Our client sought to streamline operations and optimize supply chain costs with data-driven decisions. Addressing capacity constraints, they migrated to a centralized data platform. To manage the rising demand for data analytics, they engaged HCLTech to leverage our global presence and record of delivery value. The implementation of DevOps best practices and business-aligned automation improved efficiency. This simplified data access to unlock novel business opportunities for data-driven decision-making and faster innovation.
The Challenge
Overcoming barriers to leveraging manufacturing data analytics
With the expanding analytics needs of data scientists, the company realized the need for high delivery throughputs. They also needed to support the operational efficiency of their central supply chain data platform and sought better cross-vendor collaboration. The process had been hindered by:
- A near absence of data-collecting tools and mechanisms
- Limited data computing power in existing support systems
- Lack of transformation tools to convert data into business intelligence (BI) insights
The Objective
Democratize and centralize governance of business-critical data
Our client was looking for a trusted partner with a DevOps thought leadership mindset to implement best-in-class DevOps strategies and enforce high accountability. The business needed to map its selling systems, draw raw material insights, spot leaks in the supply chain and study fulfillment patterns. Some of the client's key objectives for the partnership included:
- Better management of inventory to enable timely decision-making
- Migrating applications to improve processes
- Improving quality of service and saving resource costs
The Solution
A Core Data Lake
We determined that a new Core Data Lake would satisfy our client's data needs. The process began with understanding stakeholder requirements, then isolated applications were migrated to a unified Microsoft Azure ecosystem to accommodate escalating supply chain data volumes. Data management at scale was optimized, providing insights via dashboards and reports. Data movement was automated with Azure, producing data analytics. The process included sourcing data from:
- SAP to trace back source data on the raw level
- Global business warehouses (BWs) for inventory-level data
- Regional BWs linked to transportation
- Regional transport management systems to be updated weekly
- Integrated distribution planning for forecasting
- Transport and warehouse (T&W) BI to generate and share T&W data with the BI application DOMO
Data was collected from these environments, ingested through Data Factory and Databricks and transformed into sandbox setups. This process created easy-to-read, consumer-facing tables on the top of Azure’s file system. HCLTech was responsible for all data enhancements and changes related to:
- Inventory
- Transportation
- Intelligent daily forecasting
- Supply chain synchronization
- Transactions
- Shipments
The impact
Supply chain data operations efficiency and cost improvements
HCLTech adopted industry-best continuous integration/continuous deployment (CI/CD) standards to execute a seamless development and deployment process. By making data visible for users, HCLTech drove efficiencies, reduced operational costs and created a superior user experience.
The customized, unified solution enhanced our client’s intelligent data operations at scale for agility and innovation.
- Assisted in timely financial decision-making by generating BI related to:
- Inventory management of raw and finished goods
- Trucking, loading and routing in transportation
- Forecasting and planning
- Added value through process improvement — with one exception, all applications have been migrated
- Achieved a 99% increase in quality of service in only six months, downsizing staffing estimates and saving resource costs
- Utilized client’s existing resources optimally
