Journal of Recent Activities in Production (e-ISSN: 2581-9771)

Investigation of an Adaptable Production System Model for High-Mix Operations using Discrete-Event Simulation

2026-08-05T08:43:26+00:00

Modern manufacturing plants struggle to satisfy customer requirements in high-mix production settings, where wide product variety leads to operational inefficiencies. Repeated changeovers create substantial machine downtime because of lengthy setup times, and workloads are often distributed unevenly, placing excessive strain on certain workstations. These issues cause delayed deliveries, higher operating expenses, and wasted resources. This research examined a flexible production system model for high-mix operations through discrete-event simulation. A comprehensive workflow of a flexible manufacturing system was created, featuring a process flow diagram with routing sequences for fifteen product types, such as aluminum pistons, steel parts, copper alloy components, and cylinder liners. The model proved highly accurate, showing a 0.996 correlation between simulation results and real production data. The improved layout significantly enhanced performance, raising average resource utilization from 78% to 92%. Findings suggest cellular layouts are best suited to high-mix environments. Additionally, a hybrid scheduling rule achieved the best tradeoff between shorter lead times and dependable deliveries. A cause-and-effect analysis identified frequent setups, fixed routing, and rigid layouts as key downtime drivers in the baseline system. The validated model serves as a useful decision-making aid for manufacturers aiming to boost flexibility and efficiency in high-mix production.

Industry 4.0 Enabled Smart Manufacturing: A Comprehensive Review of Emerging Technologies

2026-08-18T04:34:13+00:00

The rapid advancement of digital technologies has fundamentally transformed manufacturing systems, leading to the emergence of smart manufacturing under the Industry 4.0 paradigm. This review examines the role of key enabling technologies, including the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), Digital Twins, Big Data Analytics, Cloud Computing, and intelligent automation, in modern production environments. This study presents a comprehensive assessment of peer-reviewed studies published between 2013 and 2024 to identify technological developments, industrial applications, implementation strategies, and current research trends. The reviewed literature indicates that the adoption of Industry 4.0 technologies enhances production efficiency, product quality, operational flexibility, predictive maintenance, and real-time decision-making through data-driven manufacturing. The paper also discusses major implementation challenges, such as investment requirements, cybersecurity concerns, workforce skill gaps, and technology integration issues, particularly for small and medium-sized enterprises (SMEs). Finally, future research priorities are highlighted to support the development of scalable, secure, sustainable, and human-centric smart manufacturing systems capable of meeting the evolving demands of next-generation industries.

Failure Part Analysis and Preventive Maintenance of Bottle Filling Machine and Bottle Capping Machine

2026-09-15T06:46:58+00:00

Bottling and packaging lines depend on the reliable operation of mechanical, pneumatic, electrical, and control components, and a failure in even one small component can stop the complete production line. This paper presents a failure-part analysis and preventive maintenance study of two machines commonly used together on a bottling line: a bottle filling machine, used for dispensing a measured quantity of liquid product into bottles, and a bottle capping machine, used for placing and tightening caps on the filled bottles. Using a functional decomposition and criticality-classification approach drawn from reliability-centered maintenance and failure mode and effect analysis practice, the study identifies individual failure-prone components, their typical failure modes, effects on production, detection methods and preventive or corrective actions, for a continuous operating basis of 24 hours per day, 7 days per week, and 365 days per year (8,760 operating hours annually). A qualitative criticality classification and a structured preventive maintenance checklist are presented, along with a framework for future failure-data collection to support calculation of MTBF, MTTR and availability.

Digital Twin-Based Production Sustainability Index for Dynamic Manufacturing Sustainability Assessment: A Fuzzy MCDM and LSTM-PSO Framework

2026-09-17T08:15:08+00:00

Sustainable manufacturing has become a key challenge for industrial competitiveness and environmental stewardship. However, many sustainability assessment frameworks remain static and do not adequately represent dynamic changes in production systems. This article proposes a Digital Twin-Based Production Sustainability Index (DTPSI) that integrates digital-twin technology, Multi-Criteria Decision-Making (MCDM), and machine learning for dynamic sustainability assessment. The framework combines environmental, economic, and social dimensions in a composite index driven by Industry 4.0 technologies for continuous data collection and evaluation. The fuzzy Analytic Hierarchy Process (AHP) determines indicator weights while accounting for uncertainty in expert judgment. Long Short-Term Memory (LSTM) neural networks optimized using Particle Swarm Optimization (PSO) are used to forecast future sustainability performance. The methodology is demonstrated in a precision-manufacturing case study, where the reported results show a 46% increase in the composite sustainability index, a 26.5% reduction in energy consumption per unit, and a 32% reduction in CO₂ emissions over the study period. The DTPSI framework provides a decision-support approach for production planning and process improvement and illustrates how Industry 4.0 technologies can support the human-centric and sustainable objectives associated with Industry 5.0.