Smart Fleet Maintenance 360 Summit 2023 
Advancing The Digital Economy in Rolling Stock Maintenance
#SFM360

Modeling Business and Maintenance Processes, Improving Asset Lifecycle and Financial Forecasting to Meet Cost Targets in Maintenance Planning

“We model the business process. So, we start from a high-level view, what are the main responsibilities of our division? And then we go a step deeper and we have the different process like the maintenance execution, how do we do the vehicle entry management release management, how do we do the planning of the rolling stock planning of the interventions and
per block that we define”

• Building a process map, having a clear understanding of the capabilities and tasks within each process to increase efficiencies between the business and maintenance
• What digital systems are operators implementing to make sure they keep in line with their asset management costs
• Investing in engineering methodology, creating a roadmap to keep engineering and maintenance in line with CAPEX and OPEX especially for ageing fleets
• Optimising continuous improvement in engineering to improve the input of information, collecting precise data for modelling and forecasting
• Examining the requirements in modelling maintenance processes, maintenance execution, release management, maintenance intervention planning
• Analysing and incorporating engineering data, bills of materials and logistics report analysis into an asset management system to improve preparation for maintenance

• How to increase availability and reliability of rail systems with efficient digital asset management enabled by Railigent X
• Ensuring optimal value creation by considering risks, performance, and costs over the whole lifecycle, from conceptual design to renewal or disposal
• How can you create transparency and support strategic decision-making based on commercial data, data from operation and maintenance and artificial intelligence

Real World Experiences of Optimizing Asset Management and Statistical Modelling to Improve Life Cycle Management in Fleet Maintenance

• What level of investment is required to construct digital infrastructure required to forecast future maintenance cost of on-board systems across the lifetime of the train
• New digital systems to build precise asset libraries, generating accurate data models of fleets maintenance plans, configurations, and logistics to increase reliability
• Challenges integrating asset management systems of different vehicles in terms of technology, available data, and age of fleets to improve forecasting and maintenance
• Examine how optimizing asset management can complement sustainability targets in rail, reducing waste, transportation, and material storage
• What systems and tools are operators deploying to optimise asset management of documents in rail, going paperless to reduce risk and improve accuracy in reporting

10:30 Morning Networking Break

To What Degree Can Digital Twin Assist in Developing a Circular Business Model to Improve Sustainability and Management of End-of-Life Materials

“The digital twin has two sides you can use sensory on one side to monitor the trains etc. But the other side is you can send request saying okay, this part is going to break in a few weeks or a month from now then this can be a trigger. We have a lot of technical people who work on the maintenance planning etc. We have a lot of stock in rolling stock maintenance and if you use sensors, you can forecast what you use on spare parts and can lower your stock and if you use additive manufacturing with that, and it's almost on demand printing.”

• Implementing digital tools to connect planning and maintenance to increase availability, reduce procurement cost and improve resilience in the supply chain
• Examine how a digital supply chain can help the industry facilitate the inflow and out flow of materials, recycling parts and materials to improve sustainability
• Understand the opportunities and benefits of automating sensor information with digital twin to improve maintenance planning and material forecasting
• Creating a digital library to connect logistics and the maintenance worlds together, implementing a digital twin for a leaner, more resilient and sustainable supply chain
• Implementing additive manufacturing, sensor information and digital twin to optimise parts availability, increase local production of parts and reduce stock levels

“The digitalisation of rolling stock, primarily to enhance customer experience and improve reliability, has led to the transition from analogue systems connected individually or via serial bus, through to digital, software-based systems that operate on a common network. All software needs maintenance, either to keep it performant, manage and respond to identified defects, or to introduce new functionality to extend the life of the asset.”

• Improve integrity and transparency of digital maintenance operations - carry out updates remotely in a secure, controlled environment
• Decrease maintenance-related downtime while improving quality and consistency of maintenance operations - perform automated tasks on multiple devices across multiple train units simultaneously
• Reduce likelihood of erroneous or malicious access to systems - secure access management & traceability for service staff

1:00 PM Lunch Networking Break

Implementing A Data-Based Maintenance Strategy to Reduce Interventions, Increase Reliability and Optimize Lifecycle Asset Management

“Asset Management wise, I think it's especially important to have the data-based maintenance. There are different aspects on how you how you plan your interventions, and you want to do it as much as possible in a data driven way. You need to collect and find the data that is relevant to be able to plan your interventions to help. Well today, we wait as a figure of speech until there is a problem. and then we ask for an intervention on that vehicle. The challenge is finding those data points in the whole big data environment, because with all the telemetry on the newer rolling stock, there is an abundance of data but the challenge is to find the links, to find the structures within that data, and all the extra elements that have their impact on the reliability of the rollingstock. To make those provision of the breakdowns”

• Working with data management system suppliers to improve the interfaces on their platforms to accept data from third party sub systems vendors
• What digital tools are operators implementing to identify structures within maintenance data that could have an impact on the reliability of the rollingstock
• Digital solutions to improve the response to defect notifications, understanding how many defaults of defects you have had with so many notifications of the same problem
• Does the cost of capturing and processing large volumes of data restrict the value of data in smaller rail organisations?

The Challenge of Continuous Improvement with Data Quality, Gathering Data Consistently, Improving Classification and In-Put of Data

• How are train operators implementing new strategies and processes to solve the challenges of continuous improvement with data quality
• Optimising the classifications, categorization of assets and getting people to put in data consistently to improve root cause analysis
• Moving from paper to handheld devices to increase reduce errors and improve accuracy in reporting work orders
• Implementing training to support the use of handheld devices, providing guidance, and embedding new behaviour
• Introduce mandatory information requirements to move the work order to the next step
• Setting up asset hierarchies and the coding behind them, type of equipment, how is it arranged and how is it all connected

Implementing Machine Learning and AI to Find New Insights in Data, Delivering A Breakthrough for Predictive Maintenance Systems

“I think today it's a break in development to use machine learnings in predictive maintenance systems. You can assist this break using new technology about explain-ability of machine learnings”

• Big data management, what are the challenges in extracting clean machine generated RCM data to pick up patterns and trends that might indicate an impending failure
• Increasing the repeatability and accuracy of algorithms to accurately forecast defects in a particular code with so many categories of codes to analyse from fleet components
• Building trust in the forecast to get the people using the tool, trusting the model
• Implementing machine learning to focus on issues that can impact maintainability, analysing congestion and weather data to understand impact on failures

Data Driven Approach vs Engineering Driven Approach to Develop Digital Twin for Health Prediction Based on Historical Systems Knowledge

• Availability of data for health prediction in Rolling Stock
• Generating a digital twin for health prediction – data driven approach vs. engineering driven approach
• Using machine learning for smart diagnosis and failure rectification
• Combination of reliability driven maintenance optimization and health prediction to optimize maintenance efficiency

Innovating Through Robotics and Automation to Meet the Challenge of Having Less People Available in The Workforce for Maintenance Activities

“I'm head of maintenance, so I probably would go to conference for optimization, for maintenance processes, automation. Because we see it's really, really going to be a problem to get the right amount of people for doing all the maintenance”

• Optimising workshop processes, delivering the right level of resources required to carry out each maintenance task
• Benefits of using robotics and automation to mitigate the challenge of reduced number of maintenance engineers in the workshop
• How are operators automating maintenance process to eliminate paper from the shop floor, using handheld tablets to increase accuracy and frequency loop in rail
• Applying automation to increase productivity on the shop floor and reduce time spent on servicing, what new cost-effective technologies are operators implementing
• Updating engineering policies and methodologies to reduce waste in the working organization, using data to challenge the action for each maintenance intervention
• What technologies are operators using for configuration management to put more triggers into the maintenance system to alert maintenance technicians

5:40 PM END OF DAY 1