古银杏仁销量预测分析数据

本数据的应用场景包括：（1）生产计划与库存管理：通过参考销量预测数据，古银杏仁的制造商可以更精确地规划生产活动，避免过量生产导致的库存积压或生产不足导致的缺货情况，销售商根据预测情况进行合理备货。（2）市场策略制定：销量预测数据可以帮助古银杏仁销售企业了解市场趋势和客户需求，从而制定更有效的市场策略，包括定价策略、促销活动和新产品推广策略等。（3）供应链优化：准确的销量预测能够帮助古银杏仁生产企业优化供应链管理，包括原材料采购、生产进度控制和物流安排。（4）风险管理：在不确定的市场环境中，准确的销量预测能够帮助古银杏仁生产企业识别潜在的市场风险，并采取相应的风险管理措施，如调整生产计划或采取对冲策略等。1.数据采集：采集公司的销售和订单信息以及后台库存信息。2.数据处理：对采集到的原始数据进行处理，去除缺失和异常数据，并根据时间和产品进行汇总。 3.数据分析：采用加权移动平均法预测销量，预测销量S=（S1*k1+S2*k2+S3*k3）/（k1+k2+k3），S1：本月的销量，S2：上月的销量，S3：上上月的销量，k1、k2、k3为权重系数，根据S1、S2和S3对下月销量预测值的影响程度确定，分别为4.5、3.5、2，本数据样例中S1为2024年4月的销量，S2为2024年3月的销量，S3为2024年2月的销量，S为5月的预测销量。库存健康监测P=实际库存/预测销量，P＜1.5，“库存不足”，1.5≤P≤2.5，“库存健康”，P>2.5“库存积压”。4.数据应用：通过销量的预测，可以帮助企业提前合理预测销量，库存应该备货多少，若库存不足，则发出预警信号，需要及时考虑补货，若库存积压，则需要推出活动及时清理库存。

Application scenarios of this dataset include: (1) Production planning and inventory management: Manufacturers of Ginkgo biloba kernels can plan production activities more accurately by referring to sales forecast data, avoiding overstock caused by overproduction or stockouts caused by underproduction, while sellers can reasonably prepare stock based on the forecast results. (2) Marketing strategy formulation: Sales forecast data can help Ginkgo biloba kernel sales enterprises understand market trends and customer needs, so as to develop more effective marketing strategies including pricing strategies, promotional activities and new product promotion strategies, etc. (3) Supply chain optimization: Accurate sales forecasts can help Ginkgo biloba kernel manufacturers optimize supply chain management, including raw material procurement, production schedule control and logistics arrangement. (4) Risk management: In an uncertain market environment, accurate sales forecasts can help Ginkgo biloba kernel manufacturers identify potential market risks and take corresponding risk management measures, such as adjusting production plans or adopting hedging strategies, etc. 1. Data collection: Collect the company's sales and order information as well as background inventory information. 2. Data processing: Process the collected raw data, remove missing and abnormal data, and aggregate the data by time and product. 3. Data analysis: The weighted moving average method is adopted to forecast sales, and the formula for forecasted sales S is S = (S1*k1 + S2*k2 + S3*k3)/(k1 + k2 + k3), where S1 represents the sales volume of the current month, S2 represents the sales volume of the previous month, S3 represents the sales volume of the month before last, and k1, k2, k3 are weight coefficients determined based on their influence degrees on the forecasted sales volume of the next month, with values of 4.5, 3.5 and 2 respectively. In the sample of this dataset, S1 is the sales volume of April 2024, S2 is the sales volume of March 2024, S3 is the sales volume of February 2024, and S is the forecasted sales volume of May. Inventory health monitoring indicator P is calculated as P = actual inventory / forecasted sales volume, with the following judgment criteria: when P < 1.5, it is marked as "insufficient inventory"; when 1.5 ≤ P ≤ 2.5, it is marked as "healthy inventory"; when P > 2.5, it is marked as "overstocked inventory". 4. Data application: Through sales forecasting, enterprises can reasonably predict sales volume in advance and determine the appropriate inventory stock level. If the inventory is insufficient, an early warning signal will be issued, and timely replenishment should be considered; if there is overstocked inventory, promotional activities should be launched to clear the inventory in a timely manner.

搜集汇总

背景与挑战

背景概述

该数据集为古银杏仁销量预测分析数据，包含2469条记录，每月更新，采用加权移动平均法预测销量，适用于生产计划、库存管理、市场策略制定等场景。