茶几需求量预测数据

本数据聚焦于预测不同地区茶几的需求量，为公司（经销商）及外部相关方提供了重要的决策依据，具有显著的应用价值。具体体现在以下方面：
对于公司(经销商)而言，它能够帮助优化库存管理，依据未来30天的需求量预测值合理安排采购与仓储，避免库存积压或缺货，从而降低运营成本并提升客户满意度。
对于同行企业而言，可通过分析消费者对茶几的偏好趋势，优化产品设计，把握小户型家居“一物多用”的市场需求。
对于生产商而言，可据此安排板材切割、表面处理与五金装配等工序的生产计划，协调物流包装方案，降低运输破损率，并支持快速响应区域市场的风格与尺寸偏好变化。1. 数据采集
采集公司茶几的销售数据，包括统计时间、客户编号、客户所在地区、订单数量（台）、订单金额（人民币元）。
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：使用SUMIFS函数对订单数量进行累加，分别计算出茶几过去365天、90天和30天的总需求量。 （2）建立需求量预测模型：未来30天茶几需求量预测值=[（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 predicting the demand for tea tables in different regions, providing critical decision-making support for the company (dealer) and external stakeholders, with significant application value, which is reflected in the following aspects:

For the company (dealer), it can help optimize inventory management, reasonably arrange procurement and warehousing based on the 30-day future demand forecast, avoid overstocking or stockouts, thereby reducing operating costs and improving customer satisfaction.

For peer enterprises, they can analyze consumer preference trends for tea tables, optimize product design, and grasp the market demand for multifunctional furniture in small-sized homes.

For manufacturers, it can be used to arrange production plans for processes such as plate cutting, surface treatment, and hardware assembly, coordinate logistics and packaging schemes, reduce transportation damage rates, and support rapid response to changes in regional market preferences for styles and sizes.

1. Data Collection
Collect the sales data of the company's tea tables, including statistical time, customer ID, customer's region, order quantity (units), and order amount (RMB yuan).

2. Data Preprocessing
Clean the collected data, remove duplicate records, and handle missing values.

3. Data Processing and Analysis
(1) Calculate historical demand: Use the SUMIFS function to accumulate the order quantity, and calculate the total demand for tea tables over the past 365 days, 90 days, and 30 days respectively.
(2) Establish a demand forecasting model: The 30-day future tea table demand forecast = [(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. Among them, 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 values on the 30-day future demand forecast. Since the algorithm pays more attention to 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

该数据集专注于茶几产品的需求量预测，包含586条每日更新的企业销售数据，涵盖客户信息、历史订单及未来30天预测值。它通过算法模型结合长期与短期历史需求趋势，为经销商、同行企业和生产商提供库存优化、市场分析和生产计划支持，具有显著的实际应用价值。