Investigating the Environmental and Economic Impact of Loading Conditions and Repositioning Strategies for Pallet Pooling Providers
Journal of Cleaner Production
Pallets are fundamental assets critical to worldwide supply chain logistics. This research develops models for closed-loop pallet pooling providers to understand the environmental and economic impact of customer characteristics and design options. First, an analytical model is developed to quantify the effects of repair facility location and pallet service conditions on a pallet pooling system's economic and environmental performance. Next, a simulation model is developed to investigate two common operational policies, crossdocking and take-back, and to quantify the impact of pallet handling and loading conditions and customer network structures on several key performance indicators. Results indicate that pallet handling and loading conditions are the most important factors determining the cost and carbon equivalent emission of a pallet pooling operation. Better pallet handling and appropriate loading increase the percentage of pallets that can be repositioned with little or no repair. This increases the radius within which a closed-loop pallet pooling system is feasible. Under random handling/loading conditions and distances, a crossdocking approach satisfies demand with 28% fewer pallets than a take-back policy. This is due to a quicker reissue time under a crossdocking approach. However, associated costs and emissions of the two policies are nearly identical due to the increased transportation costs associated with crossdocking. The models and insights proposed in this work can help support decision making by pallet pooling providers to determine operational regions and customer selection, among other network design trade-offs.
Tornese, Fabiana, Jennifer A. Pazour, Brian K. Thorn, Debjit Roy, Andres L. Carrano.
"Investigating the Environmental and Economic Impact of Loading Conditions and Repositioning Strategies for Pallet Pooling Providers."
Journal of Cleaner Production, 172: 155-168.
doi: 10.1016/j.jclepro.2017.10.054 source: https://reader.elsevier.com/reader/sd/pii/S0959652617323466?token=48CF30CB385219B9C7A54260BC876088882AFA82873C1A79F9254F5D54B401AAA5753F955051BC7B91E62CE7A08EF6A8