Every single event in your supply chain generates data, and the opportunities to utilize all that data seem endless. What sounds intimidating at first is actually a real opportunity for your business: In today’s world, where volatility in consumer behavior is at an all-time high, you can leverage this data to accurately predict your demand with machine learning. Let us introduce you to driver-based forecasting:
Why driver-based forecasting is the key ingredient to success
The biggest challenge in forecasting is how to come to a fact-based and realistic plan and forecast. Although in every business there are internal and/or external factors that have a causal relation to demand, it can be hard to bring the right drivers for demand into your forecast. What if your forecasting method would do this for you, so you can focus on more important business decisions? By using demand drivers as the basis for forecasting, you can process and deliver the right information to the right person’s desk at the right time.
How does driver-based forecasting work: Complement your forecast with the right spices
The already mentioned internal and/or external factors can help you foresee how the demand of your products is going to develop. Examples for internal drivers are promotions, orders, or a product plan. External drivers are for example point of sale information, holidays, or the weather.
Traditional forecasting methods, such as time series forecasting, don’t take this data into account. The statistical forecast needs to be manually enriched by your planners. This requires a lot of time and skill from the planners and introduces a risk of human bias.
With machine learning we can identify demand drivers for your business and learn from the past data of these drivers to enhance the predictive power of your forecast. This approach is much more fact based, offers an automated way to get insights, and enables the planners to balance time and effort.
The recipe for starting driver-based forecasting
Are you ready to steer your business based on more precise forecasts but don’t know where to start? Creating a value-adding machine learning model can be challenging. In our next blog we will talk about the hurdles companies might face when implementing driver-based forecasting and how to overcome them.
Can’t wait to start improving your forecast performance? Taste the future with driver-based forecasting: Contact us now!