In many industries, including those dealing with commodity goods, pricing changes are rampant and can make generating an accurate cash forecast especially difficult. The only way to deal with these fluctuationsis to have a solid cash forecasting methodology to address the dynamic nature of pricing changes, customer payment habits and changing business conditions.
The best approach for cash forecasting will focus on defining a reasonable scope and an appropriate level of precision. This in turn will lead to being able to easily compare actuals to forecast values and determine variances. The challenge is to easily access distributed financial data in a timely manner, store historical cash flow data for trend analysis and allow simulation of multiple cash forecast revisions based on multiple sources of input. These sources of input could be individual collector information, promises-to-pay from customers as well as historical payment data.
When dealing with pricing changes, you need to identify trends and cycles from the past and apply them to the cash forecasts. If pricing changes are seasonal, for example, the reflected payment history can be taken into consideration while generating the forecast. Similarly, each forecast revision can be updated to reflect other criteria such as known projected pricing changes, thresholds set by collectors, etc. In this manner, simulations of different forecasts, along with realistic acceptable variance levels, can bring a higher level of predictability to cash flow processes.
Short term cash forecasts based on receipts and disbursements are even more powerful when you go beyond billings and vendor invoices - the real power lies when you take into account anticipated events on the supply and demand side. More on this later...
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