SDM Resource Planning Tool

Engineering Resource Forecasting Tool - built using ChatGPT o1-preview sourcecode

Input Parameters

Funded Team Size:

Total number of engineers the budget allows for.

Current Available Engineers:

Number of engineers currently available and working.

Average Hiring Time (weeks):

Time from initiating hiring to an engineer's start date. Consider reducing this to hire engineers faster.

Ramp-up Time (weeks):

Time for new hires to reach full productivity.

Annual Attrition Rate (%):

Percentage of engineers expected to leave annually. Lowering this can help retain more engineers.

Efficiency Factor (% productive time):

Average productive time per engineer. Improving this increases effective capacity.

Annual Leave Days per Engineer:

Total leave days per engineer per year.

Public Holidays per Year:

Total public holidays per year.

Week to Close Funding Gap:

Target week to reach the funded team size. Adjust this to a later week if the target is unattainable.

Monthly Resource Summary

Weekly Resource Plan

Frequently Asked Questions (FAQ)

1. How are SDE-Weeks and SDE-Days calculated?

SDE-Weeks and SDE-Days represent the effective engineering capacity after accounting for factors like efficiency, leave days, and public holidays. Here's a detailed explanation:

Definitions:

• SDE-Weeks: Sum of the effective engineer contributions per week within a month.
• SDE-Days: Sum of the effective engineer contributions per day within a month.

These metrics account for factors such as leave days, public holidays, and the efficiency factor, which impact the actual productive time of engineers.

Calculation Steps:

1. Calculate Weekly Leave and Holiday Days per Engineer:
   • Weekly Leave Days = Annual Leave Days / 52
   • Weekly Public Holidays = Annual Public Holidays / 52
2. Calculate Weekly Available Working Days per Engineer:
   • Weekly Available Days = 5 - (Weekly Leave Days + Weekly Public Holidays)
3. Calculate Effective Engineers per Week:
   • Effective Engineers = Total Ramped Up Engineers × Efficiency Factor × (Weekly Available Days / 5)
4. Calculate SDE-Weeks for a Month:
   • SDE-Weeks = Sum of Effective Engineers over the weeks in the month
5. Calculate SDE-Days for a Month:
   • SDE-Days = Sum of (Effective Engineers × Weekly Available Days) over the weeks in the month

Example: If you have 1 engineer with an efficiency factor of 100%, annual leave days of 20, and public holidays of 10:

• Weekly Leave Days ≈ 0.3846 days/week
• Weekly Public Holidays ≈ 0.1923 days/week
• Weekly Available Days ≈ 4.4231 days/week
• Effective Engineers ≈ 0.8846 per week
• SDE-Weeks for a 4-week month ≈ 3.5384
• SDE-Days for a 4-week month ≈ 15.664

This explains why the SDE-Weeks may not be exactly equal to the headcount or the number of weeks, as it accounts for reduced availability due to leave, holidays, and efficiency.

2. How are SDE-Weeks and SDE-Days calculated?

SDE-Weeks and SDE-Days represent the effective engineering capacity after accounting for factors like efficiency, leave days, and public holidays. They are calculated by multiplying the number of effective engineers by the number of available working days per week or per month.

3. Why doesn't the number of SDE-Weeks match the headcount?

The SDE-Weeks may not match the headcount because they account for the efficiency factor, leave days, public holidays, and attrition. These factors reduce the actual productive time of engineers, resulting in SDE-Weeks being less than the total headcount.

4. How does the efficiency factor impact the calculations?

The efficiency factor represents the percentage of productive time an engineer is expected to contribute. It accounts for non-productive activities like meetings, training, or other duties. A lower efficiency factor reduces the effective engineering capacity.

5. How are leave days and public holidays accounted for?

Leave days and public holidays are distributed evenly throughout the year to calculate the average weekly available working days per engineer. They reduce the number of days an engineer is available to work each week.

6. Why does the model show attrition even when I have just started hiring?

Attrition is calculated based on the total headcount, including existing and new hires. Since attrition represents the expected percentage of engineers leaving over time, it affects the team throughout the year, even if hiring has just begun.

7. How is the hiring rate calculated?

The hiring rate is computed using a simulation that adjusts the rate until the funded team size is reached by the specified week. It considers hiring time, ramp-up time, attrition, and caps the total headcount at the funded size.

8. Can I manually adjust the headcount in the monthly summary?

Yes, you can edit the "Headcount Available" column in the monthly summary table. The tool will update the calculations and charts to reflect your changes, assuming all engineers are fully ramped up for simplicity.

9. What does the "Effective Engineers" line represent on the chart?

The "Effective Engineers" line shows the number of engineers adjusted for efficiency, leave days, public holidays, and ramp-up time. It reflects the actual productive capacity of your engineering team over time.

10. Why is the funded team size represented as a dashed line?

The funded team size is shown as a dashed red line to indicate the maximum number of engineers your budget allows. It serves as a reference point on the chart to compare your actual headcount against the funded size.

11. How does ramp-up time affect the model?

Ramp-up time is the period new hires take to reach full productivity. During this time, their contribution to the effective engineering capacity is not at 100%. The model accounts for this by delaying the addition of new hires to the fully ramped-up engineer count.

12. Why is the "Cumulative Attrition" line important?

The "Cumulative Attrition" line shows the total number of engineers expected to leave over time due to attrition. It's important for planning replacement hires and understanding how attrition affects your team's capacity.

13. Can I adjust the attrition rate during the year?

The tool currently uses a constant annual attrition rate distributed evenly throughout the year. To model changes in attrition rate over time, you would need to adjust the input and regenerate the forecast.

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.

8. Flaws in the model according to Claude sonet 3.5

• Linear Attrition Model:Flaw: The model assumes a constant attrition rate throughout the year. Improvement: Implement a more dynamic attrition model that accounts for seasonal variations or specific events (e.g., higher attrition after bonus payouts or during economic shifts).
• Simplified Ramp-Up:Flaw: The model treats ramp-up as a binary state (not ramped up vs. fully ramped up).Improvement: Implement a gradual ramp-up curve where productivity increases incrementally over time.
• Constant Hiring Rate: Flaw: The model assumes a constant hiring rate to reach the target. Improvement: Allow for variable hiring rates or incorporate hiring freezes/spikes based on business cycles or budget constraints.
• Limited Efficiency Factor:Flaw: Uses a single efficiency factor for all engineers. Improvement: Implement role-based or seniority-based efficiency factors, and allow for efficiency improvements over time.
• Lack of Project-Specific Planning:Flaw: The model focuses on general capacity without considering specific project needs.Improvement: Integrate project planning features to match capacity with anticipated project demands.
• Simplified Leave and Holiday Model:Flaw: Assumes even distribution of leave and holidays throughout the year. Improvement: Allow for more realistic modeling of leave patterns and country-specific holidays.
• Limited Risk Modeling: Flaw: Doesn't account for uncertainties in hiring, attrition, or productivity. Improvement: Implement Monte Carlo simulations or other risk modeling techniques to show a range of possible outcomes.
• Fixed Time Horizon:Flaw: The model is limited to a one-year forecast. Improvement: Allow users to adjust the time horizon for longer-term planning.
• Lack of Cost Considerations: Flaw: The model focuses on headcount without considering associated costs. Improvement: Incorporate cost modeling to show budget implications of different growth scenarios.
• Limited Handling of Over-Hiring: Flaw: The model caps hiring at the funded size but doesn't address potential over-hiring scenarios. Improvement: Add logic to handle scenarios where actual headcount exceeds funded size, including potential downsizing strategies.
• Simplified Hiring Pipeline: Flaw: Assumes all hires take the same amount of time and always succeed. Improvement: Model a more realistic hiring funnel with drop-offs and variable time-to-hire based on role or market conditions.
• Lack of Historical Data Integration:Flaw: The model doesn't leverage historical data for predictions. Improvement: Allow users to import historical data to calibrate the model to their organization's patterns.
• Limited Visualization Options:Flaw: Only provides a single chart type. Improvement: Offer multiple visualization options (e.g., stacked area charts, Sankey diagrams) for different insights.
• No Scenario Comparison: Flaw: Users can only view one scenario at a time. Improvement: Allow users to save and compare multiple scenarios side-by-side.
• Lack of External Factors Consideration: Flaw: The model doesn't account for external market factors. Improvement: Incorporate adjustable parameters for market conditions that could affect hiring ease or attrition rates.