Quality Management provides the operational spine for controlling conformance at every point where defects are created or detected. Inspection plans and checklists define what to measure, how, against which specification, and at what sampling frequency, whether the trigger is incoming goods, an in-process operation, or final release. Non-conformance reports (NCR) capture deviations the moment they are found, with disposition paths for rework, scrap, use-as-is, or return to supplier. Corrective and preventive action (CAPA) takes recurring or systemic failures through root cause analysis to verified resolution.

Statistical process control (SPC) monitors measured characteristics over time, holding processes inside control limits and surfacing drift before it becomes scrap. Supplier quality tracks the conformance of what arrives at the dock, scoring vendors and governing their approved status. Audit management plans, schedules, and records internal and external audits, linking findings back to the corrective actions that resolve them. Together these capabilities turn quality from a set of disconnected inspections into a continuous, evidenced discipline.

The data model is organised around a small set of durable entities that mirror how quality practitioners actually work. An inspection plan is a versioned specification: a set of characteristics, each with a nominal value, tolerances, a measurement method, and a sampling rule. When a plan is executed against a lot, operation, or receipt it produces an inspection result, a timestamped record of measured values and a pass or fail judgement per characteristic.

A failed result, or an issue raised independently, becomes a non-conformance report, which carries the affected item, quantity, defect classification, severity, and disposition. Where the root cause warrants systemic action, an NCR is escalated into a CAPA record holding the investigation, root cause, action plan, owners, and effectiveness check. Control charts aggregate inspection results into SPC series with calculated control limits and process capability indices. Supplier quality records and audit records complete the model, each linking back to the items, sites, and processes they concern. Every entity is defect-classified and traceable, so a single measured value can be followed forward to the CAPA it triggered and the audit finding it satisfied. The schema is built on PostgreSQL 16 through Prisma 7, which gives these relationships referential integrity rather than convention.

The module runs three interlocking workflows. The inspection cycle is the everyday path: a receipt, production operation, or shipment triggers the applicable inspection plan, an inspector or an automated gauge records results, and the item is released or held. A hold does not stall silently; it raises a non-conformance that routes to the responsible function.

The non-conformance to CAPA cycle governs remediation. An NCR is triaged, dispositioned, and, where a pattern or high severity is present, promoted to a corrective and preventive action. CAPA moves through a disciplined sequence: containment, root cause analysis, action definition, implementation, and effectiveness verification, with the record closing only once the fix is proven to hold. The audit cycle plans audits against clauses and processes, records findings during execution, and converts each finding into a tracked action so that no observation is left without an owner or a due date. Because these workflows share the same underlying records, an audit finding, a supplier defect, and a shop-floor NCR can all converge on one corrective action rather than three parallel efforts.

The functional depth of Quality Management lies in its fidelity to recognised standards and its calculated rigour. The module is structured around ISO 9001 and related quality standards, so inspection characteristics, non-conformance handling, corrective action, and audit management map directly onto the clauses an assessor expects to see. This alignment is not cosmetic: audit plans reference the requirements they test, and CAPA records document the closed-loop control that the standard demands.

On the measurement side, SPC is genuinely statistical. Control charts compute mean and range or standard-deviation limits from observed data, and process capability indices quantify how comfortably a process sits within specification. Sampling plans encode accept and reject criteria rather than leaving them to judgement. Across the module, controls are explicit: version control on inspection plans so results are always tied to the specification in force, segregation between the person who raises a non-conformance and the person who dispositions it, and mandatory effectiveness verification before a CAPA can close. The result is a quality record that withstands both an internal audit and an external certification body, because the evidence, the calculation, and the approvals are all captured in one place.

Quality Management is deliberately a bridge module, and its value grows through its integrations. It draws inspection triggers and production context from Manufacturing & Operations, so in-process and final inspections attach to real work orders and operations. With Supply Chain it governs incoming inspection and supplier quality: goods receipts raise inspections, and defect history feeds the vendor scoring and approved-supplier status that purchasing relies on.

Audit findings, controls, and conformance evidence flow into Governance, Risk & Compliance, where quality becomes part of the wider control and risk picture rather than a siloed function. Remediation and sign-off run on Flow Management, which orchestrates the CAPA and approval workflows: routing, escalation, due-date enforcement, and the review gates that move a corrective action from open to verified. Because all four modules share the same platform and data layer, a supplier defect recorded here is the same record the buyer sees, the compliance officer audits, and the workflow engine escalates, with no reconciliation or re-keying between systems.

Nashua 360 treats AI Workers as first-class users of the module, holding accounts and permissions like any other operator. In conversational use, a quality engineer can ask an AI Worker to summarise open non-conformances by defect class, trace which supplier is driving a spike in incoming rejects, or explain why a control chart breached its limits, and receive an answer grounded in the module's live data rather than a generic response.

Beyond query, AI Workers execute actions within their granted authority: opening an NCR from an out-of-tolerance result, drafting a CAPA with a proposed root cause and action plan for human review, or scheduling an audit. They run continuous anomaly and exception detection, flagging SPC drift, recurring defect signatures, and suppliers trending toward loss of approved status before a threshold is formally crossed. They perform document and data extraction, reading inspection certificates, supplier test reports, and gauge output into structured inspection results. And they act as decision support and review nodes in Flow Management workflows: an AI Worker can sit as an approval or review step on a CAPA or disposition, applying policy consistently, surfacing the evidence a human approver needs, and escalating only the cases that genuinely require judgement. The intent is not to remove the quality professional but to remove the manual overhead around them.