Recommendations to improve dead stock management in garment industry using data analytics

Sugumaran, Poonkuzhali; Sukumaran, Vinodhkumar

doi:10.3934/mbe.2019409

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Based suggestion framework planning to give expanded significance of the proposals. In the review, C. Skiada et al produced affiliation rules utilizing genuine retail location (POS) information to give proposals and to get a handle on the client's necessities and direct while shopping on the web or disconnected [27][28][29][30].…”

Section: Research Backgroundmentioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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Attributable to challenges of social event enormous volumes of material area information in a setting of less mining research, foreseeing the qualities of articles of clothing turns into a significant open issue which gets increasingly more consideration from the materials research local area. In this exploration work, the field of data mining endeavors to anticipate clothing insurance factors completely plan on understanding the computational individual of learning. Qualities of dress learning are being explored as a methodology for settling on the decision and utilization of getting ready data and their outcomes. It is seen from the results got by experimentation that the linear regression hushes up engaging as a result of adequacy as far as high forecast rate and linear regression can find the dress protection execution in a most proficient way in contrast with any remaining inclining calculations tested.

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Section: Research Backgroundmentioning

confidence: 99%

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Wang

2022

Wireless Communications and Mobile Computing

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“…Stok mati (dead stock) didefinisikan sebagai persediaan yang telah lama berada di unit penyimpanan, sehingga menjadi kadaluarsa, dan tidak mempunyai peluang jual dalam durasi waktu tertentu [1]. Durasi ini bervariasi tergantung pada jenis produk, jenis pasar, dan posisi produk dalam rantai pasokan [2].…”

Section: Pendahuluanunclassified

Pemodelan Dinamika Sistem untuk Sistem Produksi dan Inventori dalam Remanufakturing Proses Studi Kasus: Perusahaan Kimia di Indonesia

Noor

Jayson

Akbar

et al. 2022

Journal of Information System

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Pengendalian stok barang retur dengan proses manufaktur ulang (remanufacturing) merupakan hal penting bagi banyak perusahaan. Seringkali, kompleksitas proses remanufaktur seperti dalam industri kimia, membuat stok produk retur meningkat dalam inventori. Penelitian ini mengkaji sejumlah strategi pengendalian stok retur menggunakan kerangka closed loop supply-chain remanufacturing dan simulasi sistem dinamik. Pertama-tama, peneliti membuat sebuah model sistem dinamik untuk sistem closed loop supply-chain remanufacturing dengan durasi analisis 60 minggu dan satuan waktu minggu. Model ini divalidasi dengan membandingkan Stock Recoverable Inventory hasil simulasi dengan data yang dikumpulkan dari sebuah perusahaan manufaktur cat. Setelah diperoleh kesesuaian yang baik, model digunakan untuk mengkaji empat skenario pengendalian stok barang retur. Skenario 1 mengkaji proses remanufaktur di mana barang retur yang diterima dari konsumen perlu melalui proses inspeksi. Skenario 2 mengkaji proses remanufaktur dengan peningkatan kapasitas remanufaktur. Skenario 3 mengkaji diterapkannya kebijakan jumlah maksimum dari stok remanufaktur. Skenario 4 mengkaji keadaan di mana kapasitas remanufaktur ditingkatkan dan remanufacturing dan menurunkan lead time remanufacturing. Dari simulasi ke-empat skenario tersebut, dengan meningkatkan kapasitas remanufacturing dan menurunkan lead time remanufacturing selain pengendalian stok remanufacturing yang membaik, total cost dapat dikurangi.

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“…They trained a forecasting model using these style images to represent the trend over time. Yet another application of data mining extensively worked upon is recommender systems [27,28]. An excellent overview of the existing apparel recommendation systems is presented in [29].…”

Section: Research Backgroundmentioning

confidence: 99%

Garment Categorization Using Data Mining Techniques

Jain

Kumar

2020

Symmetry

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The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

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Recommendations to improve dead stock management in garment industry using data analytics

Cited by 6 publications

References 12 publications

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Research Design of Fashion Elements Identification of Clothing Based on Decision Tree Algorithm and IoT

Pemodelan Dinamika Sistem untuk Sistem Produksi dan Inventori dalam Remanufakturing Proses Studi Kasus: Perusahaan Kimia di Indonesia

Garment Categorization Using Data Mining Techniques

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