Controlling cash flow by optimizing sales forecasts
Thanks to the different sales forecasting methods based on artificial intelligence algorithms, the company will be able to anticipate possible cash flow problems. We explain how the variation in sales volume has a direct impact on cash flow management.

What is cash flow?
A complex indicator of cash inflows and outflows
Cash flow is an indicator of the level of cash a company has over a given period of time. It is the sum of all cash flows into (income) and out of (expenses and expenditures) a company. In the case of a positive cash flow, the company’s cash position is considered to be in surplus: there are more cash inflows than outflows in its bank accounts. Conversely, a negative cash flow is synonymous with a cash deficit with more money going out.
Cash flow is often mistakenly confused with self-financing capacity which compares the company’s cash revenues and expenses in order to deduce the resources that would potentially remain at its disposal at the end of a fiscal year. On these remaining resources would depend:
- its capacity to finance investments;
- its ability to repay any loans;
- its capacity to pay its debts to suppliers;
- the amount of dividends paid to shareholders.
The calculation of self-financing capacity does not take into account the payment terms granted to customers and accepted by suppliers: the result obtained is therefore a potential cash flow. To get a more accurate idea of the state of its cash flow, the company must then associate the change in working capital requirements (WCR) with the self-financing capacity. The WCR represents the company’s financing needs generated by these famous payment delays.
The main categories of cash flow to be distinguished
Cash flow is divided into several categories of flows, listed in a cash flow table to facilitate the management of these cash flows. The calculation of the company’s cash flow therefore has several levels:
- cash flow from operations;
- investment cash flow;
- financing cash flow;
- free cash flow to investors and shareholders.
To achieve its business plan, every company must optimize each level of cash flow. In order to achieve this, it is in the interest of companies to make a sales forecast as accurate as possible.

Forecast your sales to optimize your WCR
Sales management and cash management: inseparable processes
The forecast of realized sales and the corresponding turnover are included in the company’s cash flow plan. The cash flow plan is a financial forecast document that lists all expected cash inflows and outflows over a given period. Optimized sales forecasts ensure the reliability and accuracy of the cash flow plan, and therefore better financial management. By forecasting its sales, the company has visibility on the cash inflow thanks to the products or services sold to its customers.
Among all the financial indicators, it is on the working capital requirement that the sales forecast has the most impact. Indeed, the calculation of the WCR results from the difference between:
- current assets (inventories and receivables) which represent the expenses necessary for the conduct of the business, thus generating a need for financing;
- current liabilities (trade payables, social security and tax liabilities) which generate financial resources to finance the company’s operating cycle.
Companies will therefore have to improve inventory management and optimize the payment terms granted to customers and accepted by suppliers, through accurate and rational forecasts.
Sales forecasts to anticipate payment deadlines
Firstly, the forecasts made will enable the companies concerned to adapt the payment deadlines for trade receivables, the objective being to boost incoming cash flow. This means speeding up the receipt of payments from customers while maintaining the quality of the relationship with each customer.
It is also in the company’s interest to negotiate longer payment terms with suppliers. By having previously reduced the payment terms granted to customers, the company ensures that it will have the necessary liquidity to repay its supplier debts at the end of the negotiated period. For example, if the company anticipates a large volume of sales, it can justify its request to negotiate payment terms by increasing the volume of purchases from its supplier. Moreover, these purchases should ideally be spread out according to the supplier’s payment terms to limit cash flow mismatches (outgoing and incoming payments).
Optimization of inventory and supply management
Based on sales forecasts, the company can define the ideal stock level and thus reduce the inventory turnover time. It will then avoid a time lag between the expenses for production and the receipts following the sale of the products.
However, by reducing inventory turnover times, the company exposes itself to the risk of stock outs or overstocks. The implementation of inventory and supply management processes adapted to the forecasted sales volumes is therefore essential to limit working capital requirements.
If the forecasts announce an increase in sales, the company will have to increase its level of stock to avoid stock-outs and not lose revenue due to the impossibility of selling its products and satisfying the customer.
On the other hand, if forecasts predict a drop in sales, the company will have to reduce overstocks to avoid unnecessary logistics costs related to storage (buildings, machinery, electricity, salaries, maintenance costs, etc.).
By optimizing sales forecasting, companies can anticipate their cash flows to better control them. The reduction in working capital requirements (WCR) and the resulting increase in self-financing capacity guarantee the sustainability of the company’s activity.
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Quantitative methods for forecasting product sales
While qualitative forecasting is subjective, quantitative forecasting is based on objective and precise calculations. These methods are used when the analysis of historical data gives enough information to build a successful sales forecasting model. Quantitative models consist of reproducing the structure of the past to forecast the future, by extrapolating past data.
Simple quantitative methods
Simple quantitative methods are based on the use of relatively simple mathematical formulas. Although they do not take into account the demand variables that can affect the data, they still allow for the first approximate forecasts to be made of possible variations in demand.
To do this, the company can use a mathematical formula specific to one of these three calculation methods:
- the extreme points method;
- the double mean method, known as Mayer’s method;
- the least squares method.
The moving average method
The moving average method eliminates fluctuations in sales data due to seasonal and random variations. Moving averaging involves dividing a given period (year, quarters, months, etc.) into several periods and calculating each average. For example, sales can be averaged for each three-month period in a year. To forecast the next year’s sales, simply average the averages for the same periods and so on. This forecasting model is best suited for a company that is already well established in its market and has a large data history.
Exponential smoothing methods
Exponential smoothing of a data set is based on the idea that the future depends more on the recent past than on the more distant past. In this sense, the smoothing method gives more weight to the most recent sales data. This model is most suitable for growing companies. Exponential smoothing methods include:
- simple exponential smoothing, only suitable for data series with no trend or seasonality;
- double exponential smoothing, suitable for data series with a trend;
- Holt’s method, an improved version of double smoothing;
- Winters’ method, adapted to data series with seasonality and trend.
Regression and correlation methods
The regression method consists of showing the evolution of a variable in relation to another variable (simple regression) or to several other variables (multiple regression). For example, the regression model can analyze the relationship between sales revenue (dependent variable) and advertising expenditure (independent variable).
The correlation expresses the strength of the relationship between the variables under study. The calculation of the correlation coefficient allows to measure the degree of correlation: if the correlation is strong, the sales forecast will be all the more precise and reliable.
All these qualitative and quantitative forecasting methods are not exclusive. By combining the methods, the company can limit the uncertainties and maximize the reliability of the forecast.
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