|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/demand-analytics
课程评论：没有评论
课程名称：需求分析 课程概述：欢迎来到需求分析课程，这是供应链管理和营销领域中最受欢迎的技能之一！通过北美一家领先炊具制造商的真实案例和数据，您将学习需求规划和预测的数据分析技能。完成本课程后，您将能够： 1. 通过建立和验证需求预测模型来提高预测的准确性。 2. 通过识别影响需求的驱动因素（例如时间、季节性、价格和其他环境因素）并量化其影响，来更好地刺激和影响需求。 AK是北美一家领先的炊具制造商。其新推出的顶级产品在市场上逐渐获得动能。然而，在旺季进行价格调整时，意外引发了显著的需求激增，这让AK感到非常意外，导致了大量积压订单。AK面临着因顾客不满和加班生产及加急运输相关的高成本而失去市场动能的风险。准确的需求预测对于增加收入和降低成本至关重要。识别需求驱动因素并评估其对需求的影响，可以帮助公司更好地影响和刺激需求。 课程大纲： 1. 欢迎！ - 课程简介：在第一周，您将了解AK MetalCrafters（北美领先的炊具制造商）在推出新产品时面临的危机，以及AK如何成功利用需求分析解决这一危机。您还将学习需求规划和预测的一般原则，以及它在企业综合业务规划中的作用。 2. 预测趋势 - 课程介绍：在需求分析的第二周，您将把第一周学习的一般原则付诸实践，通过建立和解释线性模型来预测趋势（如新产品推出）。您还将学习数据收集、预处理和可视化技术，这些都是模型构建的关键。 3. 预测价格和其他环境因素的影响 - 课程介绍：在需求分析的第三周，您将验证并改进第二周建立的线性模型，通过分析其错误以识别缺失变量，然后建立一个多元回归模型，不仅捕捉趋势，还考虑价格和其他环境因素的影响。 4. 预测季节性 - 课程介绍：在需求分析的最后一周，您将通过将季节性纳入第三周构建的需求预测模型来进一步改进模型，以捕捉错误中的周期性模式；您将学习如何建模和格式化分类变量，以及如何创建和测试您的预测。 希望您喜欢这门课程！
Name:Welcome!
Description:Welcome to the exciting world of Demand Analytics! In Week 1, you will learn the crisis that AK MetalCrafters (a leading cookware manufacturer in North America) faced in launching new products, and how AK successfully resolved the crisis using Demand Analytics. You will also learn the general principles of demand planning and forecasting, and how it fits into a firm's integrated business planning.
Name:Predicting Trend
Description:Welcome to Week 2 of Demand Analytics! In Week 1, you learned the general principles, now in Week 2, you will put them to action by building and interpreting a linear model for predicting the trend (as in new product introduction). You will also learn data collection, pre-processing and visualization techniques, which are critical to model building.
Name:Predicting the Impact of Price and Other Environmental Factors
Description:Welcome to Week 3 of Demand Analytics! In Week 2, you built a linear model to predict the trend. In this week, you will validate and improve the model by first analyzing its errors to identify missing variables and then building a multiple regression model to capture not only the trend but also the impact of price and other environmental factors.
Name:Predicting Seasonality
Description:In this last week of Demand Analytics, you will further improve your demand forecasting model built in Week 3 by including seasonality to capture the periodic patterns in the errors; you will learn how to model and format categorical variables, and how to create and test your forecast.
Welcome to Demand Analytics - one of the most sought-after skills in supply chain management and marketing! Through the real-life story and data of a leading cookware manufacturer in North America, you will learn the data analytics skills for demand planning and forecasting. Upon the completion of this course, you will be able to 1. Improve the forecasting accuracy by building and validating demand prediction models. 2. Better stimulate and influence demand by identifying the drivers (e.g., time, seasonality, price, and other environmental factors) for demand and quantifying their impact. AK is a leading cookware manufacturer in North America. Its newly launched top-line product was gaining momentum in the marketplace. However, a price adjustment at the peak season stimulated a significant demand surge which took AK completely by surprise and resulted in huge backorders. AK faced the risk of losing the market momentum due to the upset customers and the high cost associated with over-time production and expedited shipping. Accurate demand forecast is essential for increasing revenue and reducing cost. Identifying the drivers for demand and assessing their impact on demand can help companies better influence and stimulate demand. I hope you enjoy the course!
