How to use demand planning statistical models to enhance the value of your sales input during the forecasting process.
Demand Planning directly affects the business financial plan, pricing, capex decisions, customer segmentation and resource allocation. Considering the criticality and implication of this process, Demand Planners and Managers need to continually evaluate their current Demand Planning process and ensure that the Demand Plan generated is holistic, relevant and timely.
What is Demand Forecasting? Demand Forecasting is the process in which the historical sales data is used to develop an estimate of expected forecast of customer demand.
How global businesses can build a collaborative demand planning process
Here are some guidelines on selecting the right statistical forecasting methods for your business.
During the last storm, I was watching the snow plows go to work and thinking about the amount of planning that must go into the resources needed to deal with the snow - what with salt, and plows needed. That must be a whole supply chain.
Should you factor returns in your forecast error calculation? In this article, we’ll use a sample data set, to demonstrate if you should consider returns when calculating your forecast errors.
An open order is defined as an order placed by the customer which is under process and is yet to be fulfilled by the supplier. For effective analysis, open order data needs to be recorded daily in an ERP system. A minimum of twelve to eighteen months of open order history is required for your sales forecasting analysis and fine-tuning process. A shorter period could render unreliable and skew the results of your data analysis.
Demand sensing helps us identify the actual customer order trends and helps us improve the near-term forecast. Strategic actions like demand shaping for knowingly increasing or decreasing the demand for the product can be undertaken by sensing demand signals.
Over the past few months, we’ve been running simulation tests on different demand forecasting methods: Winter’s additive & multiplicative, seasonal and robust seasonal. Then, we used MAPE to determine the forecast accuracy for each method. Here’s what we found.