How to use demand planning statistical models to enhance the value of your sales input during the forecasting process.
A guide on how to improve material planning using a more detailed volume allocation of customer orders. It is a common business practice to write up yearly contracts for the volume. Very often, this is done to extend volume discounts to the customer. That is obviously a benefit to the customer. The supplier benefits by knowing how much to budget for in terms of production through the year. They can also count on the revenue coming in.
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's the effect of customer order lead time in inventory management and safety stock calculations?
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.
To set the foundation for this discussion, let us first look at the definition of order lead time. Order lead time is the time gap between the date when a customer places an order and when they expect to receive the product. Typically, in a B2B environment, the expectation is that there will be some gap between the two dates, and in many cases, this gap can be negotiated.
Imagine a demand planner working with 10,000 unique combinations. One of the not so envious tasks for this person would be to generate statistical forecast for all these combinations. These days, the statistical forecasting tools available on the market can forecast these combinations using a list of forecasting methods and figure out which method works best for a particular combination.