This study aims to propose a solution for guiding the configuration of complex product lines by addressing challenges such as high variability, customization issues and decision-making complexity.
A design science approach was adopted to develop and evaluate ConfiLog, a goal-driven method leveraging process mining techniques. ConfiLog uses event logs to uncover configuration process models, interprets them using a Goal-Question-Metric framework and generates guidance via a recommendation engine. Its effectiveness was tested through controlled experiments involving 226 students configuring a bike from a product line comprising over a billion possible configurations.
ConfiLog reduced the average time of the configuration process by 81%, achieving a 100% completion rate with customized recommendations. Additionally, ConfiLog demonstrated scalability, maintaining execution times under one second for models with up to 3,000 variants.
The experiments were conducted with students in controlled settings, offering initial insights but limiting applicability to real-world industrial contexts, where dynamic stakeholder goals and diverse user requirements remain unexplored.
ConfiLog enhances customer experience, competitiveness and retention in e-commerce, particularly in high-variability sectors such as automotive, fashion, apparel and electronics, where personalization is crucial. It is also well-suited for other variability contexts, including ERP integration and COTS management. Beyond these applications, ConfiLog demonstrates broad applicability in public services like healthcare and transportation, showcasing its versatility.
ConfiLog supports iterative, interactive and goal-driven configurations. It ensures flexibility and consistency by recommending variants and scheduling the configuration process.
