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When a supplier fails to comply with social and environmental standards, the buyer’s reputation suffers. Reputation costs can typically be very high for the buyer, whereas the supplier’s liability is often limited. Conventional procurement strategies such as dual sourcing mitigate the buyer’s operational risk, but they often do so at the expense of increasing its reputation risk and sourcing costs. In this paper, we propose a risk-sharing contract for managing the buyer’s reputation concerns. We find that by sharing some of the supplier’s operational loss, the buyer may (in some conditions) decrease its reputational risk, but this has to be balanced against an increase in the operational risk. Risk sharing also reduces sourcing costs because the buyer takes on some of the worst-case loss of a wealth-constrained supplier. These results suggest that risk sharing can be superior, as a procurement strategy, to conventional approaches such as dual sourcing or penalty contracts. This is true when reputation and sourcing costs are a significant concern and operational costs are not that high. Under some conditions, the buyer may choose risk sharing even if it increases reputation risk in order to reduce procurement costs. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

Having a good estimate of a vessel's handling time is essential for planning and scheduling container terminal resources, such as berth positions, quay cranes (QCs) and transport vehicles. However, estimating the expected vessel handling time is not straightforward, because it depends on vessel characteristics, resource allocation decisions, and uncertainties in terminal processes. To estimate the expected vessel handling time, we propose a two-level stochastic model. The higher level model consists of a continuous-time Markov chain (CTMC) that captures the effect of QC assignment and scheduling on vessel handling time. The lower level model is a multiclass closed queuing network that models the dynamic interactions among the terminal resources and provides an estimate of the transition rate input parameters to the higher level CTMC model. We estimate the expected vessel handling times for several container load and unload profiles and discuss the effect of terminal layout parameters and crane service time variabilities on vessel handling times. From numerical experiments, we find that by having QCs cooperate, the vessel handling times are reduced by up to 15 %. The vessel handling time is strongly dependent on the variation in the QC service time and on the vehicle travel path topology.

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