Prysma CAUSALITY - Root Cause Analysis Methodology

What Is Prysma CAUSALITY?

Prysma CAUSALITY is not a generic root cause analysis tool. It is a causal integration platform engineered to eliminate the recurrence of industrial failures by enabling investigations that scale seamlessly from physics-of-failure to organizational design, depending on where the true drivers of failure reside.

The Problem with Traditional RCA

Most RCA methods fail because they are structurally constrained—either stuck in technical details or limited to superficial human-factor checklists. Prysma CAUSALITY breaks this pattern by providing both the methodological rigor and the technological flexibility to follow the evidence wherever it leads, without artificial boundaries between disciplines.

1. Follows the Full Causal Chain—From Molecule to Management

Failure is rarely isolated to a single domain. At its origin may be material fatigue (physics), but its recurrence is often enabled by inadequate inspection intervals (process), poor data quality (information systems), or misaligned performance metrics (organization).

Prysma CAUSALITY enables teams to map all layers:

Physical Layer: Metallurgical degradation, vibration patterns, seal erosion
Human Layer: Operator actions, maintenance execution, procedural adherence
Systemic/Organizational Layer: Incentive structures, training gaps, resource allocation, decision-making protocols

This multi-level diagnosis ensures that corrective actions break the entire causal chain—not just one link. An investigation doesn't stop at "bearing failure due to lubrication loss"—it continues to ask: Why was lubrication missed? Was the procedure unclear? Was there pressure to skip steps? Does the maintenance schedule conflict with production targets?

2. Deep Integration with Physics-Based Models When Needed

When the root lies in failure physics, Prysma CAUSALITY integrates directly with advanced analytical tools to validate hypotheses. The platform connects to reliability modeling systems, degradation analysis tools, and physics-based simulation environments.

This allows investigators to:

Import degradation curves from failure prediction models
Validate time-to-failure assumptions against physics-based data
Test sensitivity to operating conditions using simulation results
Link equipment stress profiles to lifecycle cost (LCC) models

3. Structured Logic Trees Prevent Premature Closure

One of the most common failure modes in RCA is premature conclusion—stopping at the first plausible explanation. Prysma CAUSALITY combats this through formal logic tree construction:

Every hypothesis must be tested and verified
Assumptions are flagged and challenged systematically
Alternative pathways are explored before conclusions
Evidence must be attached for each causal node
Traceability back to original data sources (SCADA, work orders, inspection logs)

This prevents "blame culture" and forces objectivity. If a bearing failed, the system asks: Was it overload? Misalignment? Poor lubricant? Contamination? Each branch is evaluated independently until the dominant mechanism is confirmed.

4. Organizational Root Causes Are Not Optional—They Are Required

Many tools treat organizational factors as an afterthought. Prysma CAUSALITY makes them mandatory investigative territory. Investigations must identify latent organizational causes that create conditions for failure.

Examples include:

Maintenance strategies based on calendar time instead of condition monitoring
Performance indicators that reward uptime but penalize downtime reporting
Training programs that don't reflect actual operating complexity
Asset management processes disconnected from financial planning (LCC)

These are not "soft" issues. They are systemic vulnerabilities that allow technical failures to recur. By embedding organizational analysis into the methodology, Prysma CAUSALITY ensures that solutions go beyond replacing parts—they fix broken systems.

5. Closed-Loop Verification Ensures Recurrence Is Actually Prevented

Most RCA processes end when a report is approved. Prysma CAUSALITY ends only when effectiveness is proven. Corrective actions are tracked with:

Owner assignment and accountability
Implementation deadline tracking
Real-time status monitoring
Post-intervention verification protocols

The goal is not just action completion—it's non-recurrence. This requires follow-up:

Has the failure reoccurred since corrective action?
Has system availability improved measurably?
Have warranty claims or rework rates decreased?

6. Built for Cooperative Analysis Across Disciplines and Sites

Failures span departments and locations. So should the investigation. Prysma CAUSALITY runs on the SharkIP® platform, a proprietary architecture designed for cross-functional collaboration.

This enables:

Remote experts to contribute to field investigations
Centralized oversight while empowering local teams
Consistent application of methods across sites
Knowledge transfer without creating dependencies

7. Designed for Continuous Improvement, Not One-Time Fixes

Prysma CAUSALITY treats every investigation as part of a larger performance improvement assurance process. It enables:

Trend analysis across failure events
Benchmarking performance across units or sites
Sensitivity testing of proposed corrective actions
Integration with reliability and maintenance systems

This transforms RCA from a reactive exercise into a proactive resilience engine.

Implementation Process

Event Definition: Clearly define the failure event, its impact, and initial observations
Data Collection: Gather physical evidence, operational data, maintenance records, and organizational context
Causal Chain Mapping: Build logic trees across physical, human, and organizational layers
Hypothesis Testing: Validate each causal link with evidence, physics-based models, or expert analysis
Root Cause Identification: Identify all root causes (direct, latent, organizational) that enabled the failure
Corrective Action Design: Develop actions that break the entire causal chain, not just one link
Implementation Tracking: Monitor corrective action execution with accountability and deadlines
Effectiveness Verification: Validate that recurrence has been prevented through operational data

Case Study: Chemical Processing Facility Reliability Improvement

Challenge

A chemical processing facility experienced recurring failures in a critical regeneration unit, resulting in production losses and safety concerns. Traditional RCA efforts had identified equipment-level causes but failures continued to recur, suggesting deeper systemic issues.

Prysma CAUSALITY Application

Conducted multi-layer investigation from equipment physics to organizational decision-making
Integrated degradation models with operational data to validate failure mechanisms
Identified organizational root causes including conflicting performance metrics
Developed corrective actions spanning equipment upgrades, procedural changes, and maintenance strategy modifications
Implemented closed-loop tracking with effectiveness verification metrics

Outcome

The systematic approach revealed that while equipment degradation was the immediate cause, organizational factors (maintenance scheduling conflicts, inadequate condition monitoring, and production pressure) created the conditions for recurrence. By addressing all causal layers, failure recurrence was eliminated and unit availability improved significantly.

When to Use Prysma CAUSALITY

This platform is essential when:

→Failures continue to recur despite previous RCA efforts and corrective actions
→High-impact events require investigation across technical, human, and organizational domains
→Complex systems require integration between failure physics and business processes
→Organizations need to build institutional capability for systematic failure prevention
→Regulatory or safety requirements demand rigorous, traceable causal analysis

Why Prysma CAUSALITY Works

Industrial failures do not respect silos. Neither does Prysma CAUSALITY. Its effectiveness comes from three core strengths:

Depth: It allows you to go as deep as needed—down to material science or up to corporate governance
Integration: It connects technical findings to business outcomes via traceable logic (PDEL®)
Discipline: It enforces verification, accountability, and closure until recurrence is proven to be prevented

Unlike black-box AI tools or templated RCA software, Prysma CAUSALITY doesn't hide complexity—it structures it so decisions become inevitable, not arbitrary. It doesn't just analyze failure. It engineers environments where failure cannot recur.

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