By Y. J. (Ian) Ho📧, S. Liu📧, J. Pu, and D. Zhang
In Production and Operations Management, 2022. Ahead of print online. https://doi.org/10.1111/poms.13816
Technological applications disrupt the way to assess risks in the auto-insurance business. Contrasted with the common practice based on static demographics, usage-based insurance predicts risks using driving data collected from Internet-of-things–enabled telematics. This study proposes a novel solution leveraging the synergy between big data and hierarchical modeling. We specifically consider two aspects of mobility, namely, trait and trajectory, monitored by global positioning system (GPS), on-board diagnostics, and in-vehicle cameras in real time. Traits here refer to drivers’ distinctive driving behaviors (styles), whereas trajectories consist of the vehicle motion sequences and the contextual factors on trips. We operationalize semantic features of the two to assess risks at both trip and driver levels. Using fine-granular driving data and crash reports, we find that behavioral traits play a significant role in predicting crashes, given individual heterogeneity and temporal dynamics. In a series of empirical validations, the proposed solution outperforms the current practice and alternative predictive models considered by prior literature. We show that the mobility-based models are superior to the demographic-based ones. Moreover, our model achieves the comparable performance of neural networks, improving the recall of class-weighted logistic regression, nested support vector machine, and cost-sensitive random forests by 44.23%, 29.18%, and 24.59%, respectively. Last, our approach is robust, data independent, and computationally efficient for skewed and small samples. This study provides several managerial implications and a blueprint for the auto-insurance industry to operationalize IoT-enabled risk assessments in the era of 5G communication.
Keywords: Driving risk; Usage-based insurance; Internet of things; On-board diagnostics; In-vehicle camera
Purpose. This paper aims to provide the results of a survey of courses dedicated to the field of supply chain management in higher education. This research is unique because it represents the first large-scale study of graduate supply chain management courses taught at universities globally. Design/methodology/approach. Content analysis was performed on each syllabus to identify the actual course content: requirements, pedagogy and content emphasis. This aggregated information was used to compare historical research findings in this area, with the current skills identified as important for career success. This data provides input for a gap analysis between offerings in higher education and those needs identified by practitioners. Findings. Data gathering efforts yielded a sample of 112 graduate courses representing 61 schools across the world. The aggregate number of topics covered in graduate courses totaled 114. The primary evaluation techniques include exams, projects and homework. Details regarding content and assessment techniques are provided along with a gap analysis between the supply chain management course content and the needs identified by APICS Supply Chain Manager Competency Model (2014). Originality/value. The goal is to use this data as a means of continuous improvement in the quality and value of the educational experience on a longitudinal basis. The findings are designed to foster information sharing and provide data for benchmarking efforts in the development of supply chain management courses and curricula in academia, as well as training, development and recruitment efforts by professionals in the field of supply chain management.
Stochastic simulation requires input probability distributions to model systems with random dynamic behavior. Given the input distributions, random behavior is simulated using Monte Carlo techniques. This randomness means that statistical characterizations of system behavior based on finite-length simulation runs have Monte Carlo error. Simulation output analysis and optimization methods that account for Monte Carlo error have been in place for many years. But there is a second source of uncertainty in characterizing system behavior that results from error in estimating the input probability distributions. When the input distributions represent real-world phenomena but are determined based on finite samples of real-world data, sampling error gives imperfect characterization of these distributions. This estimation error propagates to simulated system behavior causing what we call input uncertainty. This chapter summarizes the relatively recent development of methods for simulation output analysis and optimization that take both input uncertainty and Monte Carlo error into account.