A Digital Twin Architecture for Integrating Lean Manufacturing with Industrial IoT and Predictive Analytics

Abstract

The convergence of Lean manufacturing and Industry 4.0 requires digital infrastructures capable of transforming high-frequency telemetry into actionable insights. However, architectures that integrate near real-time data with closed-loop process control remain scarce, particularly in the food-processing industry. This study proposes a “Lean 4.0” framework based on a six-layer Digital Twin (DT) architecture to digitise waste detection and optimise a medium-scale bakery. The methodology integrates a heterogeneous Industrial Internet of Things (IIoT) network comprising 17 ESP32 (Espressif Systems, Shanghai, China)-based monitoring nodes. Data collection is managed via an edge-centric MQTT–InfluxDB (version 2.7, InfluxData, San Francisco, CA, USA) data pipeline. Furthermore, the analytics layer employs discrete-event simulation in Siemens Plant Simulation (version 2302, Siemens Digital Industries Software, Plano, TX, USA), constraint programming with Google OR-Tools (version 9.8, Google LLC, Mountain View, CA, USA), and machine learning models (Isolation Forest and SARIMA). Multi-month validation in a brownfield bakery, including a 60-day continuous monitoring test, demonstrated that the proposed architecture reduced production cycle time by 24.4% and inter-operational waiting time by 51.2%. Moreover, manual planning time decreased by 87.4% through the use of low-code scheduling interfaces. In addition, state-based control of critical ovens reduced energy consumption by 23.06%. These findings indicate that combining deterministic simulation and combinatorial optimisation with data-driven analytics provides a scalable blueprint for implementing cyber-physical systems in food-processing SMEs. This approach effectively bridges the gap between traditional Lean principles and data-driven smart manufacturing.

More by Shyrailym Adilkyzy

• Edge-deployed digital twin services for real-time industrial sensor orchestration: architecture and case study in smart manufacturingAsia Conference on Edge Intelligence and Service Computing · 2026
• AI-powered meeting transcription and summarization system based on Jitsi Meet, Jigasi, and VoskInternational Conference on Advanced Sensing and Intelligent Systems · 2026
• Closed-Loop Digital Twin for Energy-Efficient Scheduling in Food Manufacturing SystemsAsia Conference on Cognitive Engineering and Intelligent Interaction · 2025
• Detection of Industrial Process Anomalies Using Isolation Forest and DBSCAN in a Real Manufacturing EnvironmentAsia Conference on Cognitive Engineering and Intelligent Interaction · 2025