MBA elective - Models for Strategic Planning - Session 1
Models for Strategic Planning
Session 1 contents
About the Course
The Modeling Approach
Introduction to Optimization Models
© 2009 Ph. Delquié
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Models vs. Managerial Judgment
Judgment without Models:
you’re limited
Models without Judgment:
you’re dangerous
Models don’t take decisions. People do.
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MSP Course Material
Lecture slides, Solutions and other files
distributed via website
Software
MS Excel with “Add-ins”
• Solver
(Part I)
• TreePlan (Part II)
• Crystal Ball (Part III)
Recommended text
The Art of Modeling with Spreadsheets,
Powell and Baker, Wiley, 2007
(includes software)
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Synopsis of Business Analytics
DATA
Prediction Models
Help you extract
information from data
INFORMATION,
KNOWLEDGE
Decision Models
Help you exploit
information to make
better decisions
ACTION
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Course Contents
Optimization Models
extract maximum value from resources
Dynamic Decision Models
manage, and profit from, uncertainty
Value Models
ensure consistent, rigorous valuations
Individually, suited to
different situations
Together, tackle a wide
array of business issues,
tactical and strategic
Simulation Models
evaluate and manage complex risks
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The Building Blocks of Decision Models
Uncertainties
Objectives
Outcomes
Decisions
Constraints
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The Modeling Approach – Skills you will learn
1. Framing the issues – Conceptual
How should I think about this problem?
Am I working on the right problem?
Model = management’s best current understanding of the issue
⇒ Not “truth”, not free from judgment, 6
2. Designing – Technical
How do I capture this in a model structure?
Capture just enough detail to support decision making
⇒ Find a balance between practicality and accuracy
3. Generating insights – Analytical
How do I exploit the model to go beyond what I already know?
⇒ Generate solutions; discover alternatives, trade-offs, boundaries
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The Modeling Approach – Model vs. Reality
Model differs from reality
- Structurally:
the equations do not correspond
precisely to the actual situation
- Parametrically: we are unable to determine
all coefficients precisely
⇒ Solution to the model is not an exact solution to the real problem
GIGO principle
“Garbage In, Garbage Out”
The quality of recommendations from a model
is as good as the quality of input data.
⇒ Need to carry out Sensitivity Analysis
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Issues in Building and Using Models
• Lack of standards in business modeling
• Bugs can have a substantial impact on business
(see e.g. www.EuSpRIG.org)
• Ethics in Decision Modeling?
- Frame manipulation
- Hidden assumptions
- Misrepresentation of stakeholders’ preferences
- Outcome manipulation
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Part I. BUILDING OPTIMIZATION MODELS
Purpose:
Find how to best use
the resources available
(e.g., money, labor, time)
Decisions
so as to
Achieve a best level of
something you care about
(e.g. profit, cost, risk)
Objectives
Helps you extract maximum value from resources, activities, and
portfolios
Allows you to explore vast, complex combinations of possibilities
Enables you to develop insights into the key trade-offs inherent in
your business activities
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Guiding principle for building an Optimization Model
Describe the whole situation in terms of three elements:
1. Decision Variables
2. An Objective
3. Constraints
A surprisingly wide array of management problems can
be thought of in those terms
Let’s review these three model elements in turn6
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Building an Optimization Model (1)
1. Decision variables
• Represent parameters under management’s control,
to be decided by management
• Should be defined so as to describe all possible
alternative decisions
Examples:
- whether to support an R&D project or not
- number of salespeople to hire
- production level in a given period
- amount to buy from a given supplier
- how much to bid
Together, the values of the decision variables define a
policy or plan of action.
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Building an Optimization Model (2)
2. Objective function
• Defines the goal of the problem, the target to be optimized
• Provides a criterion to compare alternative solutions
• Expressed as a function of the decision variables
Examples:
Profit
Cost
Risk
Market Share
→
→
→
→
Maximize
Minimize
Minimize
Maximize
Objective function = an outcome variable
can be controlled through decision variables, not directly
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Building an Optimization Model (3)
3. Constraints
• Describe what’s actually feasible6
- technically
quantity produced cannot exceed production capacity
- legally
employees may not work more than 8 hours per shift
- logically
all parts of the budget must add up to 100%
• Must be expressed as a function of the decision variables
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In summary...
An optimization model is defined by
- decision variables X1, X2, . . . , Xn
whose values we want to decide
- an objective function f (X1, X2, . . . , Xn)
which describes the quantity to be optimized,
i.e. the objective of the problem
- constraints gj(X1, X2, . . . , Xn) ≤ bj j = 1, . . ., m
which reflect the economic, legal and technical
realities under which we must operate
Issue: what if there are multiple objectives?
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Optimization Models: a worked-out example
Mathematical Model Formulation:
• Decision variables
X1 = number of MP3 Players to produce
X2 = number of CD Players to produce
X3 = number of Radio Players to produce
• Objective function
Maximize: Profit = $75·X1 + $50·X2 + $40·X3
• Constraints
50,000 ≤ X1 ≤ 150,000
50,000 ≤ X2 ≤ 100,000
50,000 ≤ X3 ≤ 90,000
3·X1 + 2·X2 + 1·X3 ≤ 400,000 hrs
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A spreadsheet implementation of the model:
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Optimization with Excel’s Solver
Solver is found in the Tools menu (if not, go to Add-Ins6)
• Solver Parameters dialog box
Set Cell = the Objective Function (select Max or Min)
Changing Cells = the Decision Variables
Constraints = the Constraints
• Solver Options dialog box
To specify that all decision variables must be ≥ 0,
check the Assume Non-Negative box !
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To do for next session<
Review Solution Set 1
to be posted on website shortly
Prepare Exercise Set 2
Form a workgroup of 5 or 4 members
send email to [email protected]
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