A Novel Mathematical Model for Quality Optimization of Textile Products During Shipment

Safira Hutama  Putri; Fadil Abdullah; Nurmalinda Rahmawati; Camillana Calistafo  Dayani; Afriani Kusumadewi

doi:10.30656/intech.v10i2.8695

Authors

Safira Hutama Putri Telkom University https://orcid.org/0009-0008-6291-8796
Fadil Abdullah Telkom University https://orcid.org/0009-0002-7280-1723
Nurmalinda Rahmawati Universitas Bakrie
Camillana Calistafo Dayani Politeknik STTT Bandung https://orcid.org/0009-0005-4444-1064
Afriani Kusumadewi Universitas Insan Cendikia Mandiri

DOI:

https://doi.org/10.30656/intech.v10i2.8695

Keywords:

Monitoring , Quality, RSM, Shipping, Textiles

Abstract

The process of shipping products in the textile industry is essential as it determines the effectiveness of the business. However, non-ideal shipping can result in damage to the shipped products. Therefore, this study aims to develop a mathematical model to optimize the shipping process by assessing the textile product damage rate against environmental parameters such as humidity and temperature and shipping parameters such as shipping duration. The model developed using the Response Surface Methodology (RSM) statistical approach is based on linear and nonlinear models. The results showed that the linear model had a better coefficient of determination as a model validation parameter, with a coefficient of determination of 0.83. This value shows the effectiveness of optimizing the shipping process as a monitoring effort to determine the damage to textile products against several parameters that affect it. The results of this study have implications for the field of textile science, especially regarding the dynamics of distribution or logistics, as well as the application of mathematical applications in the textile field. In addition, the practical implications of this research are expected to be used as a monitoring effort for the textile industry to determine the impact of environmental parameters and shipping duration on the products they ship.

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References

Abdullah, F., Rahmawati, N., & Putra, V. (2023). Penerapan Algorithma Genetika Pada Masalah Penugasan Maklon di Industri Garmen dan Apparel. Jurnal Sains Dan Informatika, 9(1), 15–23. https://doi.org/10.22216/jsi.v9i1.1522

Bala, K. B., Arshad, M. F., & Noh, M. K. (2017). System dynamics simulation and modelling. In Springer Texts in Business and Economics (1st ed., Issue 1). springer. https://doi.org/10.4324/9780203112694-14

Chan, E. M. H., Cheung, J., Leslie, C. A., Lau, Y. Y., Suen, D. W. S., & Tsang, C. W. (2024). Revolutionizing the Textile and Clothing Industry: Pioneering Sustainability and Resilience in a Post-COVID Era. Sustainability (Switzerland), 16(6), 1–17. https://doi.org/10.3390/su16062474

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623

Harsanto, B., Primiana, I., Sarasi, V., & Satyakti, Y. (2023). Sustainability Innovation in the Textile Industry: A Systematic Review. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15021549

Heldayani, & Yuamita, F. (2022). Perbaikan Work Station Dan Pengukuran Waktu Kerja Dalam Menentukan Waktu Standar Guna Meningkatkan Produktivitas Pada Lini Kerja Spot Assembly (Studi Kasus Pt Indonesia Thai) Summit Auto. Jurnal Ilmiah Multidisiplin, Vol.1, No.(2810–0581), 2954–2956. https://journal-nusantara.com/index.php/JIM/article/view/688

Jadhav, S. B., Chougule, A. S., Shah, D. P., Pereira, C. S., & Jadhav, J. P. (2015). Application of response surface methodology for optimizing textile effluent biodecolorization and its toxicity perspectives using plant toxicity, plasmid nicking assays. Clean Technologies and Environmental Policy, 17(3), 709–720. https://doi.org/10.1007/s10098-014-0827-3

Jerath, K., Kim, S. H., & Swinney, R. (2017). Product quality in a distribution channel with inventory risk. Marketing Science, 36(5), 747–761. https://doi.org/10.1287/mksc.2017.1041

Köksal, D., Strähle, J., Müller, M., & Freise, M. (2017). Social sustainable supply chain management in the textile and apparel industry-a literature review. Sustainability (Switzerland), 9(1), 1–32. https://doi.org/10.3390/su9010100

Kumar, S., & Chong, I. (2018). Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. International Journal of Environmental Research and Public Health, 15(12). https://doi.org/10.3390/ijerph15122907

Marolleau, A., Salaun, F., Dupont, D., Gidik, H., & Ducept, S. (2017). Influence of textile properties on thermal comfort. IOP Conference Series: Materials Science and Engineering, 254(18). https://doi.org/10.1088/1757-899X/254/18/182007

Masruroh, N. A., & Prasetyorini, A. V. (2015). Model Penjadwalan Pengiriman Pasokan pada Strategi Multi-Supplier dengan Variasi Harga dan Lead Time untuk Permintaan Stokastik. Jurnal Teknik Industri, 17(1), 35–46. https://doi.org/10.9744/jti.17.1.35-46

Moazzem, S., Crossin, E., Daver, F., & Wang, L. (2022). Environmental impact of apparel supply chain and textile products. Environment, Development and Sustainability, 24(8), 9757–9775. https://doi.org/10.1007/s10668-021-01873-4

Mohd Rozalli, N., Chin, N., & Yusof, Y. (2014). Simultaneous multiple responses modelling, optimization and correlation of Asian type peanuts (Arachis hypogaea L.) roasting using response surface methodology. Acta Alimentaria, 43(1), 142–157. https://doi.org/10.1556/AAlim.43.2014.1.15

Mourya, V., Bhore, S. P., & Wandale, P. G. (2024). Multiobjective optimization of tribological characteristics of 3D printed texture surfaces for ABS and PLA Polymers. Journal of Thermoplastic Composite Materials, 37(2), 772–799. https://doi.org/10.1177/08927057231185710

Patwary, S. (2020). Clothing and textile sustainability: Current state of environmental challenges and the ways forward. Textile and Leather Review, 3(3), 158–173. https://doi.org/10.31881/TLR.2020.16

Putra, V. G. V., & Mohamad, J. N. (2022). Response surface methodology and artificial neural network modeling of work of adhesion on plasma-treated polyester–cotton-woven fabrics. Journal of Adhesion Science and Technology, 37(6). https://doi.org/10.1080/01694243.2022.2053349

Putra, V. G. V., & Mohamad, J. N. (2023). A novel model for predicting tenacity and unevenness of ring-spun yarn: a special case in textile engineering. Mathematical Models in Engineering, 9(3), 102–112. https://doi.org/10.21595/mme.2023.23406

Putra, V. G. V., Rosyid, M. F., & Maruto, G. (2017). New theoretical modeling for predicting yarn angle on OE yarn influenced by fibre movement on torus coordinate based on classical mechanics approach. Indian Journal of Fibre and Textile Research, 42(3), 359–363. https://nopr.niscpr.res.in/handle/123456789/42726

Sakhi, D., Elmchaouri, A., Rakhila, Y., Abouri, M., Souabi, S., Hamdani, M., & Jada, A. (2020). Optimization of the treatment of a real textile wastewater by coagulation– flocculation processes using central composite design. Desalination and Water Treatment, 196, 33–40. https://doi.org/10.5004/dwt.2020.25929

Samura, L., Pratama, M. D., Galih, V., Putra, V., Achmad, F., Yusuf, Y., & Abdullah, F. (2024). A new mathematical model for optimizing laser cutting parameters to improve fabric quality. Mathematical Models In Engineering, 10(4), 1–15. https://doi.org/10.21595/mme.2024.24204

Seikh, A. H., Mandal, B. B., Sarkar, A., Baig, M., Alharthi, N., & Alzahrani, B. (2019). Application of response surface methodology for prediction and modeling of surface roughness in ball end milling of OFHC copper. International Journal of Mechanical and Materials Engineering, 14(1). https://doi.org/10.1186/s40712-019-0099-0

Setiawati, A. E., & Kusnadi, J. (2021). Optimization of fermentation time and grain concentration for water kefir production from butterfly pea flower (Clitoria ternatea). IOP Conference Series: Earth and Environmental Science, 924(1). https://doi.org/10.1088/1755-1315/924/1/012081

Suprayogi, S., & Paillin, D. B. (2018). Algoritma Genetika untuk Pemecahan Masalah Rute Kendaraan dengan Ukuran dan Campuran Armada, Trip Majemuk, Pengiriman Terbagi, Produk Majemuk, dan Kendaraan dengan Kompartemen Majemuk. Jurnal Teknik Industri, 19(2), 115–124. https://doi.org/10.9744/jti.19.2.115-124

Suprayogi, S., & Ramdhani, H. (2015). Model Optimisasi untuk Penjadwalan Ulang Perjalanan Kereta Api. Jurnal Teknik Industri, 17(2). https://doi.org/10.9744/jti.17.2.97-104

Warasthe, R., Brandenburg, M., & Seuring, S. (2022). Sustainability, risk and performance in textile and apparel supply chains. Cleaner Logistics and Supply Chain, 5(July), 100069. https://doi.org/10.1016/j.clscn.2022.100069

Yue, W. (2023). Export duration and export product quality of firms: evidence from China. Journal of Applied Economics, 26(1). https://doi.org/10.1080/15140326.2023.2285129