The Power of Metrics:
Key Performance Indicators: Taming the Metrics Chaos
Previous columns discussed a top-down approach for developing key performance indicators (KPIs) that leveraged the "alignment pyramid" to translate mission/vision into strategies, objectives, critical success factors and finally KPIs. That approach results in identifying those "vital few" KPIs that reflect a comprehensive business perspective and ensure that all employees are operating from the same enterprise scorecard. However, in today's realistic, dynamic and competitive environment, senior management often mandates that KPIs be developed "yesterday" by leveraging existing metrics shipped with the performance management, BI and ERP dashboard/application packages. The challenge is considerable because most of the applications ship with 1,000+ metrics. Over the next several months, we will explore several Six Sigma techniques - affinity analysis, correlation analysis and root cause analysis - that can assist us in winnowing out KPI metrics from the "plain vanilla" metrics.
Although business users may be ecstatic that they are receiving tons of metrics "right out of the box," many of the metrics either measure similar process activities or have comparable effects on the organization's critical success factors. The Six Sigma methodology of affinity analysis provides an effective technique for eliminating metric chaos and converting the myriad metrics into a focused group of KPI metrics. This technique allows disperse metrics to be clustered into naturally related and meaningful categories that reflect consistent process dimensions. Affinity analysis includes the following activities:
- Generate metrics. This step can be ignored for our example because metrics are vendor-provided. Otherwise, idea-generation sessions would be needed to develop an initial set of candidate metrics.
- Cluster metrics. The metrics are clustered into metric groups by answering questions such as, "Which metrics are similar?" and "Which metrics are connected to each other?"
- Create affinity cards. Create an affinity card that has a short statement describing each metric group.
- Cluster related affinity cards. Assemble all the individual metrics in a group under their respective affinity cards, and then group the affinity cards under even broader groups.
- Create an affinity diagram. Arrange the metrics and affinity cards on a whiteboard or flip cart. Draw outlines of the metric groups with the affinity cards at the top of each group.
These steps have been used to create our strategic supply chain management (SCM) affinity diagram (see Figure 1). Initially, there existed more than 200+ SCM metrics, which we were able reduce to four meaningful groups with four KPI metrics each. The key groupings of supply chain delivery, responsiveness, costs and asset utilization measure distinctly different characteristics and processes of the supply chain. In the "delivery" group, the most relevant KPIs are processing accuracy, perfect order fulfillment, forecasting accuracy and schedule adherence. Keep in mind that the groupings build on the "dimension" concept discussed in my October 2004 column. An alternative approach could include SCM groupings by various business challenges such as demand forecasting, manufacturing scheduling, order fulfillment, strategic sourcing or customer service.
Figure 1: SCM Affinity Diagram
For more information on related topics visit the following related portals...
Corporate Performance Management (CPM) and
Scorecards and Dashboards.
Kent Bauer is the managing director, Performance Management Practice at GRT Corporation in Stamford, CT. He has more than 20 years of experience in managing and developing CRM, database marketing, data mining and data warehousing solutions for the financial, information services, healthcare and CPG industries. Bauer has an MBA in Statistics and an APC in Finance from the Stern Graduate School of Business, New York University. A published author and industry speaker, his recent articles and workshops have focused on KPI development, BI visioning and predictive analytics. Please contact Bauer at email@example.com.