HKSM

Methods used to produce reliable activity duration and effort estimates so a workable schedule can be built. They convert scope and performance data into point or range values that support sequencing, resourcing, and commitments.

Key Points

• Select the technique based on data quality, task repeatability, and uncertainty.
• Favor range-based estimates over single numbers to expose risk and variability.
• Document the basis of estimate, assumptions, constraints, and data sources.
• Involve the people who will perform the work and validate with subject matter experts.
• Calibrate with historical data, benchmarks, and organizational productivity rates.
• Convert effort to duration using resource calendars, availability, and skill mix.
• Add reserves based on quantified risk, not blanket percentages.
• Iterate estimates as scope, design, and risks evolve; keep traceability.

Purpose of Analysis

To translate scope and resource capability into credible durations that can drive the schedule model, critical path, and commitments. The analysis balances speed, cost, and risk so stakeholders understand likely dates and confidence levels.

It also enables scenario testing, resource smoothing, and negotiation by providing defensible numbers grounded in data and expert insight.

Method Steps

• Define estimating approach by work type: analogous, parametric, three-point, bottom-up, or a hybrid.
• Break work to a consistent estimating unit (work packages or activities) and clarify acceptance criteria.
• Gather reference data: historical projects, productivity rates, vendor quotes, and risks.
• Apply the chosen technique per activity: compute point or range values and note assumptions.
• Convert effort to duration using team availability, calendars, and resource constraints.
• Aggregate estimates, identify major drivers, and sanity-check against benchmarks.
• Quantify uncertainty with ranges or three-point values; recommend contingency or buffers.
• Review with the team and stakeholders; refine based on feedback and new information.
• Record the basis of estimate and load results into the schedule model for sequencing and analysis.

Inputs Needed

• Work breakdown structure, activity list, and activity attributes.
• Scope details, acceptance criteria, and definition of done.
• Historical data, analogs, benchmarks, and parametric rates.
• Resource calendars, availability, skill profiles, and productivity data.
• Risk register entries that affect duration or performance.
• Assumptions, constraints, and organizational estimating guidelines.
• External inputs such as vendor quotes and contract terms.

Outputs Produced

• Activity duration estimates as point values, ranges, or three-point sets.
• Basis of estimates capturing methods, data sources, and assumptions.
• Effort estimates and resource needs by role or skill.
• Recommended reserves or buffers linked to specific risks.
• Updated activity attributes and estimating worksheets.
• Confidence indicators such as P50 or P80 dates when modeled in the schedule.

Interpretation Tips

• Use analogous for early, high-level forecasting; switch to parametric or bottom-up as detail improves.
• Choose three-point estimating when uncertainty is material and decision-makers want confidence ranges.
• Validate parametric rates with recent, comparable work to avoid context mismatch.
• Cross-check estimates by applying a second technique to high-risk or critical-path work.
• Separate effort from duration; resource contention and calendars drive elapsed time.
• Tie reserves to specific risks and remove them when risks are retired.
• Continuously update the basis of estimate to preserve credibility and traceability.

Example

A team must estimate two activities before building the schedule. For data migration, they use parametric estimating based on a validated rate of 1,500 records per hour. With 12,000 records, effort is 8 hours. Considering an 80% effective day, duration is 8 hours ÷ (8 hours × 0.8) ≈ 1.25 days.

For system testing, uncertainty is high, so they use three-point estimating: O = 3 days, M = 5 days, P = 9 days. Expected duration (beta) is (3 + 4×5 + 9) ÷ 6 = 5.33 days; standard deviation is (9 − 3) ÷ 6 = 1.0 day. They plan for 5.3 days with a 1–2 day reserve on the path and load these values into the schedule model.

Pitfalls

• Relying on single-point numbers that hide uncertainty and lead to overcommitment.
• Using parametric rates from dissimilar projects or technologies without calibration.
• Confusing effort with duration and ignoring resource availability and calendars.
• Double-counting risk by padding activity estimates and adding separate contingency.
• Not documenting the basis of estimate, making future updates and audits difficult.
• Failing to re-estimate after scope changes, design decisions, or risk responses.
• Groupthink or optimism bias when experts are not independent or data is ignored.

PMP Example Question

While developing the schedule, you have detailed activities and team input that includes optimistic, most likely, and pessimistic durations. The sponsor requests a delivery date with a stated confidence level. What should you do next?

1. Use analogous estimating at the project level and add a 20% buffer.
2. Ask functional managers for single-point estimates and average them.
3. Apply three-point estimating to activities and use the results in a schedule simulation to derive a confidence date.
4. Perform bottom-up estimating and then crash the critical path to meet the target date.

Correct Answer: C — Apply three-point estimating to activities and use the results in a schedule simulation to derive a confidence date.

Explanation: Three-point estimating captures uncertainty, and simulation converts ranges into confidence-based dates. Analogous or single-point approaches do not quantify probability, and crashing is a response after analysis, not a substitute for it.