Smart  Fleet Maintenance 360 Summit 2023

Advancing The Pace of Digitalization, Growing Insights from Maintenance Data to Transform Financial Forecasting, Maintenance Planning & Lifecycle Management 


15-16 March 2023

Hybrid Event: In-Person, On-line & On-Demand

Day One, Wednesday 15th March

8:45 AM Chair Opening Remarks


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. We go and talk with the business to see how things are done, what how the tasks are defined, who is responsible, what are the roles and then the next step, if we find those defined tasks is to see if there are ways that we can do that per workshop, because we know there are differences between the workshops in those aspects and one of the projects we started is to find the best practices between the business processes we modelled and to try and implement this into one standardised approach between the workshops”

Senior Process Manager, Belgian Train Operator

10:00 AM Real World Experiences of Optimizing Asset Management and Statistical Modelling to Improve Life Cycle Management in Fleet Maintenance

10:30 AM Morning Networking Break

11:00 AM Developing Knowledge Management Systems Within Train Operations, Building Digital Systems to Keep Engineering Knowledge In-House

“We are trying to create a platform because we have people who have been in the railway for 20-30 years. We also have people who have joined recently. So now the question is how to share that knowledge of the experienced people. We look at what knowledge they know that is not documented in the manual so this can be stored in the platform that can be shared with the younger people, and whenever an incident happens, there will be an investigation report prepared, then you look at lesson learned from the incident accident, maybe there's something thing you should have done recommended by the experienced people, but it's not documented. It's not part of the training, nobody knows. You want to capture this and input in the knowledge management platform”

Assistant Vice President (Rolling Stock), Public Transport Operator Singapore


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, you can have a printer on site maintained by our OEMs like Alstom Siemens and you can print printed down which can also be digitalized and monitored by the OEM or us. So, we create more local production and I think the digital twin you create the Digital Library to connect all the worlds together and make it more lean more resilient, etc. I think that's an interesting story because then you can connect with people who are really in maintenance planning, etc.”

3D printing program manager/Strategic Buyer, Passenger Railway Operator Netherland

12:30 PM Unlocking Digital to Create More Local Production, Avoiding Future Supply Chain Disruptions to Limit the Impact on Maintenance Regimes

1:00 PM Networking Lunch 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. What we should try to achieve is finding the elements that tell us, watch out, because now we see that in a matter of days or hours we will have a technical breakdown, perhaps there are some data paths, data points that that give us the indication that something could go wrong. 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 are that have their impact on the reliability of the rollingstock. To make those provision of the breakdowns”

Senior Process Manager, Belgian Train Operator

2:20 PM Transferring Maintenance Data into Insights, Correlating Data to Finding Unintended Insights to Improve Reliability in Maintenance

“So basically, there's data out there, which in itself doesn’t tell me anything. But the lack of change, that there's no change in this data tells me that something else is broken and that's an insight that we're now putting into operation. So, there's a data set that's used for something completely different that gives me a new insight, I'm sure that somebody has found a way to use data that we have available, that we just haven't thought that there will be a correlation to what do I don’t know”

Fleet Manager, Regional Train Operator Denmark

2:50 PM The Challenge of Continuous Improvement with Data Quality, Gathering Data Consistently, Improving Classification and In-Put of Data

“To have a continuous data management, because we are in a model and we need real structured input of data and of course, to have the precise recording the computerised management systems that means if you detects this part has faults, we know we have to put that kind of fault into the data list that as an operator we will have to take out, if you say you can lose a part you need to have a very simple way for the breaker who will do the maintenance to say okay, this part has gone, I just have to tick a box for this for very specific items. And then if we record that at the operator level, we will have a precise model to put into your asset management model. So, we need an engineering loop to analyse very precisely, Item by item, to prepare the report and the work order. So, we have continuous improvement into the engineering department because we need more precise, and we need to populate our databases with quite relevant and precise record. The more records you have, the more precise and the better model you have”

Rolling Stock Maintenance Innovation Project Leader, National Train Operator France

3:10 PM Integrating Digital Twin and AI to Unleash Predictive Capabilities in Rail, Achieving a Data Driven Approach to Rolling Stock Maintenance

• How are operators implementing a digital twin to provide real time monitoring of trains and infrastructure, improving reliability and availability in fleet operations
• Using 3D models to improve asset classification and optimise maintenance scheduling, integrating experience and maintenance knowledge into your 3D models
• Examine how digital twin can make maintenance more data driven, reducing cost in maintenance, increasing availability and asset uptime
• What level of accuracy do digital twins’ systems have in interpreting variations in the input noise data from the train to decipher normal factors in the data from real failures
• What digital tools and systems are operators using to integrate mechanical data and dynamic data to create a wholistic digital twin for maintenance

3:40 PM Afternoon Networking Break


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

16:10 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”

Data scientist, Railway Technology Consulting Company France

4:40 PM Explaining Machine Learning, Investing in Expert Systems To Develop Algorithms Codes Based On Historical Systems Knowledge

“The difference between both systems its expert system relies only on knowledge you have. Okay. So, right away, you have a lot of knowledge. We have expert system everywhere in every company, it's your systems, you know the physics behind it and so you can use expert system, but it's costly to develop because you have to write code to do it. It will not write by itself. And on the other end, you have machine learning, which is relies only on data. You don't have to have knowledge to build it enough of the rights code to let the system code by itself. So, you have a pros and cons of both solutions, that you have one which is black box and one, which is not because it relies only on your knowledge”

Data scientist, Railway Technology Consulting Company France


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”

Head of Maintenance, Regional and Commuter Train Operator, Germany

6:10 PM END OF DAY 1

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