Why do companies use rolling forecasts? : a case study on the rolling forecast as a management tool

Abstract

This thesis focuses on the use of rolling forecast as a management tool in companies. Increasingly dynamic business environments make it important for companies to be more responsive to adjust to changes. Through a case study approach, we aim to explore the drivers behind the use of rolling forecasts, and how the rolling forecast is used as a management tool in a company, namely OffCo. The study uses qualitative research methods, explicitly semi-structured interviews with employees in OffCo to collect the data for the thesis. Both the company and the respondents in this study are anonymized. Our main findings show that there are two drivers that are specifically important for the use of rolling forecasts in OffCo; uncertainty and financial position. An interesting finding is that these drivers interact and are amplified by each other. OffCo have experienced increased uncertainty in recent years due to a dramatic drop in the oil price in 2014. This resulted in a weakened financial position causing challenges for the company. Due to the weak financial position, the consequences for uncertainty caused by fluctuating oil prices are more severe. Further, we find that the budget and the rolling forecast is closely linked together. The processes of forecasting and budgeting in OffCo are interdependent, and the two tools have the same level of detail in information content. Rolling forecast is seen as a tool to improve the accuracy of the budget which is quickly outdated. The main purposes of the rolling forecast are therefore short-term decision making and planning regarding resources in the company. Moreover, the rolling forecast is used for performance evaluation in the same way as with the budget. The close link between the two tools seems to create confusion whether the rolling forecast is a target or a realistic prediction of future outcomes. Through studying the use of rolling forecasts in OffCo, some potential improvements for the forecasting process have been detected. These include shortening the time horizon of the rolling forecast, reducing the level of details included in the forecast, and utilizing access to technology to make the process more efficient.