Automating the financial end of month - a case study
Updated: Aug 15, 2019
In finance departments the end of month is a stressful time as a significant work needs to be completed in a short timeframe. Highly paid accountants often need to pull all-nighters to collate the data they need in order to close off the month. When they are finally able to review the results they are in an exhausted state and have little time for analysis and insights.
The following case study highlights how a finance team at Australia Post was able build automation skills and reduce their month end workload doing mundane, repetitive and error prone data collation.
As one of the team exclaimed :
"We now have more time to spend on analysis and quality control rather than data entry"
The Project Accounting team support the financial set up and closure, forecasting and month end reporting processes for the $300m capital works program. The team are required to do significant repetitive and manual work such as extracting data from core systems, collating data submissions, distributing reports, extracting email attachments, uploading forecasts and re-keying funding requests submitted as PDF files.
All the collating and re-keying work means that qualified staff have little time to spend on analysis and value add activities.This is particularly challenging at peak times such as month end and quarterly forecasting times.
What they did
A working group was established to automate key processes over 3 months. The team consisted of 2 capital analysts and a managing accountant. A specialist automation lead from RPA Solutions was engaged to lead and instruct the group at weekly workshops.
The team’s objectives were as follows :
- Learn new techniques and skills which could be passed on to the wider team in the future.
- Automate manual processes to free up qualified staff’s time and allow them to do more value add activities and be able to better support the team’s stakeholders.
- Freedom to experiment with new tools and not be focussed solely on the end outcome of the process.
Processes automated :
- The Minor Works Request Process was reduced from 16 Minutes to 5 Minutes per request
- The Quarterly Forecasting Process was reduced from 12 Minutes to 3 minutes per request
- Automated the emailing and creation of individual reports
- Automated stripping and storage of email attachments
New skills obtained :
- A structured approach to problem solving and an ability to identify future potential automation opportunities.
- Programming capability with automation tools