Journal of Production Management and Optimization

Integrating Lean and Agile Practices for Enhanced Production Efficiency in Manufacturing: A Comparative Analysis

Aldo Hermaya Aditiya Nur Karsa — Fri, 29 Aug 2025 00:00:00 +0000

This study examines the impact of integrating lean and agile (leagile) practices on production efficiency in manufacturing, with a focus on sectors such as the automotive, electronics, and consumer goods industries. Using a mixed-methods approach, data were collected from 30 companies implementing lean, agile, or combined leagile practices. Findings indicate that firms using agile practices achieved higher production efficiency, with a 25% reduction in lead times and improved responsiveness compared to firms employing only lean or agile practices. Lean-focused firms reported the highest waste reduction, while agile practices contributed significantly to operational flexibility. Despite the benefits, challenges such as high implementation costs and specialized training were noted. This study supports theories on organizational adaptability and resource-based perspectives, suggesting that integrating lean and agile practices can strike a balance between efficiency and flexibility. Practical implications underscore the need for phased implementation and employee training to optimize leagile integration. Future research could investigate agile practices in additional sectors and explore the role of emerging technologies in enhancing production efficiency.

The Role of Digital Twin Technology in Enhancing Production Forecasting and Inventory Optimization

Komarudin Komarudin — Sat, 30 Aug 2025 00:00:00 +0000

This study explores the impact of digital twin technology (DTT) on production forecasting accuracy and inventory optimization across various manufacturing sectors, including automotive, electronics, and consumer goods. Using a mixed-methods approach, data were collected from 25 companies that employed DTT in their production processes. Results indicate that DTT significantly enhances forecast accuracy by an average of 20%, improves inventory turnover by 15%, and reduces stockout occurrences by 18%. These findings support the potential of DTT to enable real-time data analysis, predictive modeling, and adaptive decision-making, thereby aligning with the goals of digital transformation. However, challenges such as high implementation costs, robust data infrastructure requirements, and a need for technical expertise were noted, particularly for smaller firms. Practical implications suggest phased implementation and staff training to optimize DTT adoption. This study contributes to the understanding of DTT's role in production management. It offers recommendations for industry-wide adoption, with future research suggested in broader sectors and integration with artificial intelligence.

Optimization of Resource Allocation in Production Management: A Machine Learning Approach

Nurhaliza Nurhaliza — Fri, 29 Aug 2025 00:00:00 +0000

This study explores the application of machine learning (ML) algorithms to optimize resource allocation in production management across various manufacturing sectors. Using a quantitative research approach, data were collected from 20 manufacturing firms employing ML solutions in production. The results indicate that ML-driven approaches, particularly reinforcement learning (RL) and supervised learning models, significantly enhance production efficiency by reducing downtime, improving cost efficiency, and optimizing resource utilization. On average, firms using ML achieved a 30% increase in cost efficiency and a 20% reduction in lead times. Reinforcement learning was found to be particularly effective in complex, variable production environments, while supervised learning provided reliable predictions in stable demand scenarios. However, companies encountered challenges with data quality, high initial costs, and the need for specialized skills. Practical implications suggest that phased implementation and continuous staff training can mitigate these challenges, ensuring smoother integration. The study concludes that ML offers substantial advantages in resource allocation, with further research recommended on the long-term impacts of ML across different industries and the role of advanced ML models, such as deep learning, in production optimization.

Maximizing Production Throughput Using Real-Time Data Analytics and Predictive Maintenance

Wahyu Eko Saputro — Sat, 30 Aug 2025 00:00:00 +0000

This study examines the impact of integrating real-time data analytics and predictive maintenance on maximizing production throughput in manufacturing sectors, including automotive, electronics, and pharmaceuticals. Using a quantitative approach, data were gathered from 30 companies with established predictive maintenance and real-time data systems. Results indicate a 25% increase in production throughput and a 30% reduction in unplanned downtime across companies implementing both technologies. Real-time data analytics enabled responsive decision-making, while predictive maintenance facilitated timely equipment interventions, thereby enhancing operational efficiency and cost-effectiveness. The findings support digital transformation theory, showcasing the value of transitioning from reactive to proactive production management. However, barriers such as high initial costs and a lack of technical expertise were noted, particularly for smaller firms. Practical recommendations include phased implementation and targeted employee training to optimize technology adoption. The study contributes to the field of production management by highlighting the potential of combined digital solutions to enhance throughput and supply chain resilience, with suggestions for broader cross-industry applications and integration with artificial intelligence to optimize processes further.

Balancing Sustainability and Efficiency: Analyzing the Impact of Green Supply Chain Practices on Production Optimization

Rafi Farizki — Sat, 30 Aug 2025 00:00:00 +0000

This study examines the impact of green supply chain management (GSCM) practices on production optimization in manufacturing sectors, including automotive, electronics, and consumer goods. Using a mixed-methods approach, data were gathered from 30 companies that implement sustainable practices in sourcing, waste reduction, and green logistics. The findings indicate that GSCM practices enhance both sustainability and efficiency, with firms reporting an average 15% increase in cost efficiency, 20% reduction in waste, and 12% improvement in lead times. Sustainable sourcing, waste minimization, and green logistics contributed significantly to these operational gains. However, challenges such as high initial costs and the need for specialized skills were noted. The study aligns with resource-based theory, which suggests that sustainable resource management creates competitive advantages. Practical implications indicate that GSCM provides dual benefits, addressing both environmental responsibility and operational efficiency. Future research should extend these findings to a broader range of industries and explore the role of advanced technologies, such as AI and IoT, in optimizing GSCM practices.