Prompt for this article: Great I think we have good baseline version to start with. Now using your PhD capabilities, write up an accompanying blog post that details the model, the thinking behind it, and call out the limitations using a critical eye, suggesting improvements for the next version.
Prompt for Image using Dall-E: "An isometric illustration of a diverse engineering team collaborating in a modern office space. They are gathered around a large table filled with charts, graphs, and laptops, discussing and planning. Behind them, a wall-sized calendar and a whiteboard display timelines, Gantt charts, and upward-trending graphs. Include elements like gears and clock icons subtly integrated into the design to symbolize engineering and time management. Use a professional, clean art style with vibrant yet muted colors to convey a serious but engaging atmosphere.
Engineering Resource Forecasting: A Comprehensive Model for Team Planning
By ChatGPT o1-Preview (took about 4 hours to get the tool to version 1)
Effective resource planning is crucial for engineering teams aiming to meet project deadlines, optimize productivity, and manage budgets efficiently. The Engineering Resource Forecasting Tool is designed to help managers and team leads forecast their engineering resources over a year, considering various real-world factors such as hiring rates, attrition, ramp-up times, efficiency, leave days, and public holidays.
In this blog post, we'll delve into the model behind the tool, explore the thinking that shaped its development, critically analyze its limitations, and suggest improvements for future iterations.
Understanding the Model
1. Objectives of the Tool
The primary goals of the Engineering Resource Forecasting Tool are to:
- Forecast Team Size: Predict how the engineering team will grow or shrink over time.
- Calculate Effective Capacity: Determine the actual productive capacity of the team, considering factors that reduce availability.
- Assist in Planning: Provide insights for hiring strategies, resource allocation, and project scheduling.
2. Key Components of the Model
The model incorporates several essential elements:
- Funded Team Size: The maximum number of engineers that the budget allows.
- Current Available Engineers: The starting point for the forecasting, representing the existing team.
- Hiring Rate and Time: The rate at which new engineers are hired and the average time it takes for them to join.
- Ramp-Up Time: The period new hires require to reach full productivity.
- Attrition Rate: The expected percentage of engineers who will leave the team annually.
- Efficiency Factor: The proportion of time engineers spend on productive work.
- Leave Days and Public Holidays: Time when engineers are not available due to vacations and holidays.
3. Model Calculations
a. Effective Engineers
The number of effective engineers is calculated weekly by adjusting the total number of fully ramped-up engineers for efficiency and availability:
Effective Engineers = Total Ramped-Up Engineers × Efficiency Factor × (Weekly Available Days / 5)
b. Weekly Available Days
Engineers aren't available all five working days each week due to leave and public holidays:
Weekly Available Days = 5 - ((Annual Leave Days + Public Holidays) / 52)
c. Attrition Modeling
Attrition reduces the team size over time:
- Weekly Attrition Rate:
Weekly Attrition Rate = Annual Attrition Rate / 52
- Cumulative Attrition: The total number of engineers who have left the team up to a given week.
d. Hiring and Ramp-Up Process
- Hiring Pipeline: Represents engineers who have accepted offers but haven't started yet.
- Ramping Engineers: New hires who are in the process of ramping up to full productivity.
e. SDE-Weeks and SDE-Days
- SDE-Weeks: Sum of effective engineer contributions per week within a month.
- SDE-Days: Sum of effective engineer contributions per day within a month.
These metrics provide a more granular view of the team's productive capacity.
Thinking Behind the Model
1. Realistic Representation
The model aims to mirror real-world scenarios by considering:
- Time Delays: Hiring and ramp-up times introduce delays between initiating a hire and gaining full productivity.
- Non-Productive Time: Leave, holidays, and non-productive activities reduce actual working time.
- Attrition Effects: Engineers may leave the team, impacting capacity and necessitating replacement hires.
2. Balancing Simplicity and Complexity
While the model incorporates multiple factors, it maintains usability by:
- Using Average Rates: Distributes leave, holidays, and attrition evenly over the year for simplicity.
- Assuming Linear Processes: Hiring and ramp-up are modeled as linear processes without accounting for fluctuations.
3. Providing Actionable Insights
By visualizing the forecast through charts and tables, the tool helps managers:
- Identify Hiring Needs: Determine the hiring rate required to meet team size goals.
- Plan for Attrition: Anticipate capacity reductions due to attrition.
- Adjust Strategies: Modify inputs to explore different scenarios and their outcomes.
Limitations and Critical Analysis
Despite its usefulness, the model has several limitations:
1. Uniform Distribution Assumptions
- Leave and Holidays: The model assumes that leave days and public holidays are evenly distributed throughout the year. In reality, leave may cluster around certain periods (e.g., summer vacations, year-end holidays), leading to temporary drops in capacity.
- Attrition Timing: Attrition is modeled as a steady rate, but actual departures may occur unpredictably and in clusters.
2. Lack of Granular Ramp-Up Modeling
- Binary Productivity: The model assumes engineers are either ramping up or fully productive after the ramp-up period. It doesn't account for gradual increases in productivity during ramp-up.
- Individual Variations: All new hires are treated identically in terms of ramp-up time and efficiency, ignoring individual differences.
3. Hiring Constraints and Real-World Challenges
- Hiring Rate Feasibility: The model calculates the hiring rate needed to meet targets but doesn't consider the practical challenges of achieving that rate, such as market conditions or recruitment resource limitations.
- Candidate Pipeline Variability: It doesn't account for fluctuations in candidate availability or acceptance rates.
4. Simplified Efficiency Factor
- Static Efficiency: The efficiency factor is constant over time, not accounting for potential improvements or declines due to training, process changes, or team dynamics.
- Team Composition: It doesn't differentiate between engineers with different skill levels or specializations.
5. Absence of Project Dependencies
- No Task Modeling: The model focuses on resource capacity without linking it to specific project tasks or timelines.
- Overtime and Workload Peaks: It doesn't account for periods when engineers might work overtime or when workload exceeds capacity.
6. Economic and External Factors
- Budget Changes: The funded team size is assumed constant, ignoring potential budget cuts or increases.
- External Events: Unforeseen events like economic downturns, pandemics, or natural disasters are not modeled.
Suggestions for Improvement
To enhance the model's accuracy and applicability, future versions could consider:
1. Incorporating Seasonal Variations
- Variable Leave and Holiday Distribution: Model leave and public holidays based on historical patterns to reflect periods of low and high availability.
- Attrition Peaks: Adjust attrition rates to account for known periods of higher turnover.
2. Enhanced Ramp-Up Modeling
- Gradual Productivity Increase: Implement a ramp-up curve where productivity increases incrementally over the ramp-up period.
- Individualized Ramp-Up: Allow different ramp-up times for different roles or experience levels.
3. Realistic Hiring Constraints
- Recruitment Capacity Limits: Introduce maximum hiring rates based on recruitment team capacity.
- Candidate Pipeline Dynamics: Factor in time-to-fill variations, candidate drop-off rates, and competition in the job market.
4. Dynamic Efficiency Factor
- Efficiency Over Time: Allow the efficiency factor to change over time, reflecting process improvements or team fatigue.
- Role-Based Efficiency: Differentiate efficiency factors for various roles or seniority levels.
5. Integration with Project Planning
- Task-Level Planning: Link resource capacity to project tasks and milestones to assess whether capacity meets project demands.
- Risk Modeling: Incorporate risks and uncertainties that could impact timelines and capacity.
6. Economic Scenario Planning
- Budget Flexibility: Allow for adjustments in the funded team size based on potential budget changes.
- External Shocks: Introduce scenarios for external events that could affect hiring, attrition, or productivity.
7. User Input Flexibility
- Custom Time Frames: Enable users to adjust the time frame of the forecast beyond a single year.
- Data Import: Allow users to import historical data to better tailor the model to their organization's patterns.
Conclusion
The Engineering Resource Forecasting Tool provides a solid foundation for planning and visualizing engineering team capacity over time. By accounting for critical factors like hiring, ramp-up, attrition, efficiency, leave, and holidays, it offers valuable insights for managers.
However, like any model, it simplifies complex real-world dynamics to remain practical and user-friendly. Recognizing its limitations is essential for interpreting the results accurately and making informed decisions. By incorporating suggested improvements, future versions can offer even more robust and precise forecasting, aiding organizations in navigating the challenges of resource planning.
About the Author
[Your Name] holds a PhD in [Your Field] and has extensive experience in project management, resource planning, and organizational development. Passionate about leveraging data-driven models to solve practical problems, [Your Name] contributes to advancing tools and methodologies that enhance operational efficiency.
Note: The Engineering Resource Forecasting Tool is intended as a planning aid and should be used in conjunction with professional judgment and other planning tools. Always consider your organization's specific circumstances when making staffing decisions.
