Resources TRIZ Cases

State-machine monitoring detects productive and unproductive heavy-duty vehicle states to reveal interdependencies and improve workflow utilization.

Machine learning predicts group cooperation rates and corrects action rankings, reducing manual effort in organization-specific measure planning.

Maps issue structures into KPI trees that link causal relations with responsible departments for clearer operational control and decisions.

A control management system modifies data objects and control scopes to close compliance gaps across data processes with less manual reconfiguration.

Edit and validate only the changed branch of an organizational tree, cutting activation time and memory use while preserving rule consistency.

Dynamic programming tables and discretized integer groups cut subset sum matching time while preserving complete financial record reconciliation.

Relative complexity scoring and user preferences balance radiology study assignments, reducing uneven workloads and physician stress.

Predicts shipment transport modes and uncertainty to score environmental impact and recommend lower-emission process flow changes.

Smart contracts assign integrity ranks to participants and transactions, enabling priority-based blockchain validation with traceability and secure data access.

Visual case modeling auto-provisions services and supports event-driven, ad hoc workflows for varied case instances without coding.

Blockchain-linked IDs and use history clarify ownership of combined user content and automate copyright revenue sharing.

Interactive map controls let customers set asset position, orientation, door direction, and truck clearance to reduce delivery placement errors.

Drag-and-drop scheduling updates job duration by device capacity and warns of conflicts before assignment.

Multimodal sensor and dashcam data build driver profiles, rank vehicle-task matches, and improve delivery assignment accuracy.

Models vehicle options as constrained packages to forecast valid configurations and align production with capacity and supply chain rules.

User inquiry messages expose bottleneck steps, enabling process chart updates through branch routes or step subdivision for clearer workflows.

Highlighted explored and unexplored map regions reduce tourist decision fatigue while improving destination recommendations from user history.

AI turns historical and external supply chain data into a single pick-pack-ship view for faster, more accurate packaging and shipping decisions.

Employee IP and cloud data are filtered for false positives to recommend accurate office locations and reduce site planning costs.

Multi-frequency RFID interrogators and a global database automate warehouse item location and movement tracking with less manual scanning.

Retrofitted cooler sensors and a data module capture traffic, door, stock, and refrigeration data to improve restocking, maintenance, and energy use.

Satellite and pipeline data are combined to predict pipe leakage risk and rank investigation priorities across water networks.

Compares ERP configurations across segments, legal entities, and environments to prevent setup errors and implementation failures.

Automated analysis identifies, applies, and verifies BI artifact, metadata, and configuration improvements to cut execution time and maintenance burden.

When service endpoints degrade, workflow definitions are modified to divert calls into staging areas, preventing overload and easing recovery.

Supervised learning predicts ATC sector complexity in real time, cutting computation load and enabling dynamic sector and controller assignment.

Historical time-sequence matching bootstraps schedules for new users, improving forecasting under absenteeism and shifting availability.

Deep reinforcement learning models collector workforce dynamics to improve restructuring accuracy, flexibility, staffing, and debt recovery.

Evaluates candidate real estate for coordinated data centers by balancing safety rate, building conditions, and profitability.

Multi-factor certainty scoring for sensor and operation data helps facility recovery plans match actual conditions with better execution accuracy.

Dynamic graph neural modeling tracks object behaviors and interactions over time to improve battlefield threat probability prediction.

Maps an avatar's virtual location into enterprise-compatible data to generate location-aware reports without leaving the metaverse.

Reusable grouped attribute values replace repeated hazard model runs, cutting pipeline asset risk forecasting time and compute load.

Calculates product-specific carbon impact from material, manufacturing, and user-linked data to improve environmental evaluation accuracy.

Tracks device energy use and local grid carbon intensity to aggregate accurate media playback emissions across diverse screens and power sources.

Forecasts field-specific resource needs and assigns available pesticides, fertilizers, or equipment by yield, profit, and environmental criteria.

Real-time operational, usage, and configuration data make aircraft life cycle assessment faster and more accurate for footprint reduction.

Calculates product-specific environmental indicators by combining material and manufacturing prediction data for more precise CO2 evaluation.

Theoretical optimum NDVI profiles turn satellite crop data into actionable anomaly detection for irrigation, reseeding, and nitrogen adjustment.