Presentation on theme: "MGS3100 General Modeling Chapter 1: Introduction."— Presentation transcript:
MGS3100 General Modeling Chapter 1: Introduction
THE MODELING PROCESS Managerial Approach to Decision Making Manager analyzes situation (alternatives) Makes decision to resolve conflict Decisions are implemented Consequences of decision These steps Use Spreadsheet Modeling
A Detailed View of the Modeling Process 1. Diagnose the problem 2. Select relevant aspects of reality 3. Organize the facts; identify the assumptions, objectives, and decisions to be made 4. Select the methodology 5. Construct the model 6. Solve the model (generate alternatives) 7. Interpret results (in “lay” terms!) 8. Validate the model (does it work correctly?) 9. Do sensitivity analysis (does the solution change?) 10. Implement the solution 11. Monitor results
THE MODELING PROCESS Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World
The Modeling Process Management Situation Decisions Model Analysis Results Intuition Abstraction Interpretation Real World Symbolic World Managerial Judgment
Reasons for Using Models Models force you to: Be explicit about your objectives Think carefully about variables to include and their definitions in terms that are quantifiable Identify and record the decisions that influence those objectives Identify and record interactions and trade-offs among those decisions
Reasons (cont.) Consider what data are pertinent for quantification of those variables and determining their interactions Recognize constraints (limitations) on the values that those quantified variables may assume Allow communication of your ideas and understanding to facilitate teamwork
Types of Models
Building Models Performance Measure(s) Decisions (Controllable) Parameters (Uncontrollable) Exogenous Variables Model Consequence Variables Endogenous Variables The “Black Box” View of a Model
MODELING VARIABLES Modeling Term Management Lingo Formal DefinitionExample Decision Variable Lever Controllable Exogenous Investment Input Quantity Amount Parameter Gauge Uncontrollable Exogenous Interest Rate Input Quantity Consequence Outcome Endogenous Output Commissions Variable Variable Paid Performance Yardstick Endogenous Variable Return on Measure Used for Evaluation Investment (Objective Function Value)
Examples of Decision Model Assumptions - Profit Models If it is beyond your control, do not consider it! Overhead costs - a convenient fiction - we ignore Sunk costs - we ignore Depreciation - only include if we can use to shield future taxes Costs are linear in the short term
Building Models Symbolic Model Construction Mathematical relationships are developed from data. Graphing the variables may help define the relationship. Var. X Var. Y Cost A Cost B A + B
Modeling with Data Consider the following data. Graphs are created to view any relationship(s) between the variables. This is the first step in formulating the equations in the model.
Creating the Symbolic Model
DETERMINISTIC AND PROBABILISTIC MODELS Deterministic Models are models in which all relevant data are assumed to be known with certainty. can handle complex situations with many decisions and constraints. are very useful when there are few uncontrolled model inputs that are uncertain. are useful for a variety of management problems. allow for managerial interpretation of results. will help develop your ability to formulate models in general.
Probabilistic (Stochastic) Models are models in which some inputs to the model are not known with certainty. uncertainty is incorporated via probabilities on these “random” variables. often used for strategic decision making involving an organization’s relationship to its environment. very useful when there are only a few uncertain model inputs and few or no constraints. DETERMINISTIC AND PROBABILISTIC MODELS
Deductive Modeling focuses on the variables themselves before data are collected. variables are interrelated based on assumptions about algebraic relationships and values of the parameters. focuses on the variables as reflected in existing data collections. tends to be “data poor” initially. Inferential Modeling variables are interrelated based on an analysis of data to determine relationships and to estimate values of parameters. available data need to be accurate and readily available. tends to be “data rich” initially. places importance on modeler’s prior knowledge and judgments of both mathematical relationships and data values. ITERATIVE MODEL BUILDING
DEDUCTIVE MODELING INFERENTIAL MODELING PROBABILISTIC MODELS DETERMINISTIC MODELS Model Building Process Models Decision Modeling (‘What If?’ Projections, Decision Analysis, Decision Trees, Queuing) Decision Modeling (‘What If?’ Projections, Optimization) Data Analysis (Forecasting, Simulation Analysis, Statistical Analysis, Parameter Estimation) Data Analysis (Data Base Query, Parameter Evaluation
Philosophy of Modeling Realism A model is valuable if you make better decisions when you use it than when you don’t. Intuition A manager’s intuition arbitrates the content of the abstraction, resulting model, analysis, and the relevance and interpretation of the results.
MGS3100 General Modeling Chapter 11: Implementation
Just as knowledge of Excel is insufficient without modeling concepts, your knowledge of spreadsheet modeling alone is insufficient for truly affecting decision making in organizations. INTRODUCTION Creating a model itself, although an important first step, is far from sufficient in the process of systematically improving decision making in the real world of business enterprise. Inadequate modeling is just one of the reasons why decision-makers do not make good decisions.
The purpose of this chapter is to help you understand why improving the quality of modeling alone will not necessarily lead to improved real-world decisions. This chapter will cover critical oversights that users new to the concepts of modeling make in attempting to move forward to apply those ideas in actual decision-making situations. The upside and downside potential risks of applying modeling concepts will be discussed so that you will come away with a balanced perspective of the pros and cons of applying modeling in business practical situations.
WHAT, AFTER ALL, IS A MODEL? It is difficult to define a model. One definition might be: Consider the following evolution of a model: A model is an abstraction of a business situation suitable for spreadsheet analysis to support decision making and provide managerial insights. To many managers, a model is exquisitely crafted and professionally polished in appearance, highly intuitive, self-documenting, easy to use, completely validated and generalizable enough to be applied in a variety of settings by many people.
A Prototype Model CompleteDebugged Runable by Its Author Validated with Test Data Believed to Deliver Value An Institutionalized Model Sustained by the Organization Integrated into Organization's Decision Processes Coordinated in Function with Other Models and Systems Useable by Other Managers Maintainable and Extensible by Others Need Data Supplied and Maintained by Others Effort: 10X-100X Effort: 1X A Modeling Application Usable by a Client Manager Well Documented Hardened to Reject Unusual Data Inputs Extendable by Author or Client Manager Validated with Real-World Data Known to Deliver Value Effort: 10X An Institutionalized Modeling Application Effort: 100X – 1000X
This framework is a variation of one originally proposed by C. West Churchman, et. al. Modeler,ProjectManager,DecisionMaker,Client Curse of Player Separation ClientModeler Project Manager DecisionMaker The Separation of Players Curse
The Curse of Scope Creep Narrow Modeling Project Single Model Single Objective Focused Activity Few Players Few Stakeholders Low Effort Low Cost Low Development Risk Informal Coordination & Project Management Low Project Visibility Scale Diseconomies in Information Systems for Model Scale Diseconomies in Model & Database Maintenance Deterioration in Model Use as Early Adopters Move on Low Potential Organization-wide Impact Curse of Scope Creep Wide Modeling Project Multiple (Replicated) Models Multiple Objectives Diffused Activity Many Players Many Stakeholders High Effort High Cost High Development Risk Formal Coordination & Project Management High Project Visibility Scale economies in Information Systems for Model Scale Economies in model & Database Maintenance Support for Model Use Independent of Early Adopters High Potential Organizational- wide Impact
Other Frequent Sources of Implementation Failure However, inadequate attention to political issues that arise from the use of a model is far more prevalent as a source of failure in modeling. Easily addressed issues in modeling failure are model logic, model inadequacy, etc. When a model fails, it is all too common to blame the model when in fact, it was due to inadequacies of the whole process of developing and implementing the model.
Another problem is the potential loss of continuity either during the development of a model itself or later during implementation caused by departure of key players, or the loss of organizational memory of a successful model. A source of difficulty in modeling is the attempt to develop a modeling application before assessing issues of the data availability necessary to support that application. An important consideration early in the model development phase is the matching of available data to a possibly less-adequate model as a way of avoiding implementation problems later.
An infrastructure must be created that guarantees that the data and systems will be maintained in a way that serves the users of the model. A more subtle and insidious shortcoming of modeling concerns the identification of shortcomings at one level of an organization as being caused by failures or inadequacies at a higher, often more abstract, level of the organization. In this case, the best thing to do is to tune the model to work well given other organizational inadequacies that might be addressed more effectively at a later time.