Hello, I am Alok It is a great pleasure to have you enrolled in this course on simulation model for decision-making. This short video will provide you a high-level overview of the course, including its broad objectives. Course design tools that we will use. And my general pedagogical approach for this course. Simulation modeling, in my opinion, is the most versatile of the analytical tools. It's applicable whether you are trying to get better at golf or you are interested in sport simulations, prediction about sports events and so forth. Whether you're a small business owner and are interested in getting your operations better, organized and optimized. Whether you are trying to get your factory floor to be better, optimize through streamlining of processes. Or you are interested in doing better in your investments or retirement planning, or constructing a new portfolio. Besides the versatility of the simulation and analytical tool. It's also very important to understand that we can develop the simulation models at many different levels. It can be at a very high level where all the details are hidden. And we just simply want to understand what will be the variance in our outcome. Are we going to be okay, are we going to have a high chance for loss? Or are you going to have reasonable amount of profits to do some investments? Those kinds of questions don't require modeling necessarily at a very detailed level. But we can build a very high level model. On the other hand, we can also build models that are at a very detailed level, or as we call it at a low level of specification where every detail is specified and we do that when we, for example, want to understand how can we design a better process or how we can improve the process? We can test our hypothesis is if we say Okay, what if we do this versus that? We can test that in simulation models. It can therefore provide answers to situations that have actually happened. We call these counterfactuals. So it's one of the best tool to do counterfactual analysis. Finally, we should use simulation modeling because I just love this approach. Who am I at present time, Senior Associate Dean of Faculty Affairs as well as internal operations at Carlson School of Management at University of Minnesota. I have developed, designed, and I've been a consumer and a teacher of analytics based approaches for 30 years. I say cautiously because maybe I've been doing it for longer, but for 30 years certainly I've been doing it in the academic arena and teaching executives as well as students at various levels. I've extensively used simulations for my own research, in my teaching and in my consulting practice. If you want to know more about me, please see the instructor introduction video. I have provided more details about my interests as well as experience there. In terms of the course, the broad objective of the course is to clarify the role of simulation modeling in the analytics spectrum. So analytics means different things to different people. And simulation has a major role in it. I just want to explain how it's related to other analytics approaches. We want to understand the tenets -- basic tenets of simulation modeling. What is essential to do in simulation modeling, even though it has high level of flexibility, it's very versatile. But there are certainly things that we should always do in simulation modeling, for example, doing a lot of replication analysis, doing sensitivity analysis to make sure that whatever results we are obtaining are trustworthy. We'll also understand two major classes of simulations that are used in business. Simulation. That's Monte-Carlo simulations, which is typically you can think of it as, as a modeling approach that models outcomes. Discrete event simulation, which is used to do process modeling or details of a given business process. In terms of course design, we have four modules which you can finish in four weeks. The first module introduces the probability concepts. Probabilty is probably the most essential concept that you need to understand in order to do simulations Because simulation modeling is about capturing uncertainty in future environments and the mathematical tool that we use to model these is probability. The second module focuses on probability distributions and introduces the basic principles of simulations, some best practices and tools of the trade and some secrets. On simulation modeling. The probability distributions are the way in which we can capture observed data that use Certain theoretical properties if our observed data matches one of these classical distributions that avoids us -- That allows us to use the distributions rather than raw data. It saves space and it's faster for simulation purposes. In Module 3, we develop a lots of different kinds of Monte Carlo models from the perspective of asking different levels of business questions. But also understanding how complex models can be built on top of existing models and whether or not to build those models. Depending upon the question at hand. We also do sensitivity analysis, compare results across models to see which model provides perhaps the most trustworthy answers and what are the characteristics of that? Finally, in module four, we look at discrete event simulation. Even though the two we're going to use, which I'm going to talk about next, is designed to do discrete event simulations, but through some innovations in pedagogical approaches, doing things that I have in some senses inverted. We look at the core practices involved in discrete event modeling. Specifically looking at dependencies in process, events, in a process, and then using those dependencies to define various steps so that we can model a process. The tool we are going to use in this course is Excel. This has been done to make the course as widely accessible as possible without the burden of being familiar or having any experience with computer programming. All the material is especially designed to work using available functionality in Microsoft Excel. While this is limiting, it does provide a lot of appropriate functionality. And again, as I mentioned, to some pedagogical innovations, especially for discrete event simulation for process modeling. I believe I have been able to provide an in-depth understanding And if you are comfortable with programming, you can easily port these ideas into any platform. So what we're gonna do here is we're going to look at some of the advanced Excel capabilities such as functions that we use, especially in those simulations, probability distribution functions, histograms, percentile calculations, doing what-if analysis, specifically the use of data tables and goal seek. data manipulations and lookup techniques. Circular computing, persistent computing. Automating computing during simulations to avoid unnecessary delay during development, some of these techniques or tricks that are not easily found, even though if you search a lot on the Internet, maybe you can find a few things, but they're not usually presented in a consolidated form anywhere. So I like to also note that simulations can be computing intensive. I have used a six year old Mac to build these models so that -- I simply wanted to ensure that most students of the course can comfortably develop and run the models that we have built in the course. Now if you're having trouble running these models and they're taking too long or your computers seem stuck. It could happen because it's doing a lot of configurations on the backend. One thing you can do is you can reduce the number of replications that we do in each model. So for example, I've been suggesting doing 1000 applications, but you can do 500 or even 250. Qualitatively, you should observe the similar results, although in some cases they may not be as trustworthy. I have specifically used Excel version 16.27 for Mac. The equivalent capabilities though, are presented in prior versions of Excel, both on Mac and Windows. And all the spreadsheet models that we have developed in this particular course have been tested on several versions of Excel, including, including Office 360 on both Mac and Windows. So they all work at the end, you should have no problem sending them. One of the issues that you might run into is the menu structure that is there for my version of Excel might be different from the menu structure for your version of Excel. However, this is quite easy to find. Just look at the, the specific tool that we need to use. specifically many items that we need to use and a quick website can easily show you how a particular toolbar can be accessed on your version of Excel. Now, what if analysis, some of you might have questions about whether you can use Google Sheets instead of Excel to do this. So the what-if analysis capabilities are available in Google Sheets as an add on. Now, our organizational security constraints don't allow me to add this add-on to our machines. Therefore, I have not been able to test these -- test the models that we have developed in Google Sheets. However, conceptually I do not see why they should not work. If you are allowed to and are able to install and run these add-ons. Just a little bit more about the examples and specific pedagogical approach that I have used in this course, in Module 1 and 2, the examples that I've used are from everyday life and uses the general knowledge and general sense to demonstrate the theoretical implication and their relevance to the questions that one might ask. In Module 3 and 4, I use a running example or I use a context that is similar so that we don't worry about explaining contextual details too much. Instead, we focus on modeling challenges. So for example, you want to look at model choice, interrelationship of factors that we are trying to model in an environment, business goals and appropriate modeling complexity that should be chosen and process versus outcome modeling. And when to use what type of model. Each module has lessons and has specific set of learning objectives that are mention in each module and videos that are associated with each lesson. There are lots of labs because I believe in learning by doing. And so these labs allow you to learn concepts and many a times The concepts are explained in general terms. Even when we're doing the lab. I provided all the Excel lab files. You can download these and just look at the. but I do encourage you to try and develop these on your own. That will show that you understand the concepts as well as tools as well As have a sense of challenges that might be there in developing a set of models. Finally, each module has a practice quiz and an assessment quiz. They are slightly different questions, but based on same learning goals. Practice quizzes should reconnect you with what you learn during a particular week or module. And if you don't understand why a certain answer is the correct one, usually the hint is provided in what you can Go and look back. Which video can you go and look at again, in order to reconnect with the material. Assessment quiz will test your ability to apply the stuff that we have learned and to test whether you have understood the implications of results that we obtained during building a certain model. I hope you learn a lot in this course, have fun, and can use these techniques that you learn in this course for your professional as well as personal goals.