Complex process innovation projects require a far more complex set of internal metrics that are applied and evaluated at frequent intervals to ensure that projects remain on track.
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There's no goal line on the AstroTurf at the end of your project to improve your business. No Super Bowl rings, no gold medals. So how do you know if the results were worthwhile? And more important, how do you prove it to your boss?
Establishing a methodology to measure success is one of the most important steps in any business improvement process. Without agreement in advance as to what the results of the process should be, managers who attempt significant change may find:
o The project succeeds, but you don't receive appropriate credit because other executives claim that the improvement would have occurred without your action or that their own departments are responsible for the improvement.
o You believe the project failed--because it failed to meet one of your goals, like reducing operating budgets--but your staff, using measurements like employee satisfaction or fewer customer complaints, believes it succeeded. The reverse--employees see the project as a failure while managers see it as a success--is probably less common, but not infrequent.
o The project becomes derailed or veers off in a different direction without achieving the initial objectives. Project spin-offs are not necessarily a bad thing. But determining precise project metrics in advance--especially those that will measure bottom-line impact--can make it far easier to determine if a spin-off should take precedence over the original project or should wait until initial results are achieved as planned.
Managers already know how to use major business metrics like personnel numbers and costs, size of departmental budgets, number of internal users served, and number and accuracy of customer bills processed.
Complex process innovation projects, however, require a far more complex set of internal metrics that are applied and evaluated at frequent intervals to ensure that projects remain on track. While any set of metrics must be tailored to specific situations, it is frequently helpful to draw from sets of business-process metrics used in other companies or established in business literature.
Metrics used in the Six Sigma process are good examples. Six Sigma is designed to improve quality in the processes that contribute to a final product or service so that there is little or no need to measure the quality of the ultimate outcome. Using "sigma" in the statistical sense--a measure of deviation from perfection--Six Sigma begins with the understanding that traditional customer-attitude measurements--rankings on a 10-point scale, for instance--are an artificial construct on the way customers actually experience a transaction that has gone wrong. In other words, when customers are dissatisfied, they do not experience this dissatisfaction as a comparison with other companies (by thinking something like "I guess this is about the level of accuracy I expect from most companies"). Instead, customers experience the dissatisfaction as a deviation from their expectations (by thinking, "This is the third time in four months that my bill has been wrong, and I'm really mad.")
Six Sigma was developed in the 1980s at Motorola and has since been used at such major technology-oriented companies as GE and Allied Signal. It helps organizations focus on what the user or customer sees and feels and how those experiences differ from "perfection." Those participating in the change/innovation project then focus on what it is possible for current processes to deliver and how those processes can be changed to get closer on a consistent and predictable basis to what the user/customer wants.
Measurements like those used in Six Sigma are important because ultimately, quality reduces costs, largely because companies save the money that would otherwise go into answering questions, reexamining accounts, identifying and correcting errors, and processing refunds. Additionally, processes that result in faster collections mean higher levels of interest income added to bottom lines.
About the author:
Joanne Kelley is managing director of TransFormance Group, a consulting division of SPL WorldGroup. Kelley has more than 20 years of global experience with energy companies, consulting in benchmarking, outsourcing, sales force automation, due diligence, product and service feasibility, and organizational and business alignment resulting from deregulation.
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