Management Analytics for Smart Manufacturing Transformation: A Product Lifecycle Intelligence Framework
Main article
Abstract
Smart manufacturing has advanced quickly, yet managerial adoption remains uneven because intelligence is often deployed as isolated applications rather than as a coordinated product-lifecycle system. This article develops a management-analytics perspective on smart manufacturing transformation and proposes a Product Lifecycle Intelligence framework that links strategy and organization, value-chain intelligence, management support processes, and infrastructure-capability layers through database-centered analytics. Instead of treating artificial intelligence, digital twins, industrial internet systems, and cloud-edge platforms as separate technologies, the framework interprets them as components of a common lifecycle data architecture. To operationalize the framework, the article introduces a structured scoring design across four lifecycle domains and five analytical dimensions: data integration, interoperability, decision analytics, resilience, and human-AI readiness. An illustrative management-analytics assessment is used to compare current readiness levels, identify integration gaps, and prioritize transformation actions under different enterprise scenarios. The results show that infrastructure capabilities usually mature faster than strategy-to-execution coordination, while the largest system-wide bottleneck lies in closed-loop integration between market intelligence, design knowledge, operations control, and managerial governance. The analysis further indicates that smart manufacturing programs create the greatest managerial value when database standardization, cross-functional metrics, and human-AI governance are developed together rather than sequentially. The article contributes by reframing smart manufacturing as a management-analytics problem, offering a lifecycle-wide intelligence architecture, and providing a practical roadmap that can guide enterprise transformation beyond fragmented pilot projects.
