![]() ![]() Each data point on the chart corresponds to a forecast or actual (actuals are in bold) closing cash position.x axis: shows the month each forecast is projecting the closing balance of.y axis: shows versions of the forecast (when they were produced).Here, the table shows how multiple forecast versions compare to the actual data (in bold): ![]() Time Series VisualisationĪ Time Series Visualisation allows variance analysis to be carried out across multiple forecast versions and it is usually best displayed in a table such as the one below. This means that an increase in the accuracy of forecasts produced by the Chinese entity (where there is considerable scope for improvement) would have a far greater impact on overall, company-wide forecast accuracy than focusing on the most inaccurate forecasting entity. Whereas in China, where forecast accuracy was better (though still poor as China had an 83% divergence from the actuals), the value of this discrepancy was $4.0million. For example, in the instance above, Brazil had a major deviation between forecast and actual (92%) but this only equated to a variance amount of $1.3million. This means that attention can be paid to the variations that have the greatest impact. One of the key benefits of this method of data visualisation is that it allows material variances to be quickly identified and put into context with other entities’ percentage accuracy. (Here, the different reporting entities’ variances are transposed to a common currency, US dollars). It breaks the measure into two categories, percentage variance and amount variance. The graph below visualises a comparison of forecast versus actual data for a variety of reporting entities. Forecast vs Actual Variance Visualisation For example, in the graph above we can quickly see that supplier payments (which total $10million in outgoings) cancel out the positive contributions from customer receipts, investing inflow, and dividend receipts combined. This means attention can be focused on the element(s) that will have the greatest impact. One of the key benefits of this method of data visualisation is that it highlights the extent to which each category of cash flow affects the cash balance in an easy to understand visual. The four headline outflow categories are highlighted in red (Supplier Payments, Tax, Payroll, Debt Payments). ![]() In the example below, we can see the three headline cash inflow categories highlighted in green (Customer Receipts, Investing Inflow, Dividend Receipts). Cash Walk Through GraphĪ cash walk through visualisation breaks the journey from opening cash balance to closing cash balance into a series of steps that identify the most significant contributors/detractors to the net cash balance. However, it is important to note that these examples are not exhaustive, and that many other formats of visualising cash forecasting data are available. Time Series Visualisation (analyse accuracy across multiple versions of a forecast).Forecast Versus Actual Visualisation (quickly understand forecast variances).Cash Walk Through Visualisation (shows how cash moves from an opening position to a closing position).To illustrate how data visualisation can help with data analysis, this post will review three potential ways to visualise cash forecast data. The same is true when analysing the data produced as part of a cash forecasting process. Presenting data in a graphical format often helps to highlight trends, identify anomalies, and uncover insights that will be missed when simply reading through raw data. Data visualisation is an important step in any form of data analysis. ![]()
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