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All Journal Journal of Exploratory Dynamic Problems Indonesian Journal of Innovation Science and Knowledge
Sunday, Aguh Patrick
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Religion Agriculture, Biological Sciences & Forestry Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Machine Learning Applications for Production Scheduling Optimization Sunday, Aguh Patrick; Emeka, Udu Chukwudi; Chukwumuanya, Emmanuel Okechukwu; Chikwendu, Okpala Charles
Journal of Exploratory Dynamic Problems Vol. 2 No. 4 (2025): Vol.2 No.4 2025
Publisher : Fakultas Keguruan dan Ilmu Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edp.v2i4.137

Abstract

Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry.
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 Chidiebube, Igbokwe Nkemakonam Chiedu, Ezeanyim Okechukwu Chikwendu, Okpala Charles Chukwumuanya, Emmanuel Okechukwu Emeka, Udu Chukwudi Onyeka, Nwamekwe Charles