Improving Operational Excellence for Metro Rails

Metro rail in India is a fast growing segment

with currently 940km network and expected

to grow by 1700km by 2030*.

Given the substantial YoY rise in operational

costs—nearly 40% of the total expenditure,

metro rail companies are under pressure to

optimize their operations. This entails

enhancing asset longevity, deploying

preemptive warning systems, predicting

potential

asset failures, and mitigating unplanned

downtime.

Addressing these challenges has the potential

to optimize O&M practices, ultimately

resulting in increased reliability, efficiency, and

cost- effectiveness in operations.

An integral aspect of this optimization lies in

leveraging O&M data to analyze both

internal and external datasets in real-time.

This strategic use of data is pivotal in driving

transformation within the metro rail sector.

Technologies such as Artificial Intelligence

(AI), data science, and predictive analytics

play a crucial role in this endeavor, offering

the potential to empower enterprises by

enhancing asset availability and

performance.

Through the strategic integration of these

technologies, metro rail companies can

revolutionize their operations, ensuring

smoother, more efficient service delivery while

simultaneously optimizing costs and

resources.

Enterprise

integration

midas360

Confronting Significant Challenges

Limited Real-time Visibility

Current SCADA systems lack sophistication,

hindering real-time data capture and effective

asset management.

Compromised Health of Assets

Long-term asset health must be prioritized to

avoid escalating maintenance costs over time.

SMS alerts,

mail notifications

RCA, smart

maintenance, intelligent

alarm management

Visual analytics with

BI/Role-based dashboards

Condition Monitoring

Predictive analysis

Operational analysis

Unplanned Downtimes

Integrated digital solutions are needed to

Rising Operations and Maintenance (O&M)

Expenses

Efficient management practices and strategic

Fig 2. midas360 Key Modules

Apply statistical methods, machine learning algorithms, and data mining techniques to detect patterns

indicative of asset performance degradation, anomalies, or failure precursors.

anticipate faults and minimize unplanned

downtime.

In India the daily metro ridership ranges from

0.16 to 5.0 Mill, for an estimated 1-hour

downtime the potential revenue loss could

amount to INR 320k to INR 10000k

planning are essential for mitigating the impact of

increasing maintenance costs.

Of the total operating expenses, 25% to 30% is

allocated to repairs and spare parts, with an

annual rise ranging from 2% to 6%. In a decade

this is cost expected to be doubled.

Why midas360?

midas360, the Metrorail Intelligent Decision Analytics System, stands at the forefront of innovation,

heralding a new era in metro rail systems. This cutting-edge platform employs advanced data analytics and IoT

predictive technologies to revolutionize how metro networks operate. By proactively analyzing vast amounts of

data, midas360 predicts potential issues before they occur, ensuring safer, more reliable, and more cost-

effective transportation services.

midas360 empowers operators with predictive capabilities to optimize maintenance, reduce

interruptions, and improve system performance, yielding smoother journeys for passengers and

revolutionizing urban transportation.

Fig 1. midas360 Metrorail Intelligent Decision

Analytics System

Rolling Stock

Fig 3. Rolling Stock Sub Systems

Automatic Fare Collection

Rolling stock forms the backbone of a metro system, and effective management of this equipment

system is critical to provide safe, reliable, and efficient transportation services to the public.

Fig 4. Asset Health Monitoring and Predictive Analytics

Monitoring the health of assets in real-time and generating predictive alerts based on historical performance

data by midas360 can facilitate data-driven decision-making which enable proactive maintenance measures. This

approach leads to enhanced reliability and optimized operations.

Fig 5. Rolling Stock Alarm Analysis

midas360 Alarm analysis in metro rolling stock is a valuable tool for optimizing maintenance practices, reducing

costs, enhancing safety, and improving overall service quality.

Fig 6. AFC Equipment Failure Distribution

midas360 Analyze failure distribution to identify the most common failure modes within the AFC system. This

information allows maintenance teams to focus their efforts on addressing the root causes of these failures,

reducing their occurrence in the future.

Reliability KPIs

Monitoring reliability KPI MTTR in AFC systems enables organizations to proactively manage system reliability,

minimize downtime, improve customer satisfaction, and achieve cost savings.

Ticket office machine system

Ticket vending machine

Ticket reader

Ticket office

Station unpaid concourse

Station level equipment

Station Control Room Station

computer

Ticket office machine system

Staff gate switch

Metal detectors

Electrical cabinet & devices

Excess fare office system

Baggage inspection system

AFC Gate

0.00 1.00 2.00 3.00 4.00 5.00

Fig 7. Mean Time To Repair (MTTR) Hrs

TS06

TS07

TS10

midas360 Key Benefits

4 Optimized Maintenance

6 Key Performance

Indicators

Case in Point – Innovation in Action: Machine Learning in a Transport Company in Middle East

Predictive analytics can forecast when metro

infrastructure and rolling stock components are likely to

fail, enabling maintenance teams to proactively

schedule repairs and replacements during off-peak

hours, minimizing service interruptions and reducing

operational costs.

1 Real-time Monitoring

Predictive analytics can enable real-time monitoring of

metro systems, allowing operators to quickly identify

and respond to issues as they arise, minimizing service

disruptions and maintaining operational efficiency.

By analyzing historical and real-time data, asset

performance KPIs are computed at the subsystem level,

while operational KPIs are computed at the metro level,

enabling targeted maintenance, optimized resource

allocation and improved overall system efficiency

Challenges

The lack of real-time, on-demand reports on crucial Key

Performance Indicators (KPIs) makes it difficult for

stakeholders to assess the health and efficiency of assets

promptly.

Customer Project

The company operates 79 trains and 1442 buses on 107

routes, carrying almost 7 million riders on roughly

179,000 trips a month by bus service.

Integration issues among different applications such as

fuel management, vehicle monitoring, and asset

management result in data discrepancies, hindering

accurate analysis and decision-making.

3 Asset Performance Variations in data formats related to costs and

maintenance details create challenges in

standardizing processes, affecting efficiency and

Experiencing a high frequency of corrective

maintenance indicates a reactive rather than proactive

approach to asset management, leading

By predicting the lifespan and performance of

metro assets, operators can better plan for capital

investments and asset replacements, optimizing asset

management strategies.

reliability in asset management.

Solution

to increased downtime and operational costs.

5 Cost Savings

By identifying inefficiencies in operations, maintenance,

and energy consumption, predictive analytics can help

metro operators optimize resource allocation and

reduce overall costs.

2 Data-driven Decision-making

Predictive analytics provides metro operators with

valuable insights into system performance and passenger

behavior, enabling data-driven decision- making to improve

service quality and efficiency

Fig 8. midas360 Metrorail Intelligent

Decision Analytics System - KPIs

Seeking consultancy services offers tailored insights and recommendations for implementing

smart maintenance practices, enhancing efficiency in maintenance operations for buses and

trains.

Implementing advanced analytical techniques like case-based reasoning and

reliability analysis enables proactive identification of potential failures, allowing

preemptive maintenance actions to prevent downtime.

Utilizing Monte Carlo simulation aids in predicting failure scenarios for buses and

trains, enabling proactive maintenance scheduling to optimize asset performance,

while implementing proactive alerts based on predictive

maintenance algorithms with a 78% accuracy rate facilitates timely intervention, minimizing

unexpected failures and associated downtime in subsystems.

Value Delivered

Unified Preventive Maintenance Plans

Developing unified preventive maintenance plans for

assets, streamlines maintenance schedules and reduces

the number of visits required for maintenance

activities, optimizing resource

utilization and minimizing disruptions to operations.

Cost Savings and Increased Asset

Availability

By implementing the recommended solutions,

significant cost savings of $1.2 million per year was

achieved, coupled with a 1.2% increase in asset

availability. This reduction in corrective maintenance

instances indicates improved operational reliability

and efficiency.

Reduction in Non-PM Work Orders

Ratio

The transformation from a 45/55 ratio of

non-preventive maintenance (PM) to preventive

maintenance work orders to a 70/30 ratio signifies a

shift towards a more proactive maintenance approach,

reducing the reliance on reactive maintenance

practices.

Subsystem-Level Prediction and

Planning

Accurate prediction of expected corrective maintenance

at the subsystem level enables proactive planning of spares

and maintenance tasks, optimizing inventory management

and resource allocation for improved operational

efficiency.

*Based on India Infrastructure

Research, March 2024

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company today has 1000+ Enterprise Customers and 3500+ SME Customers globally, including Fortune 500 Companies.

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