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Estimating the length of surgical cases is an important research topic, due to its significant effect on the accuracy of the surgical schedule and operating room (OR) efficiency. Several factors can be considered in the estimation; for example, surgeon, surgeon experience, case type, case start time, etc. Some of these factors are correlated, and this correlation needs to be considered in the prediction model in order to have an accurate estimation. Extensive research exists that identifies the preferred estimation methods for cases that occur frequently. However, in practice, there are many procedure types with limited historical data, which makes it hard to use common statistical methods (such as regression) that rely on a large number of data points. Moreover, only point estimates are typically provided. In this research, Kernel Density Estimation (KDE) is implemented as an estimator for the probability distribution of surgery duration, and a comparison against Lognormal and Gaussian mixture models is reported, showing the efficiency of the KDE. In addition, an improvement procedure for the KDE that further enables the algorithm to outperform other methods is proposed. Based on the analysis, KDE can be recommended as an alternative estimator of surgical duration for cases with low volume (or limited historical data).

Purpose The purpose of this paper is to propose a three-staged approach to configuration change management that uses a combination of complexity analysis, data visualization, and algorithmic validation to assist in validating configuration changes. Design/methodology/approach In order to accomplish the above purpose, the authors conducted a review of existing configuration management practices. This was followed by an in-depth case study of the configuration management practices of a major automotive OEM. The primary means of data collection for the case study were interviews, ethnographic study, and document analysis. Based on the results of the case study, a set of support tools is proposed to assist in the configuration management process. Findings Through the case study, the authors identified that the OEM used a configuration management method that largely represented the rule-based reasoning methods identified in the literature review. In addition, many of the associated challenges are present, primarily, the difficulty in making changes to the rule system and evaluating the changes. Research limitations/implications The primary limitation is that the case study was based on a single OEM. However, the results are in line with other practices identified in the literature review. Therefore, it is expected that the findings and recommendations should hold true in other applications. Practical implications A set of configuration management tools and associated requirements are identified and defined that could be used to assist companies in the automotive industry, and perhaps others, in managing their option changes as they continue to move towards full mass customization of products. Originality/value The proposed approach for configuration management has not been seen in any other organization. The value of this paper is in the effectiveness of the proposed approach in assisting in the configuration change management process.

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