The fourth industrial revolution, fueled by automation and digital technology advancements, enables us to manage manufacturing systems effectively. Its deployment in enterprises has now become increasingly important in developed and emerging economies. Many experts believe that barriers associated with Industry 4.0 implementation are critical to its success. Therefore, this study aimed to identify the major hurdles to Industry 4.0 adoption and reveal their interrelationships. Initially, the literature was thoroughly studied to determine the sixteen barriers impeding I4.0 adoption. Then, based on experts’ opinions, an integrated fuzzy-DEMATEL approach was utilized to examine the most significant challenges to I4.0 deployment. The results demonstrated the distribution of barriers in which the economic dimension played a decisive role, affecting technological, regulatory, and organizational dimensions. As observed in the barrier mapping, the lack of qualified workforce was a typical adoption barrier. Finally, the mitigation strategies developed would help managers to overcome the identified critical obstacles.

This study delves into the critical barriers hindering sustainable manufacturing practices in medium-sized ready-made garments manufacturing enterprises. Initially, barriers were identified through a literature review and expert opinions. Then, the fuzzy Delphi Method and expert consensus were used to prioritize the critical barriers. Finally, based on the insights from a focus group discussion and the supported literature, strategies to mitigate the identified critical barriers were formulated. In this study, the initial barriers identified encompassed various dimensions. The top three critical barriers were identified as higher prices of sustainable products, limited supplier commitment, and lack of access to information. Strategies to address these critical barriers include adopting collaborative business approaches, incorporating sustainability standards, investing in technology, implementing sustainable manufacturing techniques, and integrating green supply chain management practices. Overall, by focusing on these strategies, medium-sized garment manufacturing enterprises can enhance their sustainability efforts and gain economic, environmental, and social benefits.

Quality function deployment (QFD) is a management tool which is used in identifying customers' need and to provide those requirements to them with quality consciousness. This paper demonstrates how an engineering department can apply QFD to translate voices of students into technical requirements of teaching. The students' needs and requirements are identified to develop the foundation in delivering the quality services in engineering education. For QFD analysis, a survey with the students of an engineering department is structured. The developed house of quality (HOQ) illustrates the transformation process of students' requirements into instructional developments. This step-by-step application of QFD might help the management of the department to improve the teaching techniques and to deliver the quality services.

Manufacturing flexibility is a widely accepted manufacturing strategy for mitigating the negative impacts of environmental uncertainty on firm performance and is also a required strategic attribute to acclimatize mass customization and agile manufacturing. Manufacturing flexibility has been adequately studied for technology-intensive industries but remains inadequately addressed for labor-intensive industries. In this study, a framework is proposed for sorting the relevant manufacturing flexibility types for the relevant environmental uncertainty types as an initial step towards implementing manufacturing flexibility in labor-intensive industries. This study considered the RMG (ready-made garment) industries in Bangladesh, which are mostly labor-intensive, as a case. Different types of manufacturing flexibility and environmental uncertainty were identified through a deductive approach from the existing literature and theory. Then, final sorting was conducted through a focus group discussion using the analytical hierarchy process (AHP) and decision making trial and evaluation laboratory (DEMATEL) techniques. This study revealed that demand, competitor, supplier, and technology uncertainty were ranked sequentially from first to fourth. This study also revealed that demand and competitor uncertainty would be the first-line focus, and supplier uncertainty the second-line focus, of decision makers. Similarly, new product, volume, workforce, and modification flexibility were ranked sequentially from first to fourth, and these would be considered first-line focuses by decision makers. This study also showed that all types of environmental uncertainties had an internal effect (one type affects another type within the types of environmental uncertainty). Internal effects among the different types of manufacturing flexibility were also identified. This study contributes to the theory of manufacturing flexibility for labor-intensive industries and will help decision makers gradually implement manufacturing flexibility based on their capacity and goal.

This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO₂ gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO₂ gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO₂ gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively.