Challenge
- Time to Decision for the processing of personal loans took an average of 5.3 days
- Due to this long processing time, only 8% of applicant customers were taking out the loans
- The funding conversion rate of personal loans is closely linked to the “speed to yes”, therefore, reducing the throughput time can significantly increase profits
Current Situation
- The processing of personal loans was an 8 step process
- Usually, 2-3 of the process steps had very large queues (up to 341 loan applications)
- The process step with the largest queue changed regularly, depending on the day of the week and month of the year
Key Issues
- The actual time spent physically processing the application was a small fraction of the total throughput time (3%), so significant improvements in speed could be achieved through improving flow by identifying and eliminating bottlenecks. Line managers had very little knowledge of total processing
times, queue sizes and how that affected the customer conversion rates - The way the workflow was managed usually hindered flow when it should have facilitated it. Managers were keen to batch process by file type rather than abide by the “first-in-first-out” principle. Managers were also averse to moving operators between queues as they were not multi-skilled and only knew how to execute their own process step
- Poor flow masked process issues, not allowing managers to react to short-term productivity variation, cycle time imbalances, and most importantly, errors in real time. For example, if a new-hire started processing loans incorrectly, and the next step in the step had a two day backlog, the operator in that
next step would not be able to identify and inform the new operator of their errors for two days. In this time, there may have been hundreds of loans processed incorrectly - Performance measures were focussed on the wrong behaviours, e.g. working to SLAs rather than improving flow.
Solution
1. End-to-end Value Steam Mapping
- The introduction of an end-to-end visual management board assisted PL Operations Leadership to manage the PL process as a coordinated system rather than as a collection of siloed process steps
- The board encouraged an end-to-end focus to allow effective resource allocation to reduce backlogs and improve flow
- This board is now used as the basis for capacity and overtime discussions at the daily Operations buzz meeting
- The introduction of an end-to-end visual management board assisted PL Operations Leadership to manage the PL process as a coordinated system rather than as a collection of siloed process steps
- The board encouraged an end-to-end focus to allow effective resource allocation to reduce backlogs and improve flow
- This board is now used as the basis for capacity and overtime discussions at the daily Operations buzz meeting
- The value stream map had 4 key components:
- SLA graph for each process step, indicating throughput time contribution, and whether that was too slow (red zone), borderline (yellow) or on track (green).
- Queue size (WIP level) sitting in front of each team.
- Resource allocation for the day, expected inflow and planned out-flow for each queue
- The overall throughput time that customers would experience
(the sum of all the teams individual throughput time contributions)
2. Visual Active Management Scoreboards
- Active management scoreboards (short interval control) were constructed for the process steps (teams) with the highest processing times to enable hourly tracking of output versus target run rates (run-rates calculated from targets established using the ‘End-to-end visual management board’ at the daily ‘Operations buzz meeting’
- Having hourly data available enabled early identification and management of issues and therefore aided intraday capacity adjustments (where necessary). For example, if a team’s actual run-rate (red) fell BELOW the planned run-rate (blue) they would have borrowed an operator from a team whose red line was ABOVE the blue, i.e. ahead of schedule
Results
Conversion rate of applications to drawn down loans rose from 8% – 18%, and resulted in an additional $5m profit per annum to the bank
Processing time reduced from 5.3 days – 1.8 days
