Housebuilding companies are required to deal with customer complaints in the warranty period. Some of them have maintenance departments that make necessary repairs in existing buildings. Due to this service, companies accumulate records on the quality of projects, which can contribute to the understanding of occurrence defects and their causes, supporting continuous improvement. However, deficiencies in information management can make it difficult to use complaints records as a feedback source. The literature does not discuss solutions for managing information related to customer complaints, nor the use of performance metrics that can effectively provide feedback from quality problems identified. This study has two contributions: a classification structure for the types of defects identified from complaints, and indicators generated by fault tree analysis. The study was carried out in the maintenance department of a Brazilian housebuilding company. The evidence sources used were: complaint database analysis, discussion seminars, and interviews with the company representatives. The proposed solutions resulted in improvements regarding the structure and level of detail of the records. Also, the fault tree analysis made it possible to identify the most critical quality problems as well as to evaluate the level of impact of each one in project quality.
Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. However, previous studies have not provided guidance on how to improve customer complaint data collection and analysis. This research aims to devise an information management model for customer complaints in residential projects. Using Design Science Research, a study was undertaken at a Brazilian residential building company. Multiple sources of evidence were used, including interviews, participant observations, and analysis of an existing database. Natural language processing (NLP) was used to build a word menu for customers to lodge a complaint. Moreover, a recommendation system was proposed based on machine learning (ML) and hierarchical defect classification. The system was designed to indicate which defects should be investigated during inspections. 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 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.
A challenge faced by some companies in the residential building sector is to cope with the complexity introduced to respond to the increasing diversity of customer demands in a profitable and sustainable way. Mass customisation (MC) has been described as a strategy to deliver customised products at costs and delivery times similar to mass production. The implementation of this strategy can be supported by several information and communication technologies emerging in the Industry 4.0 paradigm, which has been named Construction 4.0 in the construction industry. The aim of this research work is to identify the synergistic potential between Construction 4.0 technologies and the implementation of MC practices in the construction sector. A decision matrix associating a set of MC practices and C4.0 technologies has been devised based on a literature review. Specialists assessed the relationships between items, and the Jaccard similarity index was calculated to understand which Construction 4.0 technologies should be jointly implemented to support MC strategies. As a secondary contribution, this study has also proposed a method to guide companies in the identification of technologies that can support the implementation of MC in specific contexts.
This work aims to identify: (i) practices aligned to lean principles used by companies in the construction industry (ii) performance indicators used in lean systems and (iii) barriers to the implementation of these performance indicators. A survey was developed and applied as a research tool. The first version was prepared through bibliographic review and semi-structured interviews with lean production specialists. After application and refinement cycles, the final version of the questionnaire was applied to a sample of 32 companies in the construction sector, established in the state of Rio Grande do Sul/Brazil. As a contribution of the present study, the analysis of the most used practices showed that most of the companies included in the research is in an early stage of lean implementation. In addition, some of the most used practices are associated to the indicators pointed out by the participants as being more used. This relationship indicates that there is a structured attempt to mature the implementation of practices, monitored by appropriate indicators. The lack of knowledge of employees in the implementation and use of performance indicators and the lack of investment in technology are barriers to the implementation of performance indicators.
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