Araştırma Makalesi
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Artificial Intelligence and Image Processing for Semi-finished Goods Inventory Management in Textile Industry

Yıl 2025, Sayı: 25, 20 - 33, 30.06.2025
https://doi.org/10.63673/SosyoTeknik.1730643

Öz

This study investigates the application of artificial intelligence (AI) and image processing technologies within the textile industry, specifically focusing on optimizing production processes. The primary aim was to assess the impact of AI and image processing systems on inventory management of semi-finished products, production efficiency, and labor optimization. Findings indicate substantial improvements, including a decrease in stock discrepancies, higher on-time tracking rates, significant time savings in production, and reduced labor costs. Further analysis highlights the financial advantages of technological integration, particularly in lowering production expenses. This research contributes to the literature by demonstrating the practical benefits of these technologies in production management, offering insights that could be applicable across other manufacturing sectors. Future research should explore the wider adoption of AI and image processing technologies across various industries and investigate their potential environmental impacts within production processes.

Kaynakça

  • Akhtar, W. H., Watanabe, C., Tou, Y., & Neittaanmäki, P. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633-656.
  • Amza, C. G., & Cicic, D. T. (2015). Industrial image processing using fuzzy logic. Procedia Engineering, 100, 1238–1245. https://doi.org/10.1016/j.proeng.2015.01.404
  • Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. Cirp Annals, 63(2), 773-796.
  • Hasanbeigi, A., & Price, L. (2015). A technical review of emerging technologies for energy and water efficiency and pollution reduction in the textile industry. Journal of Cleaner Production, 95, 30-44.
  • Hassan, R., Acerbi, F., Rosa, P., & Terzi, S. (2024). The role of digital technologies in the circular transition of the textile sector. The Journal of The Textile Institute, 1-14.
  • Kaur, M. (2017). Inventory and Working Capital Management: An Empirical Analysis of Indian Textile Companies. IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN (P), 2347-4572.
  • Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23(1), 111–129. https://doi.org/10.1007/s12541-021-00543-1
  • Merli, M., Ciarapica, F. E., Varghese, K. C., & Bevilacqua, M. (2024). Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study. Applied Sciences, 14(21), 9894.
  • Mohiuddin Babu, M., Akter, S., Rahman, M., Billah, M. M., & Hack-Polay, D. (2024). The role of artificial intelligence in shaping the future of Agile fashion industry. Production Planning & Control, 35(15), 2084-2098.
  • Najlae, A., Abdelouahid, L., & Abdelfettah, S. (2020). Product-driven manufacturing launch of semi-finished product. In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 1251-1260). Cham: Springer International Publishing.
  • Naqvi, S. L. H., Nadeem, M., Ayub, F., Yasar, A., Naqvi, S. H. Z., & Tanveer, R. (2024). Social and environmental impacts in textile production. In Dye pollution from textile industry: Challenges and opportunities for sustainable development (pp. 423-453). Singapore: Springer Nature Singapore.
  • Pawlicka, K. and Bal, M. (2022). Sustainable Supply Chain Finances implementation model and Artificial Intelligence for innovative omnichannel logistics. Management, 26(1), 19-35.
  • Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access, 8, 220121-220139.
  • Rathore, B. (2022). Textile Industry 4.0 transformation for sustainable development: prediction in manufacturing & proposed hybrid sustainable practices. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 223-241.
  • Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of industrial engineering and management, 9(3), 811-833.
  • Sarkar, J., Rifat, N. M., Sakib-Uz-Zaman, M., Al Faruque, M. A., & Prottoy, Z. H. (2023). Advanced Technology in Apparel Manufacturing. In Advanced Technology in Textiles: Fibre to Apparel (pp. 177-231). Singapore: Springer Nature Singapore.
  • Stendahl, M., & Eliasson, L. (2014). Integrated production of semi-finished components in sawmills, part II: Management of internal operations. Wood Material Science & Engineering, 9(1), 12-30.
  • Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enable for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267.
  • Syafrudin, M., Alfian, G., Fitriyani, N. L., & Anshari, M. (2024). Applied Artificial Intelligence for Sustainability. Sustainability, 16(6), 2469.
  • Tarachkov, M. V., Tolstel, O. V., & Kalabin, A. L. (2023). Development of an algorithm for preparing semi-finished products for packaging. In Society 5.0 (pp. 53–62). Studies in Systems, Decision and Control, Volume 437. https://doi.org/10.1007/978-3-031-37485-3_6
  • Waqar, A., Bheel, N., & Tayeh, B. A. (2024). Modeling the effect of implementation of artificial intelligence powered image analysis and pattern recognition algorithms in concrete industry. Developments in the Built Environment, 19, 100349. https://doi.org/10.1016/j.dibe.2024.100349
  • Zhang, K., & Dong, C. (2021). Using AI technology to customize manufacture product label for decision making. Research Square Preprint. https://doi.org/10.21203/rs.3.rs-447217/v1

Tekstil Sektöründe Yarı Mamul Stok Yönetimi İçin Yapay Zekâ ve Görüntü İşleme Uygulaması

Yıl 2025, Sayı: 25, 20 - 33, 30.06.2025
https://doi.org/10.63673/SosyoTeknik.1730643

Öz

Bu çalışmada, yapay zekâ (AI) ve görüntü işleme teknolojilerinin tekstil endüstrisine entegrasyonunu ve üretim süreçlerinin optimizasyonuna odaklanılmıştır. Bu araştırmanın temel amacı, yapay zekâ ve görüntü işleme sistemlerinin uygulanmasını takiben yarı mamul envanter yönetimi, üretim verimliliği ve işgücü optimizasyonundaki gelişmeleri incelemektir. Sonuçlar, stok hatalarında önemli azalmalar, zamanında takip oranlarının arttığını, çeşitli üretim süreçlerinde önemli zaman tasarrufu sağladığını ve işçilik maliyetlerinde kayda değer bir azalma olduğunu ortaya koymuştur. Ayrıca üretim maliyetleri düşmüştür ve bu da teknolojik entegrasyonun olumlu finansal etkisini göstermiştir. Bu araştırma, üretim yönetiminde yapay zekâ ve görüntü işlemenin pratik bir uygulamasını sağlayarak, bu teknolojilerin operasyonel verimliliği nasıl artırabileceğini ve maliyetleri nasıl azaltabileceğini göstererek mevcut literatüre katkıda bulunmaktadır. Bulgular, diğer imalat sektörlerinin de benzer teknolojik gelişmelerden yararlanabileceğini ve hem araştırmacılar hem de endüstri uygulayıcıları için değerli bilgiler sağlayabileceğini göstermektedir. Gelecekteki araştırmalar, bu teknolojilerin farklı endüstrilerde daha geniş uygulamalarına odaklanmanın yanı sıra, üretim süreçlerinde yapay zekâ ve görüntü işlemenin çevresel etkilerini keşfetmeye odaklanabilir.

Kaynakça

  • Akhtar, W. H., Watanabe, C., Tou, Y., & Neittaanmäki, P. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633-656.
  • Amza, C. G., & Cicic, D. T. (2015). Industrial image processing using fuzzy logic. Procedia Engineering, 100, 1238–1245. https://doi.org/10.1016/j.proeng.2015.01.404
  • Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. Cirp Annals, 63(2), 773-796.
  • Hasanbeigi, A., & Price, L. (2015). A technical review of emerging technologies for energy and water efficiency and pollution reduction in the textile industry. Journal of Cleaner Production, 95, 30-44.
  • Hassan, R., Acerbi, F., Rosa, P., & Terzi, S. (2024). The role of digital technologies in the circular transition of the textile sector. The Journal of The Textile Institute, 1-14.
  • Kaur, M. (2017). Inventory and Working Capital Management: An Empirical Analysis of Indian Textile Companies. IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN (P), 2347-4572.
  • Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23(1), 111–129. https://doi.org/10.1007/s12541-021-00543-1
  • Merli, M., Ciarapica, F. E., Varghese, K. C., & Bevilacqua, M. (2024). Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study. Applied Sciences, 14(21), 9894.
  • Mohiuddin Babu, M., Akter, S., Rahman, M., Billah, M. M., & Hack-Polay, D. (2024). The role of artificial intelligence in shaping the future of Agile fashion industry. Production Planning & Control, 35(15), 2084-2098.
  • Najlae, A., Abdelouahid, L., & Abdelfettah, S. (2020). Product-driven manufacturing launch of semi-finished product. In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 1251-1260). Cham: Springer International Publishing.
  • Naqvi, S. L. H., Nadeem, M., Ayub, F., Yasar, A., Naqvi, S. H. Z., & Tanveer, R. (2024). Social and environmental impacts in textile production. In Dye pollution from textile industry: Challenges and opportunities for sustainable development (pp. 423-453). Singapore: Springer Nature Singapore.
  • Pawlicka, K. and Bal, M. (2022). Sustainable Supply Chain Finances implementation model and Artificial Intelligence for innovative omnichannel logistics. Management, 26(1), 19-35.
  • Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access, 8, 220121-220139.
  • Rathore, B. (2022). Textile Industry 4.0 transformation for sustainable development: prediction in manufacturing & proposed hybrid sustainable practices. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 223-241.
  • Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of industrial engineering and management, 9(3), 811-833.
  • Sarkar, J., Rifat, N. M., Sakib-Uz-Zaman, M., Al Faruque, M. A., & Prottoy, Z. H. (2023). Advanced Technology in Apparel Manufacturing. In Advanced Technology in Textiles: Fibre to Apparel (pp. 177-231). Singapore: Springer Nature Singapore.
  • Stendahl, M., & Eliasson, L. (2014). Integrated production of semi-finished components in sawmills, part II: Management of internal operations. Wood Material Science & Engineering, 9(1), 12-30.
  • Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enable for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254-267.
  • Syafrudin, M., Alfian, G., Fitriyani, N. L., & Anshari, M. (2024). Applied Artificial Intelligence for Sustainability. Sustainability, 16(6), 2469.
  • Tarachkov, M. V., Tolstel, O. V., & Kalabin, A. L. (2023). Development of an algorithm for preparing semi-finished products for packaging. In Society 5.0 (pp. 53–62). Studies in Systems, Decision and Control, Volume 437. https://doi.org/10.1007/978-3-031-37485-3_6
  • Waqar, A., Bheel, N., & Tayeh, B. A. (2024). Modeling the effect of implementation of artificial intelligence powered image analysis and pattern recognition algorithms in concrete industry. Developments in the Built Environment, 19, 100349. https://doi.org/10.1016/j.dibe.2024.100349
  • Zhang, K., & Dong, C. (2021). Using AI technology to customize manufacture product label for decision making. Research Square Preprint. https://doi.org/10.21203/rs.3.rs-447217/v1
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

M. Paşa Gültaş

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 9 Mayıs 2025
Kabul Tarihi 20 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 25

Kaynak Göster

APA Gültaş, M. P. (2025). Artificial Intelligence and Image Processing for Semi-finished Goods Inventory Management in Textile Industry. Selçuk Üniversitesi Sosyal ve Teknik Araştırmalar Dergisi(25), 20-33. https://doi.org/10.63673/SosyoTeknik.1730643

 Selçuk Üniversitesi Sosyal ve Teknik Araştırmalar Dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.