Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

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

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

Special Issues: IoT and Big Data for Public Health

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.
  Figure/Table
  Supplementary
  Article Metrics

Keywords 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. Mathematical Biosciences and Engineering, 2019, 16(6): 8121-8133. doi: 10.3934/mbe.2019409

References

  • 1. P. Ignaciuk, Base-stock distributed inventory management in continuous-review logistic systems-control system perspective, 2017 22nd International Conference on Methods and Models in Automation and Robotics, (2017), 1027-1032. Available from: https://ieeexplore.ieee.org/abstract/document/8046971.
  • 2. O. Bounou, A. E. Barkany and A. E. Biyaali, Bayesian model for spare parts management, 10 th International Colloquium on Logistics and Supply Chain Management, (2017), 204-208. Available from: https://ieeexplore.ieee.org/document/7962899.
  • 3. R. Guidotti, G. Rossetti, L. Pappalardo, et al., Market basket prediction using user-centric temporal annotated recurring sequences, 2017 IEEE International Conference on Data Mining, (2017), 895-900. Available from: https://ieeexplore.ieee.org/abstract/document/8215574.
  • 4. Y. Sun, C. Liang, S. Sutherland, et al., Modeling player decisions in a supply chain game, 2016 IEEE Conference on Computational Intelligence and Games (CIG), (2016), 1-8. Available from: https://ieeexplore.ieee.org/abstract/document/7860444.
  • 5. G. S. Karakozov, G. B. Virabyan, S. V. Verlinski, et al., Construction of decision support system in business design based on integration of information technology, 2016 6th International Conference on Computers Communications and Control, (2016), 240-243. Available from: https://ieeexplore.ieee.org/abstract/document/7496767.
  • 6. H Wani and N Ashtankar, Big data in supply chain management, 2017 4th International Conference on Advanced Computing and Communication Systems, (2017), 1-4. Available from: https://ieeexplore.ieee.org/abstract/document/8014602.
  • 7. U. S. Dharmapriya, S. B. Kiridena and N. Shukla, A review of supply network configuration literature and decision support tools, 2016 IEEE International Conference on Industrial Engineering and Engineering Management, (2016), 149-153. Available from: https://ieeexplore.ieee.org/abstract/document/7797854.
  • 8. D. Das, L. Sahoo and S. Datta, A Survey on Recommendation System, Int. J. Comput. Appl., 160 (2017), 6-10.
  • 9. S. Zhang, L. Yao, A. Sun et al., Deep Learning based Recommender System: A Survey and New Perspectives, ACM Comput. Surv., 52 (2017), 1-35.
  • 10. A. Mugdha and L. Vina, Survey: Collaborative Recommender Systems Using Multiclass Co-Clustering, Int. J. Innovative Res. Comput. Commun. Eng., 5 (2017), 452-457.
  • 11. S. Sehgala, S. Chaudhrya, P. Biswasa, et al., A new genre of Recommender systems based on modern paradigms of data filtering, Proc. Comput. Sci., 92 (2016), 562-567.
  • 12. K. Kulkarni, K. Wagh, S. Badgujar, et al., A Study Of Recommender Systems With Hybrid Collaborative Filtering, Int. Res. J. Eng. Technol., 4 (2016), 2216-2219.
  • 13. S. Jeble, S. kumari and Y. Patil, Role of big data and predictive analytics, Int. J. Autom. Log., 2 (2016), 307-331.
  • 14. K. Anusha, C. Yashaswini and S. Manishankar, Segmentation of Retail Mobile Market Using HMS Algorithm, Int. J. Electr. Comput. Eng., 6 (2016), 1818-1827.
  • 15. D. S Jasim, Data mining approach and its application to dresses sales recommendation, Available from: https://www.researchgate.net/publication/293464737.
  • 16. M. A. Ullah, A Model for Predicting Outfit Sales: Using Data Mining Methods, in Emerging Technologies in Data Mining and Information Security, Springer, (2019), 813.
  • 17. U. Muhammad and A. Adeel, Dresses Attribute Sales Dataset. Available from: https://archive.ics.uci.edu/ml/datasets/dresses_attribute_sales.

 

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved