Business forecasting undertaken by managers is prone to numerous biases and limited market knowledge. Over the past few years, various statistical tools and AI programs have assisted managers in improving their forecast accuracy. Human instincts combined with statistical tools and AI programs can help construct a reliable forecasting tool. However, many companies still follow an archaic process of quarterly forecasts, which is estimated by managers of respective units. The individual estimates are then consolidated and negotiated to match the final target. In this article, our experts unveil some of the common challenges in the business forecasting process.

Common Challenges in the Business Forecasting Process

Estimating Forecast for New Products

Almost all forecast tools rely on historical data to predict future outcomes. The managers do not have any historical data to refer to in the case of evaluating performance for a new product. This problem is particularly evident in industries such as consumer electronics, fashion, and books, where new product introductions account for the bulk of sales. So, an error in forecasting can result in a loss of sales opportunity. To tackle such problems, companies usually resort to historical data of similar products and assortments to build a forecast.

Lack of Quantitative Analysis

Despite the availability of numerous forecasting tools and software, most managers simply ignore using quantitative analysis for their forecasts. A majority of the managers are habituated to use a simplistic approach such as linear extrapolation and also adjust the forecast techniques to align with their instincts. Currently, business forecasting is largely inaccurate as organizations lack access or expertise to use forecasting tools.

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Tackling Variance in Sales Volume

An organization’s sales figure is highly dynamic and dependent on a large number of factors such as economic, political, cultural, and legal factors. It can be challenging to accurately estimate the sales volume as such factors completely disrupt the historical pattern. Various predictive models are being developed to take inputs from multiple factors such as that enhance the process of business forecasting.

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