1. Data Analysis and Decision Making (Albrigth, Winston and Zappe)
Decision Making ADMI 6510
Introduction to Systems
and Models
Key Sources:
Data Analysis and Decision Making (Albrigth, Winston and Zappe)
An Introduction to Management Science: Quantitative Approaches to Decision Making (Anderson, Sweeny, Williams, and Martin), Essentials of MIS (Laudon and Laudon), Slides from N. Yildrim at ITU, Slides from Jean Lacoste, Virginia Tech, ….

2. Outline Systems Models Models and IS Modeling process
From modeling to decision making

3. customer
and
metrics
Systems
input
processing
output
feedback
What is a system? A set of elements or components that work together and interact to accomplish goals
Business systems share common characteristics, including:
A system has structure, it contains parts (or components) that are directly or indirectly related to each other;
A system has behavior, it contains processes that transform inputs into outputs
A system has interconnectivity: the parts and processes are connected by structural and/or behavioral relationships.
A system's structure and behavior may be decomposed via subsystems and sub-processes to elementary parts and process steps.
There is a “boundary.”

4. System Characteristics
Complexity
Diversity of inputs
Diversity of outputs (standard versus customized)
Role of the customer in defining the output -customization
Number of steps (cumulative variability)
Diversity of “steps” or sequences that can be used to achieve output
Number of decision makers
Number of performance measures

5. System Characteristics
Uncertainty
Relates to the “speed” in which the environment changes
Variability of inputs
Variability of environmental factors
Variability of demand/ customer requirements
Highly related to maturity
of products and markets

6. Systems Characteristics
Open vs. closed (decision)
Level and frequency of information flow with its external environment.
The role of external decisions on how the system operates and adjusts
The role of internal decisions on the external systems (it feeds)

7. System Characteristics
Adaptive vs. non-adaptive (decision)
How the system reacts/ adjusts to changes around it.
Information could be just “to know” – non adaptive
Information could be used to modify forecasts, production plans, staffing plans.
If no information flow (if closed), cannot be adaptive.
Subject to the type of decision.
Operational / day to day decisions could be highly dynamic.
Long term decisions are typically stable, but could be dynamic within its context. EG. Every month we change suppliers.

8. Systems Characteristics
Performance and standards
Efficiency: A measure of what is produced divided by what is consumed.
Effectiveness: A measure of the extent to which a system achieves its goals.
Can a system be effective but not efficient?
System variable
A quantity or item that can be controlled by the decision maker. E.g., the price a company charges for a product.
System parameter
A value or quantity that cannot be controlled by the decision maker. E.g., cost of a raw material, amount of raw material needed to make the product.

9. Models Models are representations of real systems/ objects.
Conceptual Models
Qualitative models that highlight important connections in real world systems and processes.
They are used as a first step in the development of more complex models.
Statistical Models
Characterize system based upon its statistical parameters such as mean, mode, variance or regression coefficients.
Mathematical models
Analytical models (set of equations) and numerical models (behavior / trend of data).

10. Conceptual Models

11. Other Models

12. Models
Goal is to give insight into interrelationships of key variables.
Advantages of using models:
Experimenting with models versus real systems.
Faster
Less expensive
Less risk
Does not disrupt real system
Analysis of alternatives
Used to represent “products/services” that do not exist.
Key consideration:
The level of detail: cost versus “confidence” and analysis ability.

13. Models Basic characteristics of “business” quantitative models:
Decision variables (what management controls).
Constraints and systems characteristics (what is “given”).
Objective function (the goal of the system) - an output of “running “ the model.

14. Models
Reasons for quantitative analysis/ models to decision making (why do this?)
The system/problem is complex.
The system/problem is important.
Strategic value
Risk reduction
Profits
Customer service
the system/problem is new want to get insights.
the processes/problem is repetitive, we want to increase efficiency of decision making (time and cost).

15. Models and Information Systems
Most models are data driven, data collected and managed by IS.
For example
Inventory/supply chain models
POS / stock levels
Vendor management
Transportation management
Product design models
Customer preferences
Design options
Economic indicators

16. Models and Information Systems
Yield management models
Time / volume based price structure for a product (family of products)
Complex because it involves several aspects of management control, including rate management, revenue streams management, and distribution channel management
Blends elements of marketing, operations, and financial management

17. Modeling process System observation and analysis
Identification of processes, characteristics, constraints, available data, needed data, objective functions and decision variables.
Selection of modeling technique
Issues include cost and complexity, effect of variability, what is the goal of the analysis.
Consultant problem: recommending a hammer when we need a wrench… we know a modeling method let’s use it.

18. Modeling Process 7 Steps of Problem Solving
First 5 steps are the process of decision making and often are based on models.
Define the problem.
Identify the set of alternative solutions.
Determine the criteria for evaluating alternatives.
Evaluate the alternatives.
Choose an alternative (make a decision).
Implement the chosen alternative.
Evaluate the results.

19. Modeling process Role of Assumptions
Assumptions remove some complexity -> solvable. (AKA - simplifying assumption)
Assumptions are your friends!
Assumptions are necessary to solve problems.
Without them, we are stuck with “real-world” complexity that is essentially unsolvable.
A good assumption:
Simplifies the problem statement/model significantly.
Is intuitively plausible to the “domain experts”.
from Dr. Michael Gorman, U of Dayton

20. Modeling process Model development Model verification Days… weeks ….
Information from multiple sources.
Software and data driven.
Justification of assumptions (previous slide).
Model verification
Is the model representing what we understand of the product/ system?
Does it have the right constraints? Can we eliminate any?

21. Modeling process Model validation (for existing systems)
Given a set of similar inputs would the model and the system generate the “same” results?
What if analysis/ Sensitivity Analysis
Running the model at various levels of inputs (the decision variables) …. evaluates how a solution/decision (or its performance) changes given changes to one or more of the constraints/ assumptions.
This can indicate how different options are “dominant”, based on different variable levels.
Serves to indicate solution/ recommendation robustness.

22. From Modeling to Decision Making
Model provides insights and multiple alternatives.
Management selects course of action.
Implementation and “retest”
Models are used to evaluate best implementation and keep decisions “optimal”.
Real time models: support DM using real time data.