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Scenarios

Configure allocation scenarios and understand data sources

Active Scenario

How Scenarios Work

Scenarios provide context for allocation decisions. The selected scenario influences:

Default threshold values
Risk assessment weights
Compliance requirements
Audit documentation levels

Software Development Scenario

Data Sources

In a production environment, MCAT integrates with your existing tools to extract decision context automatically:

Ticketing Systems

Jira / Linear / GitHub Issues - Issue type, priority, story points, labels
Extracted features: complexity estimate, affected components, linked incidents

Version Control

GitHub / GitLab / Bitbucket - PR size, files changed, test coverage
Extracted features: code churn, author experience, review history

Security & Quality Tools

SonarQube / Snyk / Dependabot - Vulnerability severity, code smells
Extracted features: security score, technical debt, dependency risks

CI/CD & Monitoring

Jenkins / GitHub Actions / DataDog - Build status, deployment frequency
Extracted features: failure rate, rollback history, incident correlation

Decision Types

Typical software development decisions and their recommended allocation modes:

How Jira Fields Map to MCAT Dimensions

MCAT Dimension	Jira/Ticketing Fields	Example Calculation
Uncertainty (U)	Story points, description clarity, acceptance criteria completeness	`U = 1 - (criteria_count / expected_criteria) * clarity_score`
Novelty (N)	Labels, components, similar resolved issues count	`N = 1 - min(similar_issues / 10, 1.0)`
Value Conflict (W)	Watchers count, comment threads, priority changes, linked blockers	`W = (watchers * 0.1) + (priority_changes * 0.2) + has_blockers`
Data Quality	Required fields filled, attachments, reproduction steps	`DQ = filled_fields / total_required_fields`
Confidence	Historical success rate for issue type, assignee track record	`C = type_success_rate * assignee_success_rate`

Financial Services Scenario

Data Sources

In a production environment, MCAT integrates with financial systems:

Transaction Systems

Core Banking / Payment Gateways - Transaction amount, frequency, location
Extracted features: deviation from patterns, velocity checks, amount thresholds

Risk & Compliance

AML/KYC Systems - Customer risk scores, watchlist matches
Extracted features: compliance flags, regulatory requirements, audit trails

Analytics & Fraud Detection

ML Models / Rules Engines - Fraud scores, anomaly detection
Extracted features: model confidence, rule triggers, historical patterns

Customer Data

CRM / Account Systems - Customer tenure, account history
Extracted features: relationship value, complaint history, product holdings

Decision Types

Typical financial services decisions and their recommended allocation modes:

Healthcare Scenario

Data Sources

In a production environment, MCAT integrates with healthcare systems:

Electronic Health Records

EHR/EMR Systems - Patient history, vitals, medications
Extracted features: condition severity, comorbidities, allergies

Clinical Decision Support

CDSS / Diagnostic Tools - Differential diagnoses, treatment protocols
Extracted features: guideline compliance, drug interactions, risk scores

Imaging & Diagnostics

PACS / Lab Systems - Radiology reports, lab results
Extracted features: abnormality flags, trend analysis, critical values

Scheduling & Workflow

HIS / Scheduling Systems - Appointments, bed management
Extracted features: urgency levels, resource availability, wait times

Decision Types

Typical healthcare decisions and their recommended allocation modes:

Manufacturing Scenario

Data Sources

In a production environment, MCAT integrates with manufacturing systems:

Industrial IoT & SCADA

Sensors / PLCs / SCADA - Temperature, pressure, vibration, speed
Extracted features: anomaly scores, trend deviations, threshold breaches

MES & ERP Systems

SAP / Oracle / Custom MES - Production orders, inventory, schedules
Extracted features: order priority, resource utilization, lead times

Quality Management

QMS / SPC Tools - Inspection results, defect rates, SPC charts
Extracted features: Cp/Cpk indices, control limit violations, trends

Maintenance Systems

CMMS / Predictive Maintenance - Work orders, asset health, failure predictions
Extracted features: RUL estimates, failure probability, maintenance history

Decision Types

Typical manufacturing decisions and their recommended allocation modes:

Import a Scenario Disabled

Importing scenarios is temporarily disabled. A scenario is a JSON file (pure data — no code). View JSON schema ·