| Abstract: | Despite its pivotal importance in enterprise management, cash flow forecasting gets little attention from academics perhaps because few of them have access to internal processes and data. In this article, however, the authors explain how cash flow forecasting is organized at Bayer, a large multinational company headquartered in Germany, and which factors influence the accuracy of its forecasts. The research focuses on cash flow forecasts based on the direct method, prepared three times a yearat Bayer, involving about 62,000 individual forecasting items each time. These forecasts form the basis of the company's liquidity and financial risk management, in particular, its foreign exchange risk hedging. The authors explain how local managers in Bayer's entities across the world derive the forecasts, i.e., what information they use as input, how they validate it, and how they deal with potential bias caused by managerial incentive systems. They also analyze whether forecasting processes are affected by characteristics such as business area, size, region, or specific local conditions, and ultimately whether forecasting practices and entity characteristics affect forecast accuracy. The findings show that cash flow forecasting procedures vary substantially across Bayer. While the central finance department gives general guidance on the required cash flow forecasting output and provides direction on the input to be used, there are no detailed instructions on how forecasts are to be prepared. Instead, local managers are free to determine their own forecasting practices. They use different forecasting inputs and validate forecasting inputs and output with different intensities, and they also differ in how they treat possible biases in input data. These findings document the limits of standardization and central control in large multinational corporations resulting from local managers’ need for flexibility to cope with the heterogeneity and dynamism of their environments. At the same time, however, local differentiation increases complexity and may increase errors. Quantitative analysis of forecasting errors shows that forecasts of receipts from customers (cash inflows) are more accurate than forecasts of payments to suppliers (cash outflows). Moreover, forecasting practices affect forecast accuracy. Outflow forecasts are more accurate if managers intensively validate forecasting input; inflow forecasts, if they eliminate input biases that may result from internal target setting or from other managerial incentives, and if they carefully validate their forecasting output. The study provides several insights.
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