Resources TRIZ Cases

See how recipe segmentation into apparatus-specific blocks enables automatic customization by r

See how mobile device image capture and tag-based product identification enable real-time shelf

See how a cooking board integrates pressure, cut, and weight sensors to monitor ingredient stat

See how automated sorting, counting, and priority scheduling eliminate storage space in textile

Vehicle prioritization and timed energy exchange help charging stations reduce peak-period delays and allocate charge more efficiently.

LSTM-based electrical and hydraulic flow models with thermal coupling improve integrated energy dispatch accuracy while avoiding slow convergence.

Monitors pressure and temperature during hydrogen charging to stop unsafe fills and release hydrogen when preset safety criteria are not met.

Selection-based load ordering matches distributed energy sources to consumer needs, improving fairness, resilience, and responsive grid operation.

Audio and wireless passenger detection helps correlate trip periods with driving data, improving driver scoring for safer ride matching.

Maps selected industrial assets to sensing units, derives performance indicators, and applies the right ML workflow for predictive maintenance.

Sensor data is matched to expected work steps to flag abnormal worker actions early and improve maintenance efficiency and quality.

Boxplots, effective takt modes, and takt walls reveal station fluctuations and bottlenecks from blocking, shortage, and failure time data.

Preplanned routing, location tracking, and interlock checks speed smart logistics vehicle control while reducing factory process delays.

Distributed mission data and scannable codes let unmanned vehicles be launched, redirected, and repurposed securely across wide areas.

Simulation-guided debugging helps users trace failed workflow operators, update hierarchical plans, and reach intermediate goals faster.

Iterative batching, pallet positioning, and task scheduling cut picker travel and improve warehouse layer-pick throughput.

Pre-distributed OPC UA lens data lets manufacturing machines retrieve local job sets faster, cutting latency and conversion overhead.

Simulated stacking guides multiple drones to place packages in the right order, preventing pile collapse and improving handling at delivery.

Multi-stage optimization separates mission assignment variables to coordinate diverse UAV objectives while keeping fleet scheduling tractable.

Automated sampling schedules, task assignment, and alerts help pharmaceutical facilities prevent monitoring omissions and protect GMP data integrity.

A control apparatus links processing data across multiple machines to enable product traceability without adding software to each processing unit.

Compares work cost, automation cost, monitoring load, and risk to identify which plant operator tasks are worth automating.

Entrance counters, touchscreens, and sensors feed a cloud system that adapts washroom maintenance to usage and user feedback.

A prediction model uses past medium data to set culture parameters faster, reducing trial-and-error cost while improving medium effectiveness.

Narrows facility combinations by time and cost, then selects sharable equipment to adapt production lines with less planning waste.

Unique IDs tied to nesting data let mixed ordered and WIP stock parts be distinguished and traced for accurate inventory status.

Distributed onboard and offboard event analysis prioritizes aerial vehicle incidents and speeds accurate operator recommendations.

A virtual 3D field model reflects time-series work data for real-time production monitoring without camera-driven stress on workers.

Preprocessed production, design, and layout data cut factory planning time while optimizing process order, resource use, and equipment layout.

Dynamic CI calculation and hydrogen allocation keep production and delivery within carbon intensity limits while balancing network constraints.

Block-level PLC status flags and server instructions guide machine commissioning, reducing missed steps, rework, and duplicate effort.

Alarm output changes with abnormality duration and countermeasure status, helping factories speed recognition and close the PDCA loop.

Past medium manufacturing data feeds a prediction model that selects culture parameters, cutting trial counts and search time while improving gain.

By calculating transfer duration between adjacent process nodes, this case validates time constraints to prevent sample deterioration.

Transfer time constraints between adjacent process nodes are verified to prevent sample deterioration and keep automation accurate.

Role-based plant model displays tailor operator and site worker information in one interface, improving inspection support and coordination.

Hashing existing BOM and company codes into meaningless identifiers enables unified reference codes, rewrite detection, and traceability.

Automated module replacement raises theoretical utilization in modular plants, cutting engineering effort, module count, and reconfiguration cost.

Cyclic bus broadcasts map industrial vehicle data into reusable messages, improving status visibility and enterprise integration.

Balances sensor-data age, network congestion, and operator load to choose teleoperated vehicle maneuvers that meet latency-critical conditions.

An orchestration layer coordinates heterogeneous uncrewed assets, reassigns tasks, and fills operational gaps without adding command burden.

A five-platform Industrial IoT architecture reduces data interaction errors and processing load in multi-workshop production task control.

Domain-specific sensor mapping and workflow selection improve predictive maintenance accuracy while speeding new asset integration.

Motor current and sheet count are used to estimate total power and CO2 emissions, giving users accurate reporting without full-apparatus metering.

Monitoring index indicators replace marginal revenue and cost comparisons to track production changes more accurately across market conditions.

Estimated operating rates filter recovery priorities so workers focus on stoppages that best protect overseen equipment productivity.

Staggered machine arrival times at a worksite loading area cut congestion and idle time by adapting the separation threshold over time.

Balances opportunity loss, self-investment cost, and external resource availability to choose disaster recovery actions during blackout risk.

Team status evaluation identifies operators needing support and presents work substitution options to improve cooperative plant monitoring.

Schedules blend events for rundown components without storage tanks to meet refinery commitments while minimizing cost and quality giveaway.

Role-based alarm displays track persistent abnormalities and countermeasure status to speed PDCA response across factory operations.

Real-time sensor capture and machine learning turn manual process recording into accurate, complete, and replicable industrial recipes.

Supervisor robots use alive signaling, task selection, and takeover messaging to keep distributed fleets working in low connectivity.

Correlates object, observational, and text data from multiple machines to analyze manufacturing flow across lifecycle phases.

By comparing each feed's verification data with historical records, this case blocks duplicate material loading and preserves traceability.

Distributed robot managers split wide-area jobs into local tasks with relay-point handover, reducing coordination complexity and supporting battery-aware operation.

Generates production plans in real time by balancing order changes, cost criteria, structural constraints, and process failure probability.

Semantic and multiattribute matching links work-plan tasks to machine capabilities, reducing manual planning for faster product introduction.

Real-time store status and local characteristics are combined to tailor equipment settings and cut unnecessary power use across stores.

Tracks sustainability data across IT assets to improve inventory lifecycle planning, reduce e-waste, and limit management complexity.

First-time pick metrics flag disorganized shelf locations so staff can prioritize high-sales and core categories for faster order picking.

Inverter speed trends reveal early wear in rock drilling units, helping schedule maintenance before service breaks and interruptions occur.

Aggregated workforce and outcome data reveal benchmarks that replace intuition with objective school staffing decisions and improvement targets.

Usage records and selectable periods help predict consumable replacement timing for sample analyzers, reducing interruptions from depleted supplies.

Automatic capture structures user actions and project changes into shareable variation records without manual media creation.

Intelligent evolutionary processes reduce computational time by modeling variable relationships to identify optimal parameters without exhaustive search.

A controller compares target and actual work surface profiles using position sensor signals to generate quantitative performance factor scores.

A rollout selection system manages user cohorts across multiple deployment phases to ensure balanced exposure.

Machine learning models analyze multi-stage production data to calculate a releasability metric, replacing manual sampling with automated batch assessment.

A labor tracking system allocates elapsed time across multiple projects using user-defined fields.

Virtual custom groups enable bulk operations on network objects, reducing repetitive administrative tasks and improving operational efficiency.

A rules engine validates proposed schedule changes against predefined constraints to enable multi-user editing.

A service provider coordinates inventory transfers between merchants to optimize stock levels and delivery routes.

Aggregating resource demands and calculating capital expenditure ratios enables accurate affordability modeling across multiple software projects.

Integer linear programming balances component variances across fitting lines, reducing setup change frequency and minimizing setup families.

A dynamic impact field renders weighted ESG scores as a gradient to simplify portfolio analysis.

Controller minimizes total costs under block-and-index rate structures by segmenting resource allocation into discrete time steps.

Kinetic transformation algorithm segments large-scale initiatives into manageable modules to reduce employee fatigue and improve success rates.

A system monitors external data changes to identify event pairs and generate customized integration flow templates.

Centralized repository provides context-specific editors, reducing software maintenance complexity.

Remote server verifies operator training status to prevent unqualified hematology analyzer use and ensure result accuracy.

Automated gate assignment system schedules unloading resources based on product priority and arrival estimates.

Stochastic constraint programming resolves resource consumption trade-offs by optimizing workflow completion probability within strict business constraints.

A self-contained recycling system isolates pharmaceutical containers to reform them into new unused units.

Segmented electronic data model reduces master data maintenance time by enabling reuse of manufacturing segments across different products and facilities.

A reverse scheduling algorithm fixes activity due dates and reschedules preceding tasks backward to minimize order lead times.

System aggregates orders and aligns textile panels to minimize material waste while maintaining production speed in on-demand apparel manufacturing.

A job matching system normalizes diverse assessment data into standardized suitability scores for direct comparison with employer position quotients.

A mixed integer programming system generates updated shift candidates and restarts the solver to minimize computational overhead.

Automated resource tracking system eliminates manual report collation time by using text mining to align diverse tool data with business objectives.

A computer-implemented method simulates inventory needs by determining probability distributions for lead time and demand.