Delivery forecasting & commitments
Forecast when work actually lands, and check whether what was promised matches what was scoped.
35 of 388 tools.
Audit commercial technical commitment integrity
Audit signed commercial promises against explicitly allocated technical scope, dependency order, funded capacity, acceptance criteria and evidence; expose orphan scope, double allocation, cycles, late plans and maximum contractual penalty without interpreting legal rights from engineering activity.
Audit decision execution fidelity
Audit whether approved decisions actually became verified implementation at the promised aggregate-unit and component grain, with whole-unit bootstrap uncertainty and simultaneous gates for fidelity, overdue scope, unverifiable evidence, exceptions and critical gaps.
Audit KPI threshold bunching
Detect a post-target excess concentration immediately above a governed KPI threshold: compare within-unit pre/post local mass and above-versus-below mirror asymmetry, bootstrap whole units, report density bins and a smoothed log-density jump, and explicitly refuse to equate bunching with individual gaming or intent.
Audit release risk prediction integrity
Audit a complete eligible-change release-risk cohort for point-in-time prediction lineage, exact change-to-deployment linkage, mature mutually exclusive outcomes, selective labels, score-triggered intervention contamination, calibration and false alarms before the score influences a release decision.
Audit sunk cost escalation
Audit whether cumulative sunk cost predicts aggregate project continuation after project fixed effects, checkpoint time, forward value, success probability, remaining cost, and future irreversibility, with project-cluster bootstrap uncertainty.
Calculate break even delivery date
Find the latest economically supported delivery period across coherent value, remaining-cost, recurring-contribution, operating-cost, value-decay, cost-growth and delay-cost scenarios; enforce expected NPV, positive-NPV probability and CVaR gates while keeping the economic deadline distinct from a completion forecast.
Calculate earned value forecast
Turn period-level planned value, accepted earned value, and actual cost into a correlated Bayesian CPI/SPI distribution for final cost, completion period, budget overrun, and deadline miss, with classical EAC cross-checks and an early-progress abstention gate.
Calculate execution value leakage
Translate incomplete scope, delay-driven value decay, rework and approved-exception costs into coherent expected, reserve-quantile and tail-CVaR execution leakage, with exact decision-level reconciliation to net realized value.
Calculate incremental cost effectiveness ratio
Construct a probabilistic incremental cost-effectiveness frontier from jointly aligned cost and outcome scenarios; remove strict and extended dominance before calculating ICERs, and select by expected net benefit plus a cost-effectiveness acceptability curve at organization-owned willingness-to-pay thresholds.
Calculate opportunity cost of WIP
Quantify the expected value-delay cost of the current WIP completion pattern against the Smith-rule focus sequence, including scenario probability and tail disadvantage.
Calculate value of delay to decide
Calculate a period-by-period value-of-delay curve that separates prospectively available information from waiting cost and changing action economics under coherent scenarios.
Cluster process markov archetypes
Discover privacy-eligible workflow archetypes from aggregate Markov transition counts using empirical-Bayes shrinkage, Jensen-Shannon k-medoids, silhouette quality, and posterior assignment stability.
Estimate budget contingency reserve
Size an engineering or investment contingency reserve from one coherent joint cost distribution, stress represented scenario probabilities inside a governed total-variation radius, preserve natural offsets, and exactly reconcile robust tail overrun to aggregate cost items.
Estimate causal value of execution fidelity
Estimate how much outcome value an additional unit of implementation fidelity causes by using randomized enablement as an encouragement instrument, with whole-cluster arm bootstrap, first-stage, balance, negative-control and ratio-stability gates.
Estimate competing delivery risks
Estimate age-conditional probabilities of delivery, cancellation, escalation, or remaining active with Aalen-Johansen competing risks and bootstrap intervals.
Estimate delivery delay value at risk
Translate aligned portfolio completion-date draws into expected delay loss, VaR/CVaR, dependence amplification, and initiative tail attribution.
Estimate dynamic execution factor
Extract a direction-aligned latent execution factor from aggregate metric vectors and forecast its level and velocity with a likelihood-tuned local-linear-trend state-space model.
Estimate estimate at completion distribution
Turn bottom-up component actuals and locally calibrated remaining-cost p50/p90 estimates into a correlated Gaussian-copula lognormal estimate-at-completion distribution with antithetic simulation, budget-breach probability, CVaR, correlation uplift, finite-draw error, and exactly reconciled component tail contributions.
Estimate productivity rebound effect
Estimate how much aggregate capacity released by a productivity intervention is absorbed by induced output or workload using stacked matched-cohort log difference-in-differences; separate fixed-output efficiency, induced output and total resource use, audit pretrends, reconcile the log identity, and cluster-bootstrap rebound uncertainty including backfire above 100 percent.
Forecast contract delivery and liability
Forecast remaining commercial-commitment delivery time, on-time probability, contractual penalties, acceptance cash and liquidity from a right-censored empirical-Bayes lognormal duration model, conditioning each live promise on its age and refusing sparse or unverified classes.
Forecast correlated milestone slippage
Forecast joint portfolio milestone slippage from complete historical episode-by-category planned/actual duration ratios: fit log-error marginals and a positive-definite shrinkage Gaussian copula, mix shared and idiosyncratic shocks, propagate durations through the current dependency DAG, and report joint confidence, finish distributions, and tail value at risk.
Forecast governed release competing risks
Forecast company-local rollback, hotfix and incident incidence conditional on deployment with inverse-propensity-corrected discrete-time competing risks, strict whole-release chronological validation, posterior intervals and mandatory improvement over both a simple baseline and the legacy PR score.
Forecast workflow absorption semimarkov
Forecast terminal workflow outcomes and remaining time from Bayesian transition and lognormal dwell-time posteriors over status histories.
MCMC project completion forecast
Forecast live task and project completion with a censored Bayesian lognormal model, MCMC uncertainty, dependencies, and finite parallelism.
Optimize causal release assurance portfolio
Select one release, assurance or hold option per change from prospectively identified Beta-binomial relative-risk effects while pricing delay, failure and shared common-mode loss under budget, scarce resources, mandatory controls, expected-failure and CVaR constraints with an exact or disclosed beam-search Pareto frontier.
Optimize commercial commitment portfolio
Select decline or one executable contract-term package per commercial opportunity under common delivery scenarios, period capacity, delivery budget, expected penalty, acceptance-cash, liquidity and CVaR gates; value acceptance and relationship economics and disclose exact or uncertified beam search.
Optimize learning vs earning allocation
Solve the exact finite-horizon Beta–Bernoulli bandit for allocating scarce units between a known earning baseline and uncertain actions that earn and update their posterior.
Optimize probabilistic roadmap commitment
Select the highest-value dependency-safe roadmap that satisfies a joint capacity commitment probability and optional tail-overtime limit.
Optimize reliability investment frontier
Construct a dependency- and exclusion-feasible Pareto frontier across investment cost, expected residual loss, CVaR loss, and expected downtime under coherent scenarios and explicit pair interactions, then select the least-cost evaluated portfolio clearing governed reliability targets.
Optimize reserve follow on allocation
Solve a two-stage follow-on capital problem: choose how much reserve to hold now, then choose at most one funding tier per company conditional only on the signal partition genuinely observable later, with coherent scenario value, opportunity cost, CVaR, a reserve Pareto frontier, value of available information, and exact-or-disclosed supported-policy search.
Optimize roadmap under resource substitution
Choose a value-maximizing roadmap and one explicitly validated native or substitute resource plan per initiative within all capability capacities.
Optimize stage gate funding
Value project continuation and abandonment by backward induction at each evidence gate, then select a portfolio within initial and expected follow-on capital limits.
Rank experiments by expected information gain
Rank prospective experiments by Bayesian mutual information and decision-aware expected value of sample information across explicit hypotheses, result likelihoods and decision payoffs; price usability, monetary cost and decision delay, expose recommendation-change probability, and preserve a value-information-cost-delay Pareto set.
Recommend stop continue scale decisions
Recommend stop, continue learning, or scale for aggregate initiatives using beta-binomial posterior rollout economics, independent harm gates, simulation precision, sampling cost, and opportunity decay.
Solve robust policy across causal models
Choose an aggregate policy across competing interventional causal models and a bounded posterior credal set: derive model-by-action expected utility from outcome probabilities, compute exact lower/upper utility and adversarial model weights, minimize worst-case regret, expose model disagreement and value of perfect model information, and fail closed when any causal evidence gate fails.