Manufacturing and supply networks face disruptions that affect production, suppliers and customers. Recording incidents is only the beginning: organisations also need to learn from them. Developed within the Horizon Europe NARRATE project, the Resilient Decision-Making Module (RDM) connects incident management, risk analytics, simulation and reporting in a unified digital workflow.
1. Building an Evidence Base for Resilience
The RDM’s Incident Management section records incident timelines, severity, status, events, disruptions, mitigation actions and lessons learned. It captures impacts on resources, inventory, suppliers and customers across quantity, quality, cost and time.
2. Transforming Incident Data into Risk Intelligence
The Risk Assessment Tool (RAT) combines complementary views of risk. Risk Scoring summarises severity, likelihood and aggregated impact. The Risk Matrix positions events and disruptions by likelihood and impact across acceptable, mitigation, strategic and critical zones. Damage Alert highlights network components with stronger impact evidence, helping users identify where closer attention may be needed. Time-to-Recovery estimates the likely duration of ongoing and future incidents using comparable incident records.
3. Understanding How Risks Evolve and Spread
Risk Trend Analysis identifies recurring monthly and seasonal patterns of risk occurrence. Risk Propagation Analysis examines how disruptions may cascade through interconnected network components, while Drift Analysis captures the temporal evolution of events and disruption pathways. Collectively, these predictive analytics capabilities provide organisations with enhanced visibility into network-level risk drift, enabling earlier detection of emerging vulnerabilities and more effective mitigation strategies.
4. Exploring Unforeseen Risks through Hybrid Scenario Modelling and Analytics
In the RDM, each risk is defined by a combination of event type, severity level and disruption code. Historical incident data capture only the combinations previously experienced by a manufacturing network. The wider risk space also contains other possible combinations that have not yet been seen in the network, which are defined as unforeseen events. RDM helps companies to explore unforeseen events through hybrid scenario modelling and analytics. By combining learning from historical incident patterns with the RAT’s analytical functions, the RDM generates scenarios and assesses their potential consequences. This gives companies insight into disruption situations beyond their recorded experience, helping them strengthen preparedness, support business continuity and enhance the resilience of their manufacturing networks.
5. Translating Complex Risk Analytics into Actionable Insights
The RDM translate the complex outputs generated by the RDM’s analytics engines into easy-to-understand, actionable reports. Instead of overwhelming users with large datasets, technical metrics, and detailed analytical results, the RDM presents the most important findings in plain language tailored to users with varying levels of expertise. The reports identify critical risks, vulnerabilities, and potential disruptions, while providing clear recommendations on which events require immediate attention, which can be safely monitored, and what operational actions should be taken to minimise disruption and avoid unnecessary costs. In doing so, the RDM bridges the gap between advanced analytics and managerial decision-making, enabling organisations to improve resilience through informed and timely actions.
Authors
Dr Navid Sahebjamnia
Senior Lecturer in Operations and Information Management and PI in the NARRATE Project
Brunel Business School, Brunel University of London, Uxbridge, UB8 3PH, UK
Email: navid.sahebjamnia@brunel.ac.uk
Tel: +44 (0)1895 265028
Dr Taraneh Saadati
Research Fellow at Brunel Business School
taraneh.saadati@brunel.ac.uk