The client is a major California-based wireless networking company, with a history of over 20 years. Its solutions provide clients seamless wireless network services, monitor networks meticulously, and future-proof networks for IoT (Internet of Things), the next generation of devices.
Over its growth journey, the customer has acquired various warehouses and different facilities, as well as developed proprietary warehouse management systems, adding significant complexity to its IT landscape. Due to disparate systems, with around 10+ WMS applications used across 150+ warehouses, it was a challenge to provide a self-service platform to customers to place orders and help them more effectively manage their business.
The client also aimed to integrate data across warehouses and provide a single source of truth through the consolidation of data into a data lake, to improve operational efficiency and achieve scalability based on future business needs.
The challenges the customer faced:
Brillio’s solution was to set up a Data Lake on AWS Cloud and provide the ability to take the data stored in various systems across the enterprise and consolidate it into a single data store. This would achieve easier access to various analytics and application projects.
Brillio proposed having a cloud-based solution using the ELT process that would retain the source integrity and also have a transformed layer known as SC360 that could be leveraged for analytics, reporting, API consumption, and operational use.
Standardization: Consolidate data sources and key reports across sources (SPDST/BMT/Supply-Demand plan/IBP, and remove Pnb-Pdw as a source). Standardize KPIs and design persona-specific views to enable better decision-making.
Communication/Collaboration: Enable users to share updates, make changes, and notify other users.
Visibility: Enable end-to-end view of supply chain performance like order tracking, inventory tracking, etc. to enable better decision making, and measure hierarchical performance based on stakeholder access.
Advanced Analytics: Deep dive into trends to identify areas of improvement and perform Root Cause Analysis, and enable teams to predict/prescribe actions resulting in business process optimization.
Key considerations:
AWS services used
Rds, Redshift, Dynamodb, Athena, Glue, Cognito-sync, Cloudfront, Secretsmanager, Ses, Kms, Codedeploy, Config, SNS, States, Cognito-identity, S3, Apigateway, GuardDuty, Cloudformation, Elasticloadbalancing, IAM, Elasticbeanstalk, ES, Codecommit, Cloudwatch, SSM, Lambda, Route53, EC2, Cognito-IDP, Elasticmapreduce, Datapipeline, ACM.
Third-party solutions used
Power BI, Sage Maker
With Brillio’s solution – a significant improvement of the new v2 model was achieved by implementing the new pipeline. Also, there was a significant improvement in the query performance (15 sec from 45+ sec) by normalizing multiple queries, as well as data quality improvements by incorporating an automated tested framework. It ensured uniformity of Schema being implemented for various source systems and provided a single source for all APIs and dashboards to consume the data.
Target State Architecture
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