What is Driver-Based Forecasting for Demand Planning? 

Traditional forecasting methodologies simply extrapolate trends in history in the future to create a statistics-based baseline forecast. Additionally, planners (or sometimes other departments such as sales or marketing) add impact of activities such as promotions or product launches.

This results in a final forecast based solely on the assumption of what happened in the past will continue to happen in the future. It also adds judgment and feel based information on top to create a final number that mostly compounds mistakes, leaving a higher chance of error. Since this process is repeated on a weekly or monthly basis and is mostly analog and judgment based, the learnings from cycle to cycle do not carry over. The building blocks and assumptions behind the final plan are also not systematically captured.

By contrast, driver-based forecasting surpasses traditional practice and identifies the main drivers of a business and how those drivers affect its revenue, costs, and cash flow. Drivers are the variables that influence or determine a business’s financial results, such as sales volume, price, customer retention, productivity, or quality. By using drivers, businesses can systematically capture learnings  and make plans that are more responsive to changes in their business environment.

By using drivers, businesses can simplify their forecast model and make it more responsive to changes in their business environment. Businesses can also align their forecast with strategic goals and initiatives and communicate them clearly to their stakeholders. 

What are the Benefits of Driver Based Forecasting?

Using generative AI, the impact of these drivers is now visible and can be understood by all the different stakeholders within the business. This means that the plan is fully aligned with strategic goals and initiatives of the organization and all stakeholders can better and faster decisions to achieve those goals. Other benefits include:

• Increased accuracy: businesses can improve the accuracy of their forecasts by focusing on the decisions and activities that have the greatest impact on their financial results. 
• Improved efficiency: It enables exception driven forecast management. This can help businesses to improve the efficiency of their forecasting process by simplifying the model while making it more automated. This can free up time for businesses to focus on other important tasks, such as strategic planning and decision-making rather than mundane tasks such as cleaning old history and managing SKU by SKU forecasts.
• Increased collaboration: It helps businesses to improve collaboration between different departments by providing a common framework for understanding the drivers of financial performance. This can help businesses to make better decisions that are aligned with their strategic goals.
• Enhanced decision-making: Driver-based forecasting can help businesses to make better decisions
  by providing insights into the impact of different activities and scenarios on their financial performance. This can help businesses to evaluate trade-offs and alternatives, and to make more informed decisions about their future.

If you are looking for a way to improve your forecasting process, driver-based forecasting is a great option to consider. Embracing this innovative approach can help improve the accuracy, efficiency, and collaboration of your forecasting process. Driver based forecasting ultimately allows businesses better decisions about your future.

Need to know more? You can learn more about how AI-first demand planning and forecasting solutions can advance your business to the next level here.