One of the largest retailers that operates a chain of hypermarkets, discount department stores, and grocery stores around the globe
Product Development, Web, DevOps, Cloud
Azure DevOps pipelines, Terraform, Azure CLI, PowerShell, Docker Python, Spark, Delta Lake, Airflow, Docker, Kubernetes, Java 11, Spring Boot, REST API, OpenAPI, Maven, Spring Data (JPA), OAuth 2.0, MS SQL, Azure (App Services, Load Balancers, Key Vault, Application Insights)


In large enterprises, asset management decisions to repair or replace a “bad” asset approaching the final phase of its life cycle present a big challenge. The wrong choice can directly affect profitability, leading to downtime or unnecessary costs. If you replace an asset too soon, you are wasting resources, but if you delay too long choosing repairs instead, operations can be put at risk. Sound asset management was a natural pain point, too, for our client, a leading retailer operating thousands of stores in many countries. They needed to know precisely what was the best course of action for their vast assets to improve cost efficiency. The retailer approached ITRex, its long-standing tech partner in big data services, for assistance with building an enterprise-grade data analytics solution for ML-powered decision-making.

Our task was to:
Build a training data set and prepare the data to train the client’s proprietary ML model
Collect, structure, clean and enrich data on assets and stores that operate them
Build the entire back end of the enterprise data analytics solution, pairing the ML model to the big data
Ensure that the ML model can be retrained to improve predictive performance
Build an easy-to-understand data analytics dashboard for end-users


An enterprise data analytics solution running on a cloud-based architecture has been built using DevOps best practices. By analyzing multiple inputs about a specific asset in a specific store and running cost calculations, the system allows both data analysts and non-tech savvy business users to get an instant answer about the cost efficiency of repairing vs. replacing this asset. Specifically, the system:
Allows users to select an asset from a table of attributes for analysis
Uses a predefined set of dozens inputs for cost calculations, from age, useful life, initial capital cost, book value, and critical repair cost to current resale value
Allows users to customize the inputs
Retrieves and visualizes data on historical and forecast maintenance and repair costs
Runs ML-powered calculations and displays a comparison of net present value for repair and replace scenarios, recommending which scenario is best
Displays detailed maintenance and repair cost breakdown
Retrains the ML model based on fresh asset data
enterprise data analytics solutions for asset management
ML based data analytics solution


Instant decision-making on the cost efficiency of repairing vs. replacing an asset
Data-driven accuracy based on current inputs
Substantial time savings for asset managers
Optimization of operational costs

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