UNIVERSITY OF SOUTHERN CALIFORNIA Marshall School of Business IOM 427 Designing Spreadsheet-Based Business Models Fall 2011

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1 UNIVERSITY OF SOUTHERN CALIFORNIA Marshall School of Business IOM 427 Designing Spreadsheet-Based Business Models Fall 2011 Instructor: Hiroshi Ochiumi Office: BRI 401M Office hours: 3-4pm Tuesday. Phone: (213) Course Material: Required Text: Management Science & Decision Technology, by Jeffrey D. Camm and James R. Evans (South-West College Publishing, 2000). Course Objective: Spreadsheets are convenient and widely available platforms for organizing information and performing what if analyses. Excel therefore, has become an indispensable tool for business analysis. This course will focus on structuring, analyzing and solving managerial decision problems on Excel spreadsheets. This course is not about becoming an Excel expert, but about modeling. The ultimate goal is to help you acquire the skills of logical reasoning with formal models how to improve your decision making capability. More specifically, the course will help you become an effective modeler who can build sound models to solve business problems. We will study five broad classes of managerial problems: 1. Data Analysis: How to summarize available data into useful information. The cost of collecting data has declined fairly dramatically and most firms now have a fair amount of data. The first few, perhaps the most useful, steps in understanding and structuring a business decision is to find out what data is available and organizing it to support decision making. 2. Resource Allocation: How to optimally allocate a limited pool of resources among available opportunities. This is the most common managerial problem, occurring in every functional area. Examples in finance include constructing an optimal risk-return portfolio, and capital budgeting. Examples in marketing include media planning, and sales force territory planning. In operations management resource allocation problems arise in capacity, logistics and operations planning. 3. Estimation and Forecasting: How to find important information and implications from historical data and how to extrapolate past data into the future. We will explore a handful of simple techniques for estimation and forecasting. 4. Decision Analysis/ Contingent Decisions: How to synthesize a sequence of decisions involving uncertainty. An intuitive approach to handling uncertainty is to explore the possibility of deferring a decision until some uncertainty is resolved,

2 especially when the stakes are high. If we can we should make sequence of decisions instead of one big decision. Business examples where such decision techniques are used include dynamic portfolio management, new product development, and capacity expansion planning. 5. Risk Analysis: How to incorporate uncertainty in problem parameters. Almost always managerial decisions are based on anticipated states of the business environment. Clearly as the decision horizon becomes longer there is an increase in uncertainty. Managers have to carefully consider different potential scenarios while making decisions. In this part of the course we will learn how to explicitly incorporate uncertainty into business models. Excel Skills: Previous knowledge of Excel is not required. Knowing how to enter formulae involving relative and absolute cell addresses, and how to graph using chart wizard is sufficient. We will learn Pivot Table, Filters, etc., for data processing, Solver to find optimal decisions, Time series models for forecasting, Treeplan for drawing decision trees, and Crystal Ball to simulate the effects of uncertainty. Grading: The course grade, which will be curved, is based on four homework assignments 1, Exams 1 and 2, Class and on-line Participation, Problem of the Day, and C3 project, according to the following weights: Homework 20% (5% each) Exam 1 25% Exam 2 30% Class Participation 5% Problem of the Day 5% On-line Participation 5% C3 Project 10% There will be absolutely no other assignments. Exams are in-class and closed book/notes. There are no make up exams. Homework assignments are accepted only via Blackboard. The instructor will not accept homework assignments in class. Late homework will not be graded. Blackboard: We will use Blackboard as our information center. Make sure you know how to use it. Handouts, assignments, syllabus updates, and supplementary reading materials will all be posted there. 1 There will be five homework assignments. Only the best four will count.

3 Class Continuity in a Crisis (C3): IOM427 is one of the 16 pilot courses selected by the university. During this hypothetical crisis, you would not be able to come to campus. You will work on a project from home (or anywhere that is not affected by the crisis). You will use technologies such as gotomeeting, goview, Google+, Skype, YouTube, and of course BB and Excel to complete this project. Details will be posted in BB. Notice on Academic Integrity The use of unauthorized material, communication with fellow students during an examination, attempting to benefit from the work of another student, and similar behavior that defeats the intent of an examination or other class work is unacceptable to the University. It is often difficult to distinguish between a culpable act and inadvertent behavior resulting from the nervous tensions accompanying examinations. Where a clear violation has occurred, however, the instructor may disqualify the student's work as unacceptable and assign a failing mark on the paper. Turnitin Technologies USC is committed to the general principles of academic honesty that include and incorporate the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one s own academic work from misuse by others as well as to avoid using another s work as one s own. By taking this course, students are expected to understand and abide by these principles. All submitted work for this course may be subject to an originality review as performed by Turnitin technologies ( to find textual similarities with other Internet content or previously submitted student work. Students of this course retain the copyright of their own original work, and Turnitin is not permitted to use student-submitted work for any other purpose than (a) performing an originality review of the work, and (b) including that work in the database against which it checks other student-submitted work. For Students with Disabilities Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the office as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m. - 5:00 p.m., Monday through Friday. The phone number for DSP is (213)

4 COURSE OUTLINE and ASSIGNMENTS (Tentative) Aug. 23 Course Introduction Objectives, outline, skill sets, textbook, expectations Introduction to Modeling Definition of modeling, types of models, functional area applications, modeling steps; A simple profit model in math and in Excel; Ch. 1 of CE Aug. 25 Excel Basics Workbook/sheet navigation, window split/freeze, column/row operations, sequences, absolute/relative cell references, range names, auditing tools, menu items, basic functions (Min, Max, Average, Count, etc.) Intermediate functions (If, And, Or, Sumproduct, Vlookup, Index, Match, Pmt, etc.) Aug. 30 Data Analysis Searching, sorting, filtering, etc. Ch. 2 of CE (except for the section of regression) Sep. 1 Data Analysis Cont d Auditing, Pivot Table Ch. 2 of CE

5 Sep. 6 Introduction to Optimization Optimization model components, types of optimization models, types of linear constraints, Excel model layouts and guidelines, solution possibilities Linear Programming Example: product mix (Santa Cruz MicroProducts) Math formulation, Solver setup Example: investment allocation (Kathryn) Alternative Excel models, universal linear programming template, intuitive solution Ch. 4 of CE Sep. 8 Linear Programming Example: allocation of marketing effort (Phillips, Inc.) Choosing decision variables, handling upper/lower bounds efficiently, modeling and Solver tips Example: diet problem (Colorado Cattle Company) Standard linear programming form, bottleneck constraints Homework 1 is due 4:00pm Sep. 13 Linear Programming Example: multiperiod production planning (Suzie s Sweatshirts) Network diagram, inventory balance equation, alternative models, solution intuition Example: working capital management (Vohio, Inc.) Network diagram, flow balance equation, solution diagram, solution intuition Sep. 15 Sensitivity Analysis Example: transportation problem (Hanover, Inc.) Connection with Hanover data analysis example, network diagram, special (rectangle) Excel layout Sensitivity Analysis Changing right-hand sides, bottleneck constraints, shadow prices; changing objective coefficients, basic (non-zero) variables; Solver sensitivity report

6 Sep. 20 Integer Programming Introduction: integer and binary variables, Solver setup Example: production planning (Queen City, Inc.) Difference between linear and integer solutions Example: project selection (Burke Construction) Describing logical relationships by binary variables and linear constraints, binary variables in objective function Ch. 5 of CE Homework 2 is due 4:00pm Sep. 22 Integer Programming Example: location problem (Sun Bank) I Choosing decision variables, modeling adjacency relationships, linking principal place of business with surrounding branches, creating coverage table Example: location problem (Sun Bank) II Preventing a branch from double counting Sep. 27 Non-linear Programming Introduction: nonlinear functions, nice vs. nasty functions, global vs. local optimal solutions, nonlinear constraints, Solver setup Example: advertising budget allocation (Phillips, Inc.) Determining nonlinear objective function (relationship between profit and advertising money) through regression Example: facility location (Jack s Job Shop) Determining nonlinear objective function through geometry Ch. 5 of CE Homework 3 is due 4:00pm Sep. 29 Non-linear Programming Example: Markowitz portfolio optimization model Expressing expected portfolio return from allocation variables, expressing portfolio variance from allocation variables, variancecovariance matrix, 3-stock Excel model, 30-stock Excel model, sensitivity analysis, risk-return frontier

7 Oct. 4 Review Oct. 6 Exam 1 Oct. 11 Forecasting Introduction: Time series components and decomposition, forecasting model classification, forecast errors (mean absolute deviation, root mean square error, mean absolute percentage error) Simple moving average model (data with no trend or seasonality): Selecting window size based on errors Simple exponential smoothing model (data with no tread or seasonality): Selecting the smoothing constant through data table or Solver Double moving average model (data with trend): Linear trend equation, level and trend Ch. 3 of CE Oct. 13 Forecasting with Trend and Seasonality Double exponential smoothing model (data with trend): Smoothing constants initialization, selecting smoothing constants through data table or Solver Additive seasonality model (data with seasonality) Oct. 18 Forecasting with Trend and Seasonality Multiplicative seasonality model (data with seasonality) Holt-Winters additive model (data with trend and seasonality) Holt-Winters multiplicative model (data with trend and seasonality) Forecasting with regression: Simple linear regression models, Excel add trendline tool

8 Oct. 20 Introduction to Decision Analysis Introduction: Decision alternatives, outcomes, payoff table, decision criteria (average-payoff, aggressive, conservative, opportunity-loss, and expected-value criteria) Decision tree basics: Diagram, decision nodes, event nodes, branches Drawing decision tree, Treeplan Excel add-in, rollback procedure (two methods) Ch. 6 of CE Oct. 25 Decision Analysis Example: new product introduction Drawing decision tree, Treeplan tips, sensitivity analysis by oneand two-variable data tables Solving decision tree by Treeplan Oct. 27 Decision Analysis Solving decision tree by Treeplan One-variable sensitivity analysis Two-variable sensitivity analysis Nov. 1 Review Homework 4 is due 4:00pm

9 Nov. 3 Introduction to Simulation Introduction: Simulation and risk, random numbers, histogram, probability distributions (two points, binomial, Poisson, uniform, triangular, normal), simulation modeling process Practice: Generating random numbers by Rand(), drawing distribution by histogram data analysis tool, rolling one or two dice and viewing distribution (dice roller simulator I & II) Example: profit model of a firm Creating base model, generating discrete random variables by Rand(), defining random variables in Crystal Ball, defining output cell in Crystal Ball, analyzing simulation result Ch. 7 of CE Nov. 8 Simulation Simulation using Crystal Ball Nov. 10 Simulation Example: newsvendor problem Key trade-off in newsvendor problems, profit formula, base model with fixed order quantity, Crystal Ball simulation, finding best order quantity via data table and decision table tools Example: pricing stock options Daily or weekly stock price formula, types of stock options, strike price and expiration date, fair price of an option, Crystal Ball simulation Crystal Ball tips: Crystal Ball functions and decision table tool Crystal Ball Nov. 15 Simulation Example: bidding for contract (Miller Construction Company) Modeling competitors bids with and without correlation, choosing Miller s bid through decision table tool Crystal Ball tip: correlation matrix tool Homework 5 is due 4:00pm. Nov. 17 No Class - Hypothetical Crisis on Campus Work on the C3 project from off-campus.

10 Nov. 22 No Class - Hypothetical Crisis on Campus Work on the C3 project from off-campus. Nov. 24 Nov. 29 No Class - Thanksgiving Simulation NCAA basketball tournament simulation. C3 project is due 4:00pm Dec. 1 Conclusions Dec Exam 2