LAPSE:2024.0631
Published Article
LAPSE:2024.0631
A Smart Manufacturing Process for Textile Industry Automation under Uncertainties
June 5, 2024
Most textile manufacturing companies in the world heavily rely on manual labor, particularly in the fabric inspection section, especially for cotton fabric. Establishing smart manufacturing systems like industrial automation in the textile industry for cotton fabric inspection is important for error-free inspection. The proposed make-to-order (MTO) inventory model focuses on the strategic development of a supply chain network under fuzzy uncertainty. The distinctiveness of this research lies in integrating a methodology that involves human and machine interaction, along with allocating resources to investment in smart manufacturing. This article presents a case study of the Jagatjit Cotton Textiles (JCT) manufacturing company in Punjab, India, as an example to validate the model and check the performance of SMT in the fabric inspection process in cotton TC mills. This paper contributes by developing four distinct textile supply chain models with industrial automation under triangular and trapezoidal fuzzy demand. A numerical analysis is conducted to verify the effectiveness of installing automated fabric inspection machines in the cotton plant. This article proposes an iterative solution algorithm (KDPMG) to obtain the global optimum for the proposed model. A comparative study of the proposed algorithm, KDPMG, and the genetic algorithm (GA) is presented in this study to verify the credibility of the obtained results. It is observed that KDPMG provides more appropriate solutions to the problem compared to the GA. Moreover, the computational time of KDPMG is significantly less than that of the GA. The rigorous analysis reveals that maximum profit can be achieved under trapezoidal fuzzy demand with fully automated fabric inspection technology. Using a triangular fuzzy demand pattern, the model with fully automated smart manufacturing achieves an 8.62% higher profit compared to a traditional system. Similarly, in the case of a trapezoidal fuzzy demand pattern, the adoption of automation in cotton plants can achieve an 8.69% higher profit. Hence, the implementation of smart manufacturing systems in the mending section of the cotton textile industry proves to be more profitable compared to the traditional inspection process.
Keywords
fully automated fabric inspection, fuzzy uncertainty, industrial automation, textile industry
Suggested Citation
Kaur G, Dey BK, Pandey P, Majumder A, Gupta S. A Smart Manufacturing Process for Textile Industry Automation under Uncertainties. (2024). LAPSE:2024.0631
Author Affiliations
Kaur G: Department of Mathematics, Lovely Professional University, Phagwara 144411, Punjab, India [ORCID]
Dey BK: Department of Industrial & Data Engineering, Hongik University, Wausan-ro 94, Mapo-Gu, Seoul 04066, Republic of Korea [ORCID]
Pandey P: Department of Mathematics, Lovely Professional University, Phagwara 144411, Punjab, India [ORCID]
Majumder A: Department of Mathematics, Lovely Professional University, Phagwara 144411, Punjab, India [ORCID]
Gupta S: Department of Robotics and Control Engineering, School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India [ORCID]
Journal Name
Processes
Volume
12
Issue
4
First Page
778
Year
2024
Publication Date
2024-04-12
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr12040778, Publication Type: Journal Article
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LAPSE:2024.0631
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doi:10.3390/pr12040778
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