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Forecasting is used in customer demand planning in everyday business for manufacturing and distribution companies. While the veracity of predictions for actual stock returns are disputed through reference to the efficient-market hypothesis, forecasting of broad economic trends is common. Such analysis is provided by both non-profit groups as ...
Demand forecasting plays an important role for businesses in different industries, particularly with regard to mitigating the risks associated with particular business activities. However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis. [4]
The S&OP process includes an updated forecast that leads to a sales plan, production plan, inventory plan, customer lead time (backlog) plan, new product development plan, strategic initiative plan, and resulting financial plan. Plan frequency and planning horizon depend on the specifics of the context. [1]
Demand management is the responsibility of the marketing organization (in his definition sales is subset of marketing); 2. The demand "forecast" is the result of planned marketing efforts. Those planned efforts, not only should focus on stimulating demand, more importantly influencing demand so that a business's objectives are achieved.
The history of integrated business planning can be traced back to sales and operations planning (S&OP), a process that balances demand and manufacturing resources. According to Gartner, there is a 5-stage maturity model for S&OP, and in this model, integrated business planning is denoted as Phased 4 & 5. [1]
Forecasting aims to predict what the future will look like, while planning imagines what the future could look like. Planning according to established principles - most notably since the early-20th century [2] - forms a core part of many professional occupations, particularly in fields such as management and business.
Predictive analytics is a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [3]
The Open Source initiative was originally called CFAR (pronounced See-Far, for Collaborative Forecasting and Replenishment). According to an October 21, 1996 Business Week article entitled Clearing the Cobwebs from the Stockroom, New Internet software may make forecasting a snap , "Benchmarking developed CFAR with funding from Wal-Mart , IBM ...