Synergizing Analytics and Data Engineering - A Comprehensive Case Study for Warehouse Operations

Customer Overview

One of the largest private equity firms headquartered in the UK acquired portfolio companies in the business of managing warehouses for elite people across the US and Europe. The private equity firm wanted to view the aggregated data across all their portfolio companies.

Project Overview

The private equity firm wanted to evaluate overall performance of all acquired portfolio companies with the intention to optimize their warehouse management processes and facilitate business growth. To achieve this goal, they envisioned building analytical solutions of a unified data warehouse that aggregates diverse data from all portfolio companies, with the objective of developing dashboards and reports.

Challenges

Designing a configurable solution with high scalability and availability for end-to-end data warehousing operations from collecting data from diverse sources to analytics and reporting.

• Designing a solution that is easily configurable and installed for the newly acquired portfolio companies to expedite the data retrieval and ingestion.
• Establishing connections to different data sources including relational databases, S3, SFTP etc., with the capability to acquire structured and unstructured data.
• Ensuring the timely delivery of data ingestion, pre-processing, and categorization of data to the data warehouse.
• Creating a dashboard for instant visibility into the overall warehouse management processes and generating the relevant reports for the portfolio companies on a regular basis.
• Ensuring scalability and availability of the data warehouse to accommodate high volume of data from various portfolio companies.
• Identifying objects stored in different warehouses and recognizing damage in them from the image-based data within the data source.

Solution

Building comprehensive, scalable warehouse management software for the private equity firm to monitor and evaluate the performance of its portfolio companies managing warehouse operations across multiple locations.

• We deployed and configured client and server agents to all portfolio companies. Client agents fetch diverse data from sources and transfer it to the data lake. The server agent then initiates the data pipeline to ingest data into the data warehouse.
• The client agent has the capability to establish connections with relational databases and any other sources using the connector framework.
• We have set up a data synching strategy to manage the data flow between data source and data warehouse, facilitating full, delta and real-time updates.
• The server agent has the capability to coordinate the flow of data through the pipeline, organizing data into bronze, silver, and gold categories within the data warehouse.
• Simultaneously, the server agent employs AI for image recognition and damage identification for managing further processes within the data warehouse.
• For the final outcome, we’ve created dashboards and reports that can be visualised and scheduled with configurable filters, covering the needs of various stakeholders.

Benefits

The warehouse management software we developed to provide centralized analytical and reporting solutions to our client, delivered the following business benefits:

• Our client gained speed and agility in data processing and improved resource management to oversee the performance of portfolio companies.
• By implementing Data Analytics and AI, we helped the client achieve high visibility into inventory movement for optimization and better demand-supply prediction.
• With custom dashboards and reports backed by analysis of relevant performance metrics for different departments or business areas, our client streamlined processes for high efficiency.
• By utilizing predictive analytics to optimize operations and identify new business opportunities, our client attracts, retains, and grows customers for improved profitability.

Industry

• Logistics
• Warehouse management

Technology and Integration

• Amazon Redshift
• Amazon S3
• Amazon Recognition
• Amazon SQS
• Amazon Lambda
• Tableau