How AI-Powered Incident Management Can Strengthen End-to-End Supply Chain Resilience
In this article we explore how AI-Powered Incident Management Can Strengthen End-to-End Supply Chain Resilience and become a leading advantage.
The past decade has proven both the essential role of supply chain management in global stability and the vulnerability of these systems to shocks. With so many assets and avenues to track, it can seem fruitless to put so much emphasis on risk management when uncertainty is baked into operations. Yet leaders now consistently prioritise resilience and adaptability over cost optimisation, in large part because two-thirds of all supply chains are considered fragile and prone to loss in unexpected circumstances.
The industry is, at the very least, aware of this reality. The question is: how best to address it after spending decades chasing lean efficiency at every turn? Companies can expect a month-long disruption every 3.7 years, according to McKinsey, with costs that compound over time as the world grows more interconnected. While there is no single definitive answer to this issue, it has become apparent that technology and data play a large role in every segment of each part-solution.
The shortcomings of traditional incident response
Despite technological and digital transformation driving the scale and complexity of global supply chains, incident response remains constrained by reactive, fragmented models. Transports use telematics and IoT sensors, warehouse teams monitor CCTV and access controls, and SOCs (security operations centres) might receive some or all of this information, unfiltered, from distributed devices across the country and possibly the world. This wall of data is unstandardised, as are the protocols for escalating or investigating the inciting incident.
Impenetrable information and unclear processes create three fatal vulnerabilities:
- Delayed detection
- Slow escalation
- Incomplete awareness
This applies to logistics centres monitoring global shipping, all the way down to an individual storage facility. Internal devices that do not communicate with each other require manual time and effort from on-site teams who must corroborate and correlate safety reports, shipping delays, and access breaches across different data silos before an overview can be formed. AI-powered management is most effective when the underlying infrastructure is unified, which is why finding a 3PL with pre-integrated data protocols is a critical step toward supply chain resilience.
End-to-end confusion
Without end-to-end incident management automation, incidents can be misattributed and investigated by the wrong teams. A delayed shipment might flag with logistics before security teams realise the root cause was an unauthorised entry, complicating the workflow of both.
These disruptions pile up, contributing to the industry-wide problem of unplanned downtime and its downstream financial and reputational effects.
AI’s role in strengthening supply chain resilience
Modern incident response software gathers, filters, and refines information at each step in the supply chain, presenting critical data from across sensors in a single cloud-based command dashboard.
Warehouses and distribution centres
In dynamic, high-pressure environments, incidents can easily cascade and impact other segments of the chain. AI works to strengthen warehouses by:
- Detecting unusual movement patterns, such as loitering or unauthorised personnel in restricted areas
- Flagging failed access attempts
- Identifying workplace safety concerns, such as a lack of protective equipment
- Filtering false alarms to decrease distraction, with decision-making left transparent and auditable for teams to analyse
- Highlighting when machinery needs maintenance through temperature and vibration sensor data
Adaptive automation eases the burden on security and logistics staff by notifying them when a concern requires their attention. AI tags and timestamps relevant footage, sensor readings and log entries, providing responders and investigators with complete operational awareness.
Transportation
Cargo theft is becoming more common across the US, but drivers also face pressure from tight deadlines and traffic constraints. By utilising GPS data, video and communication tools, AI can:
- Monitor routes for real-time anomalies and deviations
- Identify congestion to adjust ETA
- Correlate location with security alerts
- Trigger pre-defined response workflows to get human eyes on the situation
Drivers and dispatchers benefit from greater awareness and protection, with a streamlined emergency response that speeds up reaction time.
Crisis management
Major disruptions require cross-functional coordination, a process that relies on manual efforts and adherence to protocol. During emergencies, this is not always attainable or intuitive, and wasted seconds can be the difference between life and death. Automated AI systems, however, can:
- Notify cross-department teams and provide ongoing, real-time updates
- Log actions and footage for investigation or emergency services
- Trigger pre-defined actions, such as alerting authorities or closing doors and vents during a fire
Consistency at scale requires experienced, highly trained operators and clear communication from on-the-ground teams. The confusion and panic of an emergency can fracture even the clearest of plans, but AI suffers no such anxiety. It can orchestrate reaction and response, and manage dispatch when humans are unable.
Compliance and governance
Documentation, transparency and accountability are all core to incident management, and their importance only grows with the implementation of AI. Though AI can aid in compliance through automated time-stamped logs and structured, consistent documentation, care must be taken to ensure its actions and decisions are understood.
Not only must they be clearly presented in a manner that is legible to non-technicians, but they must also be capable of being overwritten. Human-in-the-loop management of AI, supported by IBM and OECD frameworks, remains the most consistent and reliable approach to using and maintaining AI systems.
Intelligent resilience
Traditional incident management systems are among the main reasons supply chain management finds itself in such a precarious position. They are not built to handle distributed, complex operations, and each additional moving part they attempt to incorporate into their network only adds further delays and stumbling blocks.
Operations have become more streamlined and data-driven than ever, but the overarching structures have yet to adapt. In this environment, disruptions hit hard and are difficult to anticipate, giving a sharp competitive edge to those who incorporate data-driven predictions and mitigation strategies into the foundations of their end-to-end management.
Advance your supply chain management capabilities with IoSCM. Call 0800 1422 522 today to find out more.