Improving Your Punctuality by Implementing Turnaround Management within Your Airline
Punctual Turnarounds have become a critical factor for success for airline operations, although actively controlling ground processes is still a relatively uncharted territory for many airlines. The period of time between on-blocks and off-blocks therefore very often remains a “black box” for carrier of any business model - network carriers, Low Cost, Charter Airline, Cargo Airlines or any hybrid model – even though there is great potential to save money (= delay costs), improve on-time performance and increase customer satisfaction. The goal of this presentation is to describe what Turnaround Management is about and give an initial approach for the implementation of this important airline business process.
Challenges in Connected Aircraft Data Collection & Analytics
In-flight connectivity (IFC) installations continue at major airlines, with many carriers at or near 100% fleet equipage. With installations fragmented across vendors, technologies and business models, the financial benefits of real-time operational data transmission over IFC have been slow to realize. This presentation provides a technical review of in-flight operational data collection and transmission over connectivity platforms, presents relevant challenges (including platform compatibility, intellectual property rights, competing standards, data integration and regulatory compliance) and discusses case studies of deployed IFC-based operational data solutions for analysis, planning and customer service. We provide recommendations for how operational analysis and planning teams should participate in IFC platform selection and implementation.
Predicting Airport Arrival and Departure Rates
Airport arrival rates (AARs) and airport departure rates (ADRs) vary drastically between visual meteorological conditions and instrument meteorological conditions. During inclement weather conditions, the ability to predict both the timing and magnitude of the change of the rates is critical to airline operations. Airlines can proactively allocate adequate resources (aircraft, crew and gates) so that the impact of the change of airport rates can be minimized. This presentation focuses on identifying key factors affecting airport arrival and departure rates, examining different modeling for predicting ADRs and AARs, and assessing the performance of the prediction. The work includes ten major hub airports in the United States. The time horizon of the prediction is hourly and up to twelve hours into the future.
Predictive Maintenance Check from Analysis of Airplane Sensor Data
Unscheduled maintenance drives 10% of the annual operational cost to airlines worldwide. Predictive Maintenance could reduce those costs, particularly when synchronized with airline’s operations. By using engineering expertise, statistics and machine learning on aircraft sensor and fault data, as well as analysis of an airline’s flight and maintenance schedule, we detect impending issues on the aircraft and suggest maintenance tasks in accordance with the prediction and an airline’s working rhythm. These predictive maintenance tasks will increase reliability and reduce unscheduled maintenance.
A Comprehensive Approach to Understanding the Future of Air France KLM’s CDG Hub
Scheduling and Operations have always been fighting over the hub. Should the hub be peaked in banks of activity to improve the intrinsic connectivity of the network and its attractiveness, at the risk of driving up the costs of operations? Or should we flatten the hub activity to lower the costs of operations, while reducing connectivity opportunities for passengers in the meantime? With state-of-the-art machine learning and operations research techniques, we were able to develop a comprehensive two-sided tool, one side for computing the potential revenue of flight schedules, and one side for estimating their total cost of operations. Best practices and recommendations emerging from this tool helps Air France KLM in the building of the perfect schedule.
Operational Goal Planning for Reliability and Efficiency
We present operational goal planning methods that meet the dual demands of managing and attaining competitive levels of reliability performance and operational efficiency. We start with the simplest method of applying flat Year-over-Year changes and progress to sophisticated methods that employ dynamically shifting percentiles across different reliability defect distributions. Some of the presented methodologies are versatile and therefore capable of generating sub-goals for drilldown operational subsets. They enable fitting constraints and business risk profiles that apply to a particular operating division so as to drive more realistic and efficient goal profiles from an operational ROI perspective. These methods can be applied to any data rich, operationally complex, goal-driven environment.
When ETA and Taxi Times Meet Hub Control Management: Making Aeroflot’s Hub Operations
More Efficient and an Attractive Experience for Customers
With the implementation of a hub control management system in proximity to the airport’s control center Aeroflot created the basis for a continuous sustainable improvement of its tactical connection and turnaround management. It is a holistic solution integrated with the airport’s collaborative decision making (A-CDM). Today Aeroflot successfully manages its hub disruptions in relation to passenger flows, turnaround management, taxing and take off based on their commercial value and economic implication. This case study will illustrate how we manage our hub and how important it is not only to get the systems customized to the local situation, but also to make sure that the best quality ETA and variable taxi time data feeds are supplied.
Predictive Maintenance Optimization for Aircraft Redundant Systems Subjected to Multiple
The implementation of an airline line maintenance planning optimization considering failure prognostics features is not a trivial task and requires sophisticated mathematical approaches specifically when dealing with complex and integrated systems. This presentation proposes a methodology for aircraft predictive line maintenance optimization of redundant aeronautical systems subjected to multiple wear conditions. Degradation trends and future wear values are estimated considering an implementation of a multiple model approach of the extended Kalman filter technique. Planning optimization is based on the minimization of operational costs comprising several aspects of the aviation industry such as dispatch requirements, delays, cancellations and equipment costs. A case study is conducted using field prognostics data of hydraulic systems to evidence the efficiency and benefit of the proposed methodology.
Measuring En-route Vertical Flight Inefficiencies
In Europe, short haul flights are susceptible to flight level capping restrictions during the flight planning phase. The restrictions are designed for specific city pairs where necessary. This paper describes the initial research into the impact of these restrictions on vertical flight efficiency. For flights on constrained city pairs, the maximum flight level in the flight plan is compared with the maximum flight levels of flights on unconstrained city pairs having a similar great circle distance. Since jet aircraft and turboprop aircraft have quite different optimal cruising altitudes, the study is initially limited to jet aircraft. Preliminary results show that the highest vertical flight inefficiency occurs for flights on city pairs with a great circle distance between 200NM and 300NM. Vertical flight inefficiencies up to 10,000 feet per flight are seen in the results.
The “Un-Seen” Human Factors: How to Improve your Efficiency
How can we be more efficient? Probably one of the most stated goals in the airline business nowadays. This speech would like to give an alternative and holistic approach towards this important business goal by focusing a bunch of additional soft factors - outside any automated IT support - that influence the efficiency of a department sometimes very tremendously. These factors might be for instance: Cultural influence, psychology about automation, the organization of information, etc. As these factors are not tangible, very often they are not considered when the ef ciency is below expectations. This speech shall raise the awareness for these topics.
The Renaissance Has Begun: Current State of the Airline Operations Systems Marketplace
Airlines have been using 3rd party systems solutions since the 1980s, and this robust vendor market place has helped airline users run a better operation while reducing their costs. Over the past several years, interest in operational systems has increased significantly, and this has led to a major uptick (a.k.a. Renaissance) in attention and investment from senior airline executives. In this presentation, we will cover the factors behind this improvement and how it has led to an active entrepreneurial community for new tools and techniques. We will also talk about leading-edge issues that both airline and vendor executives are confronting for the next generation of systems, which will include the current interest in Irregular Operations (IROPS) solutions.
Profit Maximizing Operational Performance (PMOP)
COOs face growing challenges: demand for great products, quick responses during disruptions, friendly crew, and of course safety, all this at a low price. Airline COOs know this well. It is operations responds every day, every flight under varying circumstances. Airlines can improve their bottom line by focusing on 'Profit Maximizing Operational Performance' (PMOP). This requires airlines a data driven, integral approach to make better tradeoffs between operational performance (primarily OTP), efficiency, customer satisfaction, and frontline staff engagement. We are convinced that the right balance across PMOP dimensions will help airlines improve profitability regardless of network strategy, geography or price point. However, no single silver bullet exists for all carriers. BCG's approach combines big data with field observations and experience and truly brings our airline experience together
A Baggage Simulation Model at Air Canada
Leveraging the global network to build an international powerhouse has been a priority over the past few years. Several new routes have been announced, Air Canada Rouge has been launched and new aircrafts have joined the fleet resulting in a significant capacity increase. To grow, Canadian airports must act as strong hubs and support the feed traffic essential to filling international flights and making them viable. Since so much international traffic is now connecting traffic, especially the 6th freedom, collaborative work needs to be done with the airports to ensure connecting processes are as smooth as possible. Streamlining of baggage handling is a good example, but also increasing capacity of the baggage handling system. This presentation describes how a simulation analysis with Arena helped estimate the baggage peak demand and the requirements for baggage connection processes.
Probabilistic Decision Making and Operations: Why it pays to play games of chance
Decisions made in the absence of probability-based analytics will not just usually be wrong, they will consistently be wrong, especially when those decisions involve weather and ATC. One of the objectives of our Decision Support Vision for the Operations Control Center is to improve how we respond to weather events, specifically in terms of the determining the timing, level of risk, and specific actions to take. We need to move away from deterministic decision making that is primarily ad-hoc and experience-based to a more data-driven process that is built on predictive analytics and accounts for uncertainty. This talk looks at some examples where probabilistic decision making could be applied, some examples in the industry of where these techniques are already being applied, and finally, our current efforts to develop this capability internally.
Situational Awareness for Proactive Disruption Management
Disruptions cost airlines a lot of money and affect the brand name of an airline. Mitigating the impact of operations disruptions for passengers, crews and the aircraft rotations is the daily business of an Operations Control Center. Hence, airlines struggle every day to keep the schedule on plan and suffer from unplanned costs for the many disruptions including delays and passenger compensation claims. Having a specific situational awareness perspective on potential operational issues like critical weather affected aircraft rotations, critical inbound/outbound passenger connections, crew duty time limits, maintenance related flight hours limits, airport related capacity restrictions just to mention a few will help airline staff to get the decision support require on each disruption. Situational awareness is key for a proactive disruption management in airline operations.
Just-in-Time Departure Operations
In many ways, airline passengers must currently survive a series of “hurry up and wait” experiences. Even if they check-in at home or on their mobile phone, they typically hurry to the airport to wait in the security screening queue, hurry to the gate to wait in the boarding queue, and then sit in their aircraft as it hurries to the runway to wait in the departure queue. In this presentation, we will provide a concept of operations and the details of a proposed set of tools to coordinate the taxi trajectory of multiple departing flights, manage the boarding and pushback time for each flight, and provide guidance to passengers in terms of when they should to proceed through security and to their departure gate.
Performance Based Contingency Fuel Planning
With the FAA’s revision of OpSpec B343 through N8900.383, performance-based contingency fuel planning is now available to US Flag flight operators. This OpSpec revision and associated guidance is meant to harmonize with ICAO Annex 6, Part 1 and take advantage of air carriers’ considerable investment in fuel-planning, tracking, and communication capabilities. Unplanned contingency fuel can be calculated based on statistical estimation under this rule rather than using a prescriptive amount (e.g. 10% of trip fuel). United Airlines collaborated with the FAA in a proof of concept that had many lessons learned. This presentation will walk through some of the lessons learned, including statistical (e.g. sample sizes, estimation, parametric vs. nonparametric methods), automation (e.g. modeling techniques, flight planning interfaces) and monitoring (e.g. performance measurements) considerations.
The Role of Social Media in Predicting & Proactively Managing Disruptions
Social media has become a normal part of life for people in society today. In aviation we see the impacts of unhappy passengers venting their frustrations immediately to the entire world due to delayed flights, lost baggage, and poor customer service. Within the past several years airlines have moved beyond using social media simply for pushing passenger notifications, and instead also use it as a customer service channel through two-way communications. As a next step in the social media evolution, Boeing’s Digital Aviation has been conducting research on how information shared by passengers can be used to predict disruptive events with a high level of confidence so that airlines can proactively take the necessary actions to avoid schedule disruptions. This talk with explore the methodologies used, the data types available for mining, and examples of successful event predictions.
Airport versus Airline Operations Control – Two Sides of the Same Coin
Today, each stakeholder in air transport is merely focusing on their own processes. Airport operations control’s focus is on turnaround management and the maximum use of airport capacity. Airlines, on the other hand, have an interest in a smooth network flow, minimizing ground times and maximizing aircraft utilization. The strict separation of interests is also re ected in supporting IT-tools. Airport Control Centers only look at the time between an inbound and an outbound leg, while Airline Operations Control masks the entire turnaround process. Even A-CDM is limited to an exchange of information among stakeholders, and no optimization from a holistic point of view takes place. A new approach will combine the two worlds by integrating the management of turnaround processes into the rotational aircraft planning within airline operations control. The presentation gives an insight on how this integration could be implemented in an operations control system.
Delay cost management at Finnair
Increased awareness in passenger rights has drawn attention to manage delay costs even closer. Finnair’s delay costs are tracked to the delay’s root cause, providing the full perspective to the reasons behind. With the traced costs, Finnair is able to develop its delay cost model and utilize this in different ways in operations and HUB control, flight planning, cost index management and business case creation.
On-Time-Performance (OTP) Evolution and Technology Leaps for Delay Cost Improvement
The On Time Performance (OTP) for the Global Airlines over the last three years showed steady behaviour. We observe an absence of major improvements for this KPI of airline competitiveness. While OTP was steady, the cost of delay increased, often significantly. In this presentation, we outline the determinants of increasing cost of delay and we present historical OTP and delay cost evolution. Then we present a forecast of delay cost evolution for the next 5 years and discuss the impact on airline productivity, airline operations and overall airline competitiveness. Following the results of this empirical study, we discuss how the emergence of new techniques in predictive and prescriptive analytics can provide a step change in disruption mitigation and prevention to support superior on time performance. This includes strategies for leveraging real time and past data, and the transformation of the resultant information into implementable actions.
One Resource Pool - Optimized Teaming Concepts to Improve Platform Productivity
With the introduction of IT systems enabling dynamic planning of operational tasks, gaining productivity by creating one centralized resource pool is a promising concept. However, the social implications of such a radical change to the work environment can compromise a successful implementation. If change is pushed towards the operational teams without their commitment, failure is guaranteed. In a strategic study for KLM Royal Dutch Airlines, ORTEC analyzed the impact on employee productivity when all turnaround platform tasks are planned using one resource pool. The goal of the study is to find a win-win situation that results in an increased productivity and happy employees. Using a custom made mixed integer linear programming model various teaming setups are evaluated. The main topic is the potential for interchanging employees between teams in multiple workforce area breakdowns while a second topic focused the optimal ratio between fixed and flex teams.
Data Driven Operational Insights to Improve Schedule Reliability
The airline’s published schedule is essentially the product that it sells to the traveling public. When that schedule is disrupted and cannot be operated on time, the public’s confidence in the airline is eroded and may ultimately lead to a loss of market share and profitability. While there has been much time and effort invested in optimizing the revenue potential of the schedule, our research found that the schedule provided by the network planning function is often not the schedule that is flown. Priorities for revenue capture may create the potential for delays due to operational constraints. Moreover, the original schedule is often so significantly changed by the day of operations that there are inherent weaknesses that increase the likelihood of delays and passenger misconnections. This presentation will describe some of the inherent weaknesses of certain schedules, how we go about measuring the likelihood of probable delays and a few means of mitigating the risk of schedule failures.
On Time Performance Maximizer: Simulation Optimization to Improve Operability
One of the critical components of an airline business is schedule planning. Schedule planning problem for large scale networks are solved with little or no stochasticity in the inputs. Highly optimized schedules show an opposite trend when compared against operational metrics. We developed a simulation based approach to measure operational performance of a given schedule under uncertainty. It simulates airline network by considering uncertainty due to passenger flow, recovery, and weather. Optimized schedules for point-to-point airline try to balance tradeoff between time allocated on ground vs flying time. Insufficient or excess ground time can cause expensive delays or under-utilization respectively. We propose to distribute right contingency at right places such that optimal on time performance can be achieved with minimum delay at minimal cost. We leverage our simulation framework and use mixed integer programming technique to optimize operability.