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Cut Operating Costs with Real-Time Predictive Analysis for Fleet Management

30 Apr, 2012

Regardless of your growth strategies that include MPS or other key initiatives, today’s aggressive and fast changing environments require effective Fleet Management to make real-time and optimized decision making to maximize operational cost savings, and reduce downtime for customers. Fleet Management businesses need to move away from old Business Intelligence (BI) analytics tools that provide a snapshot of the past, to today’s new web-based, real-time and predictive analytics tools that provide an accurate picture of the present and future.


Does your (or your customers) Fleet Management (FM) operation know which vehicles are going to soon need a costly repair and long down time? Does your Fleet Management operation allow you to use your hand held mobile device to review a detailed analysis of mechanical fitness and fuel efficiency of each of your vehicles? Does your Fleet Management operation allow you to tell which fleet supply assignments to fleet demand will result in better cost savings? Does your Fleet Management operation alert you that at a certain time today your demand will suddenly peak to double?  Does your Fleet Management operation guide you to prepare for that increased demand in future?

If your Fleet Management operation cannot do these then you need Real-time Analytics that will enable people, processes and technology to be on the same page in real-time and drive business improvements in a world with ongoing information explosion and dynamic events. You need predictive analytics to provide you with a scientific and predictive approach to automated decision making.

With real-time and predictive analytics you can catch maintenance problems early and avert fleet operating budget disasters with costly and unforeseen vehicle repairs. With real-time and predictive analytics, you can optimize the mileage of fleet, fuel consumption, and reduce expensive fuel costs.


Innovative analytics technologies unlock the business value of information that is hidden within massive amounts of data. This data may be financial data, or operational data such as in Fleet Management.

  • Massive Data can be converted into Actionable Data with real-time analytics. Real-time analytics enables you to understand, explore, validate and intelligently act on real-time events and actions that shape the success of your business.
  • Predictive Analytics intelligently predict the most optimal course of action for operations.

With the power of accurate real-time information and predictive analysis, Fleet Management operations can be optimized to achieve substantial fuel saving costs & increase revenue by improved utilization and decreased vehicle downtime:

Fleet Management can include a variety of functions that can be optimized such as vehicle financing, vehicle maintenance, vehicle telematics, vehicle tracking and diagnostics, driver management, vehicle fuel, health and safety management.

  • Fleet Management can improve efficiency, productivity and reducing overall transportation and staff costs, and provide compliance with government legislation.

In FM, things happen fast. The demand moves fast, supply moves fast, fleet positions locations fast, fleet breakdowns happen fast. Reacting quickly is the key to increasing profit. This real-time behavior depends on the ability to analyze data arriving from multiple sources, at very high rates in real-time.

By improving the efficiency there is overall cost reduction in fuel consumption and the elimination of time-wasting logistics problems, and improved customer service and retention.


MATHEMATICAL MODELS: Predictive analytics can predict future events using mathematical models.

  • Predictor variables are used to measure and predict future behavior. Example, time before next maintenance/repair need, fuel levels in fleet or current fleet on road, unmet demand for fleet, unused fleet.

ANALYTIC ENGINES: Analytic engines enable users to visualize critical business events and process performance. An optimized analytics engine can be used for business intelligence, advanced analytics, predictive modeling and rapid reporting. They have the ability to:

  • Build on real-time, in-memory data engine
  • produce real-time reports
  • create intelligent processes that use predictive analytics to automatically optimize process performance

IN MEMORY ANALYTICS: In-memory analytics is an advanced analytic methodology that allows fast querying data when it resides in a computer’s random access memory (RAM), as opposed to querying data that is stored on physical disks. In-memory analytics architectures are used in real-time and predictive analytics to make processes intelligent and “self-aware.’  A “self-aware” process can evaluate its current state based on past process performance and trends and create prediction models for future process activities:

ANALYTIC SERVER: Analytics server needs key architectural and technical capabilities that make it ideal for efficient and fast predictive analytics environments, such as:

  • Flexible architecture for massive scalability of data, queries, or users
  • Scale for complex analysis through advanced query optimization, distributed queries, and in-database analytics
  • Self-service analytics that allow business users get answers in real-time

COMPLEX EVENT PROCESSING: Complex event processing CEP technologies can be used to deriving intelligence from event data in real-time. CEP tools:

  • provide ability to absorb, aggregate, correlate and analyze events
  • It can monitor specific conditions or patterns

AUTOMATED DECISION MAKING: Automated decision making in fleet management operation can be done based on aggregate real-time trend analysis and pattern recognition with today’s in-memory analytics engine. These patterns are identified in real-time as they emerge so the decision making process can provide an immediate and appropriate actionable response.


Fleet Management can leverage the information and enhancing efficiency and reducing fuel consumption.  Fleet Management can be done on mobile platform that allows the company to track and leverage information for improved efficiency.

  • Full operational visibility in real-time
  • Improvement in operational efficiencies
  • Improves customer service and retention

Fleet Management Operations can manage its fuel consumption more efficiently using real-time and predictive analytics. It automates:

  • Logistics
  • Accuracy of information
  • Provides operational visibility in real-time throughout the fleet operational lifecycle
  • Information on fleet of vehicles, staff, shipment info can be put in a central database
  • Mobile devices for monitoring and automatic data collection
  • Resource utilization
  • Fuel utilization and management
  • Vehicle allocation
  • Real-time location information of vehicles

Fleet Management Operations can use location-based intelligence to help fleets operate efficiently with a real-time dashboard to monitor and manage mobile assets. This can be done with color-coded web dashboards and progress charts that highlight potential problems or service failures across the entire fleet, allowing managers to take corrective action, reassign mobile workers and keep customers informed, and other decision making.


Web-based Feet Management systems with Real-time and predictive analysis tools help maximize operating cost savings and reduce downtime for customers. Real-Time and Predictive Analytic tools help you to meet the dynamically changing targets in an environment that consists of dynamic customer needs, real time vehicle state, and varying driver attributes.  In real life, Fleet Management faces the challenges if a driver does not show up, there is an accident or vehicle has broken down, or an urgent special customer request has arisen. Real Time and Predictive Analytics allow you to change tactics in real-time to ensure the operation stays on track.  Advanced Analytics will help you make the best decision about which driver to assign and how to manage the remaining customer jobs, vehicles and drivers.

Author:  Dr. Satwant Kaur is hailed as the “First Lady of Emerging Technologies” in Silicon Valley, as well as her live radio show, enlightening thousands.  Intel published her book entitled “Transitioning Embedded Systems to Intelligent Environments.”  Currently, she is a Master Solutions Architect at HP.  She was the CTO of Emerging Technologies group at TIBCO. She received her doctorate in Mobile IP technologies from Oakland University in Oakland, Michigan, and also holds a Bachelor of Technology degree in Electrical Engineering with distinction from the Indian Institute of Technology in New Delhi, India. At satwant@drsatwantkaur.com.

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