Integrated ERP-BPMS approach for optimizing HCS management: personnel qualifications, maintenance costs, and contractor selection

The object of the study is the integrated ERP–BPMS framework designed to optimize housing and communal services (HCS). The most problematic area identified during audit was the fragmentation of personnel qualification assessments, maintenance cost forecasting, and outsourcing decisions, causing inconsistent performance metrics, increased diagnostic errors, and prolonged service-resolution times under budget constraints. The research implemented three approaches: an Integral Evaluation Method (IEM) for quantifying staff competencies, a Maintenance Cost Assessment module for enhanced budget planning, and an IEM-based decision mechanism for comparing in-house versus outsourced work. These methods were integrated into the Business Operation System – Central Information System (BOS CIS) with an Information Administrator (IA) role that cross-validates digital logs against manual safety and compliance checklists. Implementation increased the Integral Qualification Score from 9.8 to 64.0, reducing diagnostic errors by 66 % and issue-resolution times by 50 %. Cost prediction accuracy improved from 0.76 to 0.93, while outsourcing decisions shortened project durations by 25–28.6 % and enhanced financial efficiency by 6 %. Data retrieval times decreased by 75 %, and the Information Utilization Rate rose from 65 % to 88 %. These improvements stem from the framework's real-time processing of both positive and negative performance factors and its hybrid human-machine validation process. This synergy enables accurate, data-driven decisions that optimize resource allocation under strict budget limitations. Unlike solutions focusing on isolated elements, this integrated platform simultaneously addresses personnel development, cost modeling, and strategic outsourcing, increasing operational transparency and adaptability. The framework is recommended for large-scale service environments where uninterrupted operation, cost-effectiveness, and high service quality are essential. By combining automated analytics with oversight, it provides a scalable model for enhancing project performance and strategic decision-making in resource-constrained service sectors.