Contributors: Ruvisha Pillay, Bill Tonetti, Tracy Roche, Perry Britt

A Pivot Forecasting model is a collection of data and time-phased measures. It is sourced from your Netstock App and presented in a rich and functional user interface, providing a framework for a rigorous forecasting process.

A model can support several forecasting objectives. It can save time in the forecasting process, improve forecasting accuracy and promote accountability for forecast accuracy. It can also be used to align the sales forecast with budgeting and financial plans. One can easily switch between the various role-based perspectives. It can also be used to forecast in price, cost or margin, in addition to units.

The Pivot Forecasting model is made up of dimensions. Those dimensions, including:

Let’s explore each of these dimensions further.

An attribute is used to describe something, like a quality, feature or characteristic. With Pivot Forecasting, attributes are identified as key; non-key; editable; or numeric. Once a model is accessed, the attribute selector at the top-left of the screen will be loaded with all attributes that are available within the selected model.

Key attributes rarely change. They include items, locations or location groups, and key customer groups. The combination of distinct key attributes determines the lowest level of detail for forecasting. Examples of these are Customer ID and Item ID.

Non-key attributes often describe one or more keys. For example, an item which is a key may be part of a product group or category which is non-key. All grouping dimensions in your inventory optimization app are automatically added to your Pivot Forecasting model. Users can forecast and plan with hierarchies made from any type of attribute. For example, forecasts can be managed at item, region or customer groupings, or any combinations of these attributes.

Editable attributes can be populated in an ad hoc way, either individually or in groups by end users. Unlike most attributes which are imported, editable attributes can be changed by the user to flag items or customers for future follow-up. Editable attributes can also be system-populated descriptors such as ABC, forecastability, or velocity. In Pivot Forecasting, editable attributes begin with "Z_", and they can be updated from the right-click menu on the tree, or interactively using the Data - Attributes dashboard panel.

Pivot Forecasting models can be either monthly or weekly. The number of historic and future periods is determined by your inventory planning setup.

Time-based data is relevant to the model’s purpose. This includes transactional data for forecasting, such as shipment, invoice or sales order history, and open sales-orders. Another example of time-based data is the forecast itself, predicting future demand.

Planning items have a base unit of measure which will be the same as the base unit of measure in the inventory planning app. Conversions in Pivot Forecasting enable viewing and adjusting plans in financial or alternate units of measure.

Reference measures are configurable and used in a variety of ways. For example, reference measures can be configured for role-based demand plans reflecting sales-team forecasts. Reference measures can also display results of custom calculations. They are also configured to archive previous forecasts for forecast performance analysis. Pivot Forecasting is initially set up with four preset reference measures - one for collaborative sales forecasts and three for forecast scenario archives.

A Pivot forecasting model is a database consisting of attributes, conversions, and time-based data to support an advanced and rigorous forecasting process. Forecasts are developed and managed within the Pivot Forecasting model, and most other data is automatically sourced directly from your inventory planning app.

Have you read the related articles? Check out our Pivot Forecasting collection!