Network HR — Rebuilding Triglav's Knowledge Network

A four-round, intermediate people-analytics and talent-strategy simulation set inside Triglav Systems d.o.o., a 540-person, €72M Ljubljana enterprise-software firm building industrial-IoT platforms whose entire competitive edge is the tacit knowledge held in its engineers' heads and the informal ties between them. On 5 May 2026 a principal architect — Maja K., who the firm's first organizational network analysis (ONA) shows sat at the centre of knowledge flow — resigns, a flagship client implementation stalls, and €2.1M of contracted revenue goes at risk. The ONA reveals the truth: the knowledge-transfer index has collapsed to 41/100 (cross-squad flow down 30% year-on-year), three connectors mediate ~45% of all cross-team knowledge ties (a single point of failure), network density across the six squads is just 0.06, and the two remaining top connectors both sit in the engagement survey's flight-risk band. The CEO authorizes zero net new headcount: you must fix knowledge sharing by redeploying and connecting the people the firm already has, before two quarterly client milestones — or leadership forces a rigid functional reorganization that engineering leaders warn will make the silos permanent. Playing the Head of People & Network through Robert Kaše's social-network perspective on HRM, you work four rounds. Round 1 — Diagnose: read the ONA against the org chart (they look nothing alike), identify the connectors by betweenness centrality, the highest-leverage structural holes between zero-tie squads, and the over-concentration risk. Round 2 — Plan: design the intervention portfolio under a fixed development budget across three levers — connector investment (de-load the central few vs. the headcount-reflex external hire vs. the concentration trap of deepening dependence), bridging mechanism (relational rotations and communities of practice vs. the documentation fallacy of a wiki vs. conceding the reorg), and deploying line managers as knowledge enablers rather than inward supervisors. Round 3 — Decide under the cap: commit scarce budget and manager capacity across knowledge-capture and succession (apprenticeship that moves tacit context vs. playbooks), targeted vs. flooded cross-squad rotations, redistributing brokering load onto multiple redundant paths vs. protecting or re-anointing heroes, and funding retention for the flight-risk connectors. Round 4 — Recover: absorb two shocks — a second top connector signalling an exit and a new strategic client demanding rapid cross-squad mobilization — show the reshaped network holds without a single hero, then make the executive-committee case on the four KPIs and render the rigid reorg unnecessary. The scoring rewards reading the network not the org chart, de-loading connectors while building the right bridges, transferring tacit knowledge through relationships, redundant multipath brokering, and funded connector retention — and measurably punishes the five classic errors: the headcount reflex, connector over-investment, bridge-everything overreach, the documentation fallacy, and ignoring connector retention. Final KPIs track the knowledge-transfer index (/100), network density, connector concentration (%), network resilience, and how many of the two remaining connectors were secured.