Industrial Internet of Things (IIoT) deployments generate high-volume operational data, yet many manufacturing systems still use this data mainly for monitoring rather than closed-loop decision support. This paper presents an integrated IIoT-optimization framework for data-driven smart manufacturing. The contribution is positioned as a conceptual and simulation-based architecture rather than a fully validated industrial deployment. The framework contains four layers: physical data acquisition, analytics and processing, optimization and decision support, and presentation/application feedback. Its Adaptive Model Integrator (AMI) is implemented as a context-aware selection policy that chooses among mathematical programming, heuristic dispatching, multi-objective optimization, model predictive control, and metaheuristic solvers according to data quality, system stability, problem structure, active objectives, and available computation time. A 720-hour synthetic MATLAB simulation of a five-line, 30-machine production system with 240 virtual sensors is used to illustrate the framework. Under the stated assumptions, the AMI-enabled configuration improves simulated throughput from 852.2 to 961.0 units/hour, reduces energy consumption from 1.198 to 1.128 kWh/unit, increases quality rate from 94.46% to 95.75%, and raises OEE from 77.39% to 84.75%. These results are reported as preliminary simulation outcomes, not as evidence of field-level industrial benefit. The paper also specifies the simulation assumptions, baseline logic, AMI decision rules, limitations, and future validation requirements.
Sami Gazem Abdullah Thabet, Mohammed Baggash· 2026 6th International Confe...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
