CH 1

Break-even point in dollars

The sum of fixed and total variable cost if the number of units sold equals the break-even point

model

a real world problem scenario or environment (usually mathematical), it is not affected by a personal bias, emotions, or guesswork

3. Interpretation and Sensitivity Analysis

a. Analyzing the results and sensitivity analysis: -a model is only an approximation of reality so this type of data is called sensitivity, post optimality, or what-if analysis -sensitivity analysis is used to determine how much the solution will change if there are changes in the model or the input data b. Implement the results: -after solution is implemented, it should be clearly monitored -changing economy, fluctuating demand, and model enhancements requested by managers and decision makers are a few examples of changes that might require modification

2. Solution:

a. Developing a solution: -involved manipulating the model to arrive at the best or optimal solution to the problem -mathematical expressions or trial and error method -you may with to try all possible values for the variables: this is called complete enumeration -complex and difficult, you may be able to use an algorithm: consists of a series of steps or procedures that we repeat until we find the best solution b. testing the solution: -solution depends on the input data and the model, both require testing

Developing a Model

a. Fitting the textbook models: a manager may not always go by the textbook b. Understanding a model: -most managers simply do not use the results of a model they do not understand

Developing a solution

a. Hard-to-understand mathematics b. the limitation of only one answer: -the second problem in developing a solution is this

Acquiring Input Data

a. Using Accounting data: -one problem is that most data generated in a firm come from basic accounting reports b. Validity of data: -a lack of good, clean data means that whatever data are available often be distilled and manipulated (fudging) before being used in a model

2 types of decision modeling

deterministic and probabilistic

1-13 What is implementation and why is it important?

each step affects the implementation, may expose inadequacy, improves with user and manager involvement/ not the final step, must also monitor, some managers may be uncomfortable with it

Using spreadsheets

most decision modeling is used in spreadsheets

decision modeling

we define it as a scientific approach to managerial decision making

Defining the Problem

-Decision Analysis: typically face 4 roadblocks in defining a problem a. problems need to be examined from different viewpoints -ex(inventory problems, financial managers usually feel that inventory is too high because inventory represents cash not available for other investments, in contrast, sales managers ofter feel that inventory is too low because high levels of inventory may be needed to fill unexpected orders) b. impact on other departments: -problems do not exist in isolation and are not owned by just one department of a firm -a change in ordering policy can affect cash flows and upset production schedules -all inputs must be considered c. Beginning assumptions: -from an implementation perspective, a good solution to the right problem is much better than an optimal solution to the wrong problem d. solution updated

Quantitative VS. Qualitative Data

-any decision modeling process starts with data -processing and manipulating of raw data into meaningful information is the heart of decision modeling -managers may have to consider both qualitative and quantitative factors Quantitative: -factors such as rates of return, financial ratios, and cash flows in our decision model to guide our ultimate decision Qualitative: -factors such as pending state and federal legislation, new technological breakthroughs, and the outcome of an upcoming election, can be difficult to quantify these factors -due to presence (and relative importance) of qualitative factors, the role of quantitative decision modeling in the decision making process can vary -both factors must be considered -when there is a lack of qualitative factors, and when the problem model and input data remain reasonably stable and steady over time, the results of a decision model can automate the decision making process

probabilistic models

-assume some input data values are not known with certainty -values of some important variables will not be known before decisions are made -students can relate with coming to college and not knowing what major to choose, they used their own assumptions regarding the future and various alternatives -provide a structured approach for managers to incorporate uncertainty into their models and to evaluate decisions under alternate expectations regarding this uncertainty

Deterministic models

-assume that all the relevant input data values are known with certainty, they assume that all the information needed for modeling a decision-making problem environment is available, with fixed and known values -most common and popular is linear programming

testing solution

-managers are often asked how good a solution looks to them, all assumptions should be reviewed

Implementation- not the final step

-many action-oriented managers prefer the "dirty and quick" technique that gives immediate results -management support and user involvement are important

analyzing the results

-once a solution has been tested, the results must be analyzed in terms of how they will affect the total organization, if results suggest changes in organizational policy, the decision analysts can expect resistance

Steps involved in decision modeling: 1. Formulation

-process by which each aspect of a problem scenario is translated and expressed in terms of a mathematical model -most important and challenging step because the results of a poorly formulated problem will almost surely be incorrect (defining problem- developing model- acquiring data- develop solution- test- analyze results- implement) a. Define the problem -develop a clear, concise statement of the problem that gives direction and meaning to all the parts that follow it -perhaps most important in formulation -one problem may be related to other problems, and solving a problem without regard to its related problems may actually make the situation worse -develop specific, measurable objectives -ex) health care delivery hospital: might be to increase the number of beds, or reduce the avg number of days a patient spends in the hospital- but the real problem must be kept in mind, important to avoid obtaining specific and measurable problems that may not relate to the real problem b. Developing a model: -mathematical model: set of mathematical relationships, expressed as equations, inequalities, spreadsheets with sums and averages -variable: measurable quantity that may vary an dis subject to change, can be controllable or uncontrollable ex(how many inventory items to order) -problem parameter: measurable quantity that is inherent in the problem such as the cost of placing an order for more inventory items c. Acquiring input data: -GIGO- garbage in, garbage out: means that improper data will result in misleading changes

Goal Seek

A feature in Excel that allows users to specify a goal or target for a specific cell and automatically manipulate another cell to achieve that target

Problem Parameter

A measurable quantity that is inherent in a problem. It typically has a fixed and known value (a constant)

Variable

A measurable quantity that may vary of that is subject to change

Deterministic Model

A model which assumes that all the relevant input data and parameters are known with certainty

Probabilistic Model

A model which assumes that some input data are not known with certainty

Sensitivity Analysis

A process that involves determining how sensitive a solution is to change in the formulation of a problem

Model

A representation (usually mathematical) of a practical problem scenario or environment

Decision Modeling

A scientific approach that uses quantitative (mathematical) techniques as a tool in managerial decision making. Also known as quantitative analysis, management science, and operations research

Decision analyst

An individual who is responsible for developing a decision model

Input Data

Data that are used in a model in arriving at the final solution

1-2 What is the difference between deterministic and probabilistic models? Give several examples of each type of model

Deterministic: assume that all relevant input data values are known with certainty, they assume that all the information needed for modeling a decision making problem environment is available, with fixed and known values (linear programming) Probabilistic Models: assume that some input data values are not known with certainty, they assume that the values of some important variables will not be known before decisions are made (forecasting, queuing, simulation)

Break-Even point

Number of units sold that will result in total revenue equaling total costs (profit is $0)

1-3 What are the differences between quantitative and qualitative factors that may be present in a decision model?

Quantitative: (rates of return, financial ratios, and cash flows) looking at actual numbers, easier to measure because they are numbers Qualitative: (pending state and federal legislation, new technological breakthroughs, and the outcome of an upcoming election) not just purely numbers, not as easily measurable, For example, if you want to analyze how positively customers view one of your products, you might interview a cross-section of your customers and ask for feedback. This qualitative information is hard to express as numbers. Instead, you might analyze objective data such as how many customers buy the product again, how many make complaints, how many have warranty claims and how many return the product. You can express this quantitative information mathematically.

Formulation

Te process by which each aspect of a problem scenario is translated ad expressed in terms of a mathematical model