Building remote teams of highly skilled Africa-based software engineers to staff tech companies in the U.S. and Europe is a complex task. That’s why Andela, a global talent network that helps companies build remote engineering teams, uses a seven-level workforce planning model to match its clients with remote engineers who become long-term, embedded team members.Andela, headquartered in New York, initially used a spreadsheet for planning. However, as the company expanded into more countries, it needed a solution that could easily incorporate multiple currencies and support collaboration between finance teams across multiple time zones. Andela chose Workday Adaptive Planning to gain version control, multicurrency support, and seamless connection to mission-critical systems. It also discovered the value of scenario planning to respond to changing business circumstances in uncertain times such as during the COVID-19 pandemic. Working with Red Barn Partners, Andela deployed the solution in just three months, in time for its next planning cycle. The automation within Workday Adaptive Planning has increased productivity and data accuracy, and significantly reduced Andela’s annual planning and monthly reporting time. This allows team members to spend more time on continuous forecasting, analytics, and building a strong finance business partnership with executives. As a result, executives can now make decisions faster based on real-time information submitted by team members around the globe.
- Siloed information spread across multiple countries—Andela’s finance team, located in multiple countries across multiple time zones, initially worked together by sharing a complex spreadsheet during the annual planning cycle. With no version control, each person had to schedule time to enter updates—or risk writing over another team member’s data. Despite efforts to coordinate, overwrites and data losses still occurred, and reconciling multiple currencies caused consolidation headaches, resulting in a two- to three-month annual planning cycle.
- Labour-intensive and delayed reporting cycles—Creating month-end reports was largely a manual process of retrieving actuals from multiple sources, compiling data, consolidating results from each country, and formatting the reports. This process took the entire finance team in each country up to seven days to complete.
- Limited forecasting horizon—The lag time between data availability and reporting made it difficult to accurately forecast headcount requirements and cash flow to meet aggressive deadlines.
- Streamlined multi-country collaboration—Multiple finance team members in different time zones can now work in the planning model simultaneously. This reduces data loss, increases productivity, and cuts the annual planning process from three months to three weeks.
- Flexible reporting in record time—Month-end actuals can now be generated in five minutes, compared to one to two days previously and reporting cycle times have decreased from two weeks to two days, giving finance team members more time to analyse the data.
- Better informed forecasting—Rather than relying on spreadsheet-based cash flow forecasts created from weeks-old information, decision makers can now access continuous, dynamic forecasts based on real-time data from the entire enterprise. Using scenario planning, the company was able to quickly evaluate how the COVID-19 pandemic would affect the business, ultimately deciding to move to a fully remote operation.