How do you structure a Demand Plan? Whether you are forecasting in a spreadsheet or a sophisticated system, you need to define the structure that is the rows and the columns of what you forecast. Planning is structured into dimensions such as Item, Sales Domain and Time. And dimensions are structured into hierarchies, levels and attributes. Data for these structures are held in data streams or measures. Let's look at dimensions first. Dimensions are structures that group and categorize data in order to enable planners to analyze, answer business questions and create forecasts.

Typical Demand Planning dimensions are: Item, Sales Domain and Time. But they can also include Organization, Channel, Demand Type, Distribution Network, Suppliers and so on. Now let us look at Demand Planning hierarchies. Hierarchies define how dimension data is stored and displayed.

Multiple hierarchies can exist in each dimension.

Some typical hierarchy examples are: Gregorian calendar, inside the Time Dimension Product Category, inside the Item Dimension and Customer Group, inside the sales domain dimension. Levels of the groupings that make up the structure of each hierarchy. Typical levels for the product category hierarchy

in the product dimension are: Item to Group to Product Category to All Products Typical levels for the customer category

hierarchy in the sales domain dimension are: shipped to Address to Zip Code then City, Country, Customer and finally up to all customers. And typical levels for the Gregorian calendar

hierarchy and the time dimension are: Day to Month to Quarter to Year. Whereas Manufacturing Calendar hierarchy will only contain Day to Week, to Year. Data in a Demand Plan should be visible and editable at the various levels of each hierarchy. Traditional Demand Planning solutions have very rigid, structured hierarchies.

They have built like this to conform to system and data constraints. However, next generation planning solutions can now build digital twins of businesses, creating more complex but realistic relationship diagrams, and these can be used to create more accurate and flexible demand plans. There is one important factor to be aware of with level data, which is that data within each level of a hierarchy must be logical. Data held at levels are called level members and each level member needs to have a unique parent, a one to one relationship, not a one to many.

In the example shown the product thing one is shown belonging to two categories within the same hierarchy, but it can only belong to one. This is because when values are changed, the adjustments need to be completed up and down the hierarchy, and this cannot be achieved if a level member exists in multiple places at the same time. Hierarchies need to be logical for aggregation and disaggregation functions to work properly. Now let us look at attributes.

Attributes can be attached to level members and are useful for providing extra business insight without hierarchical, logical restrictions. For example, a product and the item level of the product category hierarchy in the item dimension might have attributes of Weight, Color, Life Cycle Status and many other useful pieces of information. But unlike levels, attributes do not need to be unique for each member. Notice that the attribute of item color can be purple for both of these examples.

Finally, there are data streams which are often called measures. The information in your Demand Plan is structured by dimensions and hierarchies, but the data is stored and interacted with by planners in measures, and these measures can be past or future, internal or external and editable or not editable. So typical demand planning measures, sales history, consensus forecast and forecast accuracy. To summarize then, dimensions, hierarchies, levels, attributes and measures are used to structure a demand plan from the lowest to the highest levels of granularity and from the earliest to the latest points of time.

These structural elements define how demand planning solutions work.