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Artificial Intelligence

Predictive Analytics Successfully Expose and Create Supply Chain Efficiencies

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Client Profile & Challenge

Our client is a $1B Regional Airline serving United States domestic markets for United, American, and Delta Airlines.

Challenge:

Our client’s Operational systems were running 24/7, and their Supply Chain group could not gain timely access to parts, orders, and issues data, resulting in inefficiencies and overspending on parts inventory.

Solution Overview

The Supply Chain group struggled with timely access to parts inventory and maintenance data. They experienced difficulty and lost valuable time tracking down issues, identifying the availability and location of parts in real-time, and monitoring and measuring usage, replacement, and failure trends. This led to inefficiencies in inventory levels or warehouse locations. Supply Chain and Maintenance data integration was an additional step in the multi-year partnership with our client to create value and operational efficiencies by sourcing their disparate data into an Azure Data Lake and leveraging Machine Learning models to generate actionable, predictive analytics.

Solutions:

Strategic Advisory, Data Architecture, Data Integration, Predictive Analytics, Data Visualization, Process Improvement, Change Management, Artificial Intelligence

Technology Used:

Azure Data Factory, Azure Data Lake, Synapse, Data Bricks, and Power BI

“AFTER” BUSINESS VALUE CREATION

With the new solution in place, our Client’s Supply Chain group can now visualize inventory and parts consumption trends by location and maintenance facility to better manage their multi-million dollar parts expenditure. Our client now has the data-driven insights they need to make better decisions, enabling them to anticipate and manage the appropriate levels of parts inventory, optimize the locations of their warehouses, and decide where their inventory should be located in order to significantly reduce downtime waiting for or procuring appropriate parts.

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