ETW2440-Topic 1-Decision Making Process PDF

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Topic 1: Decision-making process and modelling methods Learning objectives On completion, a student should be able to     

Describe steps in decision making process Understand the role of Business Analytics Comprehend various aspects of models Introduce mathematical models Describe the seven-step modeling process

Reading:

You are highly encouraged to read the following chapters on models and modeling. Cliff T. Ragsdale (R) (2015). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics, 7th Ed. Thompson. Chapter 1: Introduction to modeling and decision analysis. Winston, W.I. (2012). Practical Management Science 4th Edition, South-Western CENGAGE Learning. Chapter 1: Introduction to Modeling.

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Decision making process 1.

Identify the decision to be made.

You realize that a decision must be made. You then go through an internal process of trying to define clearly the nature of the decision you must make. 2.

Gather relevant information.

The real trick in this step is to know what information is needed, the best sources of this information, and how to go about getting it. Some information comes from periodic financial reports, performance reports, and other sources that are designed to discover problems. 3.

Identify alternatives.

In this step of the decision-making process, you will list all possible and desirable alternatives. 4.

Weigh evidence.

You must evaluate whether the need identified in Step 1 would be helped or solved through the use of each alternative.

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Decision making process (continued) 5. Choose among alternatives. Once you have weighed all the evidence, you are ready to select the alternative which seems to be best suited to you. As a manager, one will select an alternative: Least amount of risk and uncertainty Try to gauge the prospects for success Rely on their intuition and experience

6. Take action. You now take some positive action which begins to implement the alternative you chose in Step 5. 7. Review decision and consequences. In the last step you experience the results of your decision and evaluate whether or not it has “solved” the need you identified in Step 1. If it has, you may stay with this decision for some period of time. If the decision has not resolved the identified need, you may repeat certain steps of the process in order to make a new decision.

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Example 1 An investment bank is preparing to invest up to 7.5 million MYR of its cash reserves. The bank is considering the following alternatives: Investment Treasury securities Corporate bonds Loans to corporations Stocks

Expected rate of return 7.8% 8.5% 9.6% 12%

How to make a decision?

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Risk factor 0 1 2 3

Example 2 How does a company diversify its portfolio? A and B or C and D? How to make a decision?

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Decision making much more difficult  More alternatives Modern technology (e.g., internet) provides more alternatives  Errors costs more Example: Will it be appropriate if a firm chooses a route for a delivery of its products to certain destination without analyzing the alternative routes?  More uncertainty regarding future  Structural complexity of organization  Competition

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Business Analytics  Business analytics is a field of study that uses data, computers, statistics, and mathematics to solve business problems.  It involves using the methods and tools of science to drive the business decision making.  Business analytics is also sometimes referred to as operations research, management science, or decision science.  This discipline is commonly viewed as a subfield of Mathematics.  The terms “Operational Research” (British usage), “Management Science” and “Decision Science” are also used as synonyms.  Employing approaches from other mathematical sciences such as statistical analysis, mathematical modelling and, mathematical optimization, OR finds optimal (or near optimal) solutions to complex decision making problems.  OR is often concerned with finding the maximum of profit, performance or yield or minimum of loss, risk, or cost of some real world objective.  In a nutshell, the OR is not a Math in theory but it is a Math in practice.

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Applications of Business Analytics Organization

Area of application

Annual savings

Continental Airlines

Reassign crews to flights when schedule disruptions occur

$40 million

United Airlines

Plan employee work schedules at airports and reservations offices

$6 million

Samsung Electronics Reduce manufacturing times $200 million more and inventory levels revenue Procter & Gamble

Redesign the production and distribution system

$200 million

Canadian Pacific Railway

Plan routing of rail freight

$100 million

Air New Zealand

Airline crew scheduling

$6.7 million

Waste Management

Develop a route$100 million management system for trash collection and disposal

Bank Hapoalim Group

Develop a decision-support system for investment advisors

$31 million more revenue

Merrill Lynch

Manage liquidity risk for revolving credit lines

$4 billion more liquidity

General Motors

Improve efficiency of production lines

$90 million

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Models 

Models are abstracted or simplified representations of a real or planned structure / situation 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)

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 or phenomenon.



Spreadsheets provide the most convenient way for business people to build computer models.

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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

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.

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Models give us insight & understanding that improves decision making.

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Mathematical model 

In simple terms, a mathematical model is a quantitative representation, or idealization, of a real problem.



This representation might be phrased in terms of mathematical expressions (equations and inequalities) or as a series of interrelated cells in a spreadsheet.

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Examples for mathematical models are mathematical programming, simulations, network, econometric time series.

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Models that simply describe a situation are called descriptive models. Other models that suggest a desirable course of action are called optimization models.

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For example, the following is a descriptive model which describes the relationship between W, A, and S variables: 𝐴 𝑆(𝑆 − 𝐴) We will see an example for the optimization model at the end of this file. 𝑊=



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Mathematical modelling 

focuses on the real world issue – must exist some issue.



Different approaches of mathematical models

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Empirical models Based on some data

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Simulation models Computer programs that applies a set of rules

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Deterministic models Rely a set of equations or inequalities

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Stochastic models Probability, uncertainty, randomness involved

Example for decision making through simulation: Simul8 examples of completed models may be found at http://www.simul8.com/products/guidedtours/

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Example of a Mathematical Model Profit = Revenue - Expenses or Profit = f(Revenue, Expenses) or Y = f(𝑋1, 𝑋2 )

A Generic Mathematical Model Y=

f(X1, X2, …, Xn) Where:

Y = dependent variable (bottom-line performance measure) Xi = independent variables (inputs having an impact on Y) f(.) = function defining the relationship between the Xi & Y

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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 bottomline performance measure (or Y).

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The seven-step modeling process Step 1: Problem Definition The analyst first defines the organization’s problem. Defining the problem includes specifying the organization’s objectives and the parts of the organization that must be studied before the problem can be solved. In the simple queueing model, the organization’s problem is how to minimize the expected net cost associated with serving customers.

Step 2:Model Development In the third step, the analyst develops a model of the problem. In this unit, we present many methods that can be used to model systems. Models such as the equation for W, where an equation is used to relate inputs such as A and S to outputs such as W, are called analytical models. Most realistic applications are so complex, however, that an analytical model does not exist or is too complex to work with.

Step 3: Data Collection After defining the problem, the analyst collects data to estimate the value of parameters that affect the organization’s problem. These estimates are used to develop a mathematical model (step 3) of the organization’s problem and predict solutions (step 4).

Step 4:Model Verification The analyst now tries to determine whether the model developed in the previous step is an accurate representation of reality. Scrutinize model output to check for:  Faults in data, construction, coding, exclusion of variables, errors in relating variables.  If any faults found – refine the model. 16

The seven-step modeling process (continued) Step 5: Optimization and Decision Making Given a model and a set of possible decisions, the analyst must now choose the decision or strategy that best meets the organization’s objectives. Step 6: Model Communication to Management and presentation of results The analyst presents the model and the recommendations from the previous steps to the organization.          

Model specification Analyse the modelling problem in detail Precisely define all logical relationships Confirm modelling language and computing Demonstrate the model Explains how the model will be used to solve the problem Communicating the results Documentation Technical [for maintenance] User guide [specification and operating instructions]

Step 7:Model Implementation If the organization has accepted the validity and usefulness of the study, the analyst then helps to implement its recommendations. The implemented system must be monitored constantly (and updated dynamically as the environment changes) to ensure that the model enables the organization to meet its objectives.

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Critical path method (CPM); Project evaluation and review technique (PERT)

Show these steps with investment example

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Example 1 An investment bank is preparing to invest up to 7.5 million MYR of its cash reserves. The bank is considering the following alternatives: Investment Treasury securities Corporate bonds Loans to corporations Stocks

Expected rate of return 7.8% 8.5% 9.6% 12%

How to make a decision?

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Risk factor 0 1 2 3

Model components 

Input output variables - mathematical equations called functions use input and output replace the variables in an equation. The input is the known variable, while the output is the solution.



An example of a function 𝑊 =

𝐴 𝑆(𝑆−𝐴)

where W is output is

an output variable while A and S are input variables. 

Intermediate (auxiliary) variable – may not be interesting expect to simplify the output variable.

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Endogenous variable is entirely under the control of decision makers.

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Exogenous variable is not under the control of decision maker (e.g., cost of raw material).

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Demonstrate optimization using optimize() function in R.

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