天猫平台胸牌商品销量预测和库存健康情况评估数据

本数据对于公司胸牌商品的库存管理和市场推广、供应商和物流服务商的效率提升、其他销售商的市场预测等方面具有重要的应用价值，具体包括：1.通过销量预测和库存健康度评估，公司能够更准确地掌握天猫平台上胸牌商品的销售趋势和库存状况，从而减少库存积压或缺货风险，提高库存周转率。及时的销量预测和库存评估还能够让公司快速响应市场变化，抓住销售机会。2.对供应商（生产厂家）而言，通过共享销量预测和库存评估数据，能帮助供应商（生产厂家）更好地安排生产计划，提高供应商（生产厂家）的响应速度和供货质量。3.对物流服务商而言，准确的销量预测有助于物流服务商提前配置运输资源，提高物流效率。4.对其他胸牌商品的销售商（即同行）而言，本数据能为他们了解胸牌商品在天猫平台的销售趋势、预测市场动向提供参考，有助于他们调整自身的产品策略和市场布局。1.数据采集和预处理：（1）数据采集：采集公司在天猫平台上销售的胸牌商品的订单信息以及商品的后台库存信息，具体包括商品类目、商品名称、销售渠道/平台、订单编号、下单时间、购买数量、商品当前库存量。（2）数据预处理：对采集到的原始数据进行处理，去除缺失和异常数据。
2.数据统计：基于历史采集的销售和订单信息，运用SUM函数计算出商品在近30日总销量、近31-60日间的总销量和近61-90日间的总销量。
3.建立商品销量预测模型：采用加权移动平均法预测未来30日销量，未来30日预测销量=（近30日总销量*k1+近31-60日间的总销量*k2+近61-90日间的总销量*k3）/（k1+k2+k3）；其中k1、k2、k3为权重系数，反映因素对未来30日销量的影响程度，根据该类商品历史数据计算得出分别为3.2、2.0、0.8。
4.建立库存健康度评估模型：（1）库存健康度=商品当前库存量/未来30日预测销量；（2）库存健康度＜1.5时，评估为“库存不足”；1.5≤库存健康度≤3，评估为“库存健康”；库存健康度＞3时，评估为“库存积压”。

This dataset holds significant application value across multiple scenarios including inventory management and marketing of the company's name badge products, efficiency improvement for suppliers and logistics service providers, and market forecasting for other resellers. The specific applications are as follows:
1. For the company: By conducting sales forecasting and inventory health assessment, the company can accurately grasp the sales trends and inventory status of name badge products on the Tmall platform, thereby reducing the risks of overstock or stockout and improving the inventory turnover rate. Timely sales forecasting and inventory assessment also enable the company to quickly respond to market changes and seize sales opportunities.
2. For suppliers (manufacturers): By sharing sales forecasting and inventory assessment data, the company can help suppliers (manufacturers) better arrange production plans, and improve their response speed and supply quality.
3. For logistics service providers: Accurate sales forecasting helps logistics service providers allocate transportation resources in advance and improve logistics efficiency.
4. For other resellers of name badge products (i.e., industry peers): This dataset can provide them with references to understand the sales trends of name badge products on the Tmall platform and forecast market developments, helping them adjust their own product strategies and market layout.

1. Data Collection and Preprocessing:
(1) Data Collection: Collect order information and background inventory information of name badge products sold by the company on the Tmall platform, specifically including product category, product name, sales channel/platform, order number, order time, purchase quantity, and current inventory of the product.
(2) Data Preprocessing: Process the collected raw data by removing missing and abnormal data.
2. Data Statistics: Based on the historically collected sales and order information, use the SUM function to calculate the total sales of the product in the past 30 days, the total sales between days 31 and 60, and the total sales between days 61 and 90.
3. Establishment of Product Sales Forecasting Model: Use the weighted moving average method to forecast the sales volume in the next 30 days. The formula for the forecasted sales volume in the next 30 days is:
Forecasted Sales (Next 30 Days) = (Total Sales (Past 30 Days) * k1 + Total Sales (Days 31-60) * k2 + Total Sales (Days 61-90) * k3) / (k1 + k2 + k3)
Where k1, k2, and k3 are weight coefficients that reflect the impact degree of each factor on the sales volume in the next 30 days. Calculated based on the historical data of this type of product, their values are 3.2, 2.0, and 0.8 respectively.
4. Establishment of Inventory Health Assessment Model:
(1) Inventory Health Index = Current Inventory of the Product / Forecasted Sales Volume (Next 30 Days);
(2) Assessment criteria:
- When Inventory Health Index < 1.5: Assessed as "Insufficient Inventory";
- When 1.5 ≤ Inventory Health Index ≤ 3: Assessed as "Healthy Inventory";
- When Inventory Health Index > 3: Assessed as "Overstocked Inventory".

搜集汇总

数据集介绍

特点

该数据集提供了天猫平台胸牌商品的销售和库存信息，包括历史销量、未来销量预测和库存健康度评估，适用于库存管理、市场推广、供应商生产计划优化和物流资源配置等场景。