For those advisors who are fee-only or fee-based (or those with ongoing, asset-based revenue), a decline in the markets can translate into a reduction in gross revenue for their firms. 2008 is proving to be a challenging year in this respect. With stock market declines, mortgage woes and higher fuel and food prices, advisors and clients alike are likely to feel the pinch. The question is, What can be done to stabilize net profitability in such an environment? The answer may lie in how a financial practice accounts for its productivity and expenses.

A financial advisor can embrace a number of different cost-control disciplines, from quality control studies to Six Sigma practices. The latter are a set of practices designed to systematically improve processes by eliminating (or at least blunting the negative effects of) non-conforming products or service offerings. The basic methodology financial advisors would use include these steps:

1. Define the process improvement goals that are consistent with client needs and firm strategy;

2. Measure the current process and collect relevant data for future comparison;

3. Analyze to verify the relationship between or the causality of factors. Determine what the relationship is and attempt to ensure that all factors have been considered;

4. Improve the process after the analysis using techniques such as work-flow study comparisons (comparing the same process performed by different people in your firm); and

5. Control to ensure that any variances are corrected before they dampen profit. Set up and run oversight procedures and measure control mechanisms to ensure consistency.

The specific techniques of Six Sigma go way beyond these steps, but the method still shows the systematic approach you need to address process management in your firm.  The term sigma is derived from the statistical function of standard deviation. In a cost-control study, it typically refers to the number of standard deviations between the average time (for example) to complete a particular process and the nearest process specification limit.

To illustrate, let us say management assumed that it would take three hours for an employee of a firm to complete the data inputs for a financial plan. But then, at one firm, with several employees responsible for such a task, it becomes apparent that most of the plans are taking about four hours to input. Figure 1 shows what the study of these processes would look like in a statistical bell curve.

This chart reveals that most of the time, the data inputs seem to be accomplished in about four hours. However, there is one employee who manages to get the job done in two and another who takes nearly seven. Certainly this would be valuable information to the owner of the firm, as it could uncover a highly efficient employee (or else one who is cutting too many corners) and it could also uncover a training opportunity for that employee who is identified as taking too long to get the job done.

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