{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__logistics_supply_chain","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"logistics_supply_chain","title":"Shadow-tested modal damping for multi-echelon replenishment oscillation","opportunity_summary":"Fit and validate a local transition operator for connected SKU-echelon states, identify weakly damped or unstable coupled modes, and test whether bounded replenishment-parameter changes reduce reconstructed order, inventory, and backlog oscillation without violating service, capacity, or safety-stock guardrails. The proposed first step is a non-executing historical replay, not a live policy change.","adopter_authorizer":"The inventory-control owner can authorize analysis and shadow replay; the accountable supply-chain operations owner, operating through existing change controls, must authorize any later live policy change.","scores":{"meaningful_impact":{"score":4,"rationale":"If the stated coupled oscillations occur, reducing alternating shortages, excess inventory, expedites, and unstable supplier orders could materially improve service and operating performance. The packet does not establish prevalence, frequency, or realized impact."},"stakeholder_pull":{"score":2,"rationale":"The proposal names planners, operations owners, suppliers, customers, and finance stakeholders with plausible interests, but provides no evidence of expressed demand, budget ownership, adoption requests, or dissatisfaction with current methods."},"incremental_advantage":{"score":3,"rationale":"Mode-level detection and policy sensitivity could reveal coordinated instability missed by SKU-location alerts or aggregate tuning, and the holdout replay supplies a direct comparison. The advantage remains hypothetical and may disappear after conditioning on external shocks or comparing with conventional models."},"distinctiveness_plausibility":{"score":2,"rationale":"The packet distinguishes the proposal conceptually from local exception management and an aggregate bullwhip or system-dynamics rival, but prior art is explicitly unsearched, so real-world distinctiveness cannot be credited."},"technical_implementability":{"score":3,"rationale":"The required operational states and a bounded replay design are specified, making an analysis technically conceivable. Implementability is constrained by unspecified estimation and lag procedures, exogenous-input treatment, identification diagnostics, nonlinearity, non-normality, stationarity, conditioning, and mode drift."},"adoption_authority_feasibility":{"score":4,"rationale":"Authority is separated appropriately: the inventory-control owner can approve analysis, while live changes require the accountable operations owner and existing controls. Feasibility beyond shadow analysis remains uncertain because no specific organization, owner commitment, or approval pathway is evidenced."},"evidence_readiness":{"score":3,"rationale":"The candidate defines observable variables, a baseline comparison, holdout evaluation, falsifiers, guardrails, and halt criteria. Readiness is reduced by the missing estimator specification, unknown data completeness and shock labels, and replay's inability to establish real-world causal damping."},"safety_net_benefit":{"score":5,"rationale":"The first step is non-executing, excludes automatic orders and operational overrides, preserves current policy, and halts on residual, conditioning, spectral-gap, drift, service, or backlog failures. This creates substantial learning value with limited direct operational exposure."},"scalability":{"score":3,"rationale":"A common analytical pattern could potentially be reused across product families and echelon networks, but each application may require new state definitions, stationary windows, policy mappings, conditioning, and mode-stability checks. The packet does not demonstrate transfer across regimes or network scales."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"Preregister and conduct the non-executing replay for two product families across three connected echelons, including data preparation, estimator specification, shock conditioning, baseline comparison, holdout tests, diagnostics, and stakeholder review.","confidence":"LOW","assumptions":["Relevant historical state, policy, constraint, lead-time, and service data are already accessible.","No new production integration or live ordering change is included.","A small cross-functional team can complete the analysis within one or two planning cycles.","Material data reconstruction or commercial data acquisition would move the work upward."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Build a governed shadow-analysis capability for a limited operational scope, including repeatable data pipelines, model monitoring, access controls, documentation, validation, and change-control preparation.","confidence":"LOW","assumptions":["Existing planning systems can export the required review-period state without major replacement.","Deployment remains advisory and does not release orders automatically.","The organization supplies inventory-control, data-engineering, modeling, and operations expertise.","Security, compliance, and integration requirements are ordinary for internal operational analytics."]},"operational_launch":{"band_2026_usd":"1M_TO_5M","scope":"Undertake separately authorized controlled operational validation and limited production launch, including integration with planning workflows, prospective monitoring, operator training, supplier and operations coordination, independent evaluation, and rollback controls.","confidence":"LOW","assumptions":["Replay first passes identification, residual, stability, service, and backlog gates.","Live validation starts with a limited set of families and echelons rather than the full network.","Existing order-management and planning platforms can accommodate bounded policy changes.","The band includes substantial coordination and evaluation but not enterprise-wide replacement of planning systems."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Operate a limited production capability with data maintenance, periodic refitting, drift and spectral diagnostics, exception review, audit support, software infrastructure, and outcome evaluation.","confidence":"LOW","assumptions":["Coverage remains limited to selected product families and connected echelons.","Human review remains mandatory for policy recommendations.","Frequent regime changes or expansion across a large heterogeneous network could raise recurring cost.","No exact staffing, software licensing, data-platform, or compliance requirements are supplied."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"YES","reason":"The candidate states a recognizable operational problem involving recurring inventory, backlog, and order oscillations with concrete service and cost consequences, although its prevalence in any target organization remains unmeasured."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The inventory-control owner is identified for analysis and shadow simulation, and the accountable supply-chain operations owner is identified for any live policy authorization."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal claims that mode-guided bounded parameter changes will outperform ordinary local monitoring or aggregate tuning by reducing holdout modal gain and joint oscillation without breaching service, backlog, inventory, or capacity guardrails."},"bounded_next_evidence_step":{"status":"YES","reason":"A preregistered, non-executing replay on two product families and three connected echelons is bounded and includes a baseline comparison, later holdout, negative tests, guardrails, and explicit falsifiers."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The first step does not execute orders or alter commitments, explicitly excludes safety-stock and capacity overrides, and can be halted and discarded without live rollback. Separate authority is required before any operational intervention."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The first replay has a defined analytical scope, but the packet does not specify data condition, system integration, staffing, compliance, network scale, or controlled-live-validation design sufficiently to support more than low-confidence broad resource bands."}},"blocking_evidence":["No evidence establishes that a target system has repeatable endogenous coupled oscillation after conditioning on demand shocks, promotions, disruptions, seasonality, and capacity outages.","The operator estimator, lag structure, regularization, exogenous-input treatment, identification tests, and simulated-recovery criteria are not specified.","The availability, granularity, alignment, and quality of the required multi-echelon state and shock-label data are unknown.","Stable mode identity, adequate conditioning, a sufficient spectral gap, and acceptable holdout residuals have not been demonstrated.","Replay has not shown that predicted damping survives service, backlog, inventory, capacity, and subgroup guardrails.","No controlled operational evidence establishes real-world causal damping or robustness to nonlinear and changing policies.","Prior art and comparative distinctiveness are unsearched.","Stakeholder demand, data-access commitment, budget ownership, and willingness to pursue later controlled validation are unverified."],"next_evidence_step":"With an inventory-control partner, preregister the estimator, lag and exogenous-input specification, recovery diagnostics, thresholds, and guardrails; then run the authorized non-executing replay on two product families across three connected echelons. Compare mode-guided bounded parameter changes with current-policy replay and the nearest conventional aggregate-variance/service tuning rival on a later holdout. Stop if conditioning, residual, spectral-gap, or drift tests fail, and reject the opportunity if no repeatable coupled oscillation remains after shock conditioning or if predicted damping fails to reduce oscillation without worsening service, backlog, inventory, or capacity guardrails.","research_questions":["Does coupled oscillation remain repeatable after recorded demand shocks, promotions, disruptions, capacity outages, seasonality, and forecast error are conditioned out?","Which estimator, lag structure, exogenous-input representation, and regularization recover stable local dynamics without manufacturing spurious modes?","Are the identified modes sufficiently conditioned, separated, and stable across training windows, holdouts, product families, and plausible preprocessing choices?","Does mode-guided tuning improve holdout oscillation and modal gain relative to both current-policy replay and conventional aggregate tuning?","Do apparent improvements persist at SKU, customer, supplier, and location levels without hidden service or allocation harm?","What residual patterns or nonlinear regimes invalidate the local-operator approximation, particularly around shortages and capacity constraints?","Can a later controlled operational test distinguish genuine causal damping from replay assumptions while remaining reversible and separately authorized?","Do inventory-control and operations owners recognize the problem, control the required data and decisions, and value the incremental comparison enough to sponsor validation?","What existing supply-chain control, system-identification, bullwhip-mitigation, or modal-monitoring methods already make the same incremental claim?","What data engineering, governance, monitoring, staffing, and integration effort would a limited operational launch actually require?"],"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment: no external evidence was used.","Problem prevalence, organizational incidence, market size, stakeholder demand, and realized impact are unmeasured.","Prior art, adoption prevalence, and distinctiveness are unmeasured.","All resource bands are low-confidence resource-equivalent estimates, not prices or point estimates.","The causal interpretation depends on successful conditioning of external shocks and stable identification of a local operator.","Local fitted modes may be fragile under non-normality, nonlinear constraints, policy changes, aggregation, and small spectral gaps.","Replay can screen and falsify model predictions but cannot by itself demonstrate live causal effectiveness.","The 3-to-12-month horizon applies to credible shadow-replay evidence, not production deployment or demonstrated operational impact."],"closed_book_prior_art_boundary":"Prior art is explicitly UNSEARCHED. This assessment credits only the packet's internal conceptual contrast with local exception management and aggregate bullwhip or system-dynamics tuning; it makes no claim about novelty, rarity, existing products, published methods, adoption prevalence, or freedom to operate."}