Agenda Day 1
Morning pastries, fruit, tea and coffee served
Content to be confirmed shortly.
Who “owns” information generated for and through use of predictive maintenance tools is a key issue in this whole endeavor. Everyone was really optimistic about just how openly manufacturers, operators and maintenance providers would share data. If data is being generated and it has potential commercial value, whoever holds it is going to want return for their investment:
- Which airlines are ok to share data?
- How to standardize and regulate data sharing?
- Operators are the owners of raw data but how to deal with various needs from OEM and MRO to use data (health monitoring, Predictive, etc…) ?
- Who is the owner once the data is processed?
- How to ensure security and fair usage of data?
A fun ice breaker similar to bingo designed to get delegates networking around the room.
Light snacks, tea and coffee served.
Content to be confirmed shortly.
Today, Airlines are facing a mountain of problems, and one of them is in implementing technological advancements in their business. Despite facing numerous issues, the aviation industry may enter into the fourth industrial revolution. Emerging technologies like AI and machine learning are reshaping the aviation business from the inside out.
The result is reduced errors at key points in the assembly and overhaul of engines, unlocking millions of dollars in savings.
Predictive Maintenance is a new service provided by a limited set of suppliers (mainly Airbus Skywise, LHT Aviatar and AFI KLM E&M Prognos).
The maintenance of aircraft generally relies on periodic checks and/or in the expertise of engineers and the maintenance team. However, this approach is not enough to prevent unscheduled maintenance and potential AOGs, mainly because the tools used by the engineers are limited to a certain extend and are mostly based on threshold approaches that are set accordingly with previous detected failures.
Considering the digital area in which we are living, the potential of using the precious sensor data produced by the diverse parts to the airplane is not being used in its tremendous potential. The ability of computers to learn from huge amount of data is well noticed and it is here that Predictive Maintenance is inserted.
Predictive maintenance, by using smart machines (machine learning), is the one of the next steps of the aviation industry in order to incisively reduce the costs caused by AOGs and unscheduled maintenance in general.
- What are the current challenges/road blocks?
- How to work on predictive maintenance when you do not have enough historical data?
- Strategies to develop a classification when you do not have historical data
Hear from three innovative technology companies showcasing their latest and greatest solutions through short and punchy 5 min presentations.
Content to be confirmed shortly.
- What skills need to be grown to utilize new tools and technologies?
- Where are the gaps?
- What are the training opportunities available online and face to face?
- Why is there such an unawareness in training?
- How to get your work force to use big data techniques?
- How to retain/transfer knowledge gained by retiring mechanics?
- How to attract young engineering into aviation careers?
- How technology can help close the gap on a workforce shortage?
How to include AR, VR and Wearable Technology training
Light snacks, tea and coffee will be served.
This session will feature how other industries deal with predictive maintenance and data analytics such as rail and automotive.
Choose from the below 6 roundtables -
The methodology seeks to foster an environment that is good for conversations to develop. The approach gets authentic conversations started in order to encourage the sharing of ideas in a relaxed, informal and creative atmosphere. See guidelines. The roundtables are an opportunity for delegates to get a snap shot of a broad range of current issues, and engage in an informal Q & A with the session leaders. The presenters will give a short introduction and topic update, and the remaining time will be spent answering delegate questions.
Roundtable 1: What is the impact of advancements in machine learning and artificial intelligence on predictive maintenance What are those means of computing able to do that an analyst couldn't do on their own?
Roundtable 2: How to strike effective performance-based logistics contracts Where an MRO service signs up to make sure an aircraft readiness at any given moment – striking those agreements requires predicative maintenance, data, logistics, analytics.
Roundtable 3: What are the developments of Alternative Maintenance Requirements For example, development of AHM procedures to replace Preventive Schedule Requirements listed in MRB Reports.
Roundtable 4: How to detect threats using GPS disruption/positioning, navigation and timing (PNT) Security practitioners can perform analyses on large data sets to detect threats to GPS and other systems and (hopefully) close gaps before the bad guys find them and attack – a risk for which airlines are most vulnerable.
Roundtable 5: The interface between the new cloud-based big data platforms for predictive maintenance and the established ERP systems such as AMOS and TRAX • What are the use cases for their seamless integration? • What is possible? What not? • What are the biggest benefits and best practices in the industry today?
Roundtable 6: How can technology help close the gap on a workforce shortage? In terms of skills shortages, big data and analytics is the number one place of need, according to the recent KPMG CIO Survey. Nearly half (46%) of CIOs who participated in the survey said they suffered from a skilled shortage, followed by a shortage in AI skills at 38%.
Each roundatble host will share the key outcomes following their roundtable discussion
Join us for beer, wine and soft drinks.
Agenda day 2
Light pastries, fruit, tea and coffee will be served.
Recently, several airlines (Wow Air, Thomas Cook, Jet Airways, Avianca Brasil and others) have shuttered operations, and since January the coronavirus outbreak has pushed down demand for travel to and within Asia, leading carriers to ground aircraft and cut flights.
How might big data fare during a downturn?
Will airlines continue to invest in the technology?
Will they view it as more critical, as a means to wring more efficiency out of their operations? Or will they view it as an expense to be cut?
The use of artificial intelligence (AI) is expanding as a decision-making tool for airline maintenance teams at large fleet commercial airlines. Airlines based in the U.S., Europe and Asia have been quietly adopting AI tools in the form of intelligent agents for data modeling and simulation to the use of cognitive computing. The use of AI within airline maintenance strategies is evolving into an advanced and expanded use of predictive data analytics.
How to effectively use artificial intelligence to generate an accurate work order straight from the analysis of the data.
Dashboarding is the process of taking vital yet disparate business data related to a single topic and presenting it visually in one location. When seen together in this view, or “dashboard,” these data points enable meaningful analysis for a company, a department, or a team. You can think of a dashboard as a snapshot of relevant information at a single moment in time to keep users apprised at a glance.
- Provide data to look at internal reliability impact, but what can the industry do with data to demonstrate to the flying customers that safety is the highest priority?
How can manufactures, regulators, and operators work together to show a better emphasis on safety and demonstrate to the public that there is nothing being hidden when it comes to the reliability and certification of airplanes?
Light snacks, coffee and tea will be served.
In the current age of intensified digitization and connectivity, the airline industry is now dealing with a new and critical challenge, namely cyber security. Technology and digitization not only brings many advantages, but also risks associated with the challenge of finding and managing cyber vulnerabilities across complex, international operations. This complexity makes the aviation sector globally interdependent and vulnerable to hidden risks and ever-increasing threats. The airline industry is an attractive target for a large number of cyber threat actors with a multitude of motivations, ranging from stealing value in data or monetary, to causing disruptions and harm.
A discussion on how to ensure security and fair usage of data.
Automated benchmarking enables users to see much larger data sets and far more variables than traditional benchmarking exercises. Automated benchmarking is essential for maintenance performance in every aspect from TDR to O&A and cost – a lot of data needs to be translated in a meaningful way. There is currently a benchmarking oversight and big data can help with that and can produce effective benchmarks.