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Development of JAMMS 3.0 Cloud-Based Asset Maintenance Management System

orases.com

Construction

Environmental Services

Business services

Legacy System Limitations and Process Misalignment

Outdated Borland C++ legacy system lacked modern capabilities, created data silos, and failed to support core business philosophies of work planning, execution, and control. System complexity hindered integration with HR, inventory, and payroll processes.

About the Client

Global provider of facility and highway infrastructure consulting, maintenance, and management services with 500+ employees across 30+ locations

Modernization and Operational Excellence Goals

Implement cloud-based modular architecture for seamless integration
Reinforce planning, scheduling, and execution workflows
Enable offline mobile capabilities for field operations
Ensure data integrity through centralized database management
Achieve compliance with security and privacy standards
Support scalability for business growth

Core System Capabilities

Automated work order planning and scheduling
Offline-capable Android field crew application
Real-time inventory and resource management
Dynamic alert system for workflow coordination
Multi-system integration framework
Comprehensive reporting and analytics dashboard

Technology Stack Requirements

Cloud-native architecture (AWS/Azure)

24Watch® Business Suite framework

Progressive Web App (PWA) for mobile access

RESTful API integration layer

System Interoperability Needs

Human Resources Information Systems (HRIS)
Enterprise Inventory Management Platforms
Payroll Processing Systems
Geospatial Tracking Services

Operational Constraints

99.9% system uptime SLA
NIST Cybersecurity Framework compliance
Support for 500+ concurrent users
Offline functionality with auto-sync capability
Horizontal scalability for 20% annual growth

Business Transformation Outcomes

Enable $250M+ annual work order processing efficiency gains through streamlined workflows, reduce data entry errors by 70%, support 30% faster reporting cycles, and establish foundation for AI-driven predictive maintenance capabilities while maintaining 100% compliance with industry regulations.