Optimising inventory placement in a two-echelon distribution system with fulfillment-time-dependent demand

Abstract

We study a two-echelon, single-product fulfillment system where a regional fulfillment center (FC) replenishes multiple independent local distribution center (LDC) orders within an internal committed resupply leadtime, and LDCs serve end customers within a committed demand fulfillment time, or committed delivery time. Expected system-wide demand depends on the product's price, the committed delivery time, and the number of LDCs in the system. Our proposed model determines the values of product price, committed resupply time, and committed delivery time that maximise expected system-wide profit per period, while accounting for product holding costs and fixed facility costs. We characterise key properties of optimal solutions that permit an efficient solution for a fixed number of LDCs, and consider the impacts of several proposed demand growth models as the number of LDCs increases. The results of a computational study provide interesting managerial insights on how operational constraints and the scale of the distribution system influence strategic stock placement and distribution system structure.

Keywords:

• Two-echelon supply chain
• committed service time
• safety stock placement
• demand growth
• last-mile distribution

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, JG, upon reasonable request.

Notes

1 We assume that expected demand can be expressed as a function of the difference between the product's price and any variable processing and delivery costs, which we define as the product's net price.

2 Note that the optimality of a single LDC when $J\left(N\right)=1$ occurs because of the assumption that the physical customer delivery time, $T_C$, is independent of N; the same does not hold in general for the case in which $J\left(N\right)=1$ and $T_C$ is decreasing in N, which we consider in greater detail in Section 4.4.

Additional information

Notes on contributors

Yue Wang

Yue Wang is a doctoral student in the Wm Michael Barnes '64 Department of Industrial & Systems Engineering at Texas A&M University. Her research interests are in the field of operations research, with a particular focus on supply chain optimisation.

Joseph Geunes

Joseph Geunes is a professor in the Wm Michael Barnes '64 Department of Industrial & Systems Engineering at Texas A&M University. He received a PhD degree in business administration and operations research from Penn State in 1999. His research focuses on operations research with applications in production planning and supply chain management.

Xiaofeng Nie

Xiaofeng Nie received a Ph.D. degree in operations research from the University at Buffalo, Buffalo, NY, in 2008. He is currently an associate professor with the Department of Engineering Technology and Industrial Distribution and the Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX. His research interests include humanitarian logistics, homeland security, supply chain risk management, and applied operations research.