Given its scale, the client needed to address several issues impacting productivity and client experience, including:
- Current workflow routes like-items to separate queues managed by separate teams even though they share common tasks with similar skill requirements
- Too much routing between queues
- No automated method to identify, categorize and track deficient work items (‘not in good order’ or NIGO)
A more efficient operating model
Each queue has its own service level agreement, measured in lead time, ranging from 24–72 hours. While the work item may meet the individual queue target, there was no visibility into the end-to-end lead time, the true measure of client service. This can lead to a false sense of success. So, the goals for process management and simplification included:
- Consolidate like tasks and queues
- Minimize non-value added handoffs
- Standardize work item status and notes entered into processing system
- Align process flow with client lead time
Manual tracking revealed a NIGO range of 15%-72%, which requires direct customer follow up.
In order to reduce defects, the goals for exception handling included:
- Develop common definitions for NIGO items
- Create a structured approach to track and report on NIGO items
- Standardize processing and client communication of NIGO items
Partnering with the client’s lean center of excellence, we identified 33 opportunities for improvement within the Client Account Services and Asset Transfer Services teams. We created a task and attribute matrix. Then, consolidation from the matrix was validated using cycle time data gathered through several weeks of associate observation. Cycle time data was also used to create new Item per Hour productivity targets for associate accountability. Instead of measuring individual queue service levels, end to end lead time provides a more client centric metric that measures the lifecycle of an item from creation date to completion date.
The future state design also separated exception items into their own queue to create a more consistent tempo for processing items in good order. The client can now track exception rates and the specific reasons behind the exceptions. A successful six week pilot within one of the processing teams validated the results and is being piloted to two additional teams currently.