Automated or manual storage systems: do throughput and storage capacity matter? Academic Article uri icon


  • © 2018 Canadian Operational Research Society (CORS). Selecting the appropriate type of capital-intensive storage systems is an important decision for warehouse managers. However, such a decision is complex due to various available storage systems. In addition, warehouse requirements such as storage capacity and throughput influence this decision. This research provides insights that enable managers to select the suitable type of storage system which minimizes the investment and operational costs while the warehouse design requirements, in particular the storage capacity and throughput, are met. To obtain these insights, an ExcelVR-based decision support system is developed for a set of most common types of manual and crane-based automated storage systems in pallet and case warehouses. The decision support system uses the closed-form formulas from the warehousing literature and also Monte Carlo simulation to approximate the travel time in each storage system. The results show that the choice of automated or manual storage system and the associated costs depend on the required capacity and throughput. When the storage capacity and throughput are low, the manual pallet racks are the preferred storage system and incur the lowest costs. As the storage capacity and throughput increase, there is a need for more compact storage systems that can store more loads in a smaller footprint. Thus, for medium to high capacity levels, double-deep automated storage systems and deep-lane compact storage systems are the ones with the lowest investment and operational costs. The results for the case warehouses show that the investment and operational costs increase rapidly with an increase of the throughput. In particular, the increase is noticeable for operational costs of the shelf rack system and the investment cost of the miniload system where the storage capacity and throughput level are high.

author list (cited authors)

  • Zaerpour, N., Volbeda, R., & Gharehgozli, A.

citation count

  • 4

publication date

  • January 2019