Instruments TRIZ Cases

Predictive supply-demand models rank future surge opportunities and adjust compensation while supporting budget adherence.

Vehicle sensors and telematics compare driver actions with delivery protocols, providing real-time coaching and precise drop-off guidance.

This case coordinates mail servers, clients, and managed services to route, archive, and recover messages across environments.

Gradient-trained transfer functions reduce optical aberration noise in hologram restoration.

A machine-learned allocation model matches order requirements with shopper capabilities across time windows, reducing fulfillment delays.

User-defined checks validate datasets before processing and publication.

This case uses master-plan hierarchies and feedback to regenerate workflows as employee availability changes.

A stable diffusion classifier uses cross-attention scores to classify variable classes without domain-specific training.

Multi-stage 360-degree feedback measures self-awareness gaps in leaders.

Visualize demand, capacity, and logistics constraints in automotive production plans.

Visual risk graphs help investigators trace relationships and assess exposure faster.

This case combines staff tracking, ratings, predictive analysis, and proactive shift outreach to fill complex rosters efficiently.

A guided attention transfer network adapts target models without source data across classification, detection, and segmentation.

Point estimates can hide prediction certainty; FTRL gradient updates provide model-agnostic uncertainty scores for online decisions.

Queue monitoring on mobile devices predicts optimal joining times and guides users to locations, reducing time spent waiting in line.

Block-based calendar integration synchronizes project tasks and timelines across workspace pages, reducing manual updates for teams.

Measure cathodic protection current and assess coating, surface area, and exposure time to guide pipeline maintenance.

Automated monitoring correlates communication interruptions with power outage data, reducing manual triage and network downtime.

Forecasting detects data patterns and assigns location-specific tasks to users, turning analytics into timely action.

This case uses distribution center and store constraints to validate daily delivery schedules, reducing variability and bottlenecks.

Manual scheduling and rigid templates become adaptive itineraries through preference matching, constraint checks, and iterative user refinement.

Build sheets automate network component provisioning and reduce configuration errors.

A workspace orchestration service predicts client needs and creates adaptive workspace definitions to balance security with resource use.

This case scores driver routes from delivery outcomes, then presents lower-vibration paths to reduce item damage without manual review.

A digital recycling platform automates object scanning and valuation while supporting flexible collection and user collaboration.

The case splits candidate loads and uses parallel mixed-integer optimization to reduce costs while respecting facility capacity.

A confidence evaluator switches to an undertrained network below a threshold, helping classify images unlike the training samples.

Map customer clusters to supply chain models, tailoring service packages while keeping operational complexity manageable.

An AI accounting system segments product emissions, estimates routes, and recommends carbon reductions while balancing financial costs.

Carrier-specific scheduling adapts API polling to rate limits, keeping shipment records fresh for timely notifications.

This case combines chronotype data, time zones, and scheduling timelines to reduce meeting iterations and improve event timing.

This case separates automated G2P picking from shoppers while replenishing inventory from store shelves for dense, accurate fulfillment.

Individual background classes and ignore attributes improve detection accuracy when combining datasets for neural network training.

A tracing matrix connects process steps, objects, and attributes to track changes and identify multi-variant causes of adverse events.

Standardize multi-network delivery tracking and verify ETA reliability.

Real-time KPI analysis reallocates human and compute resources across processes.

Task and data flow graphs pre-position files at edge locations, while reduced-fidelity versions keep distributed users working.

An SSD engine sequences staging and delivery tasks through a GUI to reduce curbside and in-store pickup wait times.

A completeness graph fills missing entity attributes through workflow tasks, supporting scalable models without external synchronization.

A channel-split vision transformer combines depthwise convolution with partial self-attention to reduce edge inference cost.

This ACCF approach replaces periodic manual audits with continuous, enterprise-wide risk monitoring and compliance reporting.

Generative AI and tiered queuing structures capture spatial-temporal correlations while limiting error accumulation in demand forecasts.

Probabilistic damage forecasting guides dynamic inspection and maintenance decisions.

Task forms populate records across partitioned devices, improving processing efficiency, compatibility, and data security.

Individually addressable LED packages use left- and right-eye polarizer alignments to clarify 3D images and reduce headaches.

Stochastic modeling replaces ordinal ratings with measurable loss, detection, containment, and recovery factors for ROI analysis.

The delivery system tracks a user's device location and updates routing, with rescheduling or fixed-address fallback when needed.

The case uses category-level outcome models and price adjustments to optimize revenue while enforcing constraints across pricing policies.

Drones scan physical target codes and dispense supplies at marked locations, supporting aid delivery without long-range communication.

A processor receives policies and infraction records, requests resolution data, and communicates notices with feedback.