Welcome to the world of IBP models! The functions of a model are diverse and powerful. In this article, we'll explore how these models operate by gathering and transforming data from your ERP to meet your planning needs.

A model is a collection of data and time-phased measures. It is sourced from your ERP and presented in a rich and functional user interface, providing a framework for business planning objectives.

Some of the functions of a model include:

Multiple models can be used for different purposes, and forecasts and other data can be shared between them. A demand planning model for example, can share its final forecast with a supply planning model - even if the demand planning forecast is in monthly periods, at a customer-item level; and the supply plan is in weekly periods, at a warehouse-item level! The demand model forecast is an important input to the supply plan.

A model has multiple dimensions. The list of dimensions include:

Let’s explore these further.

An attribute is used to describe something, like a quality, feature or characteristic. 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 current model. These are mostly sourced from ERP master data.

Key attributes rarely change. They may 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 are used for descriptions, like item description and customer name. They are also used to represent different levels of groupings that form hierarchies. For example, an item which is a key may be part of a product group or category which is non-key. They can also be used to segregate or segment data, for example on supplier or status.

A key attribute combination (i.e. each planning item) can have only one value for each non-key attribute. In the example below, one of the attributes is "Product group," which tells us which group the item belongs to (gifting, clothing, etc.). If an item could be part of more than one product group (for example, a fancy dress could be part of both the “gifting” and “clothing” product groups), it would create a problem because non-key attributes cannot have multiple values for the same planning item. “Product group" would then have to become part of the key 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, or to group other attributes in a new way. Editable attributes can also be system-populated descriptors such as ABC, forecastability, or velocity. 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. They are also used with several built-in features, such as Item Classifications.

Models can be either monthly, weekly or daily. The number of historic and future periods is determined by planning needs.

Time-based data, called measures, is relevant to the model’s purpose. This includes transactional data for demand and supply planning, such as invoice or sales order history; open sales orders; purchase and production orders and on-hand inventory. It also includes forecasts and other kinds of data that might be included to support planning, such as POS or budget data.

Planning items have a base unit of measure. Conversions within the app enable viewing data in alternate units of measure. Conversions can be financial (price, cost, margin), logistical (weight, volume, pallets, cases), or production-related (hours).

Reference measures are configurable time-series data and are used in a variety of ways. For example, reference measures can be configured for role-based demand plans reflecting sales-team forecasts or a consensus forecast. They can also be data from external sources, like POS data or customer supplied forecasts. Reference measures can also display results of custom calculations, such as the percentage of budget sold in the current month. They are also configured to archive previous forecasts for forecast performance analysis.

Policies such as safety stock, lead times and minimum order quantities are required to produce meaningful supply plan recommendations. Some supply models may require bills of material and distribution requirement planning links. Lead times, minimum order quantities and related ordering constraints are typically maintained and sourced from your ERP, while safety stock and replenishment strategies are typically determined in the app.

If you are creating your pilot model, keep these design principles in mind:

Models are built by sourcing data from your ERP system, and sometimes from other sources, and presenting it in the app. After importing the data, the app functionality works to create a fully functional model that caters to all your planning needs. It is critical that the ERP data is accurate and complete. What you put in, is what you get out!