CHALLENGES

• Need for digitizing the vast number of physical shipment documents
• Time-intensive transportation process for document availability
• Lengthy response times of more than 24 hours affected customer experience
• Manual, cost-heavy, and cumbersome picklist and order number search process for business users
• Cumbersome document retrieval process that required the Club Car Distribution Center (CCDC) Logistic Transport team to request for physical copies from data storage, manually search for documents and required details, scan and print it and then address the queries/issues

SOLUTION

Our solutions team proposed to digitize the documents, extract data for quick search, and store documents in Cloud for faster retrieval:

• Enabled digital documentation and user-friendly search using Document AI in Google Cloud Platform (GCP)
• Implemented advanced data modelling to read, split, and upload shipping document PDFs using Document AI Warehouse using picklist number, order number, and text content
• Extracted important business attributes and file metadata and stored them in GCP Big Query data warehouse
• Implemented Looker Studio dashboard to visualize shipment documentation, capture reprinted shipment documents for business analysis, and capture and reprocess rejected documents
• Provided low-cost document archival for multi-year storage and retrieval

CHALLENGES

• Provide finance, Risk, and Regulatory data processing, storage, and enterprise-wide reporting capabilities that will increase the data's integrity, accuracy, and completeness
• Address business-critical and regulatory requirements stemming due to increasing volume and cost of inancial and regulatory data
• Compare and analyze data, provide extensive drill down and actionable insights for investigation
• High velocity and volume of transactions (projected to reach 1 Billion transactions per hour)
• Achieve standardization of huge volume post-product processor information assets
• Address requirements across 100+ countries with 52 disparate data sources

SOLUTION

• Built a scalable platform with inbuilt engines to support machine learning and pattern detection and a data monetization platform on a global scale
• Created a Cognitive Data Lake encompassing global finance and regulatory data into Hadoop-based big data infrastructure
• Standardization of huge volumes of data which is fed from the various product processors using a new technology stack
• Developed scorecards and dashboards for data quality analysis
• Developed NexGen microservices for rapid migration of data into the data lake

CHALLENGES

• Responding to quotes due to cumbersome manual processes
• Lack of centralized pricing system
• Slow quote turnaround times
• Inconsistency and inaccuracy in quote pricing
• Error-prone manual approval workflows

SOLUTION

The client partnered with LTIMindtree to implement the Pricing and Revenue Optimization Solutions (PROS) Control, Guidance, and Smart CPQ solution, marking the inception of a long-term collaborative relationship between LTIMindtree and the client. Leveraging the expertise of PROS-trained LTIMindtree professionals, the implementation was executed seamlessly across multiple regions and geographies.

CHALLENGES

• Business continuity post-acquisition — The client recently acquired a German subsidiary, with no legacy system. They needed to define consistent business and IT processes, to seamlessly fit this new entity
• Limited time available for Go-live — The ERP had to be made available within three months as per business requirements
• Ensure scalability and smooth upgrades in future — Reduce future maintenance costs and give an easily scalable product

SOLUTION

LTIMindtree proposed the client to implement Oracle Fusion Cloud to define consistent processes at an organization level. This ensured:

• Faster implementation with minimal requirement of technical manpower
• Smoother system upgrade in future
• Agile and DevOps-based approach for timely delivery
• The client was involved from the development stage itself, enabling smooth movement to testing and production environment within designated timelines

CHALLENGES

• Achieve simplification and digitalization of platforms across consumer and commercial cards
• Improve the process to enhance efficiency and client experience
• Manage regulatory compliance and card scheme compliance changes and requirements within stipulated deadlines
• Build sustainable digital platforms which support massive development and meet the demands of the future

SOLUTION

We delivered digital pods managed by LTIMindtree. This includes:

• Development and rollout of new chip card issuance and new virtual cards portal
• Risk-based dynamic authentication and wallet transactions on virtual cards
• Next-generation collections platform transformation for operational efficiencies
• API-driven standardized SWIFT payment messaging gateway for cross-border/international wire transfers
• Rolling out new features for existing mobile applications
• New payment methods and digital wallet implementation across countries
• Credit card tokenization to improve their data security

CHALLENGES

• Inaccurate forecasting
• Lengthy financial closing cycles
• Robust financial data and analytics platform

SOLUTION

• Developed a business case and reimagined 14 processes
• Created a prototype for solution adoption and month-end allocations
• Built core finance solution and rollout
• Connected 13 SAP instances to CFIN and developed 42 solutions to accelerate and simplify CFIN processes
• Conducted multiple technical upgrades (from 1511 to 1610, 1610 to 1709, and 1709 to 2021)
• Implemented a global forecasting solution
• Used tools and accelerators such as Business Rules Framework, Data Exploder and Advanced aDSO Activation

CHALLENGES

• Frequent outages caused by faulty data center infrastructure and breached vendor SLAs
• Performance affected by limited resources and use of legacy applications
• Scalability issues due to a limited number of servers in the cluster
• Dependence on the platform engineering team for infrastructure support due to access restrictions of on-prem data centers
• Lack of disaster recovery configuration due to stretched resource bandwidth
• Limited test and Continuous Integration (CI)/Continuous Development (CD) automation and unavailability of one-touch infrastructure creation

SOLUTION

We used the pipeline-driven migration approach to and drive operational efficiency and agility

• Reduced memory consumption due to Virtual Machine Scale Sets (VMSS) auto healing using a Virtual Machine Stock Keeping Unit (VM SKU) with a higher CPU core, enabling accelerated networking
• Implemented a permanent outbound load balancer on the source application so that the outbound calls have a static IP and can be whitelisted on the destination application
• Upgraded to newer versions of application servers and Java Development Kit (JDK) versions to enable Azure migration
• Unplugged all ActiveMQ dependencies from the applications and introduced Azure Service Bus for asynchronous communication
• Implemented auto-healing of application to minimize performance and availability degradation
• Recreated performance scripts of legacy applications and implemented database secret Key Vaults (KVs) to validate test cases
• Created Continuous Test (CT) pipelines for all applications and scheduled them for nightly runs