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.
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.
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.
In an ideal world, demand and supply would be steady and predictable, resulting in optimal capacity utilization and no back orders or missed customer orders. However, in the actual supply chain world variations in actual sales vs. projected sales result in lower forecast accuracy, and either overstocking or stock out situations.