Design and Implementation of the Digital Quality Intelligence Platform: A Modular Decision-Support Software Framework for Engineering Quality Intelligence
Abstract
Engineering organizations progressively generate large volumes of heterogeneous quality data through inspection records, laboratory tests, production logs, non-conformance reports, statistical audits, and project documentation. However, these data are commonly processed through disconnected spreadsheets, isolated statistical utilities, and manually interpreted dashboards. Such fragmented workflows weaken traceability, delay corrective action, and limit the reuse of engineering knowledge across projects. This paper presents the design and implementation of the Digital Quality Intelligence Platform (DQIP), a modular decision-support software framework for engineering quality intelligence. DQIP integrates data ingestion, preprocessing, statistical process control, Six Sigma quality assessment, rule-based reasoning, visualization, reporting, and recommendation generation within a layered software architecture. The framework separates domain-independent services from configurable domain knowledge so that the same platform can support multiple engineering quality scenarios without redesigning the core application. The proposed architecture is organized into presentation, application, analytics, reasoning, data management, and infrastructure layers, with service interfaces that support maintainability, extensibility, and independent module evolution. A prototype implementation using Python-based open-source components demonstrates the feasibility of combining engineering analytics and explainable decision support in a lightweight deployable platform. The evaluation focuses on functional adequacy, modularity, maintainability, responsiveness, interoperability, and cross-domain adaptability. Results indicate that DQIP offers a coherent software engineering solution for transforming raw engineering quality records into actionable intelligence while reducing manual workflow dependency. The platform establishes a reusable foundation for future integration with predictive models, digital twins, cloud services, and enterprise quality-management systems.