四川省门店油烟机销售金额预测数据

本数据通过整合四川省各个办事处下的各个门店预测月前六个月的油烟机产品的审核订单金额数据，结合地区和时间构建了门店零售业务的销售金额预测模型，为各个相关方提供了决策支持。对于供应链来说，基于销售收入预测，公司能够指导供应链端排产计划制定，优化生产资源配置。合理规划生产资源投入，科学制定生产计划，从原材料采购、生产线调配到成品仓储管理，实现全流程的精细化管控，确保产品供应既能满足市场需求，又避免因产能过剩造成资源浪费，有效提升资源利用效率。对于营销端，结合区域销售预测结果，公司可以深入洞察不同地区的消费潜力与市场竞争态势，精准定位目标市场，制定差异化的销售策略与市场推广计划。针对消费能力较强的地区、加大产品的推广力度。适用于具备历史销售数据的零售业务场景，但不可用于无历史数据的新品预测或应对突发重大事件。1.数据收集和预处理：（1）数据收集：收集预测月前六个月录入的油烟机订单的历史数据，以门店为颗粒度，涵盖各门店的订单金额、时间维度及地区属性等信息。 （2）数据预处理：对原始数据进行清洗，去除缺失值和异常数据，并对业务数据进行匿名化处理，确保符合数据安全规范。 2.地区差异分析：（1）趋势成分分析：分析数据中每个月的趋势成分。（2）地区差异分析：以整体地区为维度，分析不同地区间的销售差异效应，分析地区因素对销量的影响。 3.构建审核转开票矩阵与权重计算：（1）矩阵构建：结合历史实际审核数据，构建审核转开票矩阵。（2）权重计算：基于矩阵分析每个办事处每个过去月份的趋势性，计算对应的权重系数，反映各月份数据对预测的影响程度。4. 预测模型构建与结果输出：（1）模型构建：以门店为颗粒度，基于过去6个月的审核订单金额数据，结合趋势分析结果和地区差异调整因子，构建对于每个地区门店的销售金额预测模型。（2）预测输出：利用模型预测未来一个月各个门店的销售额，并通过审核金额权重系数计算最终收入预测值，输出各门店的销售额预测结果。以乐山的门店为例，本月预测销售金额(万元)=0.0102*预测月前6个月审核金额(万元)+0.0265*预测月前5个月审核金额(万元)+0.0611*预测月前4个月审核金额(万元)+0.0761*预测月前3个月审核金额(万元)+0.2415*预测月前2个月审核金额(万元)+0.7687*预测月前1个月审核金额(万元)。

This dataset integrates the audited order amount data of range hood products from stores under each office in Sichuan Province for the six months prior to the forecast month, and constructs a sales amount forecasting model for store-level retail business by combining regional and temporal factors, providing decision support for all relevant stakeholders. For the supply chain, based on sales revenue forecasts, the company can guide the formulation of supply chain production scheduling plans and optimize the allocation of production resources. By rationally planning production resource input, scientifically formulating production plans, and realizing refined full-process control from raw material procurement, production line allocation to finished goods warehouse management, the company can ensure that product supply meets market demand while avoiding resource waste caused by overcapacity, effectively improving resource utilization efficiency. For the marketing side, combined with regional sales forecast results, the company can gain in-depth insights into the consumption potential and market competition landscape of different regions, accurately identify target markets, and formulate differentiated sales strategies and marketing promotion plans. For regions with relatively strong purchasing power, the company can increase product promotion efforts. This dataset is applicable to retail business scenarios with available historical sales data, but shall not be used for new product forecasting without historical data or response to sudden major emergencies. 1. Data Collection and Preprocessing: (1) Data Collection: Collect historical data of range hood orders entered in the six months prior to the forecast month, taking stores as the granularity unit, covering information including each store's order amount, time dimension, regional attributes and other relevant details. (2) Data Preprocessing: Clean the raw data, remove missing values and abnormal data, and anonymize the business data to ensure compliance with relevant data security regulations. 2. Regional Difference Analysis: (1) Trend Component Analysis: Analyze the trend component of each month in the dataset. (2) Regional Difference Analysis: Taking the entire region as the analytical dimension, analyze the sales difference effects across different regions, and assess the impact of regional factors on sales volume. 3. Construction of Audit-to-Invoice Matrix and Weight Calculation: (1) Matrix Construction: Construct an audit-to-invoice matrix based on historical actual audit data. (2) Weight Calculation: Analyze the trend characteristics of each past month for each office based on the constructed matrix, and calculate the corresponding weight coefficients that reflect the degree of influence of each month's data on the forecasting result. 4. Forecasting Model Construction and Result Output: (1) Model Construction: Taking stores as the granularity unit, construct a sales amount forecasting model for stores in each region based on the audited order amount data of the past 6 months, combined with trend analysis results and regional difference adjustment factors. (2) Forecast Output: Use the model to forecast the sales amount of each store in the upcoming month, calculate the final revenue forecast value using the audited amount weight coefficients, and output the sales forecast results for each store. Taking a store in Leshan as an example, the forecasted sales amount this month (ten thousand yuan) = 0.0102 * audited amount in the 6 months prior to the forecast month (ten thousand yuan) + 0.0265 * audited amount in the 5 months prior to the forecast month (ten thousand yuan) + 0.0611 * audited amount in the 4 months prior to the forecast month (ten thousand yuan) + 0.0761 * audited amount in the 3 months prior to the forecast month (ten thousand yuan) + 0.2415 * audited amount in the 2 months prior to the forecast month (ten thousand yuan) + 0.7687 * audited amount in the 1 month prior to the forecast month (ten thousand yuan).

搜集汇总

背景与挑战

背景概述

该数据集是宁波方太营销有限公司提供的四川省门店油烟机销售预测数据，包含776条记录，每月更新，用于基于历史审核金额的加权模型预测未来销售额，支持供应链排产和营销策略优化。