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Development of an Automated Mining Equipment Management & Data Analytics Platform
  1. case
  2. Development of an Automated Mining Equipment Management & Data Analytics Platform

Development of an Automated Mining Equipment Management & Data Analytics Platform

reenbit
Energy & natural resources
Information technology

Identified Challenges in Equipment Monitoring and Data-Driven Insights for Mining Operations

The client faces difficulties in efficiently managing mining equipment such as vibrating screens, including tracking wear and tear, optimizing configurations, and assessing productivity. Additionally, they lack an integrated platform to collect, analyze, and visualize operational data, hindering informed decision-making and operational efficiency.

About the Client

A mid-to-large scale mining operation company seeking to optimize equipment management and leverage data analytics for strategic decision-making.

Goals for Enhancing Mining Operations through Data Management and Analytics

  • Develop a comprehensive equipment management system to monitor the condition and usage of vibratory mineral extraction equipment.
  • Create an intuitive platform enabling mining professionals to analyze equipment wear, configure optimal settings, and evaluate productivity metrics.
  • Design and implement an internal analytics dashboard for data collection, report generation, and actionable insights.
  • Establish secure user authentication and role-based access control for sensitive operational data.
  • Support onboarding of multiple client sites and facilitate scalable deployment of the solution.

Core Functional Specifications for the Mining Data Analytics Platform

  • Customizable digital forms for flexible data collection on equipment status, wear levels, and operational parameters.
  • Data ingestion modules to automatically gather data from various sources, including manual entry and IoT sensors.
  • Analytics engine to process collected data, detect wear patterns, and recommend optimal configurations.
  • Interactive dashboards and reports displaying equipment health, usage statistics, productivity metrics, and maintenance alerts.
  • Secure login and role-based access controls to ensure data privacy and limit sensitive information to authorized personnel.
  • Export capabilities for reports and data sharing with stakeholders.
  • Ability to onboard multiple clients with isolated data environments.
  • Administration interface for managing forms, user roles, and access permissions.

Recommended Technology Stack and Architectural Approach

.NET Framework/Core for backend development
Angular or similar JavaScript framework for frontend UI
Azure cloud platform for infrastructure, storage, and hosting
Azure Active Directory for authentication
Azure Data Factory for data integration
Azure Functions for serverless processing
Power BI Embedded for analytics visualization
Azure Storage Account for data storage
SharePoint integration for document management
Responsive UI/UX design principles

Essential External System Integrations

  • IoT sensors or machinery data sources for real-time equipment monitoring
  • Existing maintenance or ERP systems for data synchronization
  • Security protocols for user authentication and authorization
  • Reporting tools for report exports

Critical Non-Functional System Requirements

  • System scalability to accommodate onboarding of multiple clients and increasing data volume
  • High performance with real-time data processing and dashboards refresh
  • Robust security measures including Role-Based Access Control (RBAC), data encryption, and compliance with industry standards
  • Availability of 99.9% uptime for critical systems
  • Ease of use with intuitive UI and minimal training requirements
  • Maintainability and supportability for ongoing feature enhancements

Expected Business Benefits and Impact of the Data Analytics Platform

Implementation of this platform is projected to enhance operational efficiency by enabling proactive equipment maintenance, improving productivity tracking, and providing actionable insights. The client expects to onboard over 30 mining and energy sector clients, leading to increased revenue streams, improved decision-making capabilities, and a stronger competitive position in the industry.

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