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ABSTRACTPerformance measures are often outlined in the section of the service‐level agreement (SLA) of the contract between a supplier and a retailer. They are monitored periodically, and penalty and/or bonus payments are imposed in each performance review period, according to the SLA clauses. Previous studies have mostly considered a static inventory policy in analyzing SLAs. However, in practice, the supplier may have an opportunity to adjust the stock level in each inventory review period, according to the observed performance. This study analyzes the dynamic stocking decision for a supplier facing an SLA where the supplier sells a single product to the retailer. The ready rate is used to measure the performance in an SLA. To this end, models for both lump‐sum and linear penalty/bonus structures are developed, and the optimal stocking decisions for a strategic supplier are calculated using the stochastic dynamic programming approach. The results are then compared with the optimal static inventory policy, and new insights are derived to efficiently design an inventory system for the suppliers that are subject to service‐level incentives. In addition, we investigate the impact of SLA parameters—such as the length of the performance review period and incentive structures—on a supplier's performance, with the probability of meeting or exceeding the target service levels and the supplier's cost. We also consider the impact of demand distribution and inventory holding costs. Results show that under lump‐sum incentives, a longer performance review period benefits both the supplier and the buyer, given that the average ready rate increases with less variability as the length of the performance review period increases, leading to decrements in the supplier's total costs. In this scenario, there is a higher chance of gaining bonuses/avoiding penalties for a strategic supplier who adopts a dynamic inventory policy. On the other hand, under linear incentives, the impact of the performance review period on the supplier's cost and the performance measure (i.e., ready rate) is complicated and depends on the magnitude of the holding cost and the bonus and/or penalty structure of the contract. Under this scheme, the performance of a static inventory policy is highly dependent on the holding cost because a high holding cost may lead to failure to meet the contract requirements in terms of the service level.

ABSTRACTThe stochastic behavior of both transfusion (demand) and blood donations (collection) is a challenge for the blood supply chain. Although donations are not fully within the control of blood supply chain, the blood service can marginally moderate it by postponing appointments in the case of having an overstock, or by triggering a call for additional blood when faced with shortages. Such shortages are often observed as a consequence of catastrophic events. Past studies show that the response to a call for blood after a disaster is substantive. Yet the consequential impact on the supply chain is not well understood. This is due to the perishability of blood and the fact that donors are not eligible to give blood for a certain period after a donation has been made. In this study, the donation process is modeled with a Markov chain and the impact of a call for blood resulting from a disaster is investigated. This article highlights new actionable insights that aid planners to mitigate the negative impacts of a substantial response to a call for blood.

ABSTRACTDesigning effective settings for performance measures (e.g., fill rate) of a service‐level agreement (SLA) is challenging. This challenge is intensified when a firm adopts the pooling inventory model to allocate inventory/capacity to multiple buyers. Each buyer has its own service‐level contract outlining the required service level, the penalty structures, and the performance review period (PRP) length, which might not be the same as other buyers. This means the supplier requires an effective resource allocation policy whereby the different requirements of multiple buyers are integrated into a pooling model and capacities/inventories are allocated in the most effective way. Given a base‐stock replenishment policy and finite time horizon PRP, in this study we propose two new (anticipative) allocation policies—foresight linear programming (FLP) and two‐stage stochastic (TS)—and compare them with existing allocation policies. These allocation policies are developed for different penalty structures of linear, lump‐sum, hybrid, and no‐penalty settings. Results show that suppliers benefit from longer PRPs if linear or hybrid penalty structures are employed. We also find that when the length of PRP of buyers is not identical, TS is the recommending policy. Further, results provide a guideline for selecting the best resource allocation policy under various SLA terms, in particular, where buyers' PRP lengths are not identical.