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An innovative demand forecasting approach for the server industry
Technovation ( IF 12.5 ) Pub Date : 2021-08-11 , DOI: 10.1016/j.technovation.2021.102371
Yu-Chung Tsao , Yu-Kai Chen , Shih-Hao Chiu , Jye-Chyi Lu , Thuy-Linh Vu

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.



中文翻译:

服务器行业的创新需求预测方法

已经对使用社交媒体信息提高企业对客户行业需求预测准确性的方法进行了研究。但是,由于缺乏对最终消费者的评估,此类社交媒体信息不适用于企业对企业 (B2B) 行业。这引发了一些有趣的问题,包括是否有任何外部信息可用于改进 B2B 需求预测,以及是否有可能收集和利用有用的外部业务信息的实用方法。在这项研究中,我们开发了一种创新的智能需求预测方法,并将其应用于一家位于美国的 B2B 服务器公司。我们首先基于销售数据实现了时间序列和机器学习模型,并选择了最合适的模型作为基线,然后使用网络爬虫和谷歌趋势收集相关市场信号作为服务器行业的外部信息指标,最终纳入所选的基线模型,以调整预测结果以应对需求波动。实验结果表明,基线模型在不考虑收集到的外部信息指数的情况下实现了 19.77 的样本外均方误差 (MSE),在包含外部信息时实现了 11.87。因此,我们提出的方法显着提高了预测精度,在 MSE 方面提高了 63.1%,在平均绝对误差方面提高了 44.1%,在均方根百分比误差方面提高了 61.2%。因此,本研究揭示了外部信息在 B2B 行业需求预测中的价值。

更新日期:2021-08-11
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