香氛商品需求量预测数据

本数据聚焦于预测香氛商品的市场需求量，为公司（经销商）及外部相关方提供了关键的决策依据，具有重要的应用价值。具体体现在以下方面：
1.精准营销：结合需求量预测数据，识别高潜力市场，制定差异化的销售策略。针对需求增长较快的地区，提前布局销售资源，抢占市场份额。
2.优化生产计划：对制造商而言，通过预测香氛商品的需求量，科学制定生产排期，避免产能过剩或断货风险。动态调整原材料采购和包装供应计划，降低供应链成本并提升响应速度。1.数据采集：
采集公司香氛商品的销售数据，包括订单编号、客户编号、客户所在地区、订单日期、香氛类型、订单数量（个）、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：对于每种香氛商品，使用SUMIFS函数对订单数量进行累加，分别计算出其过去180天、90天和30天的总需求量。（2）建立需求量预测模型：每种香氛商品类型的未来30天需求量预测值=[(过去180天总需求量÷180*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是基于市场增长预期给出的修正值。

This dataset focuses on predicting the market demand for fragrance products, providing critical decision-making support for the company (distributor) and external stakeholders, with significant application value, which is specifically reflected in the following aspects:
1. Precision Marketing: Combine demand forecast data to identify high-potential markets and develop differentiated sales strategies. For regions with rapid demand growth, deploy sales resources in advance to seize market share.
2. Optimized Production Planning: For manufacturers, forecasting the demand for fragrance products enables them to scientifically formulate production schedules, avoiding risks of overcapacity or stockouts. Dynamically adjust raw material procurement and packaging supply plans to reduce supply chain costs and improve response speed.

1. Data Collection:
Collect sales data of the company's fragrance products, including order number, customer ID, customer's region, order date, fragrance type, order quantity (unit: piece), 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 fragrance product, use the SUMIFS function to accumulate the order quantity, and calculate the total demand over the past 180 days, 90 days and 30 days respectively.
(2) Establish demand forecast model: The 30-day future demand forecast value for each fragrance product type = [(Total demand over the past 180 days ÷ 180 * 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 impact of historical demand 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. k is a correction value based on market growth expectations.

搜集汇总

背景与挑战

背景概述

该数据集为香氛商品需求量预测数据，包含523条记录，每日更新，提供历史需求量和未来30天的需求量预测值，适用于精准营销和生产计划优化。