装修设计服务需求量预测数据

本数据聚焦于装修设计服务的需求量预测。通过分析各区域市场需求，企业可实现资源的精准配置、优化设计方案和服务计划，避免服务能力不足或资源浪费，提升运营效率和市场响应能力。对于该产品的提供商、装修公司、设计机构及相关供应链服务商而言，该预测数据为制定销售策略、资源配置和项目调度提供了重要参考，有助于根据市场趋势调整供应节奏与服务支持体系，提升产业链协同效率，推动行业高效稳定发展。1.数据采集：采集装修设计服务的销售数据，包括统计时间、订单编号、销售区域、服务名称、订单数量/套、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：该产品名称的未来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 decoration design services. By analyzing market demand across various regions, enterprises can achieve precise resource allocation, optimize design schemes and service plans, avoid insufficient service capacity or resource waste, and enhance operational efficiency and market responsiveness. For providers of this product, decoration companies, design institutions and relevant supply chain service providers, this forecasting data provides important references for formulating sales strategies, resource allocation and project scheduling, helping adjust supply rhythm and service support systems according to market trends, improving the collaborative efficiency of the industrial chain and promoting the efficient and stable development of the industry.

1. Data Collection: Collect sales data of decoration design services, including statistical time, order number, sales region, service name, order quantity (unit: set), and 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, 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 this product name = [(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 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 influence of the corresponding demand data on the 30-day future demand forecast. Since the algorithm places greater emphasis on 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

该数据集聚焦装修设计服务的需求量预测，包含695条CSV格式记录，每日更新，涵盖历史订单数据和未来30天预测值。通过加权算法模型分析长期需求趋势，旨在帮助企业优化资源配置、提升运营效率，适用于装修公司、设计机构等制定市场策略。