Magazine article Business Credit

Forecasting: A Critical Link in Business Performance Management

Magazine article Business Credit

Forecasting: A Critical Link in Business Performance Management

Article excerpt

Today's rapidly changing business environment demands that organizations have a firm grasp of their internal operations while maintaining the responsiveness and flexibility to exploit market trends and opportunities as they present themselves. Demonstrating an understanding of these issues and an ability and willingness to respond to changes are critical indicators to Wall Street that the company's long-term prospects are in capable hands. The ability to quickly and accurately align and realign resources with corporate strategies has become a necessity. With this in mind, business is starting to awaken to the growing need for Business Performance Management, both from a process standpoint as well as a technology perspective.

Integral to Business Performance Management success is a robust approach to forecasting. Forecasts--both revenue and cost--are the starting point for managing expectations both internal and external to the organization. Recognizing this, the organization's continued or expanded adoption of statistical forecasting and alerting technologies will drive more efficient planning and budgeting processes while keeping the resulting work products realistic, based on underlying trends not immediately obvious to the organization. As accuracy of forecasts improves, so does the likelihood that strategy implementation improves because businesses are acting on numbers and trends that better reflect the likely outcomes of the actions they have undertaken.

Specific benefits of accurate forecasting include:

* Reduced likelihood of earnings surprises

* Market change responsiveness

* Improved decision-making

* Reduced time spent on non-value-added activities

* Organizational "reality checks" on revenue projections and cost budgets

* Proactive identification of future consequences of current trends

Wall Street Worries--Earnings Surprises

"Improving the quality of the original earnings expectation or forecast is an obvious first step in attempting to avoid earnings surprises." (1)

This "improvement" is obviously easier said than done. The starting point, however, is to have a basic understanding of how you will improve the earnings expectation. In short, if you had a forecasting tool and an alerting technology, how and where would you deploy them? Warning signs of possible earnings surprises exist both inside and outside the organization. The decision to deploy technology to help uncover these warning signs must be based in part on some sort of cost benefit analysis. There exists a vast array of symptoms one could scan for in the larger environment, such as natural disasters, disruptions in raw material flow, or unexpected economic activity. In most instances, trying to build monitoring and forecasting technologies aimed at these external environmental factors into your organization's Business Performance Management application will yield questionable returns when compared to simple "paying attention" by people within your organization.

The reason for this is that there are generally no readily available data feeds capable of getting the appropriate data into an application in a timely enough manner to enable meaningful automated data analysis. In simply deciding what data to feed in and scouring the environment for the data itself, you will generally find the answers you are looking for. Further analysis may provide a certain elegance, but it will probably not yield significant additional value.

The more appropriate uses of forecasting and alerting technologies are to point them inwards, towards operations and specific financial indicators wherein trends may not be immediately obvious to management. In these instances, statistical forecasting can add value in excess of the cost of gathering information; therefore, application of the technology is a wise investment. Taking into account the need for raw data from which to generate business intelligence (that is, knowledge), the two most productive places in which to deploy forecasting and alerting technologies are the areas of profitability and liquidity. …

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