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

Poonkuzhali Sugumaran; Vinodhkumar Sukumaran; Poonkuzhali Sugumaran; Vinodhkumar Sukumaran

doi:10.3934/mbe.2019409

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 8121-8133. doi: 10.3934/mbe.2019409

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

Poonkuzhali Sugumaran ¹,
Vinodhkumar Sukumaran ^{2,3
,
,}

1.
Department of Information Technology, Rajalakshmi Engineering College, Chennai 602105, India
2.
Department of Computer Science & Engineering, Rajalakshmi Engineering College, Chennai 602105, India
3.
Anna University, Chennai 600025, India

Received: 21 May 2019 Accepted: 03 September 2019 Published: 06 September 2019

The garment industry has huge potential when it comes to ability to integrate IT components for efficient data analytics. Rapid changes in trends and the excessive presence of fashion variants lead to unsold garments, termed as dead stock, which affect the profitability of organizations. Hence, in the garment business, markdown planning has been an imperative for efficient management of dead stock. This research work deals with a data analytics model for improving sales by making timely suggestions to retailers to provide offers and discounts to reduce dead stock by markdown optimization. The model consists of two modules, namely a classification module and a gain optimization module. In the first module, a hybrid classifier ID3, with the AdaBoost algorithm, is built to classify garments for sales recommendation, from an apparel dataset taken from the UCI repository. The predictor categorizes the garments into moving stock and dead stock. Finally, the gain optimization module uses linear programming and bandit learning of upper confidence bounds with the Chernoff-Hoeffding inequality algorithm, to bundle dead stock with fast-moving garments by giving optimal discounts that maximize revenue. The hybrid classifier provides 98% accuracy, and thereby, the analytics improve turnover, as well as balance supply and demand in the garment industry.
- bundling pair,
- dead stock,
- garments,
- hybrid classifier,
- linear programming,
- moving stock,
- recommendation
Citation: Poonkuzhali Sugumaran, Vinodhkumar Sukumaran. Recommendations to improve dead stock management in garment industry using data analytics[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 8121-8133. doi: 10.3934/mbe.2019409

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Abstract

The garment industry has huge potential when it comes to ability to integrate IT components for efficient data analytics. Rapid changes in trends and the excessive presence of fashion variants lead to unsold garments, termed as dead stock, which affect the profitability of organizations. Hence, in the garment business, markdown planning has been an imperative for efficient management of dead stock. This research work deals with a data analytics model for improving sales by making timely suggestions to retailers to provide offers and discounts to reduce dead stock by markdown optimization. The model consists of two modules, namely a classification module and a gain optimization module. In the first module, a hybrid classifier ID3, with the AdaBoost algorithm, is built to classify garments for sales recommendation, from an apparel dataset taken from the UCI repository. The predictor categorizes the garments into moving stock and dead stock. Finally, the gain optimization module uses linear programming and bandit learning of upper confidence bounds with the Chernoff-Hoeffding inequality algorithm, to bundle dead stock with fast-moving garments by giving optimal discounts that maximize revenue. The hybrid classifier provides 98% accuracy, and thereby, the analytics improve turnover, as well as balance supply and demand in the garment industry.

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