推拉门需求量预测数据

本数据聚焦于预测推拉门产品的需求量。对公司而言，通过预测各区域对该产品的需求量，可以精准配置生产技术人员和设备资源，合理规划生产能力，避免人力资源闲置或产品供不应求的情况发生。对于建筑设计单位、家装公司、门窗经销商及相关服务商而言，这些预测数据可作为其生产计划和库存管理的重要参考。基于对未来市场需求趋势的理解，供应商可以相应调整供应和服务策略，避免原材料及成品的积压或短缺。1.数据采集：采集推拉门的销售数据，包括统计时间、分析时间、订单编号、销售区域、产品名称、订单数量/㎡、订单金额/元。
2.数据预处理：对采集的数据进行清洗，去除重复记录，处理缺失值。
3.数据加工与分析：（1）计算历史需求量：对于每个具体型号的产品名称，使用SUMIFS函数对订单数量进行累加，分别计算出其过去365天、90天和30天的总需求量。（2）建立需求量预测模型：每种推拉门产品名称的未来30天需求量预测值=[(过去365天总需求量÷365*a)+(过去90天的总需求量÷90*b)+(过去30天的总需求量÷30×c)]*30*k；其中，系数a=0.5，b=0.3，c=0.2，调整因子k=1.1。系数a、b、c反映数值对未来30天需求量预测的影响程度，由于算法更注重长期需求趋势的影响，因此a被赋予了最高的权重。调整因子k 基于市场增长预期进行修正。

This dataset focuses on demand forecasting for sliding doors. For manufacturing enterprises, forecasting the regional demand for this product enables precise allocation of production technicians and equipment resources, rational planning of production capacity, and avoidance of both idle human resources and product shortages. For architectural design firms, home decoration companies, door and window dealers and relevant service providers, such forecast data can serve as an important reference for production planning and inventory management. Based on an understanding of future market demand trends, suppliers can adjust their supply and service strategies accordingly to prevent overstock or shortage of raw materials and finished products.
1. Data Collection: Collect sales data of sliding doors, including statistical time, analysis time, order number, sales region, product name, order quantity (㎡), order amount (yuan).
2. Data Preprocessing: Clean the collected data, remove duplicate records, and handle missing values.
3. Data Processing and Analysis:
(1) Calculate historical demand: For each specific product model (identified by product name), use the SUMIFS function to accumulate order quantities, and calculate the total demand over the past 365 days, 90 days, and 30 days respectively.
(2) Establish a demand forecasting model: The 30-day future demand forecast value for each sliding door product is calculated as:
Forecasted Demand = [(Total demand over the past 365 days ÷ 365 × a) + (Total demand over the past 90 days ÷ 90 × b) + (Total demand over the past 30 days ÷ 30 × c)] × 30 × k
Where the coefficients are a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.1. The coefficients a, b, and c reflect the degree of impact of respective historical data on the 30-day future demand forecast. Since the algorithm prioritizes the impact of long-term demand trends, a is assigned the highest weight. The adjustment factor k is revised based on market growth expectations.

搜集汇总

背景与挑战

背景概述

该数据集由杭州墅景门窗科技有限公司提供，包含627条推拉门销售记录，用于预测未来30天需求量；数据每日更新，采用加权平均算法（权重偏向长期历史数据）进行预测，旨在帮助企业优化生产资源配置和库存管理，避免供需失衡。