Case Study #2
Stratava Logistics Group
Cross-System Logistics Exception Resolution
Evaluation of Decision Consistency Across Multi-System Environments
Company Overview
Company: Stratava Logistics Group
Industry: Third-Party & Fourth-Party Logistics (3PL / 4PL)
Scale: Multi-client logistics operator managing complex transportation and fulfillment networks
Stratava operates a centralized logistics environment coordinating shipments across multiple enterprise systems, including OMS, WMS, TMS, and carrier networks.
Operational Challenge
Stratava’s operations depended on multiple systems working together to manage fulfillment, transportation, and exception handling.
These environments included:
• Order Management Systems (OMS)
• Warehouse Management Systems (WMS)
• Transportation Management Systems (TMS)
• Routing and optimization platforms
• Operational policy frameworks
When disruptions occurred, such as delays, capacity constraints, or conflicting data, these systems often produced inconsistent or conflicting outputs, requiring manual resolution.
To improve efficiency, Stratava had introduced an AI-driven exception recommendation engine designed to suggest corrective actions during disruption scenarios.
However:
• AI recommendations frequently conflicted with system-level constraints
• Outputs varied across similar exception scenarios
• Recommendations lacked consistent policy enforcement
• Operations teams did not fully trust autonomous AI decisions
As a result:
• AI outputs were used selectively or ignored
• Manual decision-making remained the dominant process
• Exception resolution outcomes varied across teams and situations
The issue was not system availability or intelligence.
It was lack of coordinated decision validation across systems and AI.
Evaluation Context
To better understand decision behavior during exception scenarios, Stratava evaluated its cross-system exception resolution process using NEXUS alongside its existing systems and AI recommendation engine.
NEXUS operated as an observation and evaluation layer:
• Observing exception triggers and system outputs (OMS, WMS, TMS)
• Observing AI-generated corrective recommendations
• Evaluating decisions against policy, service-level, and operational constraints
• Generating a validated alternative resolution for comparison
This allowed Stratava to analyze how decisions were made across systems and AI under real operational conditions, without altering existing workflows.
Baseline Observations
Prior to evaluation, Stratava’s exception handling patterns reflected common multi-system challenges:
• Conflicting inputs between OMS, WMS, and TMS during disruptions
• AI recommendations that improved speed but introduced inconsistency
• Manual decisions that varied by operator experience
• Limited visibility into policy conflicts across systems
Exception resolution was functional, but not consistently reliable.
Evaluation Findings (Observed vs Evaluated)
During the evaluation, NEXUS compared:
• Current operational decisions
• AI-generated recommendations
• NEXUS evaluated outcomes
| KPI | Current Decisions | AI Recommendations | NEXUS Evaluated Outcomes |
|---|---|---|---|
| Cross-System Decision Consistency | 78% | 82% | 91% |
| Policy-Compliant Resolutions | 83% | 75% | 95% |
| Service-Level Adherence | 87% | 90% | 94% |
| Exception Resolution Time | Baseline | -12% faster | -15% optimized |
| Rework / Escalation Rate | 18% | 16% | 9% |
What Changed
NEXUS did not replace existing systems or AI.
Instead, it:
• Validated decisions across OMS, WMS, and TMS simultaneously
• Identified conflicts between system outputs before resolution
• Evaluated AI recommendations for policy and constraint alignment
• Applied consistent decision logic across all exception scenarios
The result was not just faster resolution. It was consistent and reliable resolution.
Key Insight
The company’s challenge was not exception handling capability.
It was:
• Multiple systems producing independently valid outputs
• AI introducing additional variability without governance
• Lack of a unified layer to evaluate decisions across systems
NEXUS resolved this by introducing cross-system decision validation before execution.
Business Interpretation
The evaluation demonstrated that:
• Exception handling variability is driven by system fragmentation, not lack of data
• AI improves speed, but without validation can degrade consistency
• A governance layer is required to coordinate decisions across systems and AI
Scenario Basis & Data Context
This scenario is constructed using real-world logistics operating conditions, multi-system enterprise benchmarks, and the expected evaluation behavior of the NEXUS Adaptive Intelligence System™. Results reflect comparative evaluation outcomes, not a production deployment.
NEXUS Pilot Program – Open Enrollment
Apply for Early Evaluation Access
Limited Pilot Access: NEXUS Adaptive Intelligence System™. For a limited time, we’re opening a small number of pilot spots.
• No license fee during the pilot
• Tier 1 pricing locked in just for signing up (even if not selected)
• Zero disruption; runs alongside your existing systems
The NEXUS Pilot evaluates how decisions move across your operations and shows where reliability breaks down before it impacts the business.
If you’re scaling automation or AI, this is the layer most teams are missing.