Business Cycle Forecasting

How can one provide business cycle predictions to the management team? There are several possible routes to take.

The main factor to decide upon is balancing the time needed for forecasting against the perceived value of the information. For example, if a company has a stable sales base that rarely varies, irrespective of what stage the business cycle is currently in, then there is no reason to track cycles very carefully. Also, if the analysis function is understaffed, the needs of day-to-day activities will probably supercede any demands for forecasting. However, if a manager can prove that the deleterious effects of not tracking business cycle conditions will lead to company losses that significantly exceed the cost of having extra staff on hand to perform the analysis, then this second factor disappears.

Let us assume that there is some time available for forecasting work, and that business cycles have a sufficient impact on company conditions to be worthy of review. If so, here are some possible actions to take to obtain, analyze, and report on business cycle forecasts. They are listed in ascending order of difficulty:

Report on published forecasts. There are forecasts published by nearly every major business magazine for the economy at large, which can be easily extracted, reformatted into an internal report, and presented to management, perhaps as part of the monthly financial statements. Several key advantages are that the information is fairly accurate for the entire economy, it is prepared by professional forecasters, and the information is essentially free. The problem is that each company operates in a smaller industry within the national economy, and as such is subject to “mini” business cycles that may not move in lockstep with that of the national economy. For this reason, the reported information may only be generally relevant to a company’s specific situation.
Subscribe to a forecasting service. A company can pay a significant fee, probably in the five to six figure range, to a forecasting service for more specific reports that relate to the industry in which it operates. This is a good approach for those organizations that do not have the resources to gather, summarize, and interpret economic data by themselves. However, some industries are too small to be serviced by a specialized forecasting service, or the fee charged is considered too high in comparison to the value of the information received.
Develop an in-house forecasting model. In cases where a company either wants to run its own forecasting model, or there are no forecasting services available that can provide the information, and it is deemed relevant, it is time to try some in-house forecasting. This effort can range from a minimalist approach to a comprehensive one, with each level of effort yielding better results. The first step is to find the right kinds of data to accumulate, followed by implementing a data gathering method that yields reliable data in a timely manner. Then, work with management to determine what resulting information is desired (usually a sales estimate). Then arrive at a methodology for translating the underlying data into a forecast. Next, develop a standard reporting format that imparts the results to management. This report should include the underlying assumptions and data used to arrive at the forecast, so that any changes in the assumptions are clearly laid out. Finally, there should be a methodology for comparing the results against actual data, and adjusting the forecasting methodology based on that information. Though this approach is a time-consuming one, it can yield the best results if a carefully developed forecasting system is used.

For example, let us assume that a controller of a sport rack company has elected to use the last of the above options for creating forecasting information. Sport racks is a very small niche market that creates and sells racks for skis, snowboards, bicycles, and kayaks that can be attached to the tops of most kinds of automobiles. The controller wants to derive a forecasting system that will give management an estimate of the amount by which projected sales can be expected to vary. She decides to sub-divide the market into four categories, one each for skis, snowboards, bicycles, and kayaks. Based on a historical analysis, she finds that 25% of ski purchasers, 35% of snowboard purchasers, 75% of bicycle purchasers, and 30% of kayak purchasers will purchase a car-top rack system to hold their new equipment. The typical delay in these purchases from the time when they bought their sports equipment to the time they bought sport racks was six months. The controller finds that she can obtain new sports equipment sales data from industry trade groups every three months. Given the lag time before users purchase car-top racks, this means that she can accumulate the underlying data that predicts sport rack sales and disseminate it to management with three months to go before the resulting sport rack sales will occur. Thus, she concludes that this is usable data. The next task is to determine the company’s share of the sport rack market, which is readily obtainable from the industry trade group for sport racks, though this information is at least one year old. Given the stability of sales within the industry, she feels that this information is still accurate. She then prepares the report shown in the following table. It shows total sports equipment sales for the last quarter, uses historical percentages to arrive at the amount of resulting sport rack sales, and then factors in the company’s market share percentage to determine the forecasted sales of each type of sport rack. By comparing this information to the previously forecasted sales information, the report reveals that the company should significantly ramp up its production of snowboard sport racks as soon as possible.

Description	Sports Equipment Unit Sales	% Buying Sport Racks	Company Market Share	Forecasted Company Unit Sales	Original Company Forecast	Variance

Ski	3,200,000	25	40%	320,000	300,000	+20,000
Snowboard	2,700,000	35	40%	378,000	300,000	+78,000
Bicycle	2,500,000	75	30%	562,500	550,000	+16,500
Kayak	450,000	30	30%	40,500	45,000	-4,500

The example used was for an extremely limited niche market, but it does point out that a modest amount of forecasting work can yield excellent results that are much more company-specific than would be the case if a company relied solely on the forecasts of experts who were only concerned with general national trends. For most companies, there will be a number of additional underlying indicators that should be factored into the forecasting model; however, the work associated with tracking these added data must be compared to the benefit of more accurate results, so that a manager arrives at a reasonable cost-benefit compromise.