各类针织衫销量预测和库存健康评估数据

通过监测各类针织衫的销售与库存相关数据，对销量进行预测、使库存准备更合理，从而实现企业降本增效的目的。通过预测指导生产备货，避免过剩或短缺；科学的健康度评估实时监控库存合理性，支撑采购与促销决策。最终实现库存成本最小化、资金周转高效化，并协同供应商与物流资源，全面提升运营效率与市场响应速度。1、数据采集：采集公司各类针织衫的订单信息以及库存信息，具体包括商品名称、订单编号、销售日期、购买数量、商品当前库存量。 2、数据处理：对数据进行分类、合并、累加，便于分析使用。 3、算法加工：基于采集的订单信息，运用SUM函数计算出各类针织衫在近30日总销量、近31-60日间的总销量和近61-90日间的总销量。建立各类针织衫销量预测模型：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，根据该商品历史数据计算得出k1=0.6、k2=0.3、k3=0.1。 4、数据应用：建立库存健康度评估模型：库存健康度=商品当前库存量/未来30日预测销量；当库存健康度＜0.8时，评估为“库存不足”；当0.8≤库存健康度≤1.2，评估为“库存健康”；当库存健康度＞1.2时，评估为“库存积压”。By monitoring sales and inventory data of various knitted garments, this dataset supports sales volume forecasting and optimizes inventory preparation, aiming to help enterprises reduce costs and improve efficiency. Production restocking guidance via forecasting helps avoid overstocking or stockouts; scientific health assessment monitors inventory rationality in real time, supporting procurement and promotional decision-making. Ultimately, it achieves minimized inventory costs, efficient capital turnover, coordinates supplier and logistics resources, and comprehensively enhances operational efficiency and market response speed. 1. Data Collection: Collect order and inventory information of all types of knitted garments in the company, specifically including product name, order number, sales date, purchase quantity, and current product inventory. 2. Data Processing: Classify, merge, and aggregate the data to facilitate analysis and application. 3. Algorithm Processing: Based on the collected order information, use the SUM function to calculate the total sales volume of each type of knitted garment in the past 30 days, the total sales volume from day 31 to day 60, and the total sales volume from day 61 to day 90. Establish a sales forecasting model for each type of knitted garment: adopt the weighted moving average method to forecast the sales volume in the next 30 days, with the formula: Forecasted sales volume in the next 30 days = (Total sales in past 30 days * k1 + Total sales from day 31 to 60 * k2 + Total sales from day 61 to 90 * k3) / (k1 + k2 + k3); where k1, k2, and k3 are weight coefficients reflecting the impact degree of each factor on the future 30-day sales volume, which are calculated based on the historical data of the corresponding product, with k1=0.6, k2=0.3, k3=0.1. 4. Data Application: Establish an inventory health assessment model: Inventory Health Score = Current product inventory / Forecasted sales volume in the next 30 days; When the inventory health score < 0.8, it is evaluated as "Insufficient Inventory"; When 0.8 ≤ Inventory Health Score ≤ 1.2, it is evaluated as "Healthy Inventory"; When the inventory health score > 1.2, it is evaluated as "Overstocked Inventory".

搜集汇总

背景与挑战

背景概述

该数据集专注于针织衫的销售预测与库存健康评估，包含历史销售数据、库存量及基于加权移动平均法计算的未来30日销量预测，并通过库存健康度模型实时评估库存状况（如库存积压、健康或不足）。其应用旨在帮助企业优化库存管理，减少过剩或短缺，提升运营效率和市场响应能力。