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An Intelligent Fashion Replenishment System Based on Data Analytics and Expert Judgment

  • Roberta Sirovich
  • Giuseppe Craparotta
  • Elena Marocco
Chapter
Part of the Springer Series in Fashion Business book series (SSFB)

Abstract

Retail stock allocation is crucial but challenging. The authors developed an innovative solution, successfully tested in the context of high-end fashion: collaboration between artificial intelligence and human intuition. Each week, stores are assigned a budget based on current stock levels versus potential sales, and offered to “spend” this budget with an initial data-driven recommendation on which SKU/sizes order and release. Each store manager is then given a time window, so she can modify the proposal while respecting budget constraints; and finally, the artificial intelligence optimally allocates available stock to requests based on the expected likelihood of sale minus cost of logistics, subject to management-defined constraints. Our test showed how this system outperformed the control group of stores, relying on a traditional head office-driven allocation without direct human input. The retailer boosted sales, demand cover, and stock rotation performance: an estimated 1M EUR margin/month positive impact. Moreover, the new system improved store managers morale through non-monetary incentive-driven empowerment.

Keywords

Retail Artificial intelligence Constrained optimization Forecasting Dynamic markets Innovation Luxury Fashion 

Notes

Acknowledgements

The authors gratefully acknowledge Evo Pricing for supporting this research.

References

  1. Agrawal N, Smith SA (2013) Optimal inventory management for a retail chain with diverse store demands. Eur J Oper Res 225(3):393–403CrossRefGoogle Scholar
  2. Blattberg RC, Hoch JS (1990) Database models and managerial intuition: 50% model \(+\) 50% manager. Manag Sci 36(8):887–899CrossRefGoogle Scholar
  3. Cachon GP, Lariviere MA (1999) An equilibrium analysis of linear, proportional and uniform allocation of scarce capacity. IIE Trans 31(9):835–849Google Scholar
  4. Caro F, Gallien J (2010) Inventory management of a fast-fashion retail network. Oper Res 58(2):257–273CrossRefGoogle Scholar
  5. Correa J (2007) Optimization of a fast-response distribution network. M.S. thesis, LFM, MIT, Cambridge, MAGoogle Scholar
  6. Fisher M, Rajaram K (2000) Accurate retail testing of fashion merchandise: methodology and application. Mark Sci 19(3):266–278CrossRefGoogle Scholar
  7. Furuhata M, Zhang D. Capacity allocation with competitive retailersGoogle Scholar
  8. Gallien J, Mersereau AJ, Garro A, Mora AD, Vidal MN (2015) Initial shipment decisions for new products at Zara. Oper Res 63(2):269–286CrossRefGoogle Scholar
  9. Mebane WR Jr, Sekhon JS (2011) Genetic optimization using derivatives: the rgenoud package for R. J Statist Softw 42(11):1–26CrossRefGoogle Scholar
  10. Sirovich, Marocco, Craparotta. A woman’s touch in fashion forecasting: combining analytics & expert judgement, in preparationGoogle Scholar
  11. Thomassey S (2010) Sales forecasts in clothing industry: the key success factor of the supply chain management. Int J Prod Econ 128(2):470–483CrossRefGoogle Scholar
  12. Thomassey S, Happiette M, Castelain JM (2005) A global forecasting support system adapted to textile distribution. Int J Prod Econ 96(1):81–95CrossRefGoogle Scholar
  13. Van Donselaar KH, Gaur V, Van Woensel T, Broekmeulen RA, Fransoo JC (2010) Ordering behavior in retail stores and implications for automated replenishment. Manag Sci 56(5):766–784CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Roberta Sirovich
    • 1
  • Giuseppe Craparotta
    • 1
  • Elena Marocco
    • 1
  1. 1.Department of MathematicsUniversity of TorinoTurinItaly

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