Everything You Need to Know About Demand Forecasting
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.
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
Is demand management illusively complex? Here's a look at some best practices in demand management and characterization.
A demand-driven supply chain management process, no matter the industry, is built based on some fundamental principles. These principles are applied taking into consideration the requirements of the particular industry or company involved.
Here are some guidelines on selecting the right statistical forecasting methods for your business.
A Demand-driven Supply Chain (DDSC) is defined as a supply chain management method focused on building supply chains in response to demand signals. The main force of DDSC is that it is driven by customer demand. In comparison with the traditional supply chain, DDSC uses the pull (Demand pull) technique. It gives the market opportunities to share more information and to collaborate with others in the supply chain.
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.