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)740-9918
Email: ochiumi@marshall.usc.edu
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,
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 assignments1, Exams
1 and 2, Class and on-line Participation, Problem of the Day, and C3 project, according to
the following weights:
Homework
Exam 1
Exam 2
Class Participation
Problem of the Day
On-line Participation
C3 Project
20% (5% each)
25%
30%
5%
5%
5%
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.
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
(http://www.turnitin.com) 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) 740-0776.
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;
Reading
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.
Reading
Ch. 2 of CE (except for the section of regression)
Sep. 1
Data Analysis Cont’d
Auditing, Pivot Table
Reading
Ch. 2 of CE
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
Reading
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
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
Reading
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
Reading
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
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
Reading
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
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)
Reading
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
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
Reading
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.
Nov. 22
No Class - Hypothetical Crisis on Campus
Work on the C3 project from off-campus.
Nov. 24
No Class - Thanksgiving
Nov. 29
Simulation
NCAA basketball tournament simulation.
C3 project is due 4:00pm
Dec. 1
Conclusions
Dec. 7-14
Exam 2