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Modelling and Business Decisions Robert Zimmer Room 6, 25 St James.

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1 Modelling and Business Decisions Robert Zimmer Room 6, 25 St James

2 This course is about building models and making decisions It is about organising information It is about organising information It is about being able to ask what-if questions It is about being able to ask what-if questions It is about applying powerful mathematical models (I might try to teach you some maths when you arent looking but that is incidental) It is about applying powerful mathematical models (I might try to teach you some maths when you arent looking but that is incidental)

3 Example of a decision: should I have another beer? Organising Information: Organising Information: How much money I have How much money I have How much money a beer costs How much money a beer costs How drunk am I? How drunk am I? Do I have to drive? Do I have to drive? How fat am I? How fat am I? How much do I like the people in the pub? How much do I like the people in the pub? How much do I like the people at home? How much do I like the people at home?

4 What-if questions What-if questions What if I can convince the barman to give me a half-price beer What if I can convince the barman to give me a half-price beer What if I decide that I like these people twice as much as I did What if I decide that I like these people twice as much as I did …

5 The Maths here would be difficult The Maths here would be difficult Luckily for us, Microsoft has implemented a special beer decision function: the famous Beer Decision Algorithm within excel Luckily for us, Microsoft has implemented a special beer decision function: the famous Beer Decision Algorithm within excel So all we need to do is put all of our figures on a spreadsheet, press the = button to pull up the formula function and type So all we need to do is put all of our figures on a spreadsheet, press the = button to pull up the formula function and typeBDA(A1,A2,….) or whatever

6 Then Bobs our uncle. We know whether to have the drink or not Then Bobs our uncle. We know whether to have the drink or not Because we did this on a spreadsheet it is flexible enough for us to change the parameters (that is, inputs) and find out if we should change our decisions Because we did this on a spreadsheet it is flexible enough for us to change the parameters (that is, inputs) and find out if we should change our decisions

7 Another Question What is the most money I am prepared to pay for this drink? That is at what price does the pleasure of the drink become less than its price? What is the most money I am prepared to pay for this drink? That is at what price does the pleasure of the drink become less than its price?

8 Some more questions What is the geometric shape of all the points at which the pleasure of the beer exactly matches the pain of the payment? What is the geometric shape of all the points at which the pleasure of the beer exactly matches the pain of the payment? How will my pleasure, my weight, and my mental state compare if instead of a beer I have chips? How will my pleasure, my weight, and my mental state compare if instead of a beer I have chips? or do my Java coursework? or do my Java coursework?

9 After this course you will be able to answer questions like this Some names for what we are studying: operations research, decision science, management science Some names for what we are studying: operations research, decision science, management science Our tool of choice: the humble spreadsheet. Our tool of choice: the humble spreadsheet.

10 Its not just beer: example 1 Merril Lynch Merril Lynch 5 million customers 5 million customers 16,000 financial advisors 16,000 financial advisors Developed a model to design product features and pricing options to better reflect customer value Developed a model to design product features and pricing options to better reflect customer value Benefits: Benefits: $80 million increase in annual revenue $80 million increase in annual revenue $22 billion increase in net assets $22 billion increase in net assets

11 Its not just beer: example 2 Jan de Wit Co. Jan de Wit Co. Brazils largest lily farmer Brazils largest lily farmer Annually plants 3.5 million bulbs and produces 420,000 pots & 220,000 bundles of lilies in 50 varieties. 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. Developed model to determine what to plant, when to plant it, and how to sell it. Benefits: Benefits: 26% increase in revenue 26% increase in revenue 32% increase in contribution margin 32% increase in contribution margin

12 NBC NBC Must determine program schedules Must determine program schedules Schedules must meet advertisers demographic and cost requirements Schedules must meet advertisers demographic and cost requirements Developed optimization model to determine optimal timing and pricing of commercials Developed optimization model to determine optimal timing and pricing of commercials Benefits: Benefits: $50 million increase in annual revenue $50 million increase in annual revenue

13 Our modus operandi 1. Make a mathematical model 2. Implement it in excel 3. Play with it to find out how the answers depend on the input variables 4. Use the inbuilt mathematical functions to do complicated analyses 5. Use the excel graphic packages to make diagrams

14 How you will learn to do this Option 1: You will listen to me, go to the labs, and not think about the subject in between Option 1: You will listen to me, go to the labs, and not think about the subject in between Option 2: You will not listen to me and stay home Option 2: You will not listen to me and stay home Option 3: You will listen to me and do everything I tell you to Option 3: You will listen to me and do everything I tell you to Option 4: You wont leave off your modelling practice, even to listen to me Option 4: You wont leave off your modelling practice, even to listen to me

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16 Models Everyone uses models to make decisions. Everyone uses models to make decisions. Types of models: Types of models: –Mental (arranging furniture) –Visual (blueprints, road maps) –Physical/Scale (aerodynamics, buildings) –Mathematical (what well be studying)

17 Mathematical Models Profit = Revenue - Expenses or Profit = f (Revenue, Expenses) or Y = f (X 1, X 2 )

18 Characteristics of Models Models are simplified versions of the things they represent Models are simplified versions of the things they represent A valid model accurately represents the relevant characteristics of the object or decision being studied A valid model accurately represents the relevant characteristics of the object or decision being studied So a large part of the art: is what is relevant and what can be abstracted away So a large part of the art: is what is relevant and what can be abstracted away

19 Benefits of Models Economy - it is often less costly to analyze decision problems using models. 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. 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. Feasibility - models can be used to do things that would be impossible. Models give us insight & understanding that improves decision making. Models give us insight & understanding that improves decision making.

20 Maths 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

21 Identify Problem Formulate & Implement Model Analyze Model Test Results Implement Solution unsatisfactory results

22 1.4 Seven-Step Modeling Process Step 1: Problem Definition - Define the problem including the objectives and the parts of the organization that must be studied. Step 2: Data Collection – Collect the data to estimate the value of parameters that affect the organizations problem. Step 3: Model Development – Develop an analytical or simulation model. Step 4: Model Verification – Determine whether the model is an accurate representation of reality.

23 Step 5: Optimization and Decision Making – Given the model and a set of possible decisions, the analyst must choose the decision that best meets the organizations objectives. Step 6: Model Communication to Management – The analyst presents the model and the recommendations to the organization. Step 7: Model Implementation – If the organization accepts the model then the analysts assists with implementation. Implementation must be monitored constantly to ensure that the model enables the organization to meets its objectives.

24 Models can be used for structurable aspects of decision problems. Models can be used for structurable aspects of decision problems. Other aspects cannot be structured easily and require intuition and judgment. Other aspects cannot be structured easily and require intuition and judgment. Caution: Human judgment and intuition is not always rational! Caution: Human judgment and intuition is not always rational!

25 Framing Effects Refers to how decision-makers view a problem from a win-loss perspective. 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… The way a problem is framed often influences choices in irrational ways… Suppose youve been given $1000 and must choose between: Suppose youve 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

26 Now suppose youve been given $2000 and must choose between: Now suppose youve 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

27 Initial state $1,500 Heads (50%) Tails (50%) $2,000 $1,000 Alternative A Alternative B (Flip coin) Payoffs

28 Chapter 2 Introduction to Spreadsheet Modeling

29 2.1 Introduction Excel skills are critical. There is an Excel tutorial on the CD-ROM that accompanies the book. Excel skills are critical. There is an Excel tutorial on the CD-ROM that accompanies the book. Excels features will provide insight into solving real business problems. Excels features will provide insight into solving real business problems.

30 2.2 Basic Spreadsheet Modeling: Concepts and Best Practices Most mathematical models, including spreadsheet models, involve inputs, decision variables, and outputs. Most mathematical models, including spreadsheet models, involve inputs, decision variables, and outputs. The model inputs are given values that are fixed. The model inputs are given values that are fixed. The decision variables are values that a decision maker has control over. The decision variables are values that a decision maker has control over. The model outputs are the ultimate values of interest. The model outputs are the ultimate values of interest.

31 Spreadsheet modeling is the process of entering the inputs and decision variables into a spreadsheet and then relating them appropriately, by means of formulas, to obtain the outputs. Spreadsheet modeling is the process of entering the inputs and decision variables into a spreadsheet and then relating them appropriately, by means of formulas, to obtain the outputs. Once a model is created there are several directions in which to proceed. Once a model is created there are several directions in which to proceed. Sensitivity analysis to see how one or more outputs change as selected inputs or decision variables change. Sensitivity analysis to see how one or more outputs change as selected inputs or decision variables change. Finding the value of a decision variable that maximizes or minimizes a particular output. Finding the value of a decision variable that maximizes or minimizes a particular output. Create graphs to show graphically how certain parameters of the model are related. Create graphs to show graphically how certain parameters of the model are related.

32 Good spreadsheet modeling practices are essential. Good spreadsheet modeling practices are essential. Spreadsheet models should be designed with readability in mind. Spreadsheet models should be designed with readability in mind. Several features that improve readability include: Several features that improve readability include: A clear logical layout to the overall model A clear logical layout to the overall model Separation of different parts of a model Separation of different parts of a model Clear headings for different sections of the model Clear headings for different sections of the model Liberal use of range names Liberal use of range names Liberal use of formatting features Liberal use of formatting features Liberal use of cell comments Liberal use of cell comments Liberal use of text boxes for assumptions, lists or explanations Liberal use of text boxes for assumptions, lists or explanations

33 Example 2.1 – Building a Model Randy Kitchell is a NCAA t-shirt vendor. The fixed cost of any order is $750, the variable cost is $6 per shirt. Randy Kitchell is a NCAA t-shirt vendor. The fixed cost of any order is $750, the variable cost is $6 per shirt. Randys selling price is $10 per shirt, until a week after the tournament when it will drop to $4 apiece. The expected demand at full price is 1500 shirts. Randys selling price is $10 per shirt, until a week after the tournament when it will drop to $4 apiece. The expected demand at full price is 1500 shirts. He wants to build a spreadsheet model that will let him experiment with the uncertain demand and his order quantity. He wants to build a spreadsheet model that will let him experiment with the uncertain demand and his order quantity.

34 Ex. 2.1(contd) - Building a Model The logic behind the model is simple. An Excel IF function will be used. The logic behind the model is simple. An Excel IF function will be used.

35 In this model the profit is calculated with the formula Profit = Revenue – Cost and the Cost = *B4

36 Revenue Case 1: Demand outstrips order (B3 > B4) In that case everything gets sold for 10 dollars Revenue is then simply 10*B4 (since B4 is the number ordered)

37 Revenue Case 2:You have ordered too many. That is order (B3) is less than peak demand Then you can only sell B3 at 10 dollars and the rest (B4-B3) at 4 dollars Revenue = 10*B3+4*(B4-B3)

38 Revenue Formula Revenue = IF(B3>B4,10*B4,10*B3+4*(B4-B3))

39 Profit Formula Profit = IF(B3>B4,10*B4,10*B3+4*(B4-B3)) – ( * B4)

40 More Flexibility

41 Ex. 2.1(contd) - Building a Model The formula can be rewritten to be more flexible. =-B3- B4*B9+IF(B8>B9,10*B8+B6*(B9-B8)) The formula can be rewritten to be more flexible. =-B3- B4*B9+IF(B8>B9,10*B8+B6*(B9-B8)) It can be made more readable by using range names. The formula would then read =-Fixed_order_cost- Variable_cost*Order + IF(Demand > Order, Selling_price*Order, 10*Demand+Salvage_value* (Order- Demand) It can be made more readable by using range names. The formula would then read =-Fixed_order_cost- Variable_cost*Order + IF(Demand > Order, Selling_price*Order, 10*Demand+Salvage_value* (Order- Demand)


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