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Spreadsheet Modeling & Decision Analysis
A Practical Introduction to Management Science
4th edition
Cliff T. Ragsdale
1-2
Introduction to Modeling & Problem Solving
Chapter 1
1-3
Introduction
• We face numerous decisions in life & business.
• We can use computers to analyze the potential outcomes of decision alternatives.
• Spreadsheets are the tool of choice for today’s managers.
1-4
What is Management Science?
• A field of study that uses computers, statistics, and mathematics to solve business problems.
• Also known as:– Operations research– Decision science
1-5
Home Runs in Management Science
• Merril Lynch– 5 million customers– 16,000 financial advisors– Developed a model to design product
features and pricing options to better reflect customer value
– Benefits: • $80 million increase in annual revenue• $22 billion increase in net assiets
1-6
Home Runs in Management Science
• Jan de Wit Co.– Brazil’s largest lily farmer– Annually plants 3.5 million bulbs and
produces 420,000 pots & 220,000 bundles of lilies in 50 varieties.
– Developed model to determine what to plant, when to plant it, and how to sell it.
– Benefits:• 26% increase in revenue• 32% increase in contribution margin
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Home Runs in Management Science
• NBC– Must determine program schedules– Schedules must meet advertisers
demographic and cost requirements– Developed optimization model to
determine optimal timing and pricing of commercials
– Benefits:• $50 million increase in annual revenue
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Home Runs in Management Science
• Samsung Electronics– Leading DRAM manufacturer– Semiconductor facilities cost $2-$3 billion– High equipment utilization is key– Developed comprehensive planning and
scheduling system to control WIP– Benefits:
• Cut cycle times in half• $1 billion increase in annual revenue
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What is a “Computer Model”?
• A set of mathematical relationships and logical assumptions implemented in a computer as an abstract representation of a real-world object of phenomenon.
• Spreadsheets provide the most convenient way for business people to build computer models.
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The Modeling Approach to Decision Making
• Everyone uses models to make decisions.
• Types of models:– Mental (arranging furniture)– Visual (blueprints, road maps)– Physical/Scale (aerodynamics,
buildings)– Mathematical (what we’ll be studying)
1-11
Characteristics of Models
• Models are usually simplified versions of the things they represent
• A valid model accurately represents the relevant characteristics of the object or decision being studied
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Benefits of Modeling• Economy - it is often less costly to
analyze decision problems using models.
• Timeliness - models often deliver needed information more quickly than their real-world counterparts.
• Feasibility - models can be used to do things that would be impossible.
• Models give us insight & understanding that improves decision making.
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Example of a Mathematical Model
Profit = Revenue - Expensesor
Profit = f(Revenue, Expenses)or
Y = f(X1, X2)
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A Generic Mathematical Model
Y = f(X1, X2, …, Xn)
Y = dependent variable
(aka bottom-line performance measure)
Xi = independent variables (inputs having an impact on Y)
f(.) = function defining the relationship between the Xi & Y
Where:
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Mathematical Models & Spreadsheets
• Most spreadsheet models are very similar to our generic mathematical model:
Y = f(X1, X2, …, Xn)
• Most spreadsheets have input cells (representing Xi) to which mathematical functions ( f(.)) are applied to compute a bottom-line performance measure (or Y).
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Categories of Mathematical Models
Prescriptive known, known or under LP, Networks, IP,well-defined decision maker’s CPM, EOQ, NLP,
control GP, MOLP
Predictive unknown, known or under Regression Analysis, ill-defined decision maker’s Time Series Analysis,
control Discriminant Analysis
Descriptive known, unknown or Simulation, PERT,well-defined uncertain
Queueing, Inventory Models
Model Independent OR/MS
Category Form of f(.) Variables Techniques
1-17
The Problem Solving Process
Identify Problem
Formulate & Implement
ModelAnalyze Model
Test Results
Implement Solution
unsatisfactoryresults
1-18
The Psychology of Decision Making
• Models can be used for structurable aspects of decision problems.
• Other aspects cannot be structured easily and require intuition and judgment.
• Caution: Human judgment and intuition is not always rational!
1-19
Anchoring Effects
• Arise when trivial factors influence initial thinking about a problem.
• Decision-makers usually under-adjust from their initial “anchor”.
• Example:– What is 1x2x3x4x5x6x7x8 ?– What is 8x7x6x5x4x3x2x1 ?
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Framing Effects
• Refers to how decision-makers view a problem from a win-loss perspective.
• The way a problem is framed often influences choices in irrational ways…
• Suppose you’ve been given $1000 and must choose between:
– A. Receive $500 more immediately– B. Flip a coin and receive $1000 more if
heads occurs or $0 more if tails occurs
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Framing Effects (Example)
• Now suppose you’ve been given $2000 and must choose between:
– A. Give back $500 immediately– B. Flip a coin and give back $0 if heads
occurs or give back $1000 if tails occurs
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A Decision Tree for Both Examples
Initial state
$1,500
Heads (50%)
Tails (50%)
$2,000
$1,000
Alternative A
Alternative B(Flip coin)
Payoffs
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Good Decisions vs. Good Outcomes
• Good decisions do not always lead to good outcomes...
• A structured, modeling approach to decision making helps us make good decisions, but can’t guarantee good outcomes.
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End of Chapter 1