Operational Effectiveness: International Wealth Management Company

This case study is typical for projects where the traditional Six Sigma DMAIC approach using statistical analysis should be used. The root causes and solutions of the problem are not known at the beginning of the project and data is available to drive meaningful statistical analysis make this a classical scenario for the application of the Six Sigma methodology.

A revenue-generating business unit from a leading global wealth management company wanted to lay the foundation to improve the customer experience by focusing on process efficiency. Based on benchmarking and customer feedback it was decided to run a project to improve the “New Business” process, through which all customer applications for new business were processed. The New Business process was executed in an environment that showed all the characteristics of a back office in a financial services firm:

• The end-to-end process was split across various business units, with each business unit controlling, owning and managing ‘their’ part of the process.
• Some of the process steps were automated through IT applications. (In this case the New Business process was automated using one central IT application.)
• The execution of the process was triggered by paper forms from clients. The paper based documents were scanned and then forwarded electronically to staff members for processing.
• A workflow system had been implemented to automate and monitor the execution of the process steps. The forwarding of documents from the scanner for further processing was performed and monitored through the workflow system.

The focus and objective of the project was to reduce the amount of rework on new business applications, which appeared to be the key driver in customer complaints and processing costs. There were conflicting opinions about causes and solutions, so the Six Sigma DMAIC approach was chosen to ensure that the root causes were identified through the use of data and statistical analysis. The Measure and Analysis phases used traditional Six Sigma statistical analysis (descriptive statistics, regression analysis, Pareto charting etc.) to determine the initial process performance and the root causes for rework to occur. The quality of the data was assessed to ensure that data quality were sufficient to identify root cause. Data quality in conjunction with the statistical analysis ensured that the solutions were effective in driving process efficiency. Improvements in process efficiency had a direct relationship with customer satisfaction.

Customer satisfaction has significantly increased as a direct result of this 20 week project. Financial benefits in excess of $240,000 p.a. including top-line as well as bottom-line benefits have been achieved during this time.