Integrated platforms are increasingly critical in Industrial Engineering to support decision-making in complex environments. This paper presents the design, architecture, and implementation of the INDEngine Suite, a modular framework developed to unify key industrial engineering tools within a single environment, combining optimization, forecasting, quality management, and simulation.

The research established a three-layer architecture to meet core functional requirements. The User Interaction Layer was designed with secure authentication, a centralized dashboard, and sidebar navigation for seamless accessibility. The Functional Module Layer comprises thirteen core modules, such as Production Scheduling, Forecasting, Economic Order Quantity (EOQ), Inventory Management, Quality Control, Six Sigma, Design of Experiments (DOE), Simulation, IoT Monitoring, and Berth Optimization. Each module was structured with input, analysis, and output tabs, to enhance structured learning. The Integration and Backend Utility Layer was implemented to manage data exchange, visualization, authentication, and database connectivity to ensure stability and interoperability.

A core finding of this work is the efficacy of its interconnected decision-making. For instance, forecasting outputs directly inform EOQ calculations, which subsequently influence inventory and scheduling modules. Similarly, quality control data drives Six Sigma and Design of Experiments (DOE) modules, creating a closed-loop system for continuous improvement. Furthermore, IoT monitoring provides real-time data awareness, while simulation tools enable the validation of planning decisions. This integrated design was found to effectively mirror real-world industrial systems, providing significant utility for both academic instruction and practical analysis.

This research demonstrates a viable path for aligning engineering education with strategic initiatives like Saudi Arabia’s Vision 2030, promoting digital transformation, innovation in higher education, and industrial efficiency. Future research will investigate artificial intelligence integration, enhanced optimization models, and cloud-based deployment.

Keywords: Industrial Engineering, Decision Support Systems, Educational Digital Twin, Forecasting, Scheduling, Quality Control, Saudi Vision 2030.

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Mr. Ahmed Abdelaal