Data-Centric CRM Architecture for Smart Manufacturing Ecosystems: A Framework for Lifecycle-Driven Customer Engagement

Abstract

Introduction of Industry 4.0 has radically restructured the manufacturing environment by connecting machines, streams of real-time data, and intelligent production systems, but the old platforms of customer relationship management remain independent of the intelligence in the architecture to provide active customer relationships in a lifecycle fashion. This model solves the crucial disengagement between operating systems and CRM potential by suggesting a detailed data-centric CRM design that has been tailored to the intelligent manufacturing ecosystem. Grounded in Socio-Technical Systems Theory, Digital Ecosystem Design, and the Resource-Based View, the framework conceptualizes a five-layer architecture encompassing data ingestion from IoT sensors and enterprise systems, integration of operational and customer intelligence through master data management, predictive analytics modules for proactive service interventions, CRM workflow translation of insights into engagement actions, and governance mechanisms ensuring quality, compliance, and trust. Four conceptual propositions establish theoretical relationships between architectural characteristics and organizational outcomes, linking data quality and integration completeness to predictive service accuracy, architectural integration to lifecycle engagement capabilities, predictive intelligence deployment to governance requirements, and socio-technical alignment to implementation effectiveness. The framework extends CRM scholarship into manufacturing contexts where operational data convergence creates novel engagement possibilities, positions CRM architecture as ecosystem infrastructure enabling controlled data exchange across manufacturing networks, and emphasizes that competitive advantage flows from integration architecture completeness rather than individual system sophistication. Strategic implications also emphasize data integration capabilities as important resources, lifecycle-dependent engagement as a particular orientation that needs organizational change, governance as a strategic enabler that ensures trust and transparency, and technical capabilities as the socio-technical fit that enhances performance. Managerial advice consists of creating data infrastructure before beginning to employ analytics, concentrating on two-way data movement between operational and CRM systems, and setting governance next to analytical skills, deliberately investing in organizational alignment, and executing in a modular, iterative implementation plan.