How can you serve tomorrow’s consumers if your forecasts rely on yesterday’s data?
As channels, markets and consumer segments become more complex and fast changing, companies are increasingly challenged to produce forecasts that get the right inventory to the right location at the right time.
Forecast accuracy for many CPR companies today is a challenge, with many reporting error rates as high as 50%.1 No wonder that businesses are desperately seeking better methods.
For top-performing companies, the answer is to adopt demand driven planning and forecasting (DDPF), a method that monitors and interprets demand in real time, actively shapes it, and finds the best response.
Using DDPF means:
- Forecast accuracy can rise by 30-35%, inventory reduce by 20-25% and revenue increase by 3-5%2.
- Companies can detect market changes five times faster and respond three times quicker3.
1 Gartner, ‘Why Demand Forecast Sensing Has Hit a Ceiling’, 2016
2 EY, based on an average of results reported by CP and retail companies using DDPF, 2016
3 Chase, Charles W. Next Generation Demand Management: People, Process, Analytics, and Technology. Wiley and SAS Business Series. Hoboken, NJ: John Wiley & Sons, 2016
DDPF enhances companies’ ability to shape demand. From a marketing perspective, they can better compare the return on investment of different proposed activities because they can segregate baseline demand from the way demand behaves during promotions and events.
From a supply perspective, when companies have too many products they can proactively push the market to reduce stock, and when they have too few they can remove promotions to avoid disappointing customers.
Moving to DDPF is the first step towards creating a demand driven response network.
How does DDPF work?
Rather than relying on historical sales as the primary data source, DDPF collects many different types of data including:
- Point-of-sale data
- Deliveries requested by retailers
- Stock situations at individual warehouses
- Trends on social media about particular products or brands
- Event-based data such as promotions, merchandising or weather conditions.
Demand analytics monitor incoming signals and develop a response. Techniques such as pattern recognition and machine learning enable the system to identify patterns and correlations.
Sales, marketing, trade marketing, supply chain and finance teams gain the ability to collaborate. Promotional and marketing plans, customer plans and demand plans become one seamless process.
The precision and reach of DDPF is growing, as companies exploit data from a widening range of sources, including:
- IoT-enabled cameras and light fittings that give information about empty shelves
- Dynamic shelf-edge labels that change prices according to levels of footfall within a store
- Amateur IoT-enabled weather stations that track the real-time movement of weather fronts
As more wearable devices and smart appliances come on line, companies will be able to see real consumption patterns in people’s homes and lives, and use this to make DDPF even more sensitive.
Key steps for moving to DDPF
- Embed ownership of DDPF into the sales and marketing functions, driven by IT
- Develop consumer-driven plans by integrating sell-out and sell-in data
- Increase collaboration between sales, marketing, operations, finance and IT by introducing a common database
- Develop deep, strategic relationships with customers to support joint business planning
- Implement a unique set of event-based data, covering areas such as promotions, price changes, weather forecasts and competitor activity
- Introduce product, demand and customer segmentation to allow aggregate and SKU planning
- Create a center of excellence for demand analytics, employing the latest technologies, and appoint an internal demand management champion.