Dissertations/Theses - Department of Industrial and Production Engineering
Browse
439 results
Search Results
Item APPLICATION OF PARETO ANALYSIS AND CAUSE-EFFECT DIAGRAM TO REDUCE DEFECTS IN LASTING AND FINISHING SECTIONS OF A LEATHER FOOTWEAR INDUSTRY(Department of Industrial & Production Engineering (IPE), BUET, 2025-03-25) Salimuzzaman, Md.; Ahmad, Dr. NafisThe footwear industry is one of the most promising and rapidly growing export sectors in Bangladesh. However, the manufacturing process is prone to defects and failures, which can lead to product rejections and customer complaints. With the rising demand for higher-quality products and the need to remain competitive in the global market, improving quality is essential for enhancing productivity and reducing costs associated with rejections and reworks. This study applies Pareto analysis and Cause-Effect diagrams to identify and address defects in footwear production, aiming to improve product quality. The research focuses on a leather footwear manufacturing company, specifically examining defects in the lasting and finishing processes of ladies' leather shoes. Data collected over six months were analyzed using Pareto analysis to identify the most significant defects. The analysis revealed that 82.20% of defects were concentrated in a few key areas. Additionally, Pareto analysis was used to pinpoint defects responsible for the majority of rejection and rework costs, accounting for 79.34% of the total expenses. These defects, including Leather Defects, Excess Roughing, Loose Leathers, Broken Stitch, Quarter Height Variation, Sole Damage, Poor Cementing, Vamp Length/Toe Depth Variation, Upper Damage, and Twisted Lasting, were identified as critical areas for improvement. To address these issues, hierarchies of causes for each defect type were organized, and Cause-Effect diagrams were constructed. Based on the analysis, specific recommendations were provided to mitigate the root causes of these defects, thereby reducing rejection losses and enhancing both productivity and product quality. The study concludes with actionable suggestions for further advancements in defect reduction and quality improvement in the footwear industry. Keywords: Footwear Industry, Product Quality, Lasting and Finishing, Footwear Defects, Pareto Analysis, Root Cause Analysis, Productivity, Reduction of Costs.Item Strategies for implementing circular bio economy in food supply chain a case study(Department of Industrial & Production Engineering (IPE), BUET, 2024-09-07) Sarker, Subroto; Azeem, Dr. AbdullahilOver the last few years, there has been interest among researchers and experts in the circular bio economy (CBE) concept as a possible solution for addressing social, economic and environmental issues. However, there seems to be a lack of focus on bio economy initiatives within the food supply chains (FSCs). This study aims to bridge this gap by conducting a review of existing literature and identifying the key strategies involved in implementing the CBE in the FSC. In this study, an extended literature review about the strategies for implementing CBE in the FSC was organized. Based on field surveying and face-to-face interviewing in support of the total interpretive structural modeling (TISM) technique, a total of twelve strategies were concluded. In addition, Matriced Impact Croises Multiplication Applique (MICMAC) analysis was applied to discover the driving and dependence power of each strategy. After the analysis, it was determined that the crucial strategies were "government policies" and "collaborative networks." Moreover, the study is among the first to try to determine what strategies are available to utilize CBE in the FSC in Bangladesh.Item Improving supply chain responsiveness for RMG sector of Bangladesh through drivers of industry 5.0(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-12) Chanda, Pronoy Chandra; Aziz, Dr. Ridwan AlThe relentless tide of globalization, coupled with the breakneck pace of technological evolution, has cast a spotlight on supply chain responsiveness as an indispensable factor underpinning success for businesses operating on a global scale. At the forefront of this transformation stands Industry 5.0, characterized by the seamless fusion of cutting-edge technologies such as Artificial Intelligence, Big Data analytics, and Robotics fosters a transformative synergy between human expertise and machine capabilities. Despite its pivotal role, there remains a conspicuous void in the comprehensive analysis of the driving forces propelling the adoption of Industry 5.0 to enhance supply chain responsiveness. This study thus embarks on a multifaceted approach to meticulously discern the fundamental drivers of Industry 5.0 and their intricate relationships within the contours of an emerging economy. Initially, the primary drivers are identified through an extensive literature review and feedback from experts. These drivers are then scrutinized and ranked using Pareto analysis. Subsequently, the contextual connections among these drivers are explored using interpretive structural modeling, coupled with cross-impact matrix multiplication applied to classification analysis. The findings underscore that driver of Industry 5.0 with strong driving power and low dependence power like Big data analytics to optimize supply chain cost possess strategic importance due to their proactive role to enhance supply chain responsiveness, particularly in response to the disruptions posed by the recent conflicts. Moreover, the study reveals the Cloud computing for dynamic supply chain also plays an important role to enhance Supply chain responsiveness. This study is anticipated to assist managers and decision-makers in effectively prioritizing Industry 5.0 drivers within supply chains, ultimately enhancing responsiveness. Keywords: Disruptions, Supply chain; Responsiveness; Industry 5.0; Artificial intelligence; Emerging economy.Item Experimental Investigation and Performance Evaluation of Copper Oxide/Olive Oil-Based Nanofluid in Turning Hardened Steel(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-04) Sristi, Nafisa Anzum; Zaman, Dr. Prianka BinteHardened steels are widely used in high-stress industrial applications but face significant challenges related to heat and friction, which accelerate tool wear and reduce machining efficiency. Conventional cutting fluids, predominantly mineral-based, pose environmental and health risks. To address these issues, vegetable oils are explored as eco-friendly alternatives with superior lubrication properties. Incorporating nanoparticles into vegetable oils further enhances their thermal conductivity, lubrication, and heat transfer characteristics. However, CuO/olive oil-based nanofluid remains unexplored in machining despite its potential for improved machinability. Previous studies lack systematic approaches to optimizing stable nanofluid preparation, and the correlation between nanofluid properties and machining performance is yet to be investigated. This study focuses on developing and evaluating stable CuO/olive oil-based nanofluids for enhanced performance in turning SKD 11 hardened steel using a Minimum Quantity Lubrication (MQL) system. CuO nanoparticles were synthesized using the co-precipitation method, and stable nanofluids with varying concentrations were developed through optimized ultrasonication parameters (60% intensity, 30 minutes). Comprehensive measurements, including thermal conductivity, viscosity, and contact angle, revealed that adding CuO nanoparticles improved the nanofluid's thermophysical properties, wettability, and lubrication performance. The nanofluids were applied using an MQL system to assess their impact on cutting temperature, material removal rate (MRR), cutting ratio, and chip morphology. Results showed that the nanofluid improved heat dissipation, reduced tool wear, and enhanced chip formation. The optimal cutting parameters were determined to be a feed rate of 0.137 mm/rev, a cutting speed of 134 m/min, and an MQL with 1 wt. % CuO nanofluid. The Grey Relational Analysis (GRA) method, applied for optimizing cutting parameters, proved reliable, achieving an absolute percentage error of only 1.097%. Additionally, correlation analysis indicated that properties such as kinematic viscosity, thermal conductivity, and contact angle significantly influenced machining performance. SEM and EDX results showed that cooling and lubrication significantly influence the wear of TiAlN-coated carbide inserts. MQL with CuO/olive oil-based nanofluid provides excellent wear resistance, with higher CuO concentrations improving tool performance by reducing friction and forming protective layers, minimizing material transfer from the workpiece. These findings underscore the effectiveness of CuO nanoparticles as solid lubricants in reducing tool wear. This study provides valuable insights into the relationship between nanofluid properties and machining outcomes, offering a sustainable alternative to conventional fluids while enhancing machinability. These findings pave the way for future research on nanofluids in machining processes and further optimization of cutting parameters for industrial applications.Item Unraveling the challenges of waste-to-energy transition in Bangladesh(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-01) Ruhul Ferdoush, Md.; Aziz, Dr. Ridwan AlThe recent geopolitical events, such as the conflict between Russia and Ukraine, have strained the available resources worldwide. In emerging economies like Bangladesh, which is heavily reliant on imported gas, oil, and coal, this has created a severe energy crisis. In response to the energy crisis and to support eco-friendly waste management, converting waste into energy is being recognized as a promising solution. However, introducing waste-to-energy systems in developing economies faces many intricate challenges that require careful examination. This study, therefore, aims to explore and evaluate the challenges associated with adopting a waste to energy (WtE) conversion system in emerging economies like Bangladesh. The research methodology involves identifying challenges from an extensive review of existing literature and expert feedback and then combining Bayesian theory with Best Worst Method (BWM) to evaluate the challenges. Among the 21 challenges analyzed, the ‘need for well-developed planning and incentivized policymaking’, ‘ineffectiveness in waste segregation at the source’, and ‘high cost for installation, maintenance, and infrastructure development’ appear to be the most significant challenges with weight values 0.071, 0.067, and 0.066, respectively. The study can enhance managers' understanding of the challenges faced by this sector and thus facilitate informed decision-making. The outcomes of this study are expected to enrich the existing body of knowledge, promote the diffusion of WtE technology in emerging economies, reduce dependency on the international energy market, and achieve global sustainable development goals (SDGs) such as affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), and climate action (SDG 13).Item Factors influence road accident severity in Bangladesh for heavy and non-heavy vehicles a machine learning-based comparative study(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-26) Hossain Limon, Mohammad; Mahbub, Dr. NafisaRoad accidents have become a significant issue in Bangladesh due to the staggering number of incidents yearly. Some major critical factors influence the increase in the severity of accidents which affects the national economy, social life, and public health issues. This study addresses critical gaps in road accident severity research by focusing on rural and suburban areas in Bangladesh, where traffic patterns and infrastructure differ significantly from urban centers. It leverages advanced machine learning techniques, such as Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to enhance predictive accuracy and analyze high-dimensional data. By incorporating vehicle-specific clustering, the study uncovers distinct severity patterns for heavy and non-heavy vehicles, providing targeted safety insights. Additionally, the integration of interpretability methods like feature importance, permutation importance and SHAP (Shapley Additive Explanations) ensures actionable insights into the influence of factors such as vehicle type, weather, and driver behavior, bridging the gap between prediction accuracy and practical, real-world applicability in road safety interventions. Police-reported accident datasets from 2006 to 2015 are used for this purpose. The analysis indicates that for single-vehicle collisions, the type of collision is a critical factor for both heavy and light vehicles. For heavy vehicles, the presence of road dividers significantly influences accidents, while road classification is more impactful for light vehicles. In two-vehicle collisions, factors such as the presence of dividers and movement patterns play important roles, with the availability of fitness certificates particularly affecting collisions between heavy and non-heavy vehicles. Additionally, road class, time of day, and environmental conditions are key contributors to heavy-heavy vehicle collisions, whereas location type and district characteristics are more relevant for light-light vehicle collisions. These insights highlight the importance of context-specific policy measures to improve road safety in emerging economies.Item Comprehensive analysis of accident severity determinants in Bangladesh using machine learning(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-27) Tahmid, Ahnaf; Mahbub, Dr. NafisaRoad traffic accidents are a major cause of fatalities in developing countries like Bangladesh, with the country's accident fatality rate significantly exceeding that of neighboring countries. By leveraging police reported accident data from the Accident Research Institute (ARI) at BUET, this study conducts a comprehensive analysis of the determinants of accident severity (AS) in Bangladesh using machine learning (ML) techniques. However, the dataset has been clustered based on area (urban/rural), vehicle involvement (single/two vehicles) and road class (Highways, other roads). Previous studies analyzing AS primarily use traditional statistical models, which are limited by assumptions about data distribution and linear relationships. These studies rarely employ explainable AI methods or cluster-wise analysis to identify significant factors within each cluster. To address these limitations, this study employed Explainable Artificial Intelligence approaches: permutation importance, and SHapley Additive exPlanations (SHAP) method across clusters, using tree-based Random Forest (RF), Extreme Gradient Boosting (XGBoost); classification-based K-Nearest Neighbor (KNN); and hybrid Stack model ML approaches. Analysis depicts that, stack model most effectively capture the complex structure of data for all. The result of the study indicates that, vehicle type, collision type, district, divider, surface quality, location type, time and driver age are the key variables for predicting AS. Based on further analysis this research concludes that common collision scenarios on Bangladeshi roads include hit pedestrian, head on collision, collision between heavy and light vehicles, and incidents involving drivers aged between 31 and 45 years. Based on the analysis, this study provides valuable insights for key organizations in Bangladesh, including the Bangladesh Road Transport Authority (BRTA), Roads and Highway Department (RHD), Bangladesh Police (BP), and Local Government Engineering Department (LGED).Item Exploring the barriers to implement industrial symbiosis in the apparel manufacturing industry(Department of Industrial & Production Engineering (IPE), BUET, 2024-11-11) Hossain, Mosaddeque; Aziz, Dr. Ridwan AlIndustrial symbiosis, a promising approach for sustainable industrial practices, has garnered attention for its ability to enhance resource efficiency, minimize waste, and preserve the environment through collaborative exchanges among industries. In emerging economies like Bangladesh, integrating industrial symbiosis in the manufacturing industries offers the potential to balance economic growth with environmental sustainability. However, this integration encounters various barriers that complicate the implementation. Despite research on industrial symbiosis in robust economies, studies on emerging and developed economies are still scarce. To date, no research has yet investigated the barriers hindering the performance of industrial symbiosis in the Bangladeshi apparel manufacturing sector. To address this gap, this study integrates the Bayes theorem and the Best-Worst Method to identify and prioritize barriers to the Bangladeshi apparel manufacturing sector. From extensive literature reviews and expert validation, 17 barriers were identified. Findings reveal the "lack of technology and infrastructure readiness" as the most significant barrier, followed by "lack of inter-company cooperation" and "lack of management support." The results are compared with traditional Best-Worst Method. Conquering these barriers empowers emerging economies to fortify the apparel manufacturing sector's resilience, resource efficiency, and environmental performance, fostering sustainable development via circular economy practices. This study is expected to guide policymakers and stakeholders in crafting targeted strategies for fostering steady growth and promoting sustainable development in the apparel manufacturing sector of emerging economies like Bangladesh.Item Role of minimum quantity lubrication in hard turning using vegetable oil-based molybdenum disulphide–carbon black hybrid nano-fluid(Department of Industrial & Production Engineering (IPE), BUET, 2024-08-27) Asha Rahman, Biswas; Dhar, Dr. Nikhil RanjanMachining hardened steel components has garnered significant interest because of its wide application in the automotive, press-tooling, mold-die, gear, bearing, and aerospace sectors. Working with hardened steels provides numerous advantages, but optimizing efficiency is difficult due to the high levels of heat, friction, cutting forces, and tool wear that can compromise the quality of the products. Dispensing conventional cutting fluids while machining is an effective technique but it has significant effects on both environment and human health. Therefore, it is essential to explore new environmental friendly cooling and lubrication techniques. One of these alternatives is machining with minimum quantity lubrication. It is a mixture of impinging of least amount of rice bran oil-based molybdenum disulphide and carbon black hybrid nano cutting fluid along with highly compressed air through a small nozzle results in reducing the heat produced during metal cutting. The effects of rice bran oil-based molybdenum disulphide and carbon black hybrid nano cutting fluid in minimum quantity lubrication (nMQL) on cutting performances in respect of chip formation, cutting temperature, surface roughness, and tool wear have been studied using coated carbide insert (SNMG) for medium carbon hardened steels (30 HRC, 35 HRC, 40 HRC). The same experiments have been carried out under dry condition in order to compare the results with those obtained under nMQL conditions. The result indicated that the machining with nMQL performed much better than dry machining mainly due to substantial reduction in cutting temperature enabling favorable chip-tool interaction and substantial reduction in surface roughness and tool wear. With the help of the experimental results, model of cutting temperature and surface roughness have been developed using an ANN model to understand the basic phenomenon in hard turning. Finally, the models demonstrated good agreement with experimental results to make it an acceptable model.Item Determining the critical success factors of ERP implementation in the RMG sector a case study(Department of Industrial & Production Engineering (IPE), BUET, 2021-09-27) Rahman, Sabbir; Sarwar, Dr. FerdousBangladesh is the second largest exporter in the Ready-Made Garment (RMG) sector globally. Many RMG companies in Bangladesh are attempting to implement Enterprise Resource Planning (ERP) systems but are not achieving the expected results. This challenge is not unique to Bangladesh, as ERP implementations have failed in 55% to 75% of European and American manufacturing companies. Critical Success Factors (CSFs) play a pivotal role in the various stages of ERP implementation, and the success of these implementations largely depends on these factors. This research identifies the top twelve critical factors for ERP implementation through a review of the literature and expert opinions. To rank these factors, the study employs the VIKOR method, a Multi-Criteria Decision-Making (MCDM) tool known for handling complex decision-making processes. Furthermore, the research reveals that these factors are interrelated, and this interdependence is illustrated using a Causal Loop Diagram (CLD). The CLD highlights how changes in one factor can influence others, creating a dynamic and interconnected system of dependencies. For instance, effective communication can enhance user involvement, while strong project management can improve data accuracy and business process reengineering. Additionally, the study analyzes the appropriate strategy and team structure necessary for successful ERP implementation. A well-defined strategy involves setting clear objectives, ensuring top management support, and fostering a culture of continuous improvement. The ideal team structure includes a balanced mix of technical experts, project managers, and end-users, all working collaboratively towards the common goal of successful ERP integration.