Welcome to this course advanced optimization for decision making. In the previous course of this specialization, the introductory course on optimization, you learned about the basics of optimization, how to formulate optimization problems and how to solve them using Excel. In this course, we are going to continue with that and look at more advanced problems from real world context. But before we do that, let's take a step back and look at which aspect of analytics are we talking about in this course. At the Carlson School, we use a framework called the house of analytics to help students appreciate which part of analytics they're looking at, given the broad nature of the field of business analytics. The particular content that we are going to cover in this course is contained within descriptive analytics. Now, if you think about this house of analytics framework, there are four pillars of analytics, the foundation is data engineering. That is cleaning up of the data that you have obtained from various sources in order to create a data repository that is ready for analytics projects. Within that there are four pillars or four different ways in which you can analyze the data for different types of questions. For example, the descriptive analytics is what is used in order to explore the data to find patterns and answer the question, what happened? Looking at the data, trying to figure out what happened in a given situation to understand that context better. And to have to explore the data and have some insights into that data, that's descriptive analytics. The second pillar is predictive analytics. This is the part that you will learn in one of the courses of this specialization on predictive analytics and the tools that are used for predictive analytics are meant to help answer the question, what will happen next that is, given the historical data, the trends, can you forecast what is going to happen in the future? Can you predict what's going to happen in the future and come up with those numbers projected into the future? That's what is predictive analytics dealing with, it contains data mining, machine learning, forecasting, etc. Then the third pillar is causal analytics. Here we are trying to establish a causal relationship between two factors. That is whether X truly caused Y where X and Y are two factors, Y is an outcome X is a factor that you're interested in to figure out whether the outcome is effected by that factor. And the methods that are used for cause analysis are AB testing, randomized experiments, that's all. The fourth pillar, which is the last key piece of analytics is prescriptive analytics. Where you have to use all the data, all the insights that you have gathered from descriptive, predictive and causal to prescribe the next course of action, that is the best course of action, what is the best thing we can do given the data. And that's where prescriptive analytics and the particular method of linear optimization comes into picture. In this course we are going to delve deeper into this world of prescriptive analytics by looking at many examples of linear optimization and how to apply it in different business domains in Supply Chain Finance in Human Resource and so on. So descriptive analytics really builds upon the previous pillars, often you can use predictive analytics to forecast the demand from customers in the next quarter, or to look at the availability of raw materials in the next quarter or the number of employees that you would have in your workforce in the next planning cycle, etc. We can use predictive analytics to forecast those numbers. And once you have those numbers, then the question is, what should I do as a result of these numbers? What is my best course of action? And that's where optimization comes into play. And this method of optimization, therefore, helps to prescribe the best decision or the best solution and hence is known as prescriptive analytics. So we are looking at that particular pillar of business analytics. And together these help to develop experience, professional skills in order to solve real world business problems in the domain of analytics. So, in this course, the learning objectives are how to make optimal decision in real world context and the context that we are going to look at in this course are from finance, supply chain, manufacturing and human resources. Now, once you have looked at these examples and how to approach these problems, how to formulate them and how to solve them. You can use the same concepts in many other domains you need to be limited to only these four domains. You can use it In a variety of other contexts and settings, depending on the kind of work that you are doing or what you expect to do. The tools can be broadly applied in a variety of, business problems. Looking at examples from these four domains will higher give us a better idea of how to approach this problems, how to formulate these problems and how to solve them in Excel. So we are going to formulate and solve a variety of linear optimization problems in excel looked at a large number of examples to get a better grasp of how to use optimization and apply it in a variety of business domains. So, we are going to use optimization in finance. In Module One, we're going to look at investment optimization and multi period cash flow optimization. These are very typical decision making problems in finance, in financial engineering where you have to decide how much to invest in different types of stocks and bonds. Given risk and returns similarly you have multi period cash flow problems, but there are different returns from different investment in different time periods. And therefore, you have to decide how much should I invest in different asset classes, different options now in order to get the maximum cash flow, the required time intervals, so we are going to use optimization in these types of settings. In module one, we are going to look at examples as to how to set up, these contexts as mathematical optimization models, and then solve them using Excel. After that in module two, we are going to look at optimization in supply chains. Particularly we're going to look at transportation optimization And production and inventory planning optimization that is, what's the right volume of goods that you need to produce and stock in order to have best return on investment in order to minimize the cost of stocking products in warehouses and so on. In module three, we are going to look at optimization in human resource planning. So the type of problems where we can use optimization would be in staff scheduling, that is who works on which day and in office assignment problems. When you are trying to assign a limited number of officers to employees based on their preferences for the different offices, they going to be able to set up these problems, this context, as optimization problems as a mathematical model, then we get to solve them using excel. And finally, in module four, we are going to look at optimization in production. We are going to look at product mix problems that looks at how much volume or how many units of different types of products should I produce in order to maximize profit or minimize cost. And also looking at examples of blending optimization that is, what is the right proportion of different ingredients that I need to blend in or mix in order to maximize profits. For example, so, we are going to look at application of optimization in production decisions. So, these formulations in a variety of context are going to help you understand the power of prescriptive analytics where you can use optimization in particular linear optimization. In order to formulate his problems and solve them in order to find the best outcome for the given context. And you describe the best course of action throughout this course we are going to use optimization in excel solver and we are going to see how to set up these more complex problems in excel and solve them in order to get the right insights and prescribe the right solution. So without further delay, let's delve into the course. And I wish you all success in this course.
