How AI copilots are mitigating transportation risk, enhancing delivery agility

AI copilots identify and notify incidents in real-time.

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For the longest time, supply chains were managed using reactive strategies. In other words, supply chain leaders rarely had the means to address problems before they occurred. For instance, the non-availability of on-ground tools led to delayed issue identification and stakeholder notification, prolonging decision-making. We have seen that traditional management information systems can take up to three to four days to identify operational or logistical issues. The lack of system assistance on corrective and preventive actions negatively impacts customer experience, SLA compliance and increases the risk of fraud.

Over-reliance on manual intervention, manual data entry and manual eyeball checks significantly increases the chances of errors, leading to growing costs. Not just manual dependencies, but the lack of real-time transportation visibility and actionable insights also pose an imminent threat to everyday logistics operations. These challenges are aggravated by the scale at which the logistics industry operates in terms of people working and customers serviced.

A research by Moody’s Analytics revealed that 69% of businesses say they do not have the necessary visibility over their supply chains to uncover risks in their organisational networks to avoid reputational harm.

The emergence of AI-powered copilots is transforming how logistics leaders manage risk and operational failures. Using a proactive monitoring system, AI copilots identify and notify incidents in real-time, empowering logistics managers to swiftly address operational issues as they arise. This translates to a streamlined incident management experience, improved efficiency and reduced costs.

At a high level, there are two aspects to such copilots. It’s a powerful combination of an incident generation engine and an incident management solution.

It’s a smart engine that proactively and 24x7 monitors logistics operations and creates incidents based on anomalies and preconfigured thresholds for efficiency. Let’s take a simple example. If an order is supposed to reach a customer in 30 minutes and the driver has not completed half of the journey in the first 15 minutes, the engine will automatically trigger an alert to the right person notifying them of a possible SLA breach.

This is just one use case. A copilot can trigger alerts on a wide range of real-time incidents, such as if a driver is idle for more than five minutes; if hub level attendance is below 90%; if a driver’s GPS is turned off for more than three minutes; if a rider’s shift duration falls short by two hours, and so on. All these thresholds can be easily configured based on business and operational needs.

This is a solution for managers to acknowledge new incidents, co-ordinate with the team for corrective action and ensure target KPI achievement. Going back to the above example, to stop the SLA breach from occurring, the AI copilot will leverage incident management solution capabilities and come up with recommendations to re-route the delivery or assign the order to another partner whose chances of adhering to the promised delivery TAT are much higher. Based on the hub/employee hierarchy, such a copilot identifies the right person or the responsible individual to drastically expedite corrective measures.

1. Real-time anomaly detection

Leveraging an intelligent engine, an AI copilot continuously analyses operational KPIs using advanced analytics to uncover potential issues. This proactive approach helps identify problems before they escalate into major disruptions, ensuring your operations run smoothly and efficiently.

2. Enhanced accountability and ownership

An AI copilot automatically assigns incidents to the most appropriate individual within the team based on your predefined organisational structure. This streamlines the incident management process, allowing for quicker resolution times.

3. Gain granular-level insights

A logistics manager can drill down to specific object or entity levels, such as hub or rider, to view a detailed listing of all contributing incidents that caused an operational failure. This granularity allows businesses to identify patterns and understand the root causes of issues impacting specific areas of operation, enabling targeted interventions and improvements.

4. Increased management bandwidth

Such copilots help businesses unburden their teams from 24x7 operations monitoring and drive “management by exception”. This approach empowers managers to invest their bandwidth in strategic initiatives and value-added activities, enhancing overall productivity and innovation within an organisation.

5. Data-driven decisions and reduced bias

AI copilots come with exhaustive KPI tracking that eliminates the risk of missed issues or subjective bias, driving data-driven decisions and targeted solution implementation. With 360° insights, businesses can ensure comprehensive oversight and informed decision-making across operations.

Building proactive logistics processes is essential to preemptively address potential issues, ensuring seamless operations and minimising disruptions. This leads to improved efficiency, cost savings and higher customer satisfaction by consistently meeting delivery expectations. Hence, businesses across the world are using AI copilots to reduce manual follow-ups by 82%, improve SLA adherence by 21%, reduce routing errors by 34% and enhance shipment processing throughput by four times.