江西地区童装校服需求量预测数据

本数据预测该地区客户对童装校服产品的需求量，为销售商、生产商及相关方提供关键决策支持。1.通过分析不同区域对童装校服的需求趋势，经销商可优化库存与采购计划，生产商能灵活调整产能布局，投资者可评估市场潜力。2.该预测模型同样适用于其他具有区域差异大、季节性强等特点的消费品行业，帮助相关企业精准把握市场需求，优化供应链管理，提升营销效率。最终实现资源合理配置，增强市场竞争力。1.数据采集：采集公司童装校服产品一个年度在该地区的销售数据，采集期间为分析时间所处月份的前12个月，截取的第一个月从该月第一天起，最后一个月为该月最后一天截止，例如若分析时间为2025年11月3日，分析时间所处月份为2025年11月，则采集期间为2024年11月1日至2025年10月31日。
2.数据处理：对采集的数据进行清洗，去除重复记录，处理缺失值，对采集到下单情况、货品数量，下单时间等数据进行分类、累加，便于分析使用。
3.数据加工与分析：（1）计算历史销售量：使用SUMIFS函数对订单中的货品数量进行累加，分别计算出其过去12个月、过去3个月和过去1个月的总销售量。（2）建立需求量预测模型：童装校服的未来1个月销量预测值=（(过去12个月的总销售量÷365*a)+(过去3个月的总销售量÷90*b)+(过去1个月的总销售量÷30*c)）*30，未来1个月为采集期间最后一个月的后一个月，例如采集期间最后一个月为2025年9月，则未来1个月指的是2025年10月。其中，系数a=0.1，b=0.3，c=0.6。系数a、b、c反映数值对未来30天销量预测的影响程度，由于算法更注重短期销售趋势的影响，因此c被赋予了最高的权重。

This dataset forecasts the demand for children's school uniform products in the target region, providing critical decision-making support for sellers, manufacturers and relevant stakeholders.
1. By analyzing demand trends for children's school uniforms across different regions, distributors can optimize inventory and procurement plans, manufacturers can flexibly adjust production capacity layout, and investors can evaluate market potential.
2. This forecasting model is also applicable to other consumer goods industries characterized by significant regional disparities and strong seasonality, helping relevant enterprises accurately grasp market demand, optimize supply chain management, and improve marketing efficiency, ultimately achieving rational resource allocation and enhancing market competitiveness.

1. Data Collection: The annual sales data of the company's children's school uniform products in the region over a one-year period is collected. The collection period covers the 12 months preceding the month of the analysis time, starting from the first day of the first month of the collection period and ending on the last day of the last month. For example, if the analysis time is November 3, 2025, and the analysis month is November 2025, the collection period will be November 1, 2024 to October 31, 2025.
2. Data Processing: Clean the collected data, remove duplicate records, handle missing values, and classify and accumulate data such as order details, product quantity, and order time to facilitate subsequent analysis.
3. Data Processing and Analysis:
(1) Calculate historical sales volume: Use the SUMIFS function to accumulate the product quantity in orders, and calculate the total sales over the past 12 months, past 3 months and past 1 month respectively.
(2) Establish demand forecasting model: The forecasted sales volume of children's school uniforms for the following month = ((Total sales over the past 12 months ÷ 365 * a) + (Total sales over the past 3 months ÷ 90 * b) + (Total sales over the past 1 month ÷ 30 * c)) * 30. The following month refers to the month immediately succeeding the last month of the collection period. For example, if the last month of the collection period is September 2025, the following month refers to October 2025. The coefficients are set as a=0.1, b=0.3, c=0.6. Coefficients a, b and c reflect the impact degree of respective values on the forecast of future 30-day sales. Since the algorithm pays more attention to the impact of short-term sales trends, c is assigned the highest weight.

搜集汇总

背景与挑战

背景概述

该数据集是江西地区童装校服的需求量预测数据，包含540条记录，覆盖2024年11月至2025年10月的销售信息，如订单详情、历史销售量及未来一个月销量预测值。它采用加权算法模型，重点基于短期销售趋势进行预测，旨在为销售商和生产商提供决策支持，帮助优化库存、产能和供应链管理。数据集适用于区域差异大、季节性强的消费品行业，助力企业精准把握市场需求，提升竞争力。