MOI UNIVERSITY

Operations Research (OR) is a scientific approach to decision-making that uses mathematical models, statistical analysis, and algorithms to solve complex problems in business, engineering, and management.

1. OR as a Decision-Making Tool:

• Provides quantitative support to decision-makers
• Helps in optimizing resources, reducing costs, and improving efficiency
• Enables what-if analysis to evaluate alternatives
• Supports strategic and operational decisions

2. Basic Requirements of OR:

• Clear definition of the problem
• Data availability for modeling and analysis
• Mathematical or simulation models to represent the system
• Computational tools to solve the models
• Feedback mechanisms to validate and refine the solution

3. Process of Operations Research:

Step	Description
1. Define the Problem	Identify objectives, constraints, and key variables
2. Formulate the Model	Create a mathematical or simulation model
3. Collect Data	Gather relevant data for model inputs
4. Solve the Model	Use algorithms or software to find optimal solutions
5. Validate the Model	Test against real-world scenarios
6. Implement the Solution	Apply the solution in practice
7. Monitor and Review	Continuously evaluate and refine the model

4. Classification of OR Models:

Type	Description	Example
Deterministic Models	All parameters are known with certainty	Linear Programming, Transportation Problems
Probabilistic/Stochastic Models	Parameters involve uncertainty	Queuing Models, Simulation
Static Models	Do not consider time	Inventory Models
Dynamic Models	Time is a key variable	Project Scheduling (PERT/CPM)
Descriptive Models	Describe how the system behaves	Simulation
Optimization Models	Find the best solution	Linear Programming, Integer Programming

✅ Conclusion: Operations Research is a powerful tool for decision-making, enabling structured analysis, optimization, and prediction in complex systems across various industries.

Scope of OR in Financial and Project Management:

• Project Scheduling: CPM, PERT help in planning and monitoring project timelines
• Resource Allocation: Linear Programming helps in optimizing resource usage
• Risk Analysis: Simulation and decision trees help in evaluating uncertainties
• Inventory Control: EOQ models help in minimizing holding and ordering costs
• Capital Budgeting: Optimization models help in selecting the best investment portfolio

Limitations of OR in Financial and Project Management:

Limitation	Description
Complexity and Cost	Some OR models require advanced software and expertise, increasing cost and time
Assumptions and Simplifications	Real-world systems are often too complex to model accurately
Uncertainty and Risk	OR models assume known parameters; real-life systems often involve unpredictable variables
Human and Behavioral Factors	OR cannot account for emotions, biases, or organizational politics
Time Constraints	Some OR techniques are too slow for real-time decision-making
Data Limitations	Inaccurate or incomplete data can lead to poor model performance
Model Misuse	Applying the wrong model or misinterpreting results can lead to flawed decisions

Practical Considerations:

• Integration with Management Information Systems (MIS) is essential for real-time applications
• Hybrid approaches combining OR with AI, machine learning, and heuristics are increasingly used
• User training and model interpretation are critical for successful implementation

✅ Conclusion: While OR has broad applications in financial and project management, its effectiveness is limited by data quality, model assumptions, and human judgment. A balanced approach combining OR with managerial insight yields the best results.

Feature	Iconic (Physical) Models	Analogue (Schematic) Models
Definition	Physical replicas or scaled-down versions of real systems	Symbolic or abstract representations of real systems
Representation	Visual and tangible	Graphical or mathematical
Examples	Scale models of buildings, aircraft, or machines	Flowcharts, network diagrams, equations
Use in OR	Used in preliminary design or visualization	Used for analysis, optimization, and simulation

✅ Iconic models are physical, while analogue models are abstract and more commonly used in OR for analytical decision-making.