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All Journal Letters in Information Technology Education (LITE) Indonesian Journal of Innovation Science and Knowledge
Chidiebube, Igbokwe Nkemakonam
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Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow Nwamekwe, Charles Onyeka; Vitalis, Ewuzie Nnamdi; Chidiebube, Igbokwe Nkemakonam; Nwabunwanne, Emeka Celestine; Ono, Chukwuma Godfrey
Letters in Information Technology Education (LITE) Vol 8, No 1 (2025)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v8i12025p1-13

Abstract

This research investigates the integration of Digital Twin (DT) technology within Lean Manufacturing frameworks to optimize value stream flow, minimize waste, and enhance real-time decision-making capabilities. By synthesizing foundational concepts of Lean Manufacturing and DT, the paper examines the layered DT architecture, covering the physical, virtual, and communication interfaces, alongside Lean tools like Kaizen, Kanban, and Just-in-Time (JIT) that facilitate continuous process improvement. Case studies, particularly in the automotive sector, demonstrate DT's ability to increase production efficiency through predictive maintenance and simulation-based scenario planning, supporting Lean's waste reduction objectives. However, the paper identifies key implementation challenges, including legacy system integration, workforce adaptation, and data interoperability. Additionally, cybersecurity and data integrity concerns are analysed to highlight essential protocols for safe DT deployment. Future research directions propose advancements like AI-powered DTs, blockchain for enhanced traceability, and edge computing for low-latency applications. Key insights from industry case studies underscore the transformative impact of DTs on production efficiency, organizational resilience, and sustainable manufacturing outcomes, positioning Digital Twin technology as a cornerstone for next-generation lean manufacturing systems
A Comparative Analysis Of Machine Learning Models For Inventory Demand Forecasting In A Food Manufacturing Sme Chidiebube, Igbokwe Nkemakonam; Onyeka, Nwamekwe Charles; Sunday, Aguh Patrick; Chiedu, Ezeanyim Okechukwu
Indonesian Journal of Innovation Science and Knowledge Vol. 2 No. 3 (2025): IJISK 2025
Publisher : Fakultas Pendidikan Ilmu Keguruan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijisk.v2i3.177

Abstract

Inventory demand forecasting is vital for small and medium enterprises (SMEs) in the food manufacturing sector to maintain optimal stock levels, reduce waste, and improve operational efficiency. Traditional statistical methods often fail to capture complex demand patterns, necessitating the adoption of advanced machine learning (ML) approaches. This study conducted a comparative analysis of four ML models Long Short-Term Memory (LSTM), Facebook Prophet, XGBoost, and Gradient Boosting Regressor using a three-year dataset (January 2020–December 2022) from a Nigerian food manufacturing SME. The dataset included monthly demand records for thirteen product categories. Models were evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). LSTM consistently outperformed other models, achieving the lowest RMSE and MAE values and the highest R² scores, demonstrating superior capability in capturing non-linear and temporal demand patterns. Facebook Prophet and Gradient Boosting performed moderately, with Prophet offering higher interpretability. XGBoost showed the weakest predictive performance across all metrics. The findings indicate that LSTM is the most effective model for inventory demand forecasting in SMEs with dynamic demand profiles. Incorporating advanced ML techniques like LSTM can enhance forecasting accuracy and support strategic inventory management decisions in food manufacturing SMEs.
Co-Authors Chiedu, Ezeanyim Okechukwu Nwabunwanne, Emeka Celestine Nwamekwe, Charles Onyeka Ono, Chukwuma Godfrey Onyeka, Nwamekwe Charles Sunday, Aguh Patrick Vitalis, Ewuzie Nnamdi