Use data analytics to predict demand with trend (as in new product introduction), seasonality, price elasticity and other environmental factors.
Identify the key drivers for demand and quantify their impact.
Build, validate and improve forecasting models with both continuous and categorical variables.
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!
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.
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.
Predicting the Impact of Price and Other Environmental Factors
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.
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.