随着电商时代的来临，快消类产品的出库量变化频率越来越高，幅度越来越大，对于出库的时效性的要求也愈加严苛，出库量预测已成为大部分快消类产品公司的痛点和难点。针对这一需求，本文选取了具有代表性的K公司的毛巾类产品的出库量数据进行深入研究，并搭建了一个出库量联合预测融合模型，以便对出库量进行预测。该模型为生产毛巾这一品类的公司提供了一个工具，以帮助他们进行出库量的预测，降低物流成本，同时提高客户的满意度。本文的核心工作由以下几个部分组成：首先，以K集团的出库情况作为研究对象，以相关预测为研究基础，分析了当前集团WMS系统使用情况、产品出库情况以及当前使用的预测方法。最终基于此搭建了本文的工作架构。其次，提取了与之相对应的内部特征数据并进行数据处理，在内部特征并不充足的情况下，引入购物节数据以及基于电商平台的义乌小商品指数等外部特征进行特征分析。为了进一步提高数据的有效性和预测精度，继续提出了时间偏移值选择的改进遗传算法，通过遗传算法对不同特征的数据偏移值进行选择。 然后，构建了基于LSTM、CNN、Transformer的需求预测模型，采用模型融合、联合预测的定量预测方法并进行参数调优，优化结果显示虽然单模型预测效果基本一致，且具有较好的泛化能力，但是当出库量波动范围较大时，其表现能力相差甚远。故将LSTM、CNN、Transformer三个单模型作为底层模型，以进一步提升模型精度。即构建了基于Stacking、blending的出库量预测模型。多模型融合利用了三个单模型的优势，提升了预测精度和泛化能力。最后，通过出库量预测系统的实际使用，K公司出库量预测的精度有较大提高。为了提升K集团的出库量预测水平，根据K集团在出库量预测工作中的问题，本文也提出了相关制度保障措施，包括组织保障、系统保障、制度保障等。通过这些保障措施的应用，K公司发货任务完成率提高5%，周转区域占用率降低10%，显著的降低了仓储运营的成本。

With the advent of the e-commerce era, the frequency and magnitude of changes inthe outflow volume of fast-moving consumer goods are increasing, and the demand fortimeliness of outflow shipments is becoming more stringent. Outflow volumeforecasting has become a pain point and difficulty for most fast-moving consumergoods companies. In response to this demand, this article selected the representativetowel products outflow volume data of Company K for in-depth research and built ajoint forecasting fusion model for outflow volume to predict the outflow volume. Thismodel provides a tool for companies producing towels to help them forecast outflowvolumes, reduce logistics costs, and improve customer satisfaction.The core work of this article consists of the following parts: First, taking theoutflow situation of Company K as the research object and based on related forecastingresearch foundations, the use of the current WMS system of the group, product outflowsituation, and forecasting situation were analyzed. Based on this, the work architectureof this article was built. Second, internal feature data corresponding to it was extractedand processed, and external features such as shopping festival data and the Yiwu smallcommodity index based on e-commerce platforms were introduced for feature analysis.An improved genetic algorithm for selecting time offset values was proposed, selectingdata offset values for different features through genetic algorithms to improveprediction accuracy. Next, demand forecasting models based on LSTM, CNN, andTransformer were constructed. A quantitative forecasting method of model fusion andjoint forecasting was adopted, and parameter optimization was performed. Theoptimized results show that although the single-model prediction effects are basicallyconsistent and have good generalization ability, when the outflow volume fluctuatesgreatly, their performance capabilities vary greatly. Therefore, LSTM, CNN, andTransformer were used as the underlying models to further improve the model accuracy.A outflow volume forecasting model based on Stacking and blending was constructed.Multiple model fusion effectively integrates the strengths of three individual models,leading to significant improvements in both prediction accuracy and generalizationability.Finally, through the actual use of the outflow volume forecasting system, theaccuracy of outflow volume forecasting for Company K has been greatly improved. Inorder to enhance the outflow volume forecasting level of Company K, relatedinstitutional security measures were proposed based on the problems encountered byCompany K in outflow volume forecasting work, including organizational security,system security, and institutional security. Through the application of these securitymeasures, the completion rate of delivery tasks for Company K increased by 5%, andthe turnover area occupancy rate decreased by 10%, significantly reducing the operatingcosts of warehousing.