Topic 1.1 Introduction and Overview PDF

Topic 1.1. Introduction and Overview  Introduction Organizational Hierarchy ORGANIZATIONAL LEVELS NATURE OF NATURE OF Organizational Hierarchy...

Topic 1.1. Introduction and Overview  Introduction Organizational Hierarchy ORGANIZATIONAL LEVELS NATURE OF NATURE OF Organizational Hierarchy PROBLEMS DECISION MAKING Highest Level Unstructured Non-Programmed Decisions Programmed Lowest Structured Decisions Level Module 1: Introduction and Overview Topic 1.1  Decision Making in the Themed Parks Many decisions are made, every day, every Source: google.com month, every year in any organization – such as in a themed parks. Module 1: Introduction and Overview Topic 1.1  Decision-making and problem solving Decision making Describes the process by which a course of action is selected as the way to deal with a specific problem Course of actions, part of problem solving process (occurs in every step of the process) Problem solving Refers to the broad set of activities involved in finding and implementing a course of action to correct an unsatisfactory situation Finding and implementing a course of action (e.g., SWOT and PEST analyses) Module 1: Introduction and Overview Topic 1.1  Types of decisions Programmed decisions – are those made in accordance with some habit, rule, or procedure (quantitative method) – Inventory manager of McDo will decide to order beef patty because stocks are ¾ empty – Employee selection or reorder inventory – Likely non error (info is given) Non-programmed decisions – are those that deal with unusual or exceptional problems – McDo will develop/invest in a new deep fryers; new line of product – Determine the appropriate training program – Likely with an error Module 1: Introduction and Overview Topic 1.1 Module 1: Introduction and Overview Topic 1.1  Management Science/ Operations Research The application of the scientific method to the Note the similar key analysis and solution of managerial decision words and phrases in problems. the definition you -Turban and Meredith searched for and the ones provided here. Application of scientific methods, techniques, Management Science and tools to problems involving the operations of and Operations systems so as to provide those in control of the Research are closely operations with optimum solutions to the linked and are used problems. interchangeably in this -Churchman, Ackoff, and Arnoff course. Module 1: Introduction and Overview Topic 1.1  Mathematical Techniques I. Decision Making Models These are just some of the 1. Matrix Analysis mathematical techniques we 1.1 Under Condition of Uncertainty will be learning in this 1.1.1 Maximax decision criterion subject, because Math is a 1.1.2 Maximin decision criterion 1.1.3 Laplace criterion Science, and the practice of 1.1.4 Minimax regret criterion good management requires 1.2 Under Condition of Risk the use of organized thinking 1.2.1 Expected value criterion and the scientific method in 1.2.2 Maximum likelihood decision making and problem 2. Decision Tree Analysis solving. Render et al. (2003) Module 1: Introduction and Overview Topic 1.1  Mathematical Techniques II. Forecasting Models 1. Judgmental forecast 2. Time series analysis 3. Causal forecasting III. Inventory Models (EOQ) IV. Linear Programming Models V. Work Scheduling 1. Gantt Chart 2. PERT/CPM VI. Queuing Theory Render et al. (2003) Module 1: Introduction and Overview Topic 1.1  The Quantitative Analysis Approach A scientific approach to managerial decision making whereby raw data are processed and manipulated resulting in meaningful information. -Render, Stair and Hanna Quantitative Meaningful Raw Data Analysis Information Module 1: Introduction and Overview Topic 1.1  What is the Quantitative Analysis Approach? Mathematical tools have been used for thousands of years. Quantitative analysis can be applied to a wide variety of problems. It’s not enough to just know the mathematics of a technique; one must understand the specific applicability of the technique, its limitations, and its assumptions. Quantitative factors might be different investment alternatives, interest rates, inventory levels, demand, or labor cost. Qualitative factors such as the weather, state and federal legislation, and technology breakthroughs should also be considered as information that may be difficult to quantify but can affect the decision-making process. Module 1: Introduction and Overview Topic 1.1  What is the Quantitative Analysis Approach? In solving a problem, managers must consider both qualitative and quantitative factors. For example, we might consider several different investment alternatives, including certificates of deposit at a bank, investments in the stock market, and an investment in real estate. We can use quantitative analysis to determine how much our investment will be worth in the future when deposited at a bank at a given interest rate for a certain number of years. Quantitative analysis can also be used in computing financial ratios from the balance sheets for several companies whose stock we are considering. Some real estate companies have developed computer programs that use quantitative analysis to analyze cash flows and rates of return for investment property. Module 1: Introduction and Overview Topic 1.1  What is the Quantitative Analysis Approach? In addition to quantitative analysis, qualitative factors should also be considered. The weather, state and federal legislation, new technological breakthroughs, the outcome of an election, and so on may all be factors that are difficult to quantify. Because of the importance of qualitative factors, the role of quantitative analysis in the decision-making process can vary. When there is a lack of qualitative factors and when the problem, model, and input data remain the same, the results of quantitative analysis can automate the decision-making process. For example, some companies use quantitative inventory models to determine automatically when to order additional new materials. In most cases, however, quantitative analysis will be an aid to the decision-making process. The results of quantitative analysis will be combined with other (qualitative) information in making decisions. Module 1: Introduction and Overview Topic 1.1 When do you use the Quantitative Analysis Approach? 1. The problem is complex. 2. The problem involves many variables. 3. There are data which describe the decision environment. 4. There are data which describe the value or utility of the different possible alternatives. 5. The goals of the decision maker or the organization can be described in quantitative terms. 6. Workable models are available for these situations. Module 1: Introduction and Overview Topic 1.1  Opportunities of Quantitative Approach 1. Forces managers to be quite explicit about their objectives, assumptions and way of seeing constraints. 2. Quickly points out gaps in the data required to support workable solutions to problems. 3. Permits us to examine a situation, change the conditions under which decisions are being made, and examine the effects of those changes – all without serious damage or excessive cost. 4. Forces managers to be very precise about how the variables in a problem interact with each other. Module 1: Introduction and Overview Topic 1.1  Opportunities of Quantitative Approach 5. Makes managers consider very carefully just what variables influence decisions. 6. Finds a solution to a complex problem much more quickly and often is the only way large complex problems can be solved. 7. Models a problem and its solution so that future solutions can be done by a computer Module 1: Introduction and Overview Topic 1.1  Shortcomings of Quantitative Approach 1. Makes a simplifying assumptions in order to solve it, thus produce solutions which have limitations. 2. Expensive when compared with other less sophisticate approaches. 3. Sometimes it does not represent the “real world” in which decisions must be made. 4. Models have limitations. 5. Solutions are so complex. 6. Many “real world” problems do not have an MS/OR solution. Module 1: Introduction and Overview Topic 1.1  The Quantitative Analysis Approach Quantitative Analysis Logic Historic Data Marketing Research Problem Scientific Analysis Decision Modeling ? Qualitative Analysis Weather State and federal legislation New technological breakthroughs To accompany Quantitative Analysis for Management, 8e Election outcome 1-19 by Render/Stair/Hanna Module 1: Introduction and Overview Topic 1.1  Problem Finding, Choice Making, Decision Making, and Problem Solving Decision Making Activities dealing Activities Activities Activities Activities with dealing with dealing with dealing with dealing with determining the identifying, generating evaluating and implementing existence and defining, alternative choosing the chosen importance of and solutions among solution problems diagnosing alternative problems solutions Problem Finding Choice Making Sources: Adapted from Managerial Decision Making by George P. Huber. Copyright @ 1980 Scott, Foresman and Company. Problem Solving Module 1: Introduction and Overview Topic 1.1  What is the Quantitative Analysis Approach? Defining the Problem Developing a Model Acquiring Input Data Developing a Solution Testing the Solution Analyzing the Results Implementing the Results Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 1: Define the Problem Step 1 is the most difficult. Make sure you are tackling the root cause and not the symptoms. Identify the limiting assumptions, boundaries, and/or stakeholder issues Goal: express the issue in a clear, one- sentence problem statement that describes both the initial and desired conditions. Module 1: Introduction and Overview Topic 1.1  How to Define the Problem 1. Deviation Statement contains the following: what “Should”, what “Is”, and the difference is the deviation. 2. Specify the following: IS IS NOT What Where When Extent 3. Develop Possible Causes: the distinction and changes 4. Verify what logic points out what the problem is, and what is reality Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 2: Develop a model A model is a representation of real objects or situations Types of models: -physical/ iconic model: scale models and physical replicas -analog model: Physical in form, but do not have the same physical appearance as the object being modelled, ex. speedometer -mathematical model Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 2: Develop a model A model is a representation of real objects or situations Types of models: -physical/ iconic model: scale models and physical replicas -analog model: Physical in form, but do not have the same physical appearance as the object being modelled, ex. speedometer -mathematical model Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 2: Develop a model Quantitative Analysis Models are realistic, solvable, and understandable mathematical representations of a situation. Mathematical models that do not involve risk are called deterministic models. We know all the values used in the model with complete certainty. Mathematical models that involve risk, chance, or uncertainty are called probabilistic models. Values used in the model are estimates based on probabilities. Models generally contain variables (controllable and uncontrollable) and parameters Controllable variables are generally the decision variables and are generally unknown Parameters are known quantities that are a part of the problem Module 1: Introduction and Overview Topic 1.1 Advantages of Mathematical Modeling Models can accurately represent reality Models can help a decision maker formulate problems Models can give us insight and information Models can save time and money in decision making and problem solving A model may be the only way to solve large or complex problems in a timely fashion (timely manner) A model can be used to communicate problems and solutions to others Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 3: Acquiring Input Data Input data must be accurate – GIGO rule Data may come from a variety of sources such as company reports, company documents, interviews, on-site direct measurement, or statistical sampling Garbage In Process Garbage Out Module 1: Introduction and Overview Topic 1.1  Data Gathering Data are the uncontrollable inputs If the model is relatively small and uncontrollable input values are few, model development and data preparation is combined. It does not mean that once the problem has been defined, and a general model has been produced, the problem has essentially been solved. Sometimes, data preparation cannot be easily handled by clerical staff. Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 4: Developing a Solution The analyst/ management scientist will attempt to identify the values of the decision variables that provide the “best” output for the model. The best (optimal) solution to a problem is found by manipulating the model variables until a solution is found that is practical and can be implemented Common techniques are -Solving equations -Trial and error – trying various approaches and picking the best result -Complete enumeration – trying all possible values -Using an algorithm – a series of repeating steps to reach a solution Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 5: Testing a Solution The analyst will attempt to identify the values of the decision variables that provide the “best” output for the model. Optimal solution - The specific decision-variable value or values providing the “best” output Decision alternatives may be feasible or infeasible. Both input data and the model should be tested for accuracy before analysis and implementation: New data can be collected to test the model, and results should be logical, consistent, and represent the real situation. Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 6: Analyzing the Results Understand the actions implied by the solution Determine the implications of the solution Implementing results often requires change in an organization The impact of actions or changes needs to be studied and understood before implementation Module 1: Introduction and Overview Topic 1.1  Quantitative Analysis Approach Step 7: Implementing the results Implementation incorporates the solution into the company Implementation can be very difficult. People can resist changes. Many quantitative analysis efforts have failed because a good, workable solution was not properly implemented. Changes occur over time, so even successful implementations must be monitored to determine if modifications are necessaryIncorporate the solution into the company Monitor the results Use the results of the model and sensitivity analysis to help you sell the solution to management. Module 1: Introduction and Overview Topic 1.1  Implementation – Reasons People Resist Change Implementation incorporates the solution into the company Implementation can be very difficult. People can resist changes. Many quantitative analysis efforts have failed because a good, workable solution was not properly implemented. Changes occur over time, so even successful implementations must be monitored to determine if modifications are necessary Module 1: Introduction and Overview Topic 1.1  Decision support systems Decision support systems vary greatly in application and complexity, but they all share specific features. A typical Decision support systems has four components: data management, model management, knowledge management and user interface management. An information system that provides answer to problems and that integrates the decision maker into the system as component. Module 1: Introduction and Overview Topic 1.1 Module 1: Introduction and Overview Topic 1.1 Module 1: Introduction and Overview Topic 1.1