Methodology of Operation Research
Methodology of Operation Research
Operation Research (O.R.) provides a quantitative basis for decision-making, enhancing a manager’s ability to make long-term plans and solve routine problems. It is a systematic and logical approach to provide a rational foundation for taking decisions. O.R. follows a scientific methodology involving several steps.
1. Formulating the Problem
Formulating the Problem involves defining the scope and nature of the problem to be studied. This initial step is crucial as it sets the direction for the entire study.
- Environment: Understand the context in which the problem exists, including external factors like market conditions, regulations, and competition.
- Objectives: Clearly define what the study aims to achieve. This could include cost reduction, efficiency improvement, or maximizing profits.
- Decision Maker: Identify the individuals or groups responsible for making decisions based on the O.R. study.
- Alternative Courses of Action and Constraints: List possible solutions and recognize any limitations or constraints such as budget, time, or resources.
The O.R. professional must gather sufficient data through various means such as attending conferences, making site visits, and conducting research to accurately formulate the problem.
2. Constructing a Model to Represent the System under Study
Constructing a Model involves creating a mathematical or simulation model that represents the system under study.
- Model Development: Develop a model to show relationships between different variables and predict the outcome of various actions.
- Testing and Modification: Test the model under various scenarios and modify it as necessary to ensure it operates correctly within the given constraints.
- Management Approval: The model may need adjustments based on management feedback to ensure it meets their requirements and expectations.
Models can be physical, mathematical, or a combination of both and are essential for visualizing complex systems and interactions.
3. Deriving Solutions from the Model
Deriving Solutions involves using the model to find solutions to the problem.
- Experimentation: Conduct experiments on the model, often through simulation or mathematical analysis, to determine the best course of action.
- Data Collection: Accurate data is critical. It may be gathered from experiments or experiential hunches.
- Analysis: Analyze the data to identify the optimal solution. This step ensures that the data collected does not skew the results due to inaccuracies or errors.
A robust solution depends on the quality of data and the accuracy of the model.
4. Testing the Model and the Solution Derived from It
Testing the Model ensures its accuracy and reliability in predicting real-world outcomes.
- Sensitivity Analysis: This process evaluates how changes in input variables affect the output, verifying the model’s robustness.
- Validation: Compare the model’s predictions with real-world results to ensure its validity. Adjustments may be necessary if discrepancies are found.
Testing is crucial to confirm that the model provides reliable solutions under various conditions.
5. Establishing Controls over the Solution
Establishing Controls involves setting parameters and guidelines to ensure the solution’s effective implementation.
- Communication with Management: Explain the model’s findings and the conditions under which the solution is valid.
- Identifying Weaknesses: Highlight any potential weaknesses or risks associated with the solution to prepare management for possible challenges.
- Defining Limits: Clearly define the boundaries within which the solution is applicable and effective.
Effective controls help manage the risks and ensure the solution is applied correctly.
6. Implementation of the Solution
Implementation is the final step where the solution is put into action.
- Behavioral Issues: Address any behavioral challenges among employees and stakeholders that might arise during implementation.
- Bridging the Gap: Ensure close cooperation between O.R. scientists and management to facilitate smooth implementation.
- Communication: Sell the benefits of the O.R. solution to both superiors and subordinates to gain their support and commitment.
A well-implemented solution leads to improved operations and management support, ensuring the success of the O.R. initiative.