Impact of Lean A3 Thinking on Critical Activities in Three Public Infrastructure Projects in Guayaquil, Ecuador
DOI:
https://doi.org/10.70577/asce.v5i2.954Keywords:
civil works; construction industry; total quality management; problem solving; project management; productivity; A3 thinkingAbstract
Managing critical activities in public civil works requires tools that help identify problems, analyze their causes, and implement corrective actions in a structured manner. To analyze how the application of A3 thinking, understood as a Lean tool for organizing and solving problems systematically, influences the management of critical activities in public civil works projects in Ecuador. An observational, descriptive, and analytical study with a multiple-case design was conducted in three public infrastructure projects carried out in Guayaquil during 2017–2024: the Guayaquil Fire Department River Station (Cuartel Fluvial), the Portete Community Surveillance Unit (UVC Portete), and the Correos del Ecuador Processing Center. The sample consisted of 10 critical activities in which the use of A3 thinking was recorded through structured direct observation and a standardized form that documented the identified problem, root cause, generated impact, applied action, obtained outcome, and level of improvement achieved. The analysis combined absolute and relative frequencies with open and axial coding. Quality-related problems were the most frequent, with 4 cases representing 40%, followed by time-related issues (20%), cost-related problems (10%), and mixed impacts (30%). In addition, 5 activities achieved a high improvement level, 4 a medium level, and 1 a low level; therefore, 90% of activities reached medium or high improvement. The actions with the best results were technical validation, use of supervised checklists, process standardization, quality control during execution, and interdisciplinary coordination. A3 thinking strengthened the link between cause, action, and outcome, while facilitating evidence-based decision making derived from field data. It contributed to reducing errors, rework, interferences, and planning deviations, mainly when the root cause could be directly addressed by the technical team.
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