Sensitivity analysis of system dynamics models is essentially about sensitivity of patterns of output behaviors to inputs, since system dynamics modeling is behavior pattern oriented. In this study, a regression‐based procedure for pattern sensitivity analysis is developed, by defining behavior pattern measures such as equilibrium level, trend, inflection point, or oscillation amplitude. A unique feature of the procedure is that it takes into account the possibility of a model generating multiple behavior modes. This pattern‐oriented procedure is next applied to the tipping point project management model and a generic supply line model. These test applications yield sensitivity results that are meaningful, and also consistent with previously available sensitivity information about the parameters of these models. Finally, our pattern sensitivity analysis is shown to be a useful and effective method also for oscillatory system dynamics models, an unsolved sensitivity problem previously in the literature. Copyright © 2017 System Dynamics Society

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Operators of long field‐life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end‐of‐supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end‐of‐supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end‐of‐supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross‐validation results and out‐of‐sample risk assessments show good performance of the method to identify spare parts with high end‐of‐supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high‐risk spare parts.