Proactively avoid service interruptions that cause retailers to lose thousands of dollars per minute.
Proactive anomaly detection modeling
With availability of time-series data, our biggest fortune 500 retail client, needed a solution to predict and resultantly minimize server down time 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.
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 down time so that the support team could react immediately even before the end-user had to intervene, saving hundreds of hours of lost operational productivity.
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 down time so that the support team could react immediately even before the end-user had to intervene, saving hundreds of hours of lost operational productivity.
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