From Dashboard to Decision Evaluating KPI Governance and Corrective-Action Closure

Dashboards are often treated as neutral windows onto organizational performance, yet the decisions they prompt depend on definitions, denominators, aggregation rules, refresh cycles, thresholds and follow-through mechanisms. This study develops and tests a KPI Governance and Corrective-Action Closure Framework (KGCACF) for resource-constrained organizations. The empirical component uses the Brazilian E-Commerce Public Dataset by Olist, obtained from Kaggle, comprising 99,441 orders. Seven operational KPIs were calculated under paired definitions across 86 eligible weeks: delivery lateness, on-time delivery, cancellation, average order value, repeat-customer activity, delivery cycle and approval latency. Management signals were compared against stated thresholds. The paired definitions generated 47 disputed signals in 602 metric-week comparisons (7.81%). The largest decision instability occurred for on-time delivery (18 disputed weeks), delivery cycle (13) and average order value (11). A corrective-action scenario used 26,575 exception events derived from reproducible validation, service and anomaly rules. With identical modeled review capacity, FIFO processing produced a mean high-severity closure time of 482.3 days and 9.2% high-severity SLA compliance; severity/SLA sequencing produced 4.2 days and 100.0% compliance. These are queueing results under explicit assumptions, not observed organizational effects. An evidence-gate sensitivity scenario further shows that an assumed 12% retest-failure rate would reopen 3,191 administratively closed cases. The paper contributes a KPI contract, dispute log, dashboard heat maps, decision register, corrective-action schema and closure-verification protocol. The results show that dashboard value arises not from visualization alone but from governed meaning, accountable action and verified resolution.