Summary
Knowledge management and resource enablement are pivotal enablers for organizational success as Protection, Automation, and Control (PAC) systems continue to evolve amidst increasing complexity and rapid technological advancements. The expanding scope of PAC engineering demands a level of technical depth that extends well beyond foundational domain knowledge. Protection systems exemplify this challenge, where foundational power system protection knowledge is only a starting point. Engineers today must manage multi-vendor product portfolios, diverse device categorizations, advanced configuration and engineering tools, testing and diagnostic environments, and data driven applications for network connected systems. This paper presents a structured knowledge management framework: “NextGen
Read more Read lessResource Management (NRM)” that can be adapted by organizations to systematically develop capabilities, manage complexity, and enable future-ready engineering practices. NRM framework encompasses four layers- "Competency Model”, “Learning Orchestration”, “Data for Regional Operations”, and “Insights & Governance”-into a cohesive capability pipeline.
The competency model structures multi-disciplinary skills (e.g., fundamentals of protection, automation, automation logic, engineering tools, communications, cybersecurity, quality assurance, standards, etc.) using defined proficiency levels (Beginner, Basic, Advanced,
Expert) and a weighted readiness score. Learning orchestration aligns competencies with structured, curated learning paths that encompass onboarding modules, role-specific certifications, advanced training, hands-on labs, and mentorship programs, while leveraging automated synchronization to minimize manual intervention. Data and regional operations address confidentiality, integrity, and scalability through bulk updates, clustering, role-based access, audit trails, and validation rules. Insights-implemented through dashboards such as readiness indices, gap heatmaps, and certification compliance, enable data-driven staffing, risk identification, and investment optimization. A governance model with clear assignments
(engineering leadership, regional managers, learners, data stewards) establishes accountability for model ownership, content curation, assessments, reporting, and privacy. The paper aims to provide an implementation roadmap (Discover–Design–Build–Pilot–Scale–Evolve), use cases
(Protection, automation and control staffing, cybersecurity uplift aligned to IEC 62443, entry level onboarding, multilingual capability planning), and value realization via KPIs (readiness index, gap closure, learning velocity, certification compliance, rework reduction) for organizations to manage the KMS and thereby ensure business success and continuity.
Additional informations
| Publication type | Session Materials |
|---|---|
| Reference | B5_12383_2026 |
| Publication year | |
| Publisher | CIGRE |
| Country | Germany |
| Study committees | |
| File size | 1 MB |
| Price for non member | 30 € |
| Price for member | 30 € |
Authors
TICKOO Kuldeep - Siemens AG; ISLAM Mojahidul - Siemens AG; EHTISHAM Hamza - Siemens AG
