Forecasts are the foundation of every company's business plans. Accurate forecasts enable a company to maintain appropriate staffing levels, set realistic sales targets, create effective marketing promotion mixes, build budgets that match operating expenses, and keep inventory at the right level to meet, but not exceed, customer demand.
Accurate forecasting must involve a range of activities across different departments within an organization and/or different organizations. For example, in a manufacturing company, the forecasting process may require collaboration among its manufacturing, sales and marketing departments, as well as outside companies such as raw material suppliers, wholesalers, etc. Typically, the approaches to forecasting may vary significantly from company-to-company and division-to-division. While forecasting may only involve a simple query in some companies or divisions, it can be an extensive planning process in other companies or divisions. Forecasting may be very high level in some organizations, and very detailed in other organizations. As a result, forecast data models maintained by different companies and divisions typically support varying levels of detail of the forecast data being stored.
Further, individual companies usually store forecast data in their own unique way. For example, a raw material supplier may organize forecast data in a way that is very different from the way that a manufacturing company may organize forecast data. Even within a single manufacturing company, that company may use many different application programs that employ very different schemas and data models. For example, a demand forecasting program may use a data model that is very different from the data model used by a supply forecasting program. The use of customized data models by a company and by internal applications has the advantage that it allows information to be modeled in a way that is appropriate for the business needs of the company.
Unfortunately, because of this diversity in the data models, it is not easy for the company to share its information with other companies or for internal applications to share their information.
Various attempts have been made to define standard data models so that information can be more easily shared between different organizations and applications to allow the consolidation of their disparate forecast processes into a single, comprehensive forecast. However, these data models have not been able to achieve sufficient data integration and simplicity. As a result, existing forecasts are typically inaccurate, ineffectual and difficult to verify.
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