Oracle Demand Planning is a web-based application that enables organizations to produce unconstrained forecasts for future demand and generate tactical, operational, and strategic business plans. Demand Planning captures and processes information from multiple sources and consolidates demand so that it can be summarized by item, product line, region, time, and organization. Demand Planning uses Oracle Workflow and supports control mechanisms based on an event or calendar.
Demand Planning collects time series information from many data sources including shipments, bookings, opportunities, and other forecasts. Feeds into ODP can be time series data from Oracle Shipping, bookings from Oracle Order Entry, or a forecast from Oracle Manufacturing or a third-party application. ODP allows the data to be viewed at multiple levels of aggregation. It enables you to summarize the item, geographic, organization, and time hierarchies. When used with the Advanced Planning and Scheduling system, it creates the demand forecast that will drive the planning and scheduling systems.
You should be familiar with the following terms that will be used throughout this chapter:
- Scenario A set of circumstances that might occur in the future, that you are creating a demand forecast for.
- Measure An attribute of the plan that determines its success as a plan. One such attribute might be forecast error. Another might be the profit yielded by the plan.
- Forecast A prediction of future demand in quantitative terms.
- Baseline forecast The forecast generated by the assigned forecasting method for a given scenario, before planners make any changes based on local knowledge.
- Dimension An attribute of the forecasts and planning data, that you might want to see quantities or measures aggregated by. For example, you might want to see the forecast error at various levels in the product dimension.
- Hierarchy A number of levels within a hierarchy. For example, the geography dimension can contain the location hierarchy. This extends from the customer location through countries and regions to “All Geographies.”
- Time series data Any collection information about something varying over time. Demand or shipment information is an obvious example. Quality data or machine failure information might be others.