Portfolio Simulation Workbook

Purpose

This workbook helps teams run repeatable, transparent portfolio what‑if analyses so they can compare tradeoffs between exploration, pilots, and scaling. It combines capacity planning, timing, probabilistic outcomes, and simple Monte Carlo-style summaries to reveal risk, timing interactions, and expected learning value. Use it to surface options and guide allocation decisions during portfolio reviews — not to replace judgement or governance.

When to use

• Quarterly or monthly portfolio reviews where competing projects need funding or attention.
• When you want to understand the impact of timing, capacity limits, or changed assumptions.
• To compare scenarios such as shifting resources toward exploration, accelerating a pilot, or pausing lower‑value projects.

Key outcomes you should expect

• A clear visual of capacity utilization over time (people, budget, equipment).
• Expected portfolio delivery timeline and probability-weighted value.
• Risk summaries (percentiles from Monte Carlo runs), expected number of successes, and distribution of possible outcomes.
• Sensitivity insights showing which inputs drive portfolio outcomes most.

Required inputs (spreadsheet fields)

Design the workbook so each project row contains these baseline fields. Keep the fields explicit and editable for scenario experiments.

• Project ID / Name
• Stage (exploration, pilot, scale)
• Start date / quarter and expected end date
• Resource demand profile by time bucket (FTEs, hours/week, or $ per quarter)
• Funding required per time bucket
• Probability of technical / market success (0–100%)
• Estimated benefit if successful (qualitative or numeric expected value)
• Learning value (estimated value of knowledge if pilot runs but fails; use a relative score)
• Dependencies & constraints (other projects, skills, equipment)
• Priority / strategic alignment score

Model overview

Recommended workbook structure:

1. Input sheet with editable project records and a separate capacity table (by quarter) for constraints.
2. Scenario selector where reviewers can toggle assumptions or copy scenarios (base, aggressive, conservative).
3. Simulation engine that runs many iterations sampling project success according to the provided probabilities and applying resource constraints to determine schedule slips, queuing, or cancellations.
4. Output dashboards showing distributional results: expected portfolio value, 10/50/90 percentiles, expected number of successes, capacity utilization, and schedule risk.

How to run a useful session

1. Prepare: confirm project inputs (especially resource profiles and success probabilities) with project leads ahead of the review.
2. Choose scenario(s): keep a base case and at least one alternate (e.g., shift 20% capacity to exploration).
3. Run simulations: run enough iterations (1,000–5,000) to stabilise percentiles in results.
4. Review outputs together: focus on actionable tradeoffs (what changes schedule most, what increases chance of >X value?).
5. Decide how results influence next actions: set guardrails, funding bands, learning milestones, or staged funding conditional on evidence.

Suggested visualizations

• Stacked capacity utilization by quarter (projects stacked by priority or stage).
• Probability distribution (histogram) of portfolio value with percentile markers.
• Timeline Gantt showing expected deliveries and slack when constrained.
• Tornado chart of sensitivity (which input changes move the outcome most).
• Summary table of expected successes, failures, and learning value by stage.

Interpreting results (practical guidance)

• Treat distributions as signals: look for robust decisions that perform reasonably well across many plausible futures.
• Identify capacity bottlenecks: if many scenarios show schedule slips, consider changing timing, hiring, or deprioritizing projects.
• Balance expected value and learning: exploration projects may have low expected monetary value but high learning value that de‑risks future scaling.
• Test sensitivity: if outcomes flip with small input changes, capture that risk in governance (conditional approvals, staged funding, clear exit rules).

Common pitfalls (Mal Hungers)

• Using the workbook as a single-point forecast — it is decision-support, not prediction.
• Overfitting probabilities to historical results or managerial optimism.
• Ignoring non-financial constraints (skills, regulatory timing, market windows).
• Freezing allocations as a mandate rather than an adaptive plan responsive to evidence.

Templates and tailoring

The workbook is most useful when teams adapt it to their cadence and metrics. Consider providing:

• A starter spreadsheet with input sheets, a Monte Carlo engine (using random sampling functions or add-ins), and sample dashboards.
• Variants tuned to different planning horizons (6 months for fast‑moving portfolios, multi‑year for platform investments).
• Industry-specific fields (regulatory lead time, clinical trial stages, manufacturing ramp milestones).

Capability opportunities (non-blocking suggestions)

Making this workbook interactive would increase adoption and reduce manual copying of scenarios. Practical next steps:

• Render an Interactive input form to collect project rows and capacity profiles (use platform Interactive Form rendering).
• Store scenario submissions so teams can compare historical scenarios and decisions (use the content submission JSON storage capability).
• Consider building a server-side Monte Carlo runner or embedded calculation engine later to produce percentiles automatically and surface recommended tradeoffs in the UI.

Next steps for teams

1. Download or copy the starter spreadsheet and populate it with your current projects.
2. Run a baseline simulation and one alternate scenario before your next portfolio review.
3. Use the workbook outputs to draft conditional funding decisions and learning milestones rather than fixed allocations.

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