Write short notes on : i) SMART objectives ii) Management control with project control cycle.

8 b] Write short notes on : i) SMART objectives ii) Management control with project control cycle.

i) SMART objectives

SMART objectives are a framework used to set clear and achievable goals. SMART stands for:

1. Specific: The objective should be clear and specific, so you know exactly what you’re aiming to achieve. It should answer the questions: Who, What, Where, When, and Why.
2. Measurable: The objective should be quantifiable, so you can track your progress and determine when you’ve achieved it. This usually involves numbers or specific milestones.
3. Achievable: The objective should be realistic and attainable, given your current resources and constraints. It should challenge you, but still be possible to accomplish.
4. Relevant: The objective should align with your broader goals and be relevant to your overall mission or purpose. It should matter to you and fit within your larger strategy.
5. Time-bound: The objective should have a clear deadline or timeframe by which you plan to achieve it. This helps create a sense of urgency and keeps you on track.

ii) Management control with project control cycle.

Management control with the project control cycle involves a series of systematic steps to ensure that a project stays on track and meets its objectives. The project control cycle helps managers monitor progress, make necessary adjustments, and ensure that the project achieves its goals within the defined constraints. Here’s an overview of how management control integrates with the project control cycle:

Step-by-Step Breakdown:

1. The Real World: This is where the project operates and generates data. It could be a construction site, a software development team, or any other environment where a project is undertaken.
2. Data Collection: Information is gathered from the real world. This data could be anything from project progress reports, financial figures, customer feedback, or environmental measurements.
3. Data Processing: The collected data is organized, analyzed, and transformed into meaningful information. This step involves cleaning and structuring the data to remove inconsistencies and make it suitable for further analysis.
4. Define Objectives: Before diving into data analysis, clear project objectives need to be defined. These objectives guide the entire process and help determine what kind of information is crucial to gather and analyze.
5. Modelling: Based on the defined objectives, models are created to represent the project’s behavior and predict outcomes. These models can be mathematical formulas, simulations, or other representations that help understand how different variables interact.
6. Making Decisions/Plans: Using the processed information and insights from the models, decisions are made and plans are formulated. This could involve resource allocation, risk mitigation strategies, or changes in project scope.
7. Decisions: The decisions made in the previous step are implemented in the real world. This could mean assigning tasks, acquiring resources, or executing project activities.
8. Implementation: The decisions are put into action, affecting the real world and potentially generating new data that feeds back into the cycle.
9. Actions: The implementation of decisions leads to actions being taken in the real world, which may influence the project’s progress and require further monitoring and adjustment.