Many companies with global supply networks suffer from market volatility and supply disruptions, which adversely affect both their short and long-term profits. Although using mechanisms, such as supply contracts, is useful to mitigate uncertainty, the inherent inefficiency of decentralization is a critical issue that needs to be considered at early design stages. This paper studies a supply chain problem where a buyer receives a product from a cheap but unreliable main supplier and signs an option contract with a perfectly reliable backup supplier to share supply and demand uncertainty. To build efficiency benchmark models, we first consider a centralized problem and then explore a decentralized problem where there is only a wholesale price contract between the buyer and the backup supplier. Considering the option contract, we reconstruct optimization problems and sequentially characterize the members' reservation and production policies under a voluntary compliance regime. Subsequently, we establish a win-win coordination mechanism that maximizes system efficiency and meanwhile is desirable from both contract members' perspectives. Results reveal that the proposed mechanism leads the backup supplier to choose a lower level of production capacity than the buyer's reservation amount (i.e., an underproduction policy). We realize that the existing mismatch between the members' optimal policies is caused by the buyer's phantom ordering. This paper sheds light on the effectiveness of contract-based mitigation strategies that enable firms to ensure responsive backup capacity under demand uncertainty and supply disruptions.

Purpose: Developing strategic relationships with third-party logistics (3PL) providers has long been one of the key challenges in automotive supply chains. The purpose of this paper is to propose a new approach for evaluation and indexing 3PL providers using the Kano model. Design/methodology/approach: The statistical population used in this research comprises managers from the Iranian automotive industry. The Kano evaluation approach is used to analyze the data collected and to classify the criteria used in selection of the preferred 3PL providers. Findings: The results suggest that the proposed framework, based on the Kano classification, can be a powerful tool for the automotive industry in evaluating 3PL providers. Moreover, the analyses indicate that 3PL providers need to improve their service offering in aspects that are to be found in the Kano model’s requirements, namely, must-be, one-dimensional and attractive. Originality/value: This study contributes to the supply chain management literature by being the first to classify selection indices of 3PL providers in the automotive industry using the Kano model.