What is Driver Based Forecasting (DBF)?
Driver Based Forecasting (DBF) is a method for predicting a company's future performance. Instead of relying on historical trends, DBF focuses on the factors that affect the business, such as sales, costs, and market conditions.
The method makes it possible to link operational variables with financial results, leading to more precise and dynamic forecasts. By identifying key drivers that affect the company's finances, management can make more informed decisions and optimize business strategy. DBF has its greatest strengths when conditions change and decisions need to be made on relevant information.
How driver-based forecasting works:
DBF is about identifying the drivers that affect a company’s financial performance. Many companies and businesses use driver-based forecasting as part of their financial forecasts and decision-making. For example, for a manufacturing company, drivers such as demand, labor, and material prices can be used to predict inventory needs and production costs. Similarly, retail companies can use DBF to link marketing effectiveness with sales forecasts, allowing them to optimize their campaigns and inventory management in real time.
By creating models for these factors, companies can more accurately predict their results and adjust their actions in real time.
Strengths:
- Better adaptation to changes – Focusing on driving forces and their expected future development normally provides a more realistic picture of how changes in the external environment affect business, than focusing on historical development
- Increased relevance – By analyzing drivers, companies can get more relevant and insightful forecasts
- Proactive decision-making – DBF creates better conditions for decision-makers to receive information and react to changes more quickly, which can lead to better strategic planning and risk management.
- Support for collaboration – By linking forecasts to business drivers, resources can be managed more effectively, which improves the conditions for planning and control within the business.
Weaknesses:
- Complexity in data collection – Collecting relevant data can be time-consuming, expensive and difficult. Often requires more initial analysis, data collection and a change in working methods compared to simpler, history-based methods.
- Need for continuous updating – Forecasts must be updated frequently to remain relevant, which often drives investments in new information models and system support.
- Driver error or misinterpretation – DBF is based on companies being able to identify and model the most relevant business drivers. If the wrong drivers are used or if relationships are overestimated, forecasts can be misleading.
- Dependence on expertise – Traditional methods such as historical trends or simple budget variances may be easier to understand and use for people who lack analytical or statistical skills.
Ekan Management is a driving force for realizing forward-thinking innovation in the financial and operational management of companies and businesses. Contact us if you want to know more about how the finance function can contribute to strengthening business, innovation and goal achievement for the entire organization.