Data-Driven Risk Management (Top Risks & KRIs)

This in-house program equips cargo & logistics risk managers and owners to run an end-to-end evidence-based ERM cycle. Using the attached core material, participants will:

1. Contrast conventional vs. data-driven risk management;
2. Identify and prioritize Top Risks from operational and financial data with risk maps/heat maps;
3. Design a fit-for-purpose KRI framework (definitions, thresholds, and escalation tied to risk appetite/tolerance); and
4. Communicate status through Excel/Power BI dashboards that surface early warnings for delays, theft, SLA breaches, and external shocks (e.g., weather).

By the end, teams are expected to leave with tangible outputs: a draft Top Risk Register, a KRI dictionary (with thresholds & data sources), a first-cut risk heat map, and a starter dashboard wired for ongoing monitoring—ready to iterate with real company data Learning Objectives (what participants will be able to do)

1. Explain ERM in logistics and how data improves identification, measurement, and decision-making.
2. Distinguish leading (KRI) vs lagging indicators and why KRIs are early-warning signals.
3. Build a Top Risk long-list from operational/financial loss & incident data, then narrow to Top Risks using impact × likelihood.
4. Construct a risk heat map and justify priorities with historical patterns and business impact.
5. Specify KRIs per Top Risk (definitions, formulas, units, frequency, owners).
6. Set baselines, thresholds, risk limits, and escalation paths aligned to risk appetite/tolerance.
7. Map data sources (WMS/TMS, fleet GPS, finance, customer/SLA, weather/traffic) and design a minimal data pipeline to feed dashboards.
8. Prototype an Excel/Power BI risk dashboard (Top Risks, KRI gauges, trends, heat map, regional drill-down).
9. Run an early-warning & response routine (weekly review, breach alerts, playbooks).