Concepts
Successfully managing a program requires a lot of strategic thinking. One such strategy is incorporating proper metrics monitoring into the program management approach. By forecasting, analyzing variances, developing ‘what if’ scenarios and simulations, and utilizing causal analysis, program managers can take corrective actions in the program and maintain, or in some cases improve, benefits realization. Monitoring metrics will not only enable the tracking of performance over time, but will also provide valuable insights and potential opportunities for improvement. These techniques are integral for individuals sitting for the Program Management Professional (PgMP) exam, as this knowledge significantly adds to their ability to effectively manage programs.
Forecasting
Forecasting is simply the act of predicting the future based on past and present data. Using statistical methods, program managers can predict future outcomes and therefore make adjustments and prepare for any possible scenarios that could affect the program. For example, if customer support call volumes are projected to increase, additional resources could be allocated ahead of time to cope with the high call volume.
Analyzing Variances
Variance analysis compares the planned and actual performance of the program. By doing so, program managers can identify trends, isolate problem areas, and take corrective action. Within a project, if the budgeted cost was $10,000 and the actual cost was $15,000, this would yield a unfavorable variance of $5,000. Unfavorable variances signal a need for corrective action, such as cost-cutting measures or a review of the original budget estimates.
Developing ‘What if’ Scenarios and Simulations
‘What if’ scenarios and simulations allow a program manager to prepare for and deal with possible situations before they arise. For example, what would happen if a key team member left, or a major supplier couldn’t deliver? By developing these scenarios, potential impacts can be discovered and mitigation plans developed. If a simulation demonstrates potential failure under certain conditions, such as loss of a key team member, strategies could be developed like cross-training other team members to ensure the program can withstand such a situation.
Causal Analysis
Causal analysis helps to identify the root cause of a problem by analyzing the cause and effect relationships of the variables within the program. It can aid program managers by providing a clearer understanding of the reasons behind program-related issues and helping them to avoid similar pitfalls in the future. For example, if there’s a high turnover rate, causal analysis might reveal that it’s due to low morale caused by lack of recognition. As a result, corrective action like a recognition program could be implemented.
Monitoring metrics in these four ways can significantly aid managers in taking corrective actions and maintaining, or improving, benefits realization. As such, it’s important for PgMP candidates to fully understand and incorporate these methods accordingly. Not only will this knowledge help on the PgMP exam, but it will provide them with a sound foundation for effective program management, and consequently, success.
Answer the Questions in Comment Section
Variance Analysis involves comparing actual project performance with planned or expected performance.
- True
- False
Answer: True
Explanation: Variance Analysis is a standard project management practice that involves comparing the actual project performance metrics to the planned or expected ones.
Forecasting is not a significant part of monitoring the metrics in a program.
- True
- False
Answer: False
Explanation: Forecasting can actually be vital in the program management process as it allows predictions about future results based on past data and trends.
When monitoring metrics in a program, it is not necessary to develop ‘what if’ scenarios.
- True
- False
Answer: False
Explanation: ‘What if’ scenarios help in identifying possible outcomes and challenges that may arise during the execution of a program, thus aiding in effective decision making and risk management.
Single-Choice: Which of the following techniques allows us to understand the impact of the change in input on the output?
- What if scenario
- Simulation
- Forecasting
- Causal analysis
Answer: Simulation
Explanation: Simulations make it possible to understand how fluctuations in inputs affect the outputs, which helps in robust decision-making in managing programs.
Multiple Select: Which of the following are methods for monitoring metrics in a program?
- Causal analysis
- Contingency planning
- Forecasting
- Benchmarking
Answer: Causal Analysis, Forecasting
Explanation: Both causal analysis and forecasting are standard techniques for monitoring metrics in a program. While causal analysis understanding the root cause of variances, forecasting predicts the future performance based on past trends.
Single-Choice: The ultimate objective of monitoring the metrics is to
- Improve benefits realization
- Punish underperforming team members
- Provide a comprehensive review of program manager
- None of the above
Answer: Improve benefits realization
Explanation: Monitoring program metrics helps in taking corrective actions and ensuring the overall efficiency of the program, eventually improving benefits realization.
All causal analyses involve the same approach to identifying the source of a problem.
- True
- False
Answer: False
Explanation: The cause of problems may vary widely from program to program, so the approaches to uncovering these root causes can also be different.
Multiple Select: What are some of the tools used for monitoring metrics in a program?
- Fishbone diagrams
- Forecasting
- Causal Analysis
- Time and Cost tracking software
Answer: All of the above
Explanation: All these tools are used for monitoring and controlling metrics in a program by identifying the causes of a problem (fishbone diagrams), predicting future performance (forecasting), determining the root cause of variances (causal analysis), and tracking project cost and schedule over time.
Single-Choice: What is the purpose of a ‘what if’ analysis in program management?
- Identifying potential outcomes
- Providing certainty about program results
- Ensuring program processes are strictly followed
- None of the above
Answer: Identifying potential outcomes
Explanation: A ‘what if’ analysis helps in identifying potential outcomes and challenges that may arise during the execution of a program.
Corrective actions should be taken immediately after a single negative variance is observed.
- True
- False
Answer: False
Explanation: Negative variances can occur occasionally, and it might not always indicate a larger issue. Therefore, while negative variances should be noted and investigated, immediate corrective action isn’t always necessary. However, consistent negative variances should trigger corrective actions.
True/False: Program managers should utilize causal analysis to identify potential issues and their root causes.
- True
- False
Answer: True
Explanation: Causal analysis is a critical tool for program managers for it helps to understand the root cause of a problem. Hence, identifying an issue and pinpointing its root cause can potentially prevent its recurrence or mitigate its impact.
Which of the following is an application of forecasting in program management?
- Predicting the quantities of resources required to close a program
- Forecasting potential risks that may affect the program
- Predicting the financial benefits from future programs
- All the above
Answer: All the above
Explanation: Forecasting is a versatile tool in program management. It can be used to predict resource requirements for delivering the program, identify potential risks, and predict financial benefits from prospective programs.
Great post! Monitoring metrics through forecasting and causal analysis can drastically improve program outcomes.
I appreciate how the blog emphasizes the use of ‘what if’ scenarios. It’s a powerful way to anticipate potential failures.
How do you prioritize which metrics to monitor in a program with multiple objectives?
Helpful post! I often use variance analysis to identify discrepancies between planned and actual results.
Any advice on effectively utilizing simulations for risk management in program management?
Amazing insights. The concept of ‘what if’ scenarios has always intrigued me.
This is invaluable for PgMP aspirants. Thanks!
I think this methodology is overcomplicated and adds unnecessary layers.