Learn how to unlock untapped potential by connecting the dots in forecasting
How do we get to a realistic demand forecast? The truth is simple: Our forecast should be a reflection of the plans we develop and the assumptions we make on our demand drivers. In reality, creating a plan is one thing, while making a demand-driver-based forecast is another thing.
In a poll, we asked our network what you find the most challenging in making a realistic forecast. The response was clear. You struggle to define your sales plans and the assumptions on your demand drivers. And when you manage that, turning the agreed plans into a forecast is the second hurdle.
Because creating the plan and the forecast is already challenging, actually bringing focus to where you can improve and to learning from your performance are the least of your worries.
So, how do you get to a realistic forecast then? You will need all 4 elements to drive change. Let’s start with the most pressing challenge:
Defining your plans and assumptions
Every business will have a few big drivers of demand based on which you can make assumptions for the future. When an organization focusses on assumptions first, the reasoning that leads to the forecast immediately gets less biased. To get there, we advise the following steps:
- A version of an S&OP process is a prerequisite to ensure the decisions on plans and assumptions can take place.
- A pragmatic solution to capture plans and assumptions is imperative to support your S&OP decision making.
With a focused process and a pragmatic solution to support it, you have the necessary foundation in place.
Turning your plans into a forecast
Once you have the plans in place, how can you avoid that the forecast ends up simply being a copy of the past? The more focus you put on qualitative plans and assumptions, the less time you actually need to spend on creating a forecast. With techniques like driver-based forecasting the machine delivers the forecast for you. A machine learning forecasting technique incorporates decisive aspects such as your sales data, and past and future information about your demand drivers, into a forecast. At EyeOn we currently have multiple customer cases where the machine generated forecasts outperform the manual forecasts with a substantial workload reduction.
Spotting vital review areas
But is the machine always the better option? We recommend smart-touch planning. The best forecasts are the result of a machine doing the heavy lifting and planners stepping in where their expertise is needed. With the right insights, the planners instantly spot where they can make a difference. Eventually, the more the machine learns about the products, customer, and drivers, the less intervention is needed. Want some more inspiration on this, stay tuned for the launch of our EyeOn Promo app which offers actionable insights on your promotional plan based on machine learning.
Learning from performance
Finally, planning and forecasting is not an exact science, but there must be more science to it than covered by manual forecasting. And like for every science, learning is imperative.
On the one hand, the machine learns from every single data point in combination with your qualitative plans and assumptions in a way no planner could match that.
On the other hand, also the planners need to learn. They should learn about how the machine delivers the forecast. We believe, a forecasting set-up should have machine learning elements at its core, but always in combination with explainability functionality. Next to the technical learning, the planners should also understand how they contributed to the forecast. You can create this awareness by bringing in concepts like cognitive insights. As soon as you adopt a mindset of continuous learning, forecasting isn’t a challenge anymore, but rather a way to unlock potential and achieve success.
Are you ready to take the steps towards a more realistic forecast? Please feel free to contact our expert Erik De Vos to discuss your situation and explore solutions together.