 Hello everyone. Welcome to the course of business forecasting. Myself, Pankaj Dutta, I am a professor in the area of decision science and operation research at Celesge Method School of Management, IIT Bombay. I have done my PhD from IIT Kharagpur and post-doctoral research from EFFL, Swiss Federal Institute of Technology. I have some post-research experience also, like INSA Visiting Fellow at Cultural Institute of Technology, Germany and that visiting professor at Humboldt University of Berlin, Germany. I have more than 15 years of research and teaching experience. So, let us come to the course of business forecasting. In business forecasting, what happens in the recent development of business analytics? The content of predictive analytics and machine learnings are extremely important for analyzing the data, their past behavior and making future decision of the organizations. Business forecasting helps managers to take firm decision, manage their risk and also retain the customers by analyzing the behavior or the purchase pattern of the customers. It helps in many aspects of decision making, resource allocation, optimization process of supply chain etc. through the understanding of predictive analytics models. It also helps to capture the pattern, the relationship or underlying factors of the data and the business scenarios and it helps the organizations to get insights and remain sustainable in the competitive advantages. Now, if you come to the course outline, what we are going to cover in different sessions or lectures of this course, here I have listed. So, we will focus on essentials of predictive analytics in detail and then we will understand the steps of data-driven decision making and also the introduction of machine learning through two aspects unsupervised learning and supervised learning. Then we will enter into the qualitative aspects of business forecasting like consumer survey method, we will study sales force composite where representatives opinion will be taken into consideration and then you take the decision for future planning of the organizations. And then we will discuss the committee experts or jury of experts opinion. Then we will carry forward the discussion to the most popular model or the superior model that is called Delphi method. Details of Delphi method will study through case applications and we will understand how it works in the industry and why it is so popular in decision making. This Delphi method is not only relevant to the business forecasting, it is also relevant to any statistical decision making in any organization. We study that, then we will enter into the quantitative aspects of forecasting. First we will discuss the time-search methods. Under that different models we will discuss like name method, moving average method. There will be a different type of moving average method which will cover like simple moving average, weighted moving average, then exponential moving average which is very much applicable in the stock market say. Then we will discuss different types of exponentials moving models like simple exponentials moving, then hold model. If there is a trend in the data, then how will capture the trend of the data and you make a forecast. Then we will extend the concept to the seasonality aspects like how to calculate the seasonal index because in India most of the data or the sales follow seasonal pattern. So, in that case how to capture the seasonal index and how to model it and make the forecast as per the seasonal pattern of the data. You can look at the graph and corners. So, that type of aspects also will cover through a winter hold model. Then we will extend one of the most important time-search models that is called multiplicative decomposition method which is very popular in industry. We will study that so through Excel illustration. Then we will enter into the one of the most important topic or predictive modeling or machine learning that is called ARIMA model. We will understand the SCF function, we will understand the PSF and autoregressive integrated moving average process in detail with illustration also. We will add one more new concept that is called human judgment in time-search where you can capture the future aspects the contextual information of the event into your past data and you can integrate the data with the future contextual information and you can revise your forecast of time-series data and you can make a better decision making. How to do that? That aspects also will cover through human judgment in time-search analysis. Then we will enter into the causal models like regression analysis, simple linear regression. We call it as econometric models, then multiple linear regression, then different measure of accuracy process and also we will discuss some logistic regression models which is a very important aspect of machine learning technique. Under logistic regression we will give different examples like dispersing of a loan to a customer in banking sector or a health service sector whether the claim is a right claim or wrong claim. We will discuss all these examples through logistic regression models. Then we will enter the last module that is called the simulation methods which is a integral part of business forecasting and the decision making also. So, we will study the Monte Carlo simulation then the continuous version of simulation like system dynamics and then also we will illustrate different cases through risk analytics software that is called ADRIX software. I will bring that software and I will illustrate detail of that software through different case applications. So, that will be overall content of this course. We will try to cover many other aspects through example of the case studies also and then there will be another question that once we learn all these methods, so in which context what method to select. So, therefore these are the couple of important factors like context of the forecast is very important in which context your case belongs. So, accordingly you have to select the appropriate model. Similarly, relevance and the reliability of historical data if you have a good amount of data with the completeness of the data and accuracy of the data if you have perhaps you will be able to make better forecast then degree of measure of accuracy different type of you know measure of accuracy techniques are there we will study them also which will establish your confidence in selection of a model or making a prediction. Then time period to be forecast whether you want to make forecast for the short time period or long term period that also very important in decision making you have to understand the data pattern and the requirement of the organization accordingly you have to select the model. Then the stage of product life cycle is also important. For example, if you are in startup, so maybe in early stage you will have to make a different forecast or different techniques you have to select because risk is involved more over there and but if you are a existing stage or growth stage of the company or the startup maybe you have to select a different model because risk has been reduced over there you might have a more financial aspects now so in that case which model to select to make a better business planning. Then time ability for making the analysis also very important. So, all this you know couple of factors are very important in the selection of a model and how to create a mapping bridge between your case understanding and the application of the particular predictive analytics models this particular couple of factors will help you in understanding or enhancing your skill in making applications. Now, when it comes to the application domain, here I have listed couple of area like retail manufacturing finance, healthcare, logistics, etcetera, but they are not limited they are almost in every sector you can use business forecasting and predictive analytics modeling. We will see different examples in different lectures as well as the assignments how different sectorial problem can be discussed and how you can analyze this particular data and make a better forecast through the contents of business forecasting. Now, who can enroll in this course, whether you are an undergraduate students or post graduate students to enhance your analytical skills or you know data different decision making aspects or different predictive analytics models in your project application or the statistical decision making perhaps this course will be helpful to you and also if you are a faculty or say PhD students so in that case this cutting edge methodology that we will be discussing throughout the course may be helpful to enhance your knowledge as well as the teaching skill also. And if you are belonging to the industry, perhaps it is one of the most important course to enhance your firm decision making skill as well as you know to remain competitive in the dynamic market conditions. So, I hope you will enroll in this course, enjoy the course and learn different techniques of business forecasting and predictive analytics to enhance your skill and also for your career endeavor. Thank you and see you during the course.