Akiem
AI Fleet Management

Akiem partnered with Plasma to create a unified data platform, allowing to optimize trains allocation.

  • fret trains
Real-time insights
Visibility and alerts
  • train maintenance
Improved operations
Streamlined fleet management
  • fleet monitoring
Fleet mapping
Global activity overseeing
  • akiem logo
01 / CONTEXT
Optimizing trains allocation at scale.
Real-time monitoring and informed decisions.

Plasma changes the way Akiem, as an operational fleet manager, optimizes its fleet allocation, ensuring seamless operations and maximum efficiency on a very large scale.

02 / STAKES
How to operate rail fleets in real-time:
Real-time observability

Tracking location and status of fleet assets in real-time is essential for ensuring efficiency and timely decision-making.

Normalized data sets

Standardized and consistent data is crucial for analysis, reporting, and decision-making, leading to improved efficiency and cost management.

Insights exploration

Effective exploration and use of data-driven insights are key to optimizing fleet operations and maintenance strategies.

Platform scalability

Scaling fleet management to growing volumes and evolving operational needs is vital for long-term success.

03/ Platform
AI-driven fleet management.
Built with Plasma.

Through Platform Design workshops, we define your platform’s core needs and specific capabilties.

04/ Capabilities
Fleet operating platform.
graph-1-fleet

Fleet Signals operates by collecting real-time data from trains and railway infrastructure to provide insights into their status and location.

  • Sensors and monitoring systems on trains and tracks capture data continuously
  • This data is processed to determine the real-time status and location of each train
  • Fleet operators use this information for precise allocation and scheduling
graph-2-fleet

Operational dashboards work by providing a visual and intuitive interface that consolidates essential data and KPIs for fleet management.

  • Data from various sources, including Fleet Signals, is centralized in dashboards
  • Fleet operators can easily monitor the performance, status, and allocation of trains
  • Real-time information allows for rapid decision-making
graph-3-fleet

Predictive Maintenance feature functions by leveraging data analytics and machine learning to predict when trains require maintenance, reducing downtime and costs.

  • Data from sensors and historical maintenance records are analyzed
  • Machine learning algorithms identify patterns and predict maintenance needs
  • Preventive actions are scheduled to optimize train reliability and availability
graph-model-4

Plasma's AI-Driven Decision Making feature empowers the train fleet operator by using advanced algorithms to make data-driven decisions for optimal train allocation.

  • Historical and real-time data is continuously analyzed
  • AI models identify the most efficient allocation strategies based on current conditions
  • Operators receive recommendations and insights to improve allocation and scheduling at scale
05/ Outcomes
Fleet management, transformed with data.
Resource Allocation

Our fleet management platform ensures the optimal allocation of resources, minimizing costs, and maximizing operational efficiency for fleet operators.

 

Safety and Compliance

Plasma enhances safety by predicting maintenance needs and ensuring compliance with regulations, creating a secure and reliable fleet environment.

 

AI-powered decision-making

We empower fleet operators with data-driven and automated decisions, providing insights that lead to smarter, more efficient, and more profitable operations."