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Development of a forecasting model for the prediction of product sales using search engine query data

开发使用搜索引擎查询数据预测产品销售的预测模型

基本信息

批准号：
280139258
负责人：
Dr.-Ing. Georg Ullmann
金额：
--
依托单位：
Institut für integrierte Produktion Hannover (IPH) gGmbH
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/280139258?language=en
关键词：
Development forecasting model prediction product

项目摘要

Forecasting delivery dates and quantity of sales is a big challenge concerning the logistical planning. In most cases the logistical planning is aggravated by fluctuations concerning sales. Sales fluctuations may arise due to advertising or promotion events. This uncertainty may cause delayed deliveries or quality issues regarding the material. As a consequence the logistical performance decreases such as the service level diminishes. Giving the growing importance of effective sales planning many companies rely on quantitative methods of sales forecasting. The shortcomings of these conventional forecasting approaches are in particular a low timeliness of data, a low level of detail and a high forecasting effort. The objective of this research proposal is to develop and evaluate an innovative approach for product specified sales forecasting. The forecasting model shall be based on internet search engine query data. Search engine query data contains keyword information (e. g. time, quantity) which are related with a consumption intention. Thus, it is expected to decrease forecasting errors and increase the forecast horizon compared to existing forecasting models. These potentials of search engine query data for sales forecasting shall be analyzed and revealed. Also knowledge shall be generated concerning the limitations of the achieved approach. The research forms the basis for the development of industrial-suited forecasting models which are based on search engine query data.

预测交货日期和销售数量是物流规划的一大挑战。在大多数情况下，物流规划因销售波动而恶化。销售波动可能因广告或促销活动而产生。这种不确定性可能会导致延迟交付或有关材料的质量问题。因此，后勤绩效下降，如服务水平下降。由于有效的销售计划越来越重要，许多公司依赖于销售预测的定量方法。这些传统预测方法的缺点特别是数据的及时性低、细节水平低和预测工作量大。本研究建议的目的是开发和评估一种创新的方法，用于产品指定的销售预测。预测模型应基于互联网搜索引擎查询数据。搜索引擎查询数据包含关键字信息（e. G.时间、数量），这些与消费意图有关。因此，与现有的预测模型相比，预计将减少预测误差并增加预测范围。这些潜力的搜索引擎查询数据的销售预测进行分析和揭示。此外，还应了解所采用方法的局限性。该研究形成了基于搜索引擎查询数据的适合工业的预测模型的发展的基础。