Research Paper
Engineering Computations
Tamanna Kamal; S M Atikur Rahman
Abstract
Many industrial production lines today are initially constructed and outfitted with machinery that, despite its inherent capabilities, struggles to satisfy increasing demand over time. The goal of this research is to find solutions to bottlenecks and improve the efficiency of the production line to satisfy ...
Read More
Many industrial production lines today are initially constructed and outfitted with machinery that, despite its inherent capabilities, struggles to satisfy increasing demand over time. The goal of this research is to find solutions to bottlenecks and improve the efficiency of the production line to satisfy rising productivity demands. The study primarily looks at an existing bottle production line in the edible oil industry, where the product is witnessing a boom in demand. Data on processing times at various workstations and their capacities were thoroughly collected and analyzed. Simulation emerged as the preferred tool for investigating and addressing the production line's intrinsic difficulties. Using Microsoft Excel for statistical analysis, an existing simulation model was created using Flexsim simulation, a process-oriented simulation software, to detect bottlenecks in the production line using the Processor block in the simulation. As a result, a modified model was offered to reduce waiting times by 12%, increase productivity by 6%, and increase overall profitability while efficiently dealing with rising demand across all seasons. As a result, this research represents a complete effort to identify operational challenges and present viable solutions that correspond with the industry's changing objectives. The study aims to contribute to optimizing production lines by utilizing contemporary simulation tools and statistical analysis, guaranteeing that they stay adaptive and responsive in the face of rising productivity demands.
Research Paper
Supply chain management
Ramin Pabarja; Gholamreza Jamali; Khodakaram Salimifard; Ahmad Ghorbanpur
Abstract
The Lean, Agile, Resilience, and Green (LARG) supply chains are more competitive than conventional ones. Evaluating its performance under current conditions and developing suitable strategies is crucial to enhance LARG. This study aims to create an assessment model for LARG in Iran's hospital medical ...
Read More
The Lean, Agile, Resilience, and Green (LARG) supply chains are more competitive than conventional ones. Evaluating its performance under current conditions and developing suitable strategies is crucial to enhance LARG. This study aims to create an assessment model for LARG in Iran's hospital medical equipment supply chain, especially in Hamadan. The Fuzzy Inference System (FIS) evaluates LARG across four dimensions: lean, agile, resilient, and green. Key indicators obtained from a comprehensive review of the literature and other published reports in the field of LARG were also confirmed by a focused group of experts in the medical equipment supply chain field. The findings indicate that the value LARG of the medical equipment supply chain is 0.787. Key indicators for the evaluation of LARG in the hospital medical equipment supply chain include reducing overall supply chain costs, optimizing inventory management, shortening supply chain development cycle time, increasing the introduction of new products, promoting information sharing among supply chain members, establishing flexible supply bases and sourcing, reducing fossil fuel consumption, and implementing waste management practices such as reuse and recycling of recyclable materials. This research provides managers with valuable insights into the current state of LARG and serves as a reference for formulating LARG strategies and practices. The study's results enable supply chain actors, particularly in Iran's Hamadan Province, to comprehend the key indicators for improving LARG performance in the hospital medical equipment supply chain. The proposed model can be adapted to other industries and service sectors by adjusting the indicators and assessing data availability.
