Historically it has been common to implement management methods as standalone initiatives. For example, Activity-based costing (ABC) is often implemented as a financial tool for identifying sources of profitability. Budgeting is the tool for financial planning in this same company, but may not benefit from the insights derived from a parallel but separate ABC initiative. Analytics may focus on predicting future revenues, but fail to predict future profits. Efforts to cut costs using six sigma or similar techniques may be guided by measures of quality rather than cost.

I would argue that the real value of a management method is derived by integrating it with other methods where the values compound. For example, a financial services company may have a risk department that assesses the likelihood of default on loans. The finance department in the same company may have a profit analytics system (based on ABC) that assesses the profitability of individual loans and customers.  This system may show that risky customers are more profitable. But are they sufficiently more profitable to offset this extra risk? And does this profitability cover the risk over the lifetime of the loan? These profit versus risk assessments can only be made accurately if the risk and profit analytics systems are combined into a single solution.

Budgeting is another management method that benefits from integration. In isolation, budgeting often falls short of the ideal of an accurate analytic planning tool to support management decisions. For example, forecasting of revenues and expenses are often just exercises in extrapolation of the past. Developing resources plans (e.g. headcount for a critical operational process) is often a simplistic exercise in adding or subtracting a percentage of the previous year’s spending.  However, the accuracy of forecast revenues and expenses can be substantially improved using statistical forecasting. The accuracy of resource plans can be improved using predictive models combining activity-based models and analytics. Scenario analysis is also more powerful with resource models and analytics.

Six Sigma is a widely used method to improve the quality of process outputs by discovering and eliminating the causes of defects in the process and minimizing process variability. Six Sigma is used to improve quantifiable targets such as customer service, safety, and cost. In the case of cost, why not combine Six Sigma with ABC to provide these measurements to support prioritization of Six Sigma projects and action plans, and to allow tracking of the removal of cost?

Does one plus one really equal more than two? What is your experience? Can you share with us examples from your company that illustrate this? What other methods work well together? What difficulties have you encountered, or do you envision, combining methods? How did you, or how should you, overcome these difficulties?