HKSM

A structured analysis of past project cost data to guide current budgeting decisions. It compares similar work, normalizes differences, and extracts cost drivers to validate estimates and set appropriate reserves.

Key Points

• Leverages organizational process assets and industry benchmarks to ground the budget in real results.
• Supports analogous and parametric approaches by providing credible reference values and cost rates.
• Normalizes past costs for scope, size, location, currency, and time to ensure apples-to-apples comparisons.
• Highlights cost drivers and variance patterns that affect contingency and management reserve decisions.
• Acts as a sanity check on bottom-up estimates and vendor quotes before finalizing the cost baseline.
• Produces traceable assumptions and data sources for the basis of estimate and audit readiness.

Purpose of Analysis

The review helps confirm whether proposed budgets are realistic by comparing them with actuals from comparable efforts. It also uncovers trends, such as underestimation in specific work packages, that inform reserves and funding strategies. Finance and leadership use the results to judge affordability, refine phasing, and set funding limits.

Method Steps

• Define comparables: identify past projects or work packages with similar scope, technology, complexity, and delivery model.
• Collect data: gather final costs, cost baselines, labor rates, vendor invoices, CPI/SPI, and lessons learned from OPAs.
• Screen quality: exclude incomplete, one-off, or low-quality records; note data confidence levels.
• Normalize: adjust for inflation, currency, region, scale, contract type, and major scope deltas; document factors used.
• Derive metrics: compute unit costs (e.g., cost per feature, per server, per sprint) and parametric factors.
• Compare and calibrate: contrast current estimates against historical ranges and medians; flag outliers.
• Set reserves: apply observed variance patterns to propose contingency percentages and any management reserve request.
• Validate: review findings with SMEs, finance, and procurement; reconcile with vendor quotes and bottom-up numbers.
• Record: update the basis of estimate, assumptions log, and cost database with sources and adjustments.

Inputs Needed

• Organizational process assets: cost databases, past cost baselines, final actuals, and lessons learned.
• WBS and scope details to match comparable work and identify scale factors.
• Preliminary cost estimates and vendor quotes for cross-checking.
• Labor rate cards, material price lists, and procurement terms from prior engagements.
• Economic indices for inflation, exchange rates, and regional adjustments.
• Cost management plan and estimation assumptions to ensure alignment.

Outputs Produced

• Comparable project set with normalization notes and data quality ratings.
• Parametric factors and unit-cost benchmarks relevant to the current scope.
• Adjustments to estimates and recommended contingency and management reserve levels.
• Updates to the basis of estimate and assumptions log with traceable sources.
• Inputs to the cost baseline and funding requirements, including phased spending profiles.

Interpretation Tips

• Favor medians and interquartile ranges over single-point extremes to reduce bias.
• Prioritize close comparability in scope and delivery model over sheer data volume.
• Separate recurring and nonrecurring costs to avoid skewing unit-rate benchmarks.
• Reconcile with vendor market conditions; historical rates may lag current pricing.
• Use data quality ratings to weight evidence when results conflict.

Example

A team planning a CRM rollout reviews two prior implementations. They normalize costs for a 30 percent larger user base, a different region, and 6 percent inflation. The analysis yields a unit cost of $1,850 per user. Applying this to 2,200 users gives $4.07M, versus the initial $3.6M bottom-up estimate. Given a 12 percent average variance in similar past work, they add $0.49M contingency, update the basis of estimate, and adjust the funding request accordingly.

Pitfalls

• Using outdated or incomplete data without noting confidence levels.
• Comparing dissimilar projects and drawing false conclusions.
• Ignoring economic shifts, contract type differences, or learning-curve effects.
• Double-counting risk by inflating both estimates and contingency.
• Failing to document normalization factors and assumptions for auditability.

PMP Example Question

While developing the project budget, the PM finds the bottom-up estimate lower than two similar completed projects. What should the PM do next?

1. Request vendors to rebid with fixed margins.
2. Compress the schedule to reduce labor costs.
3. Conduct a historical information review to normalize past costs and calibrate the estimate.
4. Increase the contingency by an arbitrary 20 percent.

Correct Answer: C — Conduct a historical information review to normalize past costs and calibrate the estimate.

Explanation: Reviewing and normalizing comparable actuals provides evidence-based adjustments to the budget and reserves. It is the most appropriate next step before changing schedule, vendor terms, or contingency arbitrarily.