Industry 4.0 (I4.0) encompasses a plethora of digital technologies effecting on manufacturing enterprises. Most research on this topic examines the effects in the smart factory domain, focusing on ...

The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review

A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing

Way back the development of Blockchain (BC) technology, Traditional supply chain (TSC) failed to meet the demand of customer of reasonable price with high quality. The current trend is the time to integrate and to make the existing system smarter, automated and sustainable. Researchers are working on the integration of information and communication technology with the supply chain (SC) and continuously endeavor to achieve an efficient SC. This paper is an attempt to study the use of BC technology and a try to develop efficient sustainable supply chain management (SSCM) rather than the inefficient design of supply chain management (SCM). Important variables related to BC are identified from the literature after going through discussion with academician and industry experts. These variables are further analyzed and modeled using Principal Component Analysis (PCA), Fuzzy-Decision making trial and evaluation laboratory (DEMATEL). PCA is applied to form the principal factor from these identified variables based on possible correlation. Although, the application of Fuzzy-DEMATEL is adopted to identify major causes that help to achieve sustainable supply chain (SSC) after integration of BC technology. The proposed integrated (incorporated) approach of PCA and Fuzzy-DEMATEL found six major causes namely Data safety and Decentralization, Accessibility, Laws and Policy, Documentation, Data management, and Quality. This study is revolving around the integrated approach of two streams namely BC-related Information technology and SC from Operations. This research represents the significant role of causes, which leads to the integration of BC with the SC resulting in achieving sustainability. BC technology is still in their nuance stage and this study will motivate researcher and industrial practitioners to achieve a more efficient and effective goal in SC practices to achieve sustainability.

Blockchain critical success factors for sustainable supply chain

Linking big data analytics and operational sustainability practices for sustainable business management

https://cronfa.swan.ac.uk/Record/cronfa55117/Download/55117__18125__8de5dc6470b743af9c986a7827a60118.pdf

Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges

Sustainable robust layout using Big Data approach: A key towards industry 4.0

Modeling barriers to the adoption of electric vehicles: An Indian perspective

The dynamic cellular facility layout problem (DCFLP) is a well-known NP-hard problem. It has been estimated that the efficient design of DCFLP reduces the manufacturing cost of products by maintaining the minimum material flow among all machines in all cells, as the material flow contributes around 10–30% of the total product cost. However, being NP hard, solving the DCFLP optimally is very difficult in reasonable time. Therefore, this article proposes a novel similarity score-based two-phase heuristic approach to solve the DCFLP optimally considering multiple products in multiple times to be manufactured in the manufacturing layout. In the first phase of the proposed heuristic, a machine–cell cluster is created based on similarity scores between machines. This is provided as an input to the second phase to minimize inter/intracell material handling costs and rearrangement costs over the entire planning period. The solution methodology of the proposed approach is demonstrated. To show the efficien...

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

Role of zero emission vehicles in sustainable transformation of the Indian automobile industry

The increase in competitiveness and environmental challenges drive manufacturing industry to change in layout frequently. Moreover, due to environmental challenges, use of scarce resources like electrical energy consumption (EEC) is a major issue. To tackle these rapidly changing conditions, a layout must be efficient and effective. This work proposes a bi-objective robust stochastic sustainable cellular facility layout mathematical model and embedded meta-heuristic to solve it. The proposed model simultaneously minimizes material handling cost for both inter/intra-cellular movement and EEC in cellular manufacturing systems. In today’s sensitive market, demand fluctuates very frequently, so a layout must be robust so that it can tackle all these market fluctuations. To cure the problem, robust facility layout has been designed and solved using simulated annealing (SA)-based embedded meta-heuristic. To show the validity of proposed SA-based embedded meta-heuristic, twenty-five data instances have been used and solved.

Ravi Kumar

Papers

Sustainable robust layout using Big Data approach: A key towards industry 4.0

Modeling barriers to the adoption of electric vehicles: An Indian perspective

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

Role of zero emission vehicles in sustainable transformation of the Indian automobile industry

Simulated Annealing-Based Embedded Meta-Heuristic Approach to Solve Bi-objective Robust Stochastic Sustainable Cellular Layout