This project addresses the productivity problem in the requirement management process of a Latin American Insurtech in the digital services sector. Despite over a decade of experience and regional growth, the company showed substantial gaps versus industry benchmarks in response times, demand coverage, and client satisfaction. The support process lacked standardization, accurate time estimation, and data-driven planning, which became critical with rising demand during peak periods like month-end and billing closures.
A diagnostic phase using 2024 operational records revealed an average resolution time of 24.92 days, service capacity covering only 44.96% of demand, and productivity at 0.0441 requirements/man-hour—well below expectations for a digital firm. Root causes included fragmented workflows, lack of real-time monitoring, and poor effort estimation.
To solve these issues, a structured improvement model based on BPM and LSS was designed. The solution combined BPM process modeling, Arena simulation with Input Analyzer, and statistical validation in Minitab. A new model process integrated ISO/IEC 20000-1:2018, ISO/IEC 27001:2022, PMBOK domains, and stakeholder insights. Simulation over 118 replications showed a 45% productivity increase, resolution time drop from 299 to 207 hours, and 75-day capacity rising from 53 to 76.9 requirements.
Beyond operational gains, the solution avoided extra hiring or infrastructure costs, supporting sustainability and reducing penalty risks of up to 50% on client premiums. This project confirms BPM and LSS as effective tools for small Insurtechs to achieve significant improvements through engineering analysis and without major investments.