快速消费品行业货车销售渠道销售预测系统

Seza Dursun, Ferhat Bakan, Sahika Koyun Yilmaz, M. Aktaş
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引用次数: 0

摘要

在快速消费品(FMCG)领域,运输车辆上是否有足够的产品库存直接关系到销售预测的准确性。估算不准确会导致收入损失,并增加运输和人工成本等次要成本。在目前的情况下,销售预测是基于销售人员的配送路线、知识、经验和关系。由于人员的知识和经验没有被纳入制度记忆,这些信息随着人员的变化而丢失,新人需要发展自己对路线的经验。目前,销售预测准确率计算为70%。已经确定,以产品为基础,每天损失15%,每天总共损失5%。在本研究范围内进行的研究中,研究了先进的分析和机器学习方法,这些方法可以捕捉快速消费品行业的动态,并分析有效形成的大量数据,以提高销售预测的准确性和一致性。在研究范围内,使用人工神经网络方法开发了用于销售预测的机器学习模型。我们根据基于路线、销售点和产品的召回率、精度和准确度指标来评估模型的性能。结果表明,人工神经网络在销售预测中表现良好。在实验研究中使用人工神经网络,我们实现了三个航线组作为飞行员的平均收入增长5%。销售预测准确率从78%提高到82%。
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Sales Forecasting System for Van-Sales Channel for FMCG Industry
In the Fast Moving Consumer Goods (FMCG) sector, the availability of sufficient product inventory on the delivery vehicle is directly related to the accuracy of the sales forecasts. Insufficient accuracy of the estimations leads to loss of income and increases secondary costs such as transportation and labor costs. In the current situation, sales forecasts are based on the sales personnel's delivery route, knowledge, experience, and relationships. Since the knowledge and experience of the personnel are not brought into the institutional memory, this information is lost with the personnel change, and the new person needs to develop their own experiences about the route. Currently, the sales forecasting accuracy rate is calculated as 70%. It has been determined that a daily loss of 15% on a product basis and a total of 5% daily occurs. In the study carried out within the scope of this research, advanced analytical and machine learning methods that can capture the dynamics of the FMCG industry and analyze the extensive data formed effectively are studied to increase the accuracy and consistency of sales forecasts. Within the scope of the research, machine learning models to be used for sales forecasts were developed using artificial neural networks methods. We evaluated the models' performance according to the recall, precision, and accuracy metrics based on the route, point of sale, and product. It was determined that the artificial neural networks performs well for sales forecasting. Using the artificial neural networks in the experimental study, we achieved an average of 5% revenue increase for the three route groups selected as pilots. The sales forecast accuracy rate increased from 78% to 82%.
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