#1: Lack of data quality and inability to collaborate
According to a report by Gartner, 40% of businesses fail to achieve their business objectives due to poor data quality. The world today generates more than 2.5 quintillion bytes of data every day. Collecting, cleaning, managing, storing, and analyzing this ever-increasing volume of data is no small feat. Businesses usually store data in different formats in disparate sources (ERPs, bank portals, FP&A systems, TMS), and aggregating this data becomes tedious in spreadsheets. On-premise ERP systems are typically installed locally on the company’s hardware and managed and maintained by IT staff, which makes it difficult for treasury analysts to operate in an accessible and known environment while forecasting.
While forecasting manually, front-line staff need to collect the data from different teams like A/R, A/P, HR, and FP&A and then consolidate all the data into a single sheet which is time-consuming. Due to this, the turnaround time in creating forecasts and reports increases, which delays decision-making from the C-suites. In addition, poor data management practices such as not storing data securely, using raw data for analysis without cleaning it, and collecting inaccurate data can all negatively impact your business and its operations. For instance, incomplete or inaccurate data makes the cash forecasting process inaccurate, which makes it challenging for treasury analysts to create and share accurate numbers with treasury managers and CFOs.
#2: Inability to identify different payment terms
Each customer has a unique payment pattern and behavior. Also, different invoices may have different currencies.
When predicting accounts receivable using spreadsheets, treasury analysts add variables like invoice date and average payment date but fail to add more variables to predict payment dates accurately due to the limitations. Adding 50+ variables provides a more realistic and holistic view. But, it is difficult to manually tweak as many as four variables, let alone track 50+ variables using spreadsheets.
#3: Low accuracy and frequency in cash forecasting
According to the Cash Forecasting & Visibility Survey by Strategic Treasurer, 91% of treasury analysts still use spreadsheets for forecasting and face challenges in getting an accurate forecast.
However, spreadsheets can’t factor in all the necessary forecast data, such as changing FX rates, seasonality/business cycles, and macroeconomic fluctuations. This reduces the accuracy of a cash forecast. The frequency of forecasting can’t be increased enough due to lean treasury teams and most of their time getting consumed on manual data aggregation.
#4: Less bandwidth to perform variance analysis
Lack of bandwidth hinders treasury analysts from performing variance analysis, especially when using manual tools like spreadsheets.
For most organizations, treasury analysts gather variances at the end of every year in spreadsheets by comparing past year actuals to current year actuals. Without regular analysis to understand the variance drivers and identify high variance categories, the forecast accuracy can’t be improved.
