Automated SPC & Quality Reporting with Agentic AI

Executive Summary 

A global manufacturing enterprise faced rising quality-related costs, compliance risks, and inefficiencies in reporting due to manual statistical process control (SPC) practices. Quality engineers spent hours compiling charts, validating data, and preparing reports that were ready for audit. 

By deploying Agent Analyst and Agent Instruct on a context-first, agentic intelligence infrastructure built using NexaStack’s Unified Inference Engine, Composable Agent Framework, AgentOps, and Secure Private & Edge Deployment, the company automated SPC monitoring and quality reporting. 

• Agent Analyst ingests data from MES/ERP and applies real-time SPC metrics, leveraging on-device intelligence and physical AI via NexaStack’s Edge AI & Private Cloud Compute, detecting anomalies and out-of-control points. 

• Agent Instruct uses NexaStack’s observability & evaluation layer, model risk management, and alignment & safety by design to format, validate, and distribute audit-ready reports, ensuring regulatory compliance (ISO, FDA, etc.) and full traceability. 

This shift achieved: 

• ~70% reduction in manual SPC reporting time through agentic workflows and automation via AgentOps. 

• ~40% improvement in defect and deviation detection thanks to continuous inference and anomaly forecasting using NexaStack’s RL as a Service and reinforcement-learning-based adaptive agents. 

• Zero non-compliance findings in subsequent audits, made possible by using private cloud compute, full audit trails, policy-driven guardrails, and model governance features inherent in NexaStack. 

As a result, the enterprise strengthened product quality, reduced rework and scrap costs, boosted customer confidence, and positioned itself with a scalable, secure, agentic AI foundation for future growth and innovation. 

Customer Challenge 

Business Challenges 

The customer, a global producer of precision components, struggled with outdated SPC practices and reporting workflows: 

• Manual SPC tracking: Engineers manually plotted control charts, which delayed the detection of quality deviations. 

• High compliance burden: Generating audit-ready documentation consumed a significant amount of time and resources. 

• Data silos: Quality data is spread across spreadsheets, MES, and ERP systems; a lack of a unified inference platform results in inconsistent metrics. 

• Reactive quality response: Out-of-control processes are often detected too late, resulting in scrap, rework, and operational inefficiencies. 

Business goals included: 

• Enhance real-time detection of process deviations by leveraging edge AI and on-device intelligence. 

• Reduce time and cost in generating audit reports through automated agentic workflows (AgentOps). 

• Ensure continuous compliance with ISO, FDA, and customer quality standards, using model risk management and alignment & safety by design. 

• Standardise quality monitoring across plants and regions using a unified inference engine and private cloud compute so that all agents run under the same agentic framework. 

Existing Solution Limitations 

• No automation in SPC charting or anomaly detection; dependency on manual rules rather than adaptive, learning-based agents. 

• Report preparation and validation are manual; there is a lack of observability, evaluation, and traceability. 

• Disconnected systems: MES/ERP data not integrated into a centralised, agentic AI infrastructure. 

• Inconsistent data formats; no standard pipeline for ensuring data quality across plants. 

• Weak audit trails; insufficient policy-driven guardrails and model governance. 

• Scalability problems: inability to process large volumes and multiple lines, high latency without edge / on-device inference. 

Partner Solution 

Solution Overview 

The enterprise implemented Agent Analyst and Agent Instruct, leveraging the NexaStack platform to deliver a fully agentic SPC & quality reporting system: 

• Agent Analyst: Ingests MES/ERP data; calculates SPC metrics; detects anomalies and out-of-control events with real-time inference; leverages edge AI, on-device intelligence, vision AI (where visual inspection is needed), and uses RL as a Service for trend forecasting. 

• Agent Instruct: Validates data, aligns with compliance policies; uses observability & evaluation layers, model risk management, alignment & safety by design; formats audit-ready reports; distributes via automated agentic workflows in AgentOps. 

Together, the agents enabled: 

• Real-time monitoring of process quality across lines and plants. 

• Automatic flagging of deviations, trends, and emerging drifts. 

• Automated report generation and secure distribution with full audit logs. 

• Enterprise-wide visibility into quality performance via unified dashboards built on NexaStack’s architecture. 

Targeted Industries & Use Cases 

Industry	Use Cases	Value Delivered
Manufacturing	SPC, visual defect detection, predictive maintenance	Higher yield, fewer defects, audit compliance, using Private Cloud Compute and Physical AI
Automotive & Aerospace	Quality checks, safety compliance, zero defect shipments	Reduced warranty costs; leveraging Embodied AI & vision AI for precise inspections
Pharmaceuticals & Life Sciences	GMP process control, regulatory compliance with FDA/ISO	Strong governance, model risk management, and full traceability
Food & Beverages	HACCP compliance, production monitoring	Ensured safety & regulatory integrity with edge AI & private compute
Semiconductors & High-Tech	Cleanroom SPC, wafer fab yield, defect inspection	Faster detection, reduced scrap via vision AI + RL agents

Solution Approach 

 
Monitoring & Detection 

• Agent Analyst ingests production data in real-time from MES/ERP and sensors. 

• Calculates SPC metrics, control limits, and trends via unified inference. 

• Detects out-of-control points and anomalies using vision AI and statistical thresholds. 

• Uses edge/compressed models and on-device intelligence where latency & privacy matter. 

Forecasting & Decision-Making 

• Uses reinforcement-learning agents under NexaStack’s RL as a Service to forecast process drift and tool wear. 

• Recommends corrective actions (adjust machine settings, schedule maintenance). 

• Supports human-in-the-loop intervention in case of high-severity issues. 

Automated Quality Reporting 

• Agent Instructs formats SPC charts and audit logs using standardised templates. 

• Validates compliance to regulatory/customer standards (ISO, FDA, customer contracts) using policy-based controls via model risk management and alignment & safety features. 

• Distributes reports automatically through AgentOps workflows, with full audit trails and secure private cloud deployment. 

Impact Areas & Results 

Model / AI Metrics 

• Reduced false alarms by ~35% using learning-based anomaly detection vs rigid rule-based. 

• Continuous retraining and evaluation improved accuracy and reduced drift. 

Data & Infrastructure 

• Unified SPC, MES, and ERP data into a centralised quality layer over NexaStack’s private cloud compute and edge AI architecture. 

• Increased trust in quality metrics through observability & evaluation layers and policy compliance. 

Workflow / Operational 

• Automating SPC → anomaly detection → reporting → distribution cut manual reporting time by ~70%. 

• Instant visibility and alerting across plants and production lines. 

Business Benefits 

• ~40% improvement in detection of quality issues. 

• Significant reduction in scrap, rework, and delays. 

• Zero audit non-compliance findings leveraging NexaStack’s secure, governed infrastructure. 

• More substantial customer confidence, improved product quality, and better ROI.

Lessons Learned & Best Practices 

• Standardising data collection, ensuring data quality across MES, ERP, and sensors is foundational—without that, automated inference is unreliable. 

• Cultural alignment is vital: QA engineers must trust agentic AI alerts and forecasts rather than relying on manual patterns. 

• Early integration with existing infrastructure (MES/ERP, control systems) is critical for a smooth deployment. 

• Governance, auditability, and risk management must be built in from day one, via NexaStack’s observability, alignment/safety, and model risk management features. 

• Edge/on-device intelligence & private cloud compute reduce latency, preserve privacy, and are essential for regulated environments or remote deployments.  

Future Plans 

• Expand coverage across more plants globally; deploy more agents (e.g. for predictive maintenance, embodied physical AI). 

• Adopt digital quality twins to simulate process variations; integrate RL for what-if scenarios. 

• Embed Agent GRC, Agent RAI, and Agentic Identity Management modules to strengthen trust, compliance, and governance. 

• Develop multilingual dashboards and localised agentic workflows for region-specific compliance. 

• Move toward autonomous quality assurance with self-correcting processes, sustainability reporting, and full agentic operations. 

Conclusion 

By implementing Agent Analyst and Agent Instruct on NexaStack’s agentic platform, the manufacturer transformed SPC and quality reporting into a real-time, automated, secure, and compliant system. With unified inference, private cloud compute, edge and embodied AI, and strong governance, the organisation reduced costs, improved quality, and became audit-ready. In an era where Industry 4.0 demands intelligent, autonomous operations, NexaStack helped position them at the forefront.