潮牌商品板形设计服务需求量预测数据

本数据聚焦于预测市场对潮牌商品板形设计服务的需求量，为潮牌设计工作室及独立设计师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：
1.优化设计资源配置：对设计工作室而言，通过预测市场需求量，可以合理安排设计师工作负荷，避免设计资源浪费或项目积压，提高设计效率和服务质量。同时，有助于制定合理的项目排期，确保按时完成设计任务。
2.支持个人接单规划：对独立设计师而言，基于需求量预测数据，可以更好地规划接单数量和收费标准，平衡工作强度与收入，同时把握市场流行趋势，提升个人设计竞争力。1.数据采集：
采集潮牌商品板形设计服务的订单数据，包括订单编号、客户编号、客户类型、订单日期、服务类型、订单数量、订单金额。
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：对于每种服务类型，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：每种服务类型的未来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。k是基于当前潮牌市场增长预期给出的修正值。

This dataset focuses on predicting the market demand for streetwear product pattern design services, providing critical decision-making support for streetwear design studios and independent designers, with significant practical application value. Its application values are specifically reflected in the following aspects:
1. Optimized Design Resource Allocation: For design studios, forecasting market demand allows for reasonable arrangement of designers' workload, avoiding waste of design resources or project backlogs, and improving design efficiency and service quality. Meanwhile, it helps formulate reasonable project schedules to ensure on-time completion of design tasks.
2. Individual Order-taking Planning Support: For independent designers, based on demand forecasting data, they can better plan the number of orders and pricing standards, balance work intensity and income, grasp market fashion trends, and enhance personal design competitiveness.

The dataset construction and processing workflow is as follows:
1. Data Collection: Collect order data related to streetwear product pattern design services, including order ID, customer ID, customer type, order date, service type, order quantity, and order amount.
2. Data Preprocessing: Clean the collected data, remove duplicate records, and handle missing values.
3. Data Processing and Analysis:
(1) Historical Demand Calculation: For each service type, 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) Demand Forecasting Model Establishment: The 30-day future demand forecast value for each service type 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 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 their respective values on the 30-day future demand forecast. Since the algorithm prioritizes long-term demand trends, a is assigned the highest weight. k is a correction value based on the current growth expectations of the streetwear market.

搜集汇总

背景与挑战

背景概述

该数据集包含637条潮牌商品板形设计服务的订单记录，用于预测未来30天需求量，通过历史数据加权计算（权重偏向长期趋势），支持设计工作室和独立设计师优化资源规划和接单决策。数据每日更新，以CSV格式存储，涵盖客户类型、订单数量及金额等关键字段。