Forecasting and Planning for Inventory Management in a Complex Rental Housing Unit Supply Chain Environment

  • Haixia SangEmail author
  • Shingo Takahashi
Conference paper


Given the increasing demand for rental housing units, suppliers have been forced to analyze methods to optimize both their inventory levels and opportunity losses. Although inventory forecasting and planning has been studied for several decades, studies on the circulation inventory problems are extremely limited. In this context, a discrete simulation-based approach to forecast inventory levels in a complex rental housing unit supply chain was developed. An interrelationship between the forecasting method, the initial inventory level and the inventory filling indicator was identified that can help suppliers to optimize their inventory level and opportunity losses. It is suggested that this simulation-based approach is a powerful and efficient tool for mangers involved in inventory decision making.


Rental unit Seasonal demand Inventory management Simulation 



This research was supported by the Grant-Aid for Scientific Research (B) (No 18K13954). The sample data relating to the rental housing unit supply chain operation are fictitious. The sample data relating to the rental housing unit supply chain operation are fictitious.


  1. 1.
    M.M. Helms, L.P. Ettkin, S. Chapman, Supply chain forecasting collaborative forecasting supports supply chain management. Bus. Process Manag. J. 6, 392–407 (2000)CrossRefGoogle Scholar
  2. 2.
    C. Eksoz, S.A. Mansouri, M. Bourlakis, Collaborative forecasting in the food supply chain: a conceptual framework. Int. J. Prod. Econ. 158, 120–135 (2014)CrossRefGoogle Scholar
  3. 3.
    U. Ramanathan, A. Gunasekaran, Supply chain collaboration: impact of success. Int. J. Prod. Econ. 147, 252–259 (2014)CrossRefGoogle Scholar
  4. 4.
    E. Hofmann, Big data and supply decision: the impact of volume, variety and velocity properities on the bullwhip effect. Int. J. Prod. Res. 55(17), 5108–5126 (2017)CrossRefGoogle Scholar
  5. 5.
    H. Akkermans, K. Van Helden, Vicious and virtuous cycles in ERP implementation: a case study of interrelations between critical success factors. Eur. J. Inf. Syst. 11, 35–46 (2002)CrossRefGoogle Scholar
  6. 6.
    H. Sang, S. Takahashi, R. Gaku, Big data-driven simulation analysis for inventory management in a dynamic retail environment, in Proceedings of the 25th International Conference on IE&EM (2018)Google Scholar
  7. 7.
    Papier F, Queuing models for sizing and structuring rental fleets. Transp. Sci. 42(3), 302–317 (2008)CrossRefGoogle Scholar
  8. 8.
    U. Endo, S. Morito, T. Tamayama, An approach to determine the amount of ownership in the rental business, in Proceedings of the 2014 Japan Industrial Management Association Autumn Conference, pp. 194–195 (2014)Google Scholar
  9. 9.
    S. Terzi, S. Cavalieri, Simulation in the supply chain context: a survey. Comput. Ind. 53(1), 3–16 (2004)CrossRefGoogle Scholar
  10. 10.
    P.R. Winters, Forecasting sales by exponentially weighted moving averages. Manage. Sci. 6, 324–342 (1960)CrossRefGoogle Scholar
  11. 11.
    M.C. Lovell, Seasonal adjustment of economic time series and multiple regression analysis. J. Am. Stat. Assoc. 58(304), 993–1010CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Science and Engineering, Department of Industrial and Management Systems EngineeringWaseda UniversityTokyoJapan

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