{"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__systems_cybernetics","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"systems_cybernetics","title":"Offline evaluation of coupled inventory-order risk modes","opportunity_summary":"Test whether a locally estimated decomposition of inventory, backlog, forecast, and pipeline-order dynamics can identify weakly damped joint modes and support safer coordinated replenishment adjustments than separate site-level tuning. The proposed first step is a held-out offline comparison against ordinary practice and a nonlinear system-dynamics rival; the existence, predictive value, and controllability of the modes remain hypotheses.","adopter_authorizer":"The accountable supply-chain operations owner, subject to model-risk review and approval from affected echelon leads and supplier partners where their decisions would be affected.","scores":{"meaningful_impact":{"score":4,"rationale":"If the hypothesized endogenous modes materially contribute to shortages, surplus inventory, expediting, and workload volatility, addressing them could improve several important operating outcomes simultaneously. The packet supplies no evidence about prevalence, effect magnitude, or the share attributable to endogenous coupling, preventing a very favorable score."},"stakeholder_pull":{"score":2,"rationale":"The proposal identifies planners, operations owners, customers, workers, and business units with plausible interests, but contains no interviews, commitments, purchasing signals, incident evidence, or demonstrated dissatisfaction with existing tools."},"incremental_advantage":{"score":3,"rationale":"Joint-mode diagnosis could expose amplification missed by separate inventory, backlog, and forecast-error alarms, and the claim is directly comparable with site-level tuning. Advantage remains uncertain because no held-out results show better prediction or policy performance, and the nonlinear rival may perform better under delays, saturation, or constraints."},"distinctiveness_plausibility":{"score":2,"rationale":"The modal reframe is coherently differentiated from the stated baseline and nonlinear rival, but prior art is explicitly unsearched. Distinctiveness relative to existing supply-chain control, modal-analysis, and system-dynamics practice is therefore unsupported."},"technical_implementability":{"score":3,"rationale":"An offline shadow test using historical review cycles is technically bounded and avoids live control. Implementation depends on aligned multi-echelon state data, conditioning on demand and policy changes, stationarity, identifiability, a well-conditioned basis, spectral separation, and calibrated safety thresholds, none of which is established."},"adoption_authority_feasibility":{"score":4,"rationale":"The packet names an accountable operations owner, model-risk review, affected-echelon approval, excluded actions, and rollback conditions. Cross-echelon control changes and supplier involvement could still fragment authority beyond the initial offline test."},"evidence_readiness":{"score":2,"rationale":"The problem, modal structure, and intervention effect are all labeled hypotheses, with no supplied domain data or empirical calibration. Readiness is improved by explicit falsifiers, held-out comparisons, and halt criteria, but the required budgets and thresholds are not yet defined."},"safety_net_benefit":{"score":3,"rationale":"The approach could reduce customer shortages and worker workload swings while its shadow-test design preserves the existing policy and excludes autonomous consequential actions. Those protective benefits are hypothetical, and coordinated adjustments could instead cause service, capacity, workload, or cross-mode harms."},"scalability":{"score":3,"rationale":"The analytical pattern could in principle be repeated across product-network segments using common state and review-cycle constructs. Each segment may require new data alignment, operating-window validation, threshold calibration, drift monitoring, and approval, while topology changes and nonlinear regimes limit straightforward reuse."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"Preregister and execute one offline, temporally held-out shadow study for one product-network segment, including data extraction and alignment, demand and policy conditioning, modal estimation, baseline and nonlinear-rival implementation, safety-threshold calibration, and comparative evaluation.","confidence":"LOW","assumptions":["Usable historical multi-echelon review-cycle data already exist and can be accessed without major contracting.","The study requires a small cross-functional team of supply-chain, data, modeling, and model-risk personnel.","No live orders, supplier allocations, workforce decisions, or new physical equipment are involved.","The nonlinear rival can be implemented at study resolution without a large bespoke simulation program."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Conditional startup for a governed shadow-monitoring capability on an initial network segment, including maintained data pipelines, model monitoring, planner-facing outputs, documentation, security and model-risk review, and operating procedures without autonomous ordering.","confidence":"LOW","assumptions":["The offline test clears its preregistered falsifiers and safety criteria.","Existing planning systems expose adequate integration interfaces.","Deployment remains limited to decision support for a bounded segment.","Material remediation of source-system data quality is not required."]},"operational_launch":{"band_2026_usd":"1M_TO_5M","scope":"Conditional controlled launch across multiple echelons or product segments, including system integration, segment-specific validation, change management, planner training, partner coordination, staged policy trials, and service, capacity, inventory, and workload safeguards.","confidence":"LOW","assumptions":["Live use receives separate authorization after shadow evidence; it is not implied by the candidate.","Several segments require independent calibration and validation.","Supplier or echelon coordination can be achieved without enterprise-wide replacement of planning platforms.","No major capacity or workforce redesign is included."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Recurring operation of a bounded multi-segment decision-support program, including data-pipeline maintenance, residual and drift monitoring, revalidation, model-risk reviews, incident handling, planner support, and periodic retirement or recalibration decisions.","confidence":"LOW","assumptions":["The system supplements rather than replaces existing planning operations.","Human review remains required for consequential policy changes.","Network scale and data complexity remain moderate.","Frequent topology or policy changes could increase recurring costs beyond this band."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"YES","reason":"The candidate specifies a recognizable mechanism involving coupled inventory, backlog, forecast, pipeline, delay, and replenishment states, along with observable consequences and a problem falsifier. Actual prevalence and magnitude remain unsupported but are not required to recognize and test the stated problem."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The accountable supply-chain operations owner is explicitly identified, with model-risk review and affected-echelon approval requirements."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal claims that modal identification and targeted adjustments can outperform separate site-level tuning and a nonlinear system-dynamics rival on held-out oscillation and service loss without unacceptable cross-mode harm."},"bounded_next_evidence_step":{"status":"YES","reason":"A preregistered offline shadow test on one product-network segment with temporally held-out cycles, three-way comparison, explicit problem and intervention falsifiers, and no live order changes is bounded and decision-relevant."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The authorized first step is offline, consequential actions are excluded, affected parties and reviewers are named, and halt conditions retain the existing policy. This does not authorize any later live intervention."},"implementation_cost_scope_and_range":{"status":"YES","reason":"The one-segment evidence test and conditional deployment stages can be bounded into broad resource-equivalent bands under explicit assumptions, although data readiness, integration complexity, and network scale keep confidence low."}},"blocking_evidence":["No evidence establishes that a reproducible endogenous coupled mode exists after conditioning on external demand, policy changes, and other shocks.","No held-out comparison establishes predictive or intervention advantage over coordinate-level tuning or the nonlinear system-dynamics rival.","Stationarity, state identifiability, modal-basis conditioning, spectral separation, transient-growth limits, and drift tolerances have not been demonstrated.","Residual, service-loss, capacity, workload, and cross-mode harm budgets lack definitions and calibration procedures.","No stakeholder inquiry establishes operational demand, workflow fit, or willingness to coordinate controls across echelons.","Prior art is unsearched, so distinctiveness cannot be established."],"next_evidence_step":"Preregister and run an offline shadow study on one product-network segment using temporally held-out review cycles. Compare the modal model with separate coordinate-level or site-tuning models and a nonlinear system-dynamics delay model after conditioning on external demand and policy changes. Reject the problem hypothesis if no reproducible coupled persistent or growing mode improves held-out oscillation or service-loss prediction; reject the intervention hypothesis if simulated modal-targeted adjustments fail to outperform both comparators or breach prespecified stock, service, capacity, workload, transient-growth, residual, conditioning, gap, or drift limits.","research_questions":["Do held-out transitions contain a reproducible coupled mode after conditioning on external demand shocks and policy changes?","Does the modal approach predict oscillation and service loss better than separate coordinate-level models and the nonlinear system-dynamics rival?","Are the operating window, state representation, modal basis, spectral gap, and intervention map sufficiently stable and identifiable for decision support?","How should residual, service-loss, transient-growth, conditioning, drift, inventory, capacity, and workload thresholds be calibrated before evaluation?","Do feasible modal-targeted adjustments reduce oscillation and service loss without harmful cross-mode, service, inventory, capacity, or workload effects?","Which operations owners, echelon leads, and supplier partners perceive the problem as important enough to support coordinated evaluation and possible adoption?","Is the proposed reframe materially distinct from existing supply-chain control, modal-analysis, and system-dynamics methods?"],"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["All empirical problem and intervention claims are hypotheses rather than observed results.","Problem prevalence, stakeholder demand, market size, and realized impact are unmeasured.","Prior art is unsearched, so novelty and differentiation claims are unavailable.","Costs are broad resource-equivalent bands conditional on unknown data quality, integration burden, network scale, and partner coordination.","Local modes may fail under nonstationarity, non-normal transient growth, nonlinear saturation, discrete constraints, long delays, human overrides, topology changes, or strategic responses.","The offline evidence step cannot establish authority or safety for autonomous or live policy changes."],"closed_book_prior_art_boundary":"No external search or source was used. The packet explicitly marks prior art as unsearched; therefore this assessment makes no claim about novelty, prevalence, market position, existing implementations, or superiority to methods outside the sealed comparison set."}