Monash IVF are a leading provider of IVF services to the Australian marketplace. They have 60+ clinics providing full service/low cost IVF and ultrasound services in Australia and internationally.
Their strategic challenge
The main issue for Monash IVF was they didn’t have any timely visibility over how they were performing with respect to procedures performed, how they were tracking against their targets (including market share) and if there was any need to address problem areas.
They also had difficulty tracking their labour costs across each clinic. Within the health industry and particularly IVF, there are a diverse range of specialist skills required to deliver the services. Gaining visibility over which clinics are performing better than others in terms of outcomes and labour efficiencies is critical to optimising resources and driving profitability.
Budgeting and forecasting was equally problematic, with the budgeting cycle taking months to complete with little control or visibility over the process.
What systems Monash IVF were using?
They had several operational systems for each business unit where they could extract procedure data out manually, but this required their finance and administration team to compile the data into spreadsheets with the process subject to inconsistencies and errors. It took many days to compile, including waiting for each business unit to email spreadsheets into head office, before it could be consolidated and presented. By then, the information was often out of date.
Likewise, for Budgeting and forecasting, Monash IVF were using spreadsheets and a heavy reliance on emailing files and manual data consolidation.
There was a single integrated solution proposed with two key goals. The first goal was to provide an effective and efficient reporting process for procedures performed (including market share) and to compare labour costs per procedure across clinics. The second goal was to provide a budgeting and forecasting solution that would enable a much faster and accurate planning capability enabling collaboration across all their business units.
One of the particular challenges we had was the need to capture data from multiple systems that contained transactional information. Data sources included bespoke IVF operational systems, as well as a payroll and general ledgers systems; effectively 3 disparate sources of data.
All information was integrated into a central SQL Server data warehouse before being overlayed with business intelligence and budgeting and forecasting capabilities, using IBM Analytics (Cognos Analytics and Planning Analytics).
How long did it take for the solution to be implemented?
All up it took three months, which included an analysis and discovery phase, before development and implementation of the solution, and finally, testing and training.
What are the results so far?
Without question, Monash IVF have improved the information flow to their executive management team. They can now see how the business is performing daily and have automated reports and dashboards being sent to senior managers on the volume of procedures and labour costs required to perform them. They now know the yield they are getting from those procedures and how much they are spending across each of their clinics. This allows them to provide comparisons from clinic to clinic and to identify which ones are performing more efficiently and effectively.
Budgeting cycle times have reduced by 70% and they can now easily model out multiple scenarios based on changes in market conditions and internal board requirements. Their process is now robust and more accurate, with the budget generated using driver based models and historical trends to provide their baseline. Once budgets are adopted, they are automatically integrated into the reporting platform to facilitate KPI reporting and variance analysis.
The management team is now far more proactive around making decisions or enquiring about what is causing underperformance at any clinic, at any time. The enhanced visibility has meant they can be strategic in their roles and address issues immediately as they arise. The finance team has also freed up to do more value adding work such as analysis rather than just being data inputters and aggregators buried in spreadsheets.