Emerald Transformer operates large-scale operations and network involving pick-ups and deliveries of distribution transformers across the United States. They face several challenges, including reliance on manual scheduling through spreadsheets, difficulty integrating contaminated versus non-contaminated equipment logistics, and the absence of a centralized system for tracking routes, job progress, or real-time scheduling. The need for optimized schedules and improved driver’s efficiency was particularly acute at their repair facilities. Although they have a robust ERP system in place, it was not designed for dynamic scheduling or coordination across multiple sites. Therefore, a modern, cloud-based solution was essential to unify data flows, enhance logistical planning, and enable future scalability through multi-tenancy support.

Emerald Transformer specializes in decommissioning, refurbishing, and servicing distribution transformers across the United States. With a wide facility footprint providing national coverage in CA, AZ, TX, KS, OH, GA, and FL, Emerald plays a critical role in utility and industrial operations.

AWS was chosen for its serverless capabilities, which reduced operational overhead while enabling dynamic scaling. Additionally, its support for advanced analytics, AI/ML integrations, and secure data services aligned with Emerald Transformer's need for intelligent automation. The platform's compatibility with third-party APIs, such as AWS Location Service, facilitated the seamless incorporation of geolocation, routing, and optimization logic into the scheduling solution.

SnapSoft led a multi-phase implementation tailored to Emerald Transformer’s logistics operations. The team first designed and deployed a serverless, scalable solution within Emerald Transformer’s own AWS environment using services such as Lambda, RDS PostgreSQL, and AWS Batch, all in alignment with the AWS Well-Architected Framework. Structured data ingestion through API-based integrations, supported by distance and routing data sourced from AWS Location Service. A Python-based optimization engine was developed and deployed using AWS Batch, generating schedules that accounted for route matrices, time constraints, and trailer-driver configurations. SnapSoft also delivered a secure web-based UI built with React, Amplify, and Cognito, enabling job uploads, trailer management, and interactive schedule visualization. To enhance security, the system included SSO and two-factor authentication. The technical pilot was deployed at the Florida facility, with early design simplifications refined through internal testing and iterative feedback. A user pilot with Emerald’s end users is scheduled to validate the solution in a real operational environment.

The engagement successfully digitized Emerald Transformer’s manual workflows and created a cloud-native solution tailored for logistical excellence, setting the stage for future automation and cross-facility expansion. Additional benefits included:

• Improved Scheduling Efficiency: Automated scheduling has accelerated route planning, supporting 2–5 stops per driver per day with optimized job assignments.
• Data Accuracy and Usability: Manual input from legacy sources was replaced with a more structured and validated data ingestion process. While CSV upload was used as an interim solution, the system is designed to support future integration with a dedicated web form and ERP data sources.
• Security Enhancements: The solution includes Single Sign-On (SSO) and two-factor authentication (2FA) for secure access and authentication.
• Operational Visibility: A centralized web-based UI allows dispatchers to visualize schedules, track job states, and manage drivers and trailers with greater clarity and control.
• Scalability Preparedness: Both the database and UI are architected for future multi-tenancy, supporting expansion to additional Emerald Transformer facilities
• User Empowerment: Dispatchers have the ability to adjust assignments and reconfigure schedules as needed, ensuring operational flexibility alongside system-driven optimization.

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