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Business Forecasting

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Business forecasting has always been one component of running an enterprise. However, forecasting traditionally was based less on concrete and comprehensive data than on face-to-face meetings and common sense. In recent years, business forecasting has developed into a much more scientific endeavor, with a host of theories, methods, and techniques designed for forecasting certain types of data. The development of information technologies and the Internet propelled this development into overdrive, as companies not only adopted such technologies into their business practices, but into forecasting schemes as well. In the 2000s, projecting the optimal levels of goods to buy or products to produce involved sophisticated software and electronic networks that incorporate mounds of data and advanced mathematical algorithms tailored to a company's particular market conditions and line of business.

Business forecasting involves a wide range of tools, including simple electronic spreadsheets, enterprise resource planning (ERP) and electronic data interchange (EDI) networks, advanced supply chain management systems, and other Web-enabled technologies. The practice attempts to pinpoint key factors in business production and extrapolate from given data sets to produce accurate projections for future costs, revenues, and opportunities. This normally is done with an eye toward adjusting current and near-future business practices to take maximum advantage of expectations.

In the Internet age, the field of business forecasting was propelled by three interrelated phenomena. First, the Internet provided a new series of tools to aid the science of business forecasting. Second, business forecasting had to take the Internet itself into account in trying to construct viable models and make predictions. Finally, the Internet fostered vastly accelerated transformations in all areas of business that made the job of business forecasters that much more exacting. By the 2000s, as the Internet and its myriad functions highlighted the central importance of information in economic activity, more and more companies came to recognize the value, and often the necessity, of business forecasting techniques and systems.

Business forecasting is indeed big business, with companies investing tremendous resources in systems, time, and employees aimed at bringing useful projections into the planning process. According to a survey by the Hudson, Ohio-based AnswerThink Consulting Group, which specializes in studies of business planning, the average U.S. company spends more than 25,000 person-days on business forecasting and related activities for every billion dollars of revenue.

Companies have a vast array of business forecasting systems and software from which to choose, but choosing the correct one for their particular needs requires a good deal of investigation. According to the Journal of Business Forecasting Methods & Systems, any forecasting system needs to be able to facilitate data-sharing partnerships between businesses, accept input from several different data sources and platforms, operate on an open architecture, and feature an array of analysis techniques and approaches.

Forecasting systems draw on several sources for their forecasting input, including databases, e-mails, documents, and Web sites. After processing data from various sources, sophisticated forecasting systems integrate all the necessary data into a single spreadsheet, which the company can then manipulate by entering in various projections—such as different estimates of future sales—that the system will incorporate into a new readout.

A flexible and sound architecture is crucial, particularly in the fast-paced, rapidly developing Internet economy. If a system's base is rigid or inadequate, it can be impossible to reconfigure to adjust to changing market conditions. Along the same lines, according to the Journal of Business Forecasting Methods & Systems, it's important to invest in systems that will remain useful over the long term, weathering alterations in the business climate.

One of the distinguishing characteristics of forecasting systems is the mathematical algorithms they use to take various factors into account. For example, most forecasting systems arrange relevant data into hierarchies, such as a consumer hierarchy, a supply hierarchy, a geography hierarchy, and so on. To return a useful forecast, the system can't simply allocate down each hierarchy separately, but must account for the ways in which those dimensions interact with each other. Moreover, the degree of this interaction varies according to the type of business in which a company is engaged. Thus, businesses need to fine-tune their allocation algorithms in order to receive useful forecasts.

According to the Journal of Business Forecasting Methods & Systems, there are three models of business forecasting systems. In the time-series model, data simply is projected forward based on an established method—of which there are several, including the moving average, the simple average, exponential smoothing, decomposition, and Box-Jenkins. Each of these methods applies various formulas to the same basic premise: data patterns from the recent past will continue more or less unabated into the future. To conduct a forecast using the time-series model, one need only plug available historical data into the formulas established by one or more of the above methods. Obviously, the time-series model is the most useful means for forecasting when the relevant historical data reveals smooth and stable patterns. Where jumps and anomalies do occur, the time-series model may still be useful, providing those jumps can be accounted for.

The second forecasting model is cause-and-effect. In this model, one assumes a cause, or driver of activity, that determines an outcome. For instance, a company may assume that, for a particular data set, the cause is an investment in information technology, and the effect is sales. This model requires the historical data not only of the factor with which one is concerned (in this case, sales), but also of that factor's determined cause (here, information technology expenditures). It is assumed, of course, that the cause-and-effect relationship is relatively stable and easily quantifiable.

The third primary forecasting model is known as the judgmental model. In this case, one attempts to produce a forecast where there is no useful historical data. A company might choose to use the judgmental model when it attempts to project sales for a brand new product, or when market conditions have qualitatively changed, rendering previous data obsolete. In addition, according to the Journal of Business Forecasting Methods & Systems, this model is useful when the bulk of sales derives only from a relative handful of customers. To proceed in the absence of historical data, alternative data is collected by way of experts in the field, prospective customers, trade groups, business partners, or any other relevant source of information.

Business forecasting systems often work hand-in-hand with supply chain management systems. In such systems, all partners in the supply chain can electronically oversee all movement of components within that supply chain and gear the chain toward maximum efficiency. The Internet has proven to be a panacea in this field, and business forecasting systems allow partners to project the optimal flow of components into the future so that companies can try to meet optimal levels rather than continually catch up to them.

In integrated supply chain networks, for instance, a single company in the supply chain can enter slight changes in their own production or purchasing schedules for all parties to see, and the forecasting system immediately processes the effects of those changes through the entire supply chain, allowing each company to adjust their own schedules accordingly. With business relationships and supply chains growing increasingly complex—particularly in the world of e-commerce, with heavy reliance on logistics outsourcing and just-in-time delivery—such forecasting systems become crucial for companies and networks to remain efficient.


Allen, David. "Looking Forwards." Management Accounting. March 2000.

Culberston, Scott; Jim Burruss; and Lee Buddress. "Control System Approach to E-commerce Fulfillment." Journal of Business Forecasting Methods & Systems. Winter 2000/2001.

Jain, Chaman L. "Which Forecasting Model Should We Use?" Journal of Business Forecasting Methods & Systems. Fall 2000.

Lapide, Larry. "New Developments in Business Forecasting: The Internet Does Not Eliminate the Need to Forecast." Journal of Business Forecasting Methods & Systems. Fall 2000.

McKeefry, Hailey Lynne. "Adding More Science to the Art of Forecasting." Ebn. March 5, 2001.

Safavi, Alex. "Choosing the Right Forecasting Software and System." Journal of Business Forecasting Methods & Systems. Fall 2000.

"Squeeze the Process." CMA Management. Oct 1999.

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over 2 years ago

bussiness forecasting

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6 months ago

Business Forecasting

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about 1 year ago

i see you got really very useful topics I am satisfied to discover this publish very useful for me

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about 1 year ago

Most crucially, technological forecasts should take into consideration the possible development of technologies from other fields that could spill over and affect the course of development in the field directly studied.

Read more: Technological Forecasting - Development, Technology, Foresight, and Studies

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over 6 years ago

In forecasting whether for the internet or conventional products, there is

1. A basic set of potential customers

2. A set of basic needs

3. The question of substitutability of an offering to meet the basic need.

This gives the broad population.

When it comes to taking a call on the exact demand, competence takes precedence over static estimates. Innovation and flexibility can make difficult forecasts come alive. As is widely acknowledged today market space is where the action is , not Market Place!