Problem Statement

• Siloed tooling across mainframe, plant networks, SD-WAN, cloud apps, and ERP created conflicting signals and slow incident triage.
• Executives lacked a single, standardized reliability score to compare diverse services and plants.
• NOC teams faced high alert volumes with little business context; effort-to-impact was unclear.
• Leadership demanded forecastable reliability ROI and transparent progress—without “techy jabber.”

Architecture

• DX NetOps/OI ingests flow, SNMP, SD-WAN, and performance metrics from plant, WAN, and data center.
• Exports curated alarms/metrics to Scout-itAI via secure connectors.

• RPI Engine (13 buckets) condenses thousands of KPIs into a single, standardized reliability score per business service/plant
• Blender (Six Sigma) correlates alarms + metrics in real time to isolate performance-impacting patterns.
• Trender (KAMA) tracks 100-day baselines to detect slow degradations and early drift.
• Predictor (Monte Carlo) runs up to 100,000 simulations to show how changes (capacity, patching, topology) shift future RPI.
• Agentic Workforce (orchestrator + sub-agents) automates root-cause hypotheses, escalation, and safe self-corrections with governance.
• Plain-Language Insights translates telemetry into business risk statements and recommended actions.

• Bidirectional hooks with ITSM (e.g., ServiceNow) for auto-ticketing and playbook execution.
• Executive scorecards and service heatmaps exposed to CIO/COO; NOC dashboards for real-time action.
• Cloud-agnostic coverage (AWS/Azure/GCP/on-prem) with up to 12 months history.

Results & Outcomes

• MTTR ↓ 41% via AI-guided triage and automated playbooks.
• P1/P2 incident volume ↓ 28% after Six Sigma-based noise reduction and correlation.
• Alert fatigue ↓ ~60% by focusing operations on 13 RPI buckets tied to business impact.
• SLA breach risk ↓ 32% as Trender exposed slow degradations before they became outages.
• Executive alignment: Monthly RPI scorecards enabled plant-to-plant and service-to-service comparisons in one number, boosting trust and funding for reliability work.
• Predictable planning: Monte Carlo forecasts quantified reliability ROI for network upgrades and patch windows, improving change-adoption decisions.

TCO & Operational Efficiency

• Tool Consolidation: Leverage Broadcom NetOps as the authoritative network signal, unify with app/infrastructure telemetry inside Scout-itAI—fewer overlapping licenses and dashboards.
• People Efficiency: Agentic automation offloads repetitive triage, freeing senior engineers for systemic fixes.
• Incident Cost Avoidance: Fewer high-severity incidents and faster recovery reduce downtime costs on production lines.
• SLA breach risk ↓ 32% as Trender exposed slow degradations before they became outages.
• Cloud & Data Costs: Smart sampling and reliability-bucket focus limit data egress and storage while preserving decision-grade fidelity.
• Adoption & Training:Plain-language insights shorten onboarding; RPI makes non-technical reviews efficient (less analyst time to prep decks).

Lessons Learned

Start with Business Services: Map plants/lines and critical apps to RPI early; don’t boil the ocean.

Govern the Agents: Define safe-action guardrails and rollback paths; measure success per action.

Baseline Before You Tune: Use KAMA-based trends for ~100 days to separate structural issues from noise.

Make It Executive-Ready: Lead with RPI and risk statements; keep metric firehoses behind the scenes.

Forecast to Fund: Use Monte Carlo deltas to justify network and capacity investments with quantified reliability ROI.

Close the Loop: Pipe plain-language recommendations into ITSM playbooks and track outcome KPIs (MTTR, change success rate, RPI lift).