Contingency Plans for the Wood Supply Chain Based on Bottleneck and Queuing Time Analyses of a Discrete Event Simulation

Abstract

Wood supply chain performance suffers from risks intensified by more frequent and extreme natural calamities such as windstorms, bark beetle infestations, and ice-break treetops. In order to limit further damage and wood value loss after natural calamities, high volumes of salvage wood have to be rapidly transported out of the forest. In these cases, robust decision support and coordinated management strategies based on advanced contingency planning are needed. Consequently, this study introduces a contingency planning toolbox consisting of a discrete event simulation model setup for analyses on an operational level, strategies to cope with challenging business cases, as well as transport templates to analyze outcomes of decisions before real, costly, and long-lasting changes are made. The toolbox enables wood supply managers to develop contingency plans to prepare for increasing risk events and more frequent natural disturbances due to climate change. Crucial key performance indicators including truck to wagon ratios, truck and wagon utilization, worktime coordination, truck queuing times, terminal transhipment volume, and required stockyard are presented for varying delivery time, transport tonnage, and train pick-up scenarios. The strategy BEST FIT was proven to provide robust solutions which saves truck and train resources, as well as keeps transhipment volume on a high level and stockyard and queuing time on a low level. Permission granted for increased truck transport tonnages was evaluated as a potential means to reduce truck trips, if working times and train pick-ups are coordinated. Furthermore, the practical applicability for contingency planning is demonstrated by highly relevant business cases such as limited wagon or truck availability, defined delivery quota, terminal selection, queuing time reduction, or scheduled stock accumulation. Further research should focus on the modeling and management of log quality deterioration and the resulting wood value loss caused by challenging transport and storage conditions.