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The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

Optimal clean-up procedures lie at the heart of the closeout phase of construction projects under contract law. However, due to its complexity involving multiple issues, potentially unfulfilled contractual obligations, performance claims and counter-claims, combined with consequently deteriorating stakeholder relationships, the management of closeouts is fraught with difficulties leading to suboptimal outcomes. This is particularly true where general contractor (GC) organizations do not have a claims management office (CMO) dedicated to improving such suboptimal clean-up outcomes. Thus, this study aims to develop a model by which CMOs’ may effectively manage the clean-up phase in an environment of closeout claims.

X-inefficiency theory was utilized as the theoretical lens guiding this study. The theory helps identify closeout strategies implemented by a GC, which manages completion claims through a CMO. Data were received and analyzed from a large GC, which served as the firm case study. In this case, managing the closeout completion claims was the main function of the CMO.

The average delay of closeout completion was four times greater than construction phase delays. The GC results highlighted the “economic destruction tsunami of projects,” as a root cause for these completion delays. Wrap-up activities under contract law are identified, including within the domains of statements of completion, project handover and debt settlement. Behavior strategies are also defined, including relational and contractual approaches. Moreover, a process for improving closeout claim performance is described, comprising project closeout identification, rational intra-firm behavior, closeout completion and program closeout practice.

Findings from this work can significantly contribute in X-inefficiency theory in relation to how a decrease of X-inefficiency will lead to better closeout claim performance. It also offers practical insights into how best to minimize delayed closeout completion while providing valuable lessons for stakeholders in complex infrastructure projects. Further, a model is developed that may be utilized by owners, consultancies, designers and other contractor organizations in an effort to improve closeout claim performance.