一朵财神潮玩手办需求量预测数据

本数据聚焦于预测用户对"一朵财神潮玩手办"的需求量，为品牌方及渠道商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.优化生产计划：对品牌方而言，通过预测盲盒需求量，可以科学制定生产计划，合理调配资源，避免库存积压或供应不足，提高生产效率和市场响应速度。同时，也有助于提前布局市场，制定针对性的营销策略，抢占市场份额，提升销售业绩。
2.支持渠道管理：对经销商和零售终端而言，基于需求量预测数据，可以更精准地规划库存管理和采购计划，降低运营风险，优化供应链效率，提升消费者满意度。1.数据采集：
采集"一朵财神潮玩手办"的销售数据，包括订单编号、用户编号、用户所在城市、订单日期、盲盒系列、购买数量、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：对于每个盲盒系列，使用SUMIFS函数对购买数量进行累加，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：每个盲盒系列的未来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=1.05。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是基于品牌市场增长预期给出的修正值。

This dataset focuses on forecasting the demand for the "One Fortune God Trendy Play Blind Box Figurine", providing critical decision-making support for brands and channel partners, with significant practical application value, which is reflected in the following aspects:
1. Optimizing Production Planning: For brand parties, forecasting the demand for blind boxes enables scientific production planning, rational resource allocation, avoidance of inventory overstock or supply shortages, improvement of production efficiency and market responsiveness. Additionally, it helps to lay out the market in advance, formulate targeted marketing strategies, seize market share and enhance sales performance.
2. Supporting Channel Management: For distributors and retail terminals, based on demand forecasting data, they can more accurately plan inventory management and procurement plans, reduce operational risks, optimize supply chain efficiency and improve consumer satisfaction.

1. Data Collection:
Collect sales data of the "One Fortune God Trendy Play Blind Box Figurine", including order number, user ID, user's city, order date, blind box series, purchase quantity and order amount.
2. Data Preprocessing:
Clean the collected data, remove duplicate records and handle missing values.
3. Data Processing and Analysis:
(1) Calculating Historical Demand: For each blind box series, use the SUMIFS function to accumulate the purchase quantity, and calculate the total demand over the past 365 days, 90 days and 30 days respectively.
(2) Establishing a Demand Forecasting Model: The 30-day future demand forecast value for each blind box series 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 are set as a=0.5, b=0.3, c=0.2, and the adjustment factor k=1.05. The coefficients a, b and c reflect the impact degree of the corresponding historical data on the 30-day future demand forecast. Since the algorithm prioritizes the influence of long-term demand trends, a is assigned the highest weight. k is a correction value based on the brand's expected market growth.