returns are a function of some common resources. However, consider that maximization of output may not always be needed. We have recently seen a great deal more emphasis on the quality of the pigs produced rather than the absolute number of pigs.

Mistake #7:

Picking a topic that is too intangible and difficult to measure
Many concerns, such as customer satisfaction, can just be too difficult to measure for comparative purposes. The records reported here are based on tangible outcomes that can be repeatedly measured.

Mistake #8:

Not establishing the baseline
The baseline production measures in your own herd also need to be defined correctly. The use of common record keeping rules and formulas need to be the basis for comparison. When rules and formulae differ, those differences must be thoroughly understood and then compensated in the comparisons.

Mistake #9:

Not researching benchmarking partners thoroughly
There is a general rule in benchmarking that you should not ask a question that can be answered through other methods. Benchmarking should be used in concert with other information on the characteristics of the industry. In addition, the context of these benchmarks should be understood. Times of financial stress may result in differences in performance and optimal activities.

Mistake #10:

Not having a code of ethics and contract agreed upon with partners
Direct arrangements with partners can often be limited by different expectations. A common agency for collection and interpretation is often useful. 

Such a list as this highlights the opportunities and constraints of benchmarking reproductive output on North American swine farms. The farms represented in the summaries on the following pages cooperate in the PigCHAMP Benchmarking program to define further opportunities for themselves and the industry. We hope the information created is of use to you.

Editor’s Note:John Deen, DVM PhD, is an Associate Professor at the University of Minnesota, and Sukumaran S Anil, DVM PhD, is a Research Associate at the University of Minnesota. To contact them, e-mail: deenx003@umn.edu or sukum001@umn.edu