{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T11:17:10Z","timestamp":1771240630470,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24992975"],"award-info":[{"award-number":["BR24992975"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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 \u201cLean 4.0\u201d 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\u2013InfluxDB (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.<\/jats:p>","DOI":"10.3390\/info17020196","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:14:32Z","timestamp":1771236872000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Digital Twin Architecture for Integrating Lean Manufacturing with Industrial IoT and Predictive Analytics"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3933-5476","authenticated-orcid":false,"given":"Gulshat","family":"Amirkhanova","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9306-1763","authenticated-orcid":false,"given":"Shyrailym","family":"Adilkyzy","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-0347","authenticated-orcid":false,"given":"Bauyrzhan","family":"Amirkhanov","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6109-8174","authenticated-orcid":false,"given":"Dina","family":"Baizhanova","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}]},{"given":"Siming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Matias, F., Miranda, S., Yildiz, O., Ch\u00e1vez, P., and Alvarez, J.C. 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