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AI-based failure prediction

Designing and deploying an AI-based real-time alerting system for a global logistics company

Real-time insights and early warning about system outages

For logistics providers, the availability of a well-organized system is essential for an efficient package delivery process. If the system is down, delivery will be delayed. This will negatively impact the customer relationship and the company-internal processes. The customer, a global logistics company, approached T-Systems to create a solution that would enable effective monitoring of critical interfaces to their customer-facing systems.

The challenges

Business impact: Revenue decreases due to sudden outages in systems relevant for sales
High support costs: Resource intensive processing of customer support inquiries
Negative customer experience caused by delayed communication of outages

The solutions

Development and go-live of individual machine learning models for anomaly detection based on internal metrics from production systems as well as external data
Design and deployment of a real-time alerting system notifying all relevant channels (customer support, incident response team) and systems in case of outages via e-mail, SMS, or Teams Webhook
The solution also mimics the strategies of the experts to perform root-cause analysis for incident detection

T-Systems designed and deployed an AI-based real-time alerting system notifying all relevant channels (customer support, incident response team) in case of outages

Dominik Zabel, T-Systems

Customer benefits

Rapid resolution of incidences and minimization of downtime costs
Significant reduction in incoming customer support inquiries and costs in incidence resolution
Increased end customer satisfaction

Download the reference flyer

AI-based failure prediction

Designing and deploying an AI-based real-time alerting system for a global logistics company.

(PDF, 402.1 KB)

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