{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"negative_space_design__operations_research","archetype_slug":"negative_space_design","domain_slug":"operations_research","title":"Protected Recovery Capacity with Release Triggers for Rolling-Horizon Schedules","opportunity_summary":"Test whether explicitly protected, operator-visible reserve intervals with governed release rules can absorb local schedule deviations and reduce propagated lateness, overtime, overrides, and rescheduling cost without unacceptable throughput or subgroup-service losses. The proposal is coherent and replay-testable, but the prevalence of the stated problem, empirical advantage over robust or stochastic scheduling, and distinctiveness from established buffer or slack practices are unverified.","adopter_authorizer":"The operations owner can authorize the historical shadow replay; live use would require joint approval from scheduling, frontline operations, and the accountable safety or service owner.","scores":{"meaningful_impact":{"score":4,"rationale":"The proposal targets consequential operational outcomes—lateness, overtime, disruption propagation, rescheduling cost, throughput, and equitable service—and could provide meaningful benefit where packed schedules cause propagation. The frequency and magnitude of that problem are unsupported."},"stakeholder_pull":{"score":3,"rationale":"Dispatchers, scheduling teams, operations owners, customers, and downstream processes have plausible reasons to value greater schedule reliability and fewer overrides, but the sealed candidate provides no direct evidence of expressed demand, adoption interest, or problem prevalence."},"incremental_advantage":{"score":3,"rationale":"The explicit protected-reserve state and release rule offer a testable operational difference from incidental slack and from the stated robust or stochastic rival. Advantage remains uncertain because reserve consumes productive capacity and the rival may match or exceed replay performance."},"distinctiveness_plausibility":{"score":2,"rationale":"Operator-visible protected reserve with release triggers is specifically described, but prior art is unsearched and the candidate itself identifies potentially adjacent slack, buffer, robust, stochastic, and disruption-recovery practices. Closed-book evidence cannot establish meaningful distinctiveness."},"technical_implementability":{"score":4,"rationale":"A replay on one resource pool using historical schedules, telemetry, constraints, and comparative scheduling variants is technically bounded and does not require live control. Implementation still depends on adequate event data, valid cost measures, and the ability to reproduce baseline and rival policies."},"adoption_authority_feasibility":{"score":4,"rationale":"The operations owner and the additional live-policy approvers are explicitly identified, and the first step fits the operations owner's shadow-test authority. A live rollout would be harder because scheduling, frontline, and safety or service owners must jointly approve it."},"evidence_readiness":{"score":4,"rationale":"The candidate specifies a preregistered four-week historical replay, three comparison arms, outcome metrics, throughput bounds, subgroup checks, falsifiers, and rejection rules. Readiness is limited by unknown data quality, representativeness, and availability of a faithful robust or stochastic comparator."},"safety_net_benefit":{"score":4,"rationale":"Protected capacity is designed specifically as a recovery margin against variable arrivals, durations, breakdowns, and rework, with release rules and hard-constraint protections. Benefit is hypothetical and could reverse if reserve is misplaced, released incorrectly, or worsens queues and claimant service."},"scalability":{"score":3,"rationale":"The mechanism could in principle apply to machines, crews, vehicles, or service capacity, but each resource pool would require locally valid constraints, uncertainty models, release triggers, priorities, integrations, and service safeguards. Cross-setting transfer is therefore plausible but unproven."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"10K_TO_50K","scope":"Preregister, implement, and analyze a historical replay for one resource pool and four representative weeks, including baseline, robust or stochastic rival, protected-reserve variants, subgroup metrics, and a decision report.","confidence":"LOW","assumptions":["Usable historical schedules, telemetry, priorities, due dates, and cost proxies already exist.","An existing scheduling model can be reproduced without major reconstruction.","The replay requires analyst, operations, and limited engineering labor but no new equipment or live-system modification.","Data access and internal review do not trigger extensive contracting or compliance work."]},"initial_deployment_startup":{"band_2026_usd":"50K_TO_250K","scope":"Prepare a controlled initial live implementation for one resource pool after favorable replay evidence, including policy configuration, data integration, operator-visible reserve state, release and override controls, monitoring, training, safety or service review, and evaluation design.","confidence":"LOW","assumptions":["The current scheduling platform can be extended rather than replaced.","Deployment remains limited to one resource pool.","Human approval is retained for releases and overrides during the initial deployment.","No safety-critical or statutory capacity is placed in reserve."]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Launch across several resource pools or one operational site with production integrations, governance, training, support, reliability monitoring, subgroup-service auditing, incident procedures, and comparative outcome evaluation.","confidence":"LOW","assumptions":["Resource pools share enough scheduling infrastructure to reuse core implementation components.","Existing telemetry and identity or priority data can support production monitoring.","Launch requires cross-functional scheduling, operations, engineering, compliance, and service-owner coordination.","The estimate excludes enterprise-wide replacement of scheduling systems."]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Operate and maintain the policy at a limited multi-pool or single-site scale, including monitoring, trigger recalibration, data and software support, audits, operator training, incident review, and periodic effectiveness evaluation.","confidence":"LOW","assumptions":["No dedicated large operations center or major new hardware is required.","Existing scheduling and telemetry licenses remain usable.","Reserve parameters and service protections require recurring review as demand and processing distributions change.","Human governance remains necessary for exceptions and distributional monitoring."]}},"research_burden":"MODERATE","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"YES","reason":"The candidate specifies observable packed schedules, disruption propagation, overrides, lateness, overtime, and schedule instability, and supplies a falsifiable relationship between those observations. External evidence is still needed to establish occurrence and materiality in any target setting."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The operations owner is identified as the shadow-test authorizer, while scheduling, frontline operations, and the accountable safety or service owner are identified as required live-policy approvers."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal claims that explicit protected reserve plus release rules will outperform ordinary deterministic scheduling and a robust or stochastic rival on preregistered realized-cost and service metrics within an acceptable throughput bound."},"bounded_next_evidence_step":{"status":"YES","reason":"The authorized first step is limited to one resource pool and four representative historical weeks, uses specified comparison arms and metrics, and makes no live dispatch changes."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The replay-only first step is within identified authority, excludes statutory, emergency, safety-critical, and minimum-service capacity, and includes hard-constraint, claimant-service, and throughput rejection rules. These controls do not establish that a later live deployment is safe."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The candidate bounds the replay and identifies major operational participants, permitting a broad first-evidence estimate, but it provides no details on data condition, scheduling-system architecture, integration effort, compliance requirements, number of resource pools, or deployment scale needed to support a reliable implementation range."}},"blocking_evidence":["No external evidence establishes that disruption propagation from near-full nominal schedules is frequent or materially costly in the intended adopter setting.","No comparative result shows that protected reserve and release rules outperform the deterministic baseline and the robust or stochastic rival within the throughput bound.","No prior-art review establishes distinctiveness from scheduling slack, buffers, recovery capacity, robust scheduling, stochastic scheduling, or disruption-recovery formulations.","Historical data availability, validity, representativeness, and the reproducibility of baseline and rival policies are unknown.","Replay cannot by itself establish operator behavior, selective overriding, rare-disruption performance, or safe live release decisions.","Deployment architecture and integration requirements are unspecified, preventing a confident implementation-cost range."],"next_evidence_step":"Run the authorized preregistered historical replay on one resource pool and four representative weeks, comparing the ordinary deterministic baseline, a faithfully implemented robust or stochastic rival, and protected-reserve variants with explicit release rules. Measure throughput, realized lateness, overtime, overrides, reserve use, rescheduling cost, hard-constraint violations, and subgroup service levels; reject the mechanism if the problem falsifier holds, the rival matches or exceeds its benefit, any protected service level is materially worsened, or throughput loss exceeds the preregistered bound without compensating disruption-cost reduction.","research_questions":["Do representative records show that high nominal utilization and small deviations predict propagated lateness, overrides, overtime, or instability after controlling for demand, chronic undercapacity, data error, and infeasible commitments?","Does explicit protected reserve with release rules improve total realized cost and service reliability relative to both the ordinary deterministic baseline and a robust or stochastic rival at the same hard constraints and acceptable throughput bound?","How sensitive are results to reserve size, placement, release timing, forecast error, breakdown severity, rework, and missing telemetry?","Which claimant classes gain or lose service, and do reserve placement or release decisions shift reliability toward favored customers, priorities, resources, or shifts?","Can existing slack, buffer, robust, stochastic, or disruption-recovery formulations reproduce the complete mechanism, including an operator-visible protected state and governed release rule?","What data, solver, workflow, integration, training, compliance, and monitoring work would a controlled live implementation require?","Which replay findings would plausibly survive behavioral adaptation, manager overrides, and rare disruptions in a separately authorized shadow or limited live evaluation?"],"recommendation":"PRIOR_ART_RESEARCH","uncertainty_constraints":["Closed-book assessment cannot establish prevalence, market size, prior art, novelty, realized effectiveness, or exact cost.","Scores reflect the candidate's internal specification and testability, not verified operational results.","Cost bands are resource-equivalent planning ranges based on assumed existing data and scheduling infrastructure; missing architecture and compliance details create substantial uncertainty.","The earliest horizon refers to credible comparative replay evidence, not production adoption or proven live impact.","A favorable replay would not authorize live dispatch changes or resolve behavioral and rare-event risks."],"closed_book_prior_art_boundary":"Prior art was not searched. This assessment supports only the coherence and testability of the proposed protected-reserve state and release-rule mechanism; it does not establish novelty or distinctiveness from existing slack, buffer, capacity-cushion, robust, stochastic, or disruption-recovery scheduling practices."}