Hello everyone. In this video, I'd like to give an overview of the course on Demand Analytics. Demand analytics is the art and science of applying data analytics to demand planning and forecasting, which is critical to a firm's goal of increasing revenue and reducing cost. Demand planning and forecasting is a cross-functional activity and involves supply chain, marketing, and finance people. It is closely linked to the firm's strategic and financial objectives, and often serves as the starting point of the sales and operations planning and the integrated business planning. Demand analytics is one of the most sought-after skills in supply chain management and marketing. As reported by supply chain insights, jobs like demand and supply planning and data scientist top the list of positions in greatest demand. Demand planning and forecasting is especially important, but challenging in the new product introduction, where demand can be hardly predictable, with forecast errors as high as 200 percent. Yet, accurate forecast is needed for sourcing, production, distribution, and advertising planning. Analytics can help to make accurate forecast for the demand and assess the impact of various factors on the demand. Upon completion of this course, you will be able to; first, build data-driven analytics models to predict the demand. Second, assess the impact of various factors such as time, seasonality, price, and other environmental factors on the demand. Finally, understand how demand planning and forecasting is linked to a company's strategic goals and the overall business planning. To achieve these objectives, this course covers the following topics in four weeks. In week 1, you will learn the general principles of demand planning and forecasting in practice. In week 2, we'll cover data visualization and predicting the trend. In week 3, you will learn model validation and improvement techniques, and how to predict the impact of price and other environmental factors. In week 4, you will learn to model categorical variables such as seasonality, and how to create and test forecasts. The course is designed so that over these weeks, you will learn more and more techniques to improve the model and make it sharper, better, and more accurate. At the end, you should be able to build a model to make accurate forecasts and quantify the impact of various factors on demand. Time commitment is about 2.5 hours a week, and we will use Excel and the Data Analysis Add-In as the Software. More specifically, in week 2, you'll learn how to pre-process the data and visualize the data by line and scatter charts, such as this one. Then you'll build a simple linear regression model to predict the trend. You will understand the parameter estimation, model building, and interpretation, and how to test the significance of the model. In week 3, you will learn model validation and improvement techniques to detect hidden patterns in errors of the model, such as this one, which is called Residuals. Based on the patterns identified, you will build a multiple regression model of both time and price, to significantly improve the fit of the model. Although the model is much improved, it's errors still have a periodic pattern as shown by this chart. So in week 4, we'll introduce seasonality by modeling and formatting seasons as categorical variables. The resulting model with time, price, and seasonality, accurately captured the variations in the demand data. Using the model, you can make a forecast for the whole year of 2013 as shown by the red dots on this chart. Testing the forecasts on the first three months of 2013, for which the actual sales data is available, you will find that the forecast error measured by the mean absolute percentage deviation, MAPD, is 8.9 percent, which is very accurate for a relatively new product in cookware. This introductory course on Demand Analytics, is designed for two general audience. First, those of you who are exploring a career in Supply Chain Management and Marketing, especially in demand planning, sales and operations planning, and integrated business planning. Second, those of you who are fascinated by the potential of Predictive Analytics and like to learn its applications in supply chain management and marketing. This course is designed for beginners with no prior experiences. However, it will be helpful if you have some knowledge or experiences in the following areas, such as general business acumen, Supply Chain Analytics Essentials, and Supply Chain Planning.
