2. Spreadsheet Modeling & Decision Analysis A Practical Introduction to Management Science 5 th edition Cliff T. Ragsdale

3. Introduction to Modeling & Problem Solving Chapter 1

4. 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.

5. 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

6. Home Runs in Management Science  Motorola –Procurement of goods and services account for 50% of its costs –Developed an Internet-based auction system for negotiations with suppliers –The system optimized multi-product, multi- vendor contract awards –Benefits: $600 million in savings

7. Home Runs in Management Science  Waste Management –Leading waste collection company in North America –26,000 vehicles service 20 million residential & 2 million commercial customers –Developed vehicle routing optimization system –Benefits: Eliminated 1,000 routes Annual savings of $44 million

8. Home Runs in Management Science  Hong Kong International Terminals –Busiest container terminal in the world –122 yard cranes serve 125 ships per week –Thousands of trucks move containers in & out of storage yard –Used DSS to optimize operational decisions involving trucks, cranes & storage locations –Benefits:  35% reduction in container handling costs  50% increase in throughput  30% improvement in vessel turnaround time

9. Home Runs in Management Science  John Deere Company –2500 dealers sell lawn equipment & tractors with support of 5 warehouses –Each dealer stocks 100 products, creating 250,000 product-stocking locations –Demand is highly seasonal and erratic –Developed inventory system to optimize stocking levels over a 26-week horizon –Benefits:  $1 billion in reduced inventory  Improved customer-service levels

10. 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.

11. 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)

12. 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

13. 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.

14. Example of a Mathematical Model Profit = Revenue - Expenses or Profit = f (Revenue, Expenses) or Y = f (X 1, X 2 )

15. A Generic Mathematical Model Y = f (X 1, X 2, …, X n ) Y = dependent variable (aka bottom-line performance measure) X i = independent variables (inputs having an impact on Y) f (. ) = function defining the relationship between the X i & Y Where:

16. Mathematical Models & Spreadsheets  Most spreadsheet models are very similar to our generic mathematical model: Y = f (X 1, X 2, …, X n )  Most spreadsheets have input cells (representing X i ) to which mathematical functions ( f (. )) are applied to compute a bottom-line performance measure (or Y).

17. Categories of Mathematical Models Prescriptiveknown,known or underLP, Networks, IP, well-defineddecision maker’sCPM, EOQ, NLP, controlGP, MOLP Predictiveunknown,known or underRegression Analysis, ill-defineddecision maker’sTime Series Analysis, control Discriminant Analysis Descriptiveknown,unknown orSimulation, PERT, well-defineduncertain Queueing, Inventory Models ModelIndependent OR/MS CategoryForm of f (. )Variables Techniques

18. The Problem Solving Process Identify Problem Formulate & Implement Model Analyze Model Test Results Implement Solution unsatisfactory results

19. The Psychology of Decision Making  Models can be used for structurable aspects of decision problems.  Other aspects cannot be structured easily, requiring intuition and judgment.  Caution: Human judgment and intuition is not always rational!

20. 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 ?

21. 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

22. 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

23. 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

24. 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.

25. End of Chapter 1