电梯需求量预测数据

电梯需求量预测数据本分析基于电梯需求量预测数据，结合城市化进程、房地产建设趋势、老旧小区改造政策及公共基础设施投资等关键驱动因素，对电梯产品及服务在未来不同区域、不同应用场景（住宅、商业、医院、公建等）的需求规模进行科学预判。该预测为电梯制造企业制定产能规划、优化零部台采购与生产排期提供了数据支撑，有助于提升供应链协同效率，保障项目交付能力。同时，也为区域代理商、安装服务商及维保运营团队调整市场布局、优化人力资源配置与库存管理提供决策依据，增强对市场波动的前瞻性应对能力，提升整体运营稳定性与服务响应效率。1.数据采集：采集电梯的销售数据，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/台、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。

### Elevator Demand Forecasting Dataset

This analysis is based on elevator demand forecasting data, integrating key driving factors such as urbanization progress, real estate construction trends, old residential area renovation policies and public infrastructure investment, to scientifically forecast the demand scale of elevator products and services across different regions and application scenarios (residential, commercial, hospital, public buildings, etc.) in the future.

This forecast provides data support for elevator manufacturing enterprises to formulate production capacity planning, optimize parts procurement and production scheduling, which helps improve supply chain collaboration efficiency and ensure project delivery capabilities. Meanwhile, it also offers decision-making basis for regional agents, installation service providers and maintenance operation teams to adjust market layout, optimize human resource allocation and inventory management, enhance forward-looking response capabilities to market fluctuations, and improve overall operational stability and service response efficiency.

1. Data Collection: Collect elevator sales data, including statistical time, analysis time, order number, sales region, product name, order quantity (unit: unit), order amount (unit: 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 product name corresponding to a specific model, use the SUMIFS function to accumulate the order quantities, and calculate the total demand over the past 365 days, 90 days and 30 days respectively.
(2) Establish demand forecasting model: The 30-day future demand forecast value for each product name is calculated as: [(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 coefficients a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.1. The coefficients a, b and c reflect the impact degree on the 30-day future demand forecast. Since the algorithm pays more attention to the influence 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

该数据集名为'电梯需求量预测数据'，包含518条CSV格式记录，每日更新，涵盖电梯销售订单的历史和预测数据，如订单数量、金额及未来30天需求量预测。它主要用于电梯制造企业和相关服务商进行产能规划、供应链优化和市场决策支持，通过加权算法模型预测需求，强调长期趋势分析。