The DLP is the period during which the companies are accountable for product defects. In fact, customer complaint data collected by residential building companies during the defect liability period (DLP) represent a rich and inexpensive source of information. One way of reducing building defects is to produce useful information about their occurrence and causes. The high incidence of defects is a recurring problem in residential building projects, causing rework, cost overruns, and customer complaints. The main theoretical contribution of the study is the use of advanced data management approaches for managing complaints in residential building projects, resulting in the combination of inputs from technical and customer perspectives to support decision-making. The main outcome of this investigation is an information management model that provides an effective classification system for customer complaints, supported by artificial intelligence (AI) applications that improve data collection, and introduce some degree of automation to warranty services. The system was designed to indicate which defects should be investigated during inspections. Moreover, a recommendation system was proposed based on machine learning (ML) and hierarchical defect classification. Natural language processing (NLP) was used to build a word menu for customers to lodge a complaint. Multiple sources of evidence were used, including interviews, participant observations, and analysis of an existing database. Using Design Science Research, a study was undertaken at a Brazilian residential building company. This research aims to devise an information management model for customer complaints in residential projects. However, previous studies have not provided guidance on how to improve customer complaint data collection and analysis. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences.
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