International Journal of Computer Integrated Manufacturing

Wagire, Aniruddha Anil; Kulkarni, Rushikesh

doi: 10.1080/0951192X.2024.2333026pmid: N/A

Industry 4.0 (I 4.0) technologies can significantly enhance the competitiveness, agility, and sustainability of the Indian manufacturing industry in the global market landscape. However, a lack of awareness about the potential benefits of I 4.0 inhibits its adoption within the sector. This lack of understanding, coupled with the complexities and implementation challenges associated with these technologies, disguises the relationship between benefits and adoption. This study aims to investigate the relationship between the adoption of I 4.0 technologies and the benefits expected from these technologies. A survey of 174 manufacturing organisations operating in India is conducted. The results of the survey suggest that the adoption of I 4.0 technologies has a positive relationship with operational performance benefits. The secondary benefits such as product flexibility, work-life balance and sustainability could only be considered after achieving operational performance. This paper contributes to the growing body of literature on the impact of I 4.0 on the manufacturing industry in India. Further it provides a base for future research on real benefits of I 4.0 technologies for manufacturing organisations from emerging economies.

Peng, GaoXian; Wen, YiPing; Liu, JianXun; Kang, GuoSheng; Zhang, BiMing; Zhou, MinHao

doi: 10.1080/0951192X.2024.2333024pmid: N/A

Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Naghshineh, Bardia; Carvalho, Helena

doi: 10.1080/0951192X.2024.2333011pmid: N/A

Additive manufacturing (AM), also known as 3D printing, is widely believed to enhance supply chain resilience (SCR). However, there is a lack of empirical frameworks to provide directions for practitioners and scholars in this regard. Motivated by the dynamic capabilities view, this exploratory survey research aims to overcome this gap. To this end, empirical data are collected from a heterogeneous sample of experts involved in different industries at the forefront of AM. These data are used to explore pathways through which AM adoption leads to enhancing SCR via different resilience practices. More specifically, the collected data are analyzed to explain how AM adoption affects different resilience practices and how these practices in turn affect SCR. Based on these findings, a preliminary practice-based framework is developed that can support practitioners in deploying AM-enabled resilience practices aimed at generating the supply chain (SC) capabilities necessary for dealing with SC vulnerabilities and therefore enhancing SCR. Moreover, relevant propositions are put forward that reflect these findings and open up avenues for future research.

Shi, Haoliang; Yang, Maolin; Makanda, Inno Lorren Désir; Guo, Wei; Jiang, Pingyu

doi: 10.1080/0951192X.2024.2335973pmid: N/A

The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Rajak, Sonu; Kumar, Prakash; Modi, Aayush; Swarnakar, Vikas; Antony, Jiju; Sony, Michael

doi: 10.1080/0951192X.2024.2335969pmid: N/A

The integration of Lean Six Sigma (LSS) with Industry 4.0 (I4.0) has numerous advantages for the organization. LSS and I4.0 integration in manufacturing environments face some challenges, including data integration, a lack of understanding of the strategic implications of integrating LSS and I4.0, security and data privacy issues, return on investment, and a shortage of consultants and trainers. This paper aims to analyse the barriers and their interrelationship that could hinder the manufacturing organisation from embracing LSS with I4.0. Fifteen potential barriers were identified from the literature review and by taking the opinion of experts and decision-makers. The grey decision-making trial and evaluation laboratory (DEMATEL) method was applied to find the influence of each barrier on other barriers. The cause–effect relationship between the barriers was identified and later validated by experts. Using a single case study methodology, this study prioritises the identified barriers that hinder the automotive component manufacturing industry from integrating LSS with I4.0. The top three barriers were ‘lack of long-term vision’, ‘timely and accurate data availability’, and ‘lack of automation’. This paper will help the managers to better understand which barriers could affect the integration of LSS with I4.0.

Durmaz, Nida; Budak, Ayşenur

doi: 10.1080/0951192X.2024.2335984pmid: N/A

Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

Li, Hongcheng; Cao, Yunpeng; Lei, Yubo; Cao, Huajun; Peng, Jian; Jia, Yachao

doi: 10.1080/0951192X.2024.2343677pmid: N/A

With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.