A Digital Twin Architecture for Integrating Lean Manufacturing with Industrial IoT and Predictive Analytics
Amirkhanova G. Adilkyzy S. Amirkhanov B. Baizhanova D. Chen S.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Information (Switzerland)
2026#17Issue 2
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.
bakery manufacturing , cyber physical systems , digital twin , energy optimisation , hybrid flow shop , industrial internet of things (IIoT) , lean manufacturing , machine learning , predictive analytics
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Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Data Science, Fudan University, Shanghai, 200433, China
Department of Artificial Intelligence and Big Data
School of Data Science
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