An innovative demand forecasting approach for the server industry

Research has been conducted on approaches using social media information to improve demand forecasting accuracy in business-to-customer industries. However, such social media information is not applicable to business-to-business (B2B) industries, as a result of a lack of end-consumer evaluations. This raises a few interesting questions, including whether there may be any external information that could be used to improve B2B demand forecasting, and whether practical approaches may be possible to collect and utilize useful external business information. In this study, we develop an innovative and intelligent demand forecasting approach and apply it to a B2B server company based in the United States. We first implemented time series and machine learning models based on sales data and selected the best-fitting model as a baseline, and then used a web crawler and Google Trends to collect related market signals as external information indices for the server industry, which were finally incorporated into the selected baseline model to adjust forecasting results to account for demand fluctuations. Experimental results demonstrate that the baseline model achieved an out-of-sample mean squared error (MSE) of 19.77 without considering the collected external information indices, and 11.87 when external information was incorporated. Therefore, our proposed approach significantly improved forecasting accuracy, demonstrating an improvement of 63.1% in terms of MSE, 44.1% in terms of mean absolute error, and 61.2% in terms of root mean square percentage error. Thus, this study sheds light on the value of external information in demand forecasting for B2B industries.