Advertisement

Big Data-Driven Simulation Analysis for Inventory Management in a Dynamic Retail Environment

  • Haixia SangEmail author
  • Shingo Takahashi
  • Rie Gaku
Conference paper

Abstract

Inventory management is one of the most important factors in logistics operations. However, real-world inventory systems are complexly intertwined with related elements, and determining the optimal parameters and identifying the determining factors that influence inventory changes are complex problems. In this paper, using real POS data, we propose a simulation-based algorithm to optimize automated refreshment systems in a retail environment. The inventory system is modeled and simulated, which then returns the performance functions. The expectations of these functions are then estimated by an algorithm and the optimal combination result is obtained. Based on the sensitivity analysis, the determining factor that influences inventory changes is identified. The results show that the proposed simulation-based algorithm is powerful and effective.

Keywords

Inventory management Simulation Optimization 

References

  1. 1.
    ARIMA models applied to distribution operations. IT Solut. Front. 10, 6–9 (2011)Google Scholar
  2. 2.
    A. Arisha, W.A. Hamad, Simulation optimization methods in supply chain applications. Ir. J. Manag. 90–124 (2010)Google Scholar
  3. 3.
    A.M. Law, M.G. McComas, Simulation optimization: simulation-based optimization, in Proceedings of the 2002 Winter Simulation Conference, ed. by E. Yucesan, C.H. Chen, J.L. Snowdon, J.M. Charnes (Institute of Electrical and Electronics Engineers, Inc, Piscataway, NJ, 2002), pp. 41–44Google Scholar
  4. 4.
    J.R. Swisher, D.H. Paul, H.J. Sheldon, W.S. Lee, A survey of simulation optimization techniques and procedures, in Proceedings of the 2000 Winter Simulation Conference, ed. by J.A. Joines, R.R. Barton, K. Kang, P.A. Fishwick (Institute of Electrical and Electronics Engineers, Inc, Piscataway, NJ, 2000), pp. 119–128Google Scholar
  5. 5.
    X. Wan, J.F. Pekny, G.V. Reklaitis, Simulation-based optimization with surrogate models—application to supply chain management. Comput. Chem. Eng. 29(6), 1317–1328 (2005)CrossRefGoogle Scholar
  6. 6.
    Y. Chu, F. You, Simulation-based optimization for multi-echelon inventory systems under uncertainty, in Proceedings of the 2014 Winter Simulation Conference, ed. by A. Tolk, S.Y. Diallo, I.O. Ryzhov, L. Yilmaz, S. Buckley, J.A. Miller (Institute of Electrical and Electronics Engineers, Inc, Piscataway, NJ, 2014), pp. 385–394Google Scholar
  7. 7.
    X. Zheng, M. He, L. Tang, C. Ren, B. Shao, A multiple-purpose simulation-based inventory optimization system: applied to a large detergent company in China, in Proceedings of the 2015 Winter Simulation Conference, ed. by L. Ylimaz, W.K.V. Chan, I. Moon, T.M.K. Roeder, C. Macal, M.D. Rossetti (Institute of Electrical and Electronics Engineers, Inc, Piscataway, NJ, 2015), pp. 1218–1229Google Scholar
  8. 8.
    H. Sang, S. Takakuwa, A simulation-based approach for obtaining optimal order quantities of short-expiration date items at a retail store, in Proceedings of the 2012 Winter Simulation Conference, ed. by C. Laroque, J. Himmelspach. R, Pasupathy, O. Rose, A.M. Uhrmacher (Institute of Electrical and Electronics Engineers, Inc, Piscataway, NJ, 2012), pp. 1466–1477Google Scholar
  9. 9.
    D.J. Yue, F.Q. You, Planning and scheduling of flexible process networks under uncertainty with stochastic inventory: MINLP models and algorithm. AIChE J. 59, 1511–1532 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Science and Engineering, Department of Industrial and ManagementWaseda UniversityTokyoJapan
  2. 2.Graduate School of Business AdministrationSt. Andrew’s UniversityOsakaJapan

Personalised recommendations