Lahore University of Management Sciences. DISC 212 Introduction to Management Science Spring Semester 2018 (Tentative Under review)

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1 Instructor Raza Ali Rafique Room No. SDSB room no.319 Office Hours TBA Secretary/TA Sec: Bushra Kanwal, Ext 5311 TA Office Hours TBA Course URL (if any) suraj.lums.edu.pk/~ro/ DISC 212 Introduction to Management Science Spring Semester 2018 (Tentative Under review) COURSE BASICS Credit Hours 3 Lecture(s) No. of Lec(s) Per Lecture(s) No. of Lec(s) Per Recitation/Lab (per week) No. of Lec(s) Per Recitation/Lab (per week) No. of Lec(s) Per Tutorial (per week) No. of Lec(s) Per Tutorial (per week) No. of Lec(s) Per Lecture(s) Recitation/Lab (per week) Tutorial (per week) COURSE DISTRIBUTION Core Elective Open for Student Category Close for Student Category Core COURSE DESCRIPTION This is a core course for undergraduate business students. It is designed to provide students with a sound conceptual understanding of the role that management science plays in the decision making process. It is an important introductory course in developing decision models and understanding their application to management problems. The emphasis is on models and techniques that are widely used in all industries and functional areas, including operations, finance, accounting, and marketing. Specific topics covered in this course include: techniques such as linear programming, integer programming, queuing theory and applications and basic understanding of simulation. COURSE PREREQUISITE(S) MATH 100 Pre Calculus OR MATH 101 Calculus I COURSE LEARNING OBJECTIVES (CLO) The course has three primary objectives 1. Introduce students to the concept of model driven decision making in business 2. Introduce key techniques in three broad categories of decision models typically discussed in the area of management science: descriptive, and prescriptive models 3. Develop student s ability to critically analyze a business problem, design and apply appropriate decision support tools and interpret the results generated from the tools

2 LEARNING OUTCOMES (LO) Upon successful completion of the course, students should be able to Discuss basic modeling techniques across prescriptive and descriptive decision models in the area of management science Implement these techniques as part of a spreadsheet based decision support tool Setup and solve a range of optimization problems (in different areas of application) by correctly recognizing constraints, decision variables and objective(s) Setup and solve basic predictive models by correctly identifying the appropriate technique, understanding its underlying assumptions and interpreting the results Setup and solve descriptive modelling techniques including simulation and basic queuing models by correctly describing the defining features of the queuing system, including server(s), customer(s), length of the queue, arrival rate(s) and service rate(s) Effectively communicate their problem solving approach, selected tool(s), results, limitations and implications to support the decision maker. UNDERGRADUATE PROGRAM LEARNING GOALS & OBJECTIVES General Learning Goals & Objectives Goal 1 Effective Written and Oral Communication Objective: Students will demonstrate effective writing and oral communication skills Goal 2 Ethical Understanding and Reasoning Objective: Students will demonstrate that they are able to identify and address ethical issues in an organizational context. Goal 3 Analytical Thinking and Problem Solving Skills Objective: Students will demonstrate that they are able to identify key problems and generate viable solutions. Goal 4 Application of Information Technology Objective: Students will demonstrate that they are able to use current technologies in business and management context. Goal 5 Teamwork in Diverse and Multicultural Environments Objective: Students will demonstrate that they are able to work effectively in diverse environments. Goal 6 Understanding Organizational Ecosystems Objective: Students will demonstrate that they have an understanding of Economic, Political, Regulatory, Legal, Technological, and Social environment of organizations. Major Specific Learning Goals & Objectives Goal 7 (a) Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations (Including subject knowledge). Goal 7 (b) Understanding the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker Indicate below how the course learning objectives specifically relate to any program learning goals and objectives. PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical Understanding and Reasoning COURSE LEARNING OBJECTIVES Students get a number of opportunities to demonstrate their ability to communicate effectively (CLO # 7) Students demonstrate an honest reporting and use of data (CLO #5) COURSE ASSESSMENT ITEM Only written skills can be assessed in Quizzes, Mid Term and Final

3 Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology This is an important objective of the course (CLO # 2 6) Students will learn to design and implement decision support tools primarily in Excel/LINGO/GUROBI. (CLO 2) Quizzes, and Exams Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Understanding Organizational Ecosystems Goal 7 (a) Discipline Specific Knowledge and Understanding Goal 7 (b) Understanding the science behind the decision making process Students work in groups on the project NA Comprehensive coverage of topics in elementary management science (CLO # 1 5 & LO # 1 9) Students apply appropriate methods to answer data based decision problems (CLO # 1 5) NA Quizzes, and Exams Quizzes, and Exams GRADING BREAKUP AND POLICY Quizzes (Announced): 10% (No make up quiz) : 15% (To be completed in groups number of students in a group: TBA) Midterm Examination: 30% Final Examination: 30% Attendance/CP/Instructor: 15% (5+5+5) **Note: Attendance will be taken in every class and you are strongly encouraged to attend all classes. Please make sure you arrive ahead of time. The lecture/lab will start at the appointed time. Your attendance will not be marked if you are late in the class (5 min policy). While the class/lab is in session, please turn your mobile devices off. Please do not use the lab time to browse the web or check s etc. CP will be marked based on your attendance, quality of contribution and class work submission (codes/excel files/short questions etc.). EXAMINATION DETAIL Midterm Exam Final Exam Yes/No: Combine/Separate: Duration: Preferred Date: Exam Specifications: Yes/No: Combine/Separate: Duration: Exam Specifications: Yes Combine 100 minutes TBD Closed book, closed notes, calculators allowed Yes Combine 100 minutes Closed book, closed notes, calculators allowed COURSE OVERVIEW LECTURE TOPICS RECOMMENDED READINGS OBJECTIVES Introduction Syllabus Course introduction 1 Introduce students to the area of management science and the MGS major. (CLO 1)

4 2 3 4 Introduction to Modeling Chapter 1 Familiarize students with different categories of modeling techniques and highlight respective applications, strengths and weaknesses. Introduce the idea of Good Decisions vs. Good Outcomes (CLO 1) Introduction to Optimization & Linear Programming (LP) Chapter 2 Introduce students to the concept and essential characteristics of mathematical optimization and illustrate the application of Linear Programming as an example (CLO 3) Solving Linear Programming problems Chapter 2 Introduce the basic framework for designing and solving a 2 variable LP (CLO 2, 3) 5 7 Modeling and Solving LPs in a spreadsheet Chapter 3 8 Sensitivity Analysis Chapter 4 Introduce the use of spreadsheets to setup and solve a multi variable LP (CLO 2, 3) Use of Excel Spreadsheet Introduce the use of LP in solving a range of different problems including: make vs. buy decisions, investment problems and transportation problems, blending problems, production and inventory planning and multi period cash flow (CLO 2, 3, 7) Provide students with a basic understanding of the purpose and application of sensitivity analysis (CLO 2, 3) Discuss the benefits and limitations of sensitivity analysis (CLO 2, 3, 7) 9 Simplex Method Chapter 4 Provide a glimpse into the mechanics of the LP solution discovery process (CLO 2, 3) Introduction to Network Modeling Chapter 5 Introduce the concept of analyzing a class of business problems as network models (CLO 2, 3) Introduce the use of network modeling in solving a range of problems including, transshipment, equipment replacement, transportation and generalized network flow (CLO 2, 3, 7)

5 13 14 Regression Analysis 15 MID TERM Chapter Integer Linear Programming (ILP) Chapter Introduction to Queuing Theory Chapter Introduction to Simulation Chapter 12 Demonstrate the application of linear regression models and discuss the interpretation of key numbers such as R square, betas and the concept of statistical significance Introduce the use of ILP in solving a range of different problems including: make vs. buy decisions, investment problems and transportation problems, blending problems, production and inventory planning and multi period cash flow (CLO 2, 3, 7) Introduce the basic elements of a queuing model including servers, customers, queue lengths etc. (CLO 2, 5) Introduce the basic concepts of simulation such as random variables, risk, sampling etc. (CLO 2, 5) Demonstrate the application of simulation (CLO 2, 5,7) Presentations/Review TEXTBOOK(S)/SUPPLEMENTARY READINGS Required Texts: Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science 5 th Edition by Cliff Ragsdale Supplementary: Operations Research: Applications and Algorithms 4 th Edition by Wayne L. Winston Software Resources R Software Gurobi Optimization LINGO Optimization Modeling Software for Linear, Nonlinear, and Integer Programming