The demand review is an essential step in the Sales & Operations Planning (S&OP) cycle. It’s crucial to have a good view of the actual demand. This forms the basis for accurate demand planning as input for the other steps in the process. Ideally, all the different voices within the organization are heard in this phase. To set up truly collaborative demand planning, Supply Chain also has to involve Sales in addition to Finance and Marketing.
Since your sales colleagues are closest to your customers, they hold valuable information for optimizing your supply chain operations. However, it’s not easy to convince them to invest their time in predicting the demand. From our experience, we know that a quantitative approach is the best way to win Sales over. We’re happy to share our best practices with you.
Typically, it is quite easy to manage the demand for a large part of the product portfolio based on a statistical forecast. By making a segmentation between the products with a steady, normal demand and those with high demand variability, you allow your sales reps to focus on the exceptions. This will save them a lot of time without compromising on the quality of their forecast. By also letting your sales people check the items that they don’t need to focus on, you help to reassure them.
Supply Chain doesn’t want Sales to make changes to the forecast just for the sake of it, so it’s a good idea to allow changes but to set minimum adjustment limits. For example, you could agree that Sales may only overwrite the statistical forecast if the adjustment deviates 20% or more from the proposed value. An additional rule could be that the forecast can only be overwritten if a reason is given. On the one hand this will subdue the urge of Sales to overwrite the forecast, and on the other it will stimulate Supply Chain to listen to Sales’ reasoning about market demand.
Calculating the forecast accuracy based on the source can significantly improve the demand forecast. In a simple example: suppose that the statistical forecast estimates that 80 products will be required, while Sales thinks that 100 products will be sold and Marketing predicts 120. If the actual demand turns out to be 98, then it’s quite clear that Sales provided the highest value-add in the collaborative demand planning process. Visualizing this in value-add reports will encourage all parties to give their best estimation. Some organizations take things even further and make monthly calculations per sales representative of how closely their predicted demand matches the actual demand. Their outcomes are then weighted. The demand forecast of a sales rep who achieved a very high accuracy over the past year will have a bigger impact on the consensus forecast than the demand prediction of a sales rep who produced less accurate forecasts. The ones who have less impact will make an extra effort to improve their forecasting performance.
Some companies ask whether they should reward Sales for good forecasts or when the predicted sales figure is exceeded. This is the topic of much debate, but we believe it is not advisable to financially reward Sales in such cases. In fact, forecast-driven incentives are never a good idea. First of all, Sales will always find a way to manipulate the calculation and raise the bonus. Secondly, rewards for better sales ultimately cause the organization a lot of pain. If Sales consciously issues a low forecast to make it easier to achieve the targets, that affects the rest of your organization: you will suffer product shortages, your service level will drop and that will eventually lead to dissatisfied customers.
Setting up a solid collaborative demand planning process is no walk in the park. It takes time and energy to get Sales on board. However, it’s absolutely worth the effort and there are some best practices to support you along the journey. By helping Sales to save time, by showing them their added value and by giving them enough freedom to have a justified impact on the process, you will win the support of Sales and their valuable input into the demand planning process.