|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/demand-forecasting-kpis-for-supply-chain-planning/
课程评论：没有评论
课程名称：供应链规划的需求预测指标 课程概述：本课程将教您如何使用各种预测指标（偏差、平均绝对误差、平均绝对百分比误差、加权平均绝对误差和均方根误差）来选择最佳的需求预测。最终目标是使您作为需求规划师或销售与运营规划（S&OP）负责人，能够在大规模上自动评估预测的质量，即使您有一个广泛的产品组合（包括间歇性需求和不同价格的产品）。 具体内容包括： - 偏差、平均绝对误差、平均绝对百分比误差和均方根误差的优缺点 - 这些指标对间歇性需求和异常值的反应 - 为什么平均绝对百分比误差是最糟糕的预测指标 - 如何平衡准确性与偏差选择合适的指标 - 如何使用价值加权指标评估预测质量，即使处理不同价格的产品 本课程理论与实践相结合，包含Excel的动手练习。您将在实际案例和示例中学习，通过动手实践获得经验，并能够将这些概念（以及Excel模板）直接应用于工作环境，产生立竿见影的效果。 课程包括一小时的视频内容，基于我的书籍《供应链预测的数据科学》和《需求预测最佳实践》，以及我教授专业人士和大学生的课程。整个课程预计需要您花费2至4小时完成，唯一要求是具备有限的Excel经验（如使用常见公式，如平均值和求和）。
This course will teach you how to use various forecasting metrics (Bias, MAE, MAPE, WMPAE, and RMSE) to select the best demand forecast. The end goal is that you can (as a demand planner or S & OP leader) automatically assess the quality of forecasts at scale - even if you have a wide product portfolio (including intermittency and products with different prices).Specifically, you will learn:The pros and cons of Bias, MAE, MAPE, and RMSE,How they react to intermittent demand and outliers,Why MAPE is the worst forecasting KPI, Which metric(s) to use to balance accuracy and bias? How to use value-weighted KPIs to assess the quality of your forecasts, even if you deal with various products with different prices.This course alternates theory with Do-It-Yourself exercises in Excel. You will learn by doing and gain hands-on experience with practical examples and case studies. You will be able to apply these concepts (and use the Excel templates) directly to your work environment for immediate impact.The course includes one hour of videos, and its content is based on my books (Data Science for Supply Chain Forecasting and Demand Forecasting Best Practices) and the content of the course I teach to professionals and university students. It should take you 2 to 4 hours to complete it.The course only requires limited experience with Excel (such as using usual formulas such as average and sum).
