Project Overview
Proactively avoid service interruptions that cause retailers to lose thousands of dollars per minute.
The need
With the availability of time-series data, our biggest Fortune 500 retail client needed a solution to predict and resultantly minimize server downtime across 1,000+ pharmacies in the USA. The objective was to enhance the efficiency of IT operations, allowing for rapid response when faced with system performance issues. Moreover, data health had to be monitored for timely down detection to enhance the collaboration between the on-ground teams and IT support.
The Solution
The introduction of ‘Artificial Intelligence for IT Operations (AIOps)’ in the organization meant that our team could exploit the telemetric, time series data such as CPU and memory usage, systems configuration parameters, and data related to queues in pharmacies to identify trends and anomalies. ARIMA and exponential smoothing models were selected after exploratory data and correlation analysis. This also enabled proactive system notifications in response to downtime so that the support team could react immediately, even before the end-user had to intervene, saving hundreds of hours of lost operational productivity.
The Outcome
Data-driven insights through BI
Powerful business intelligence (BI) visualization for data-driven insights
Improved end-user experience
Improved end-user experience with enhanced system performance
Accurate predictability
Achieved 80% accuracy in prediction, mitigating server outages at scale
Quick performance alerts
Timely performance alerts for proactive issue resolution
Successful application of ARIMA
Successful application of ARIMA for predictive analytics
Faster root-cause analysis and response time
Faster root-cause analysis and response time for system optimization
