Digital Marketplaces Unleashed
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However, the deployment of new technologies alone will not be sufficient to keep Changi ahead, as IT systems and applications become more readily available and commoditised. Increasingly, the “smartness” of an airport will hinge on the strategic capability to collect data and on how intelligently the data can be exploited.
Changi’s Smart Airport initiative mirrors Singapore’s Smart Nation Vision that defines Smart as the “harnessing of technology to the fullest with the aim of improving the lives of citizens, creating more opportunities, and building stronger communities”, and states that a Smart Nation is “built upon the collection of data and the ability to make sense of information”.
37.3 Why the Need for Smart Data Capability?
Data analytics will be a key driver in enabling the identification of patterns and the optimal deployment of resources to capitalise on the following opportunities. The following challenges are addressed:
Meeting the Expectations of Tech‐Savvy Customers
Many travellers today live largely in the virtual world they create and curate for themselves via the social media and online services on their mobile devices. In order to gain mindshare, we must be able to deliver personalised value to individual customers. Customers’ interactions and transactions with the airport provide an opportunity to collect data about their individual behaviours and needs, and to use that data to enhance their experience at the airport, or even to deliver new services.
Improving Partners’ Performance and Productivity
A hub will need to develop a sound plan and invest into infrastructure to reduce manpower requirements and improve productivity and service, not only for the airport operator, but also for the airport tenants and partners. Hubs will increasingly have to innovate on how data exchange around airport wide processes can help each stakeholder with their own productivity.
Excelling in Spite of Increasing Business Complexity
With high traffic growth in many hubs, there is a need to optimise existing physical capacity through deeper collaboration and information exchange among all airport partners. Airport Collaboration Decision Making (A‐CDM) is an example where airports have exploited greater accuracy of flight arrival times and their corresponding passenger loads, to better manage airport traffic levels and schedule scarce ground resources. Deep collaboration has also enabled more robust and integrated responses to crises and disruptions.
Maintaining Safety & Security
Safety and security of hubs are important aspects of hub operations. The deployment of sensors and sense‐making technology will allow operators to quickly filter through “noise” and zoom in on incidents and exceptions to enhance safety and security. With the immense data being collected and processed and the response time needed for these incidents and situations, a smart way is needed to respond effectively.
Ensuring Cyber Security and Information Assurance
Even when exploiting the use of data analytics to transform the way resources are used, one needs to Ensure to invest adequately in information assurance and cyber security in order to ensure an effective and safe operation. Cyber Security Operations Centre (CSOC) is an important smart way to collect and analyse data on cyber attack attempts, allowing to understand patterns, to identify weaknesses, and to strengthen cyber defences.
37.4 What Benefits Has Smart Data Capability Achieved so Far?
Some examples of Changi’s smart capability achieved so far include: Fuse airport operations information on maps and graphical charts, like for example the movements of planes or vehicles, or measures of on/off block timings. With this ability, the coordination and management of operations is highly improved.
Digitise the position of aircraft and airside vehicles, status of key resources (for example, aircraft parking stands, runways), and other ground sensors (for example, perimeter security sensors) on an interactive airport map. This provides ground staff with the situational awareness of the airport to better manage operations.
Help ground staff to quickly comprehend the situation on the ground such as upcoming flights and passenger volume through graphical charts of operations information. This helps operations staff to proactively prepare for possible surges in flight or passenger traffic.
Exploit big data analytics. With 58.7 million passengers travelling through the airport last year, every transaction, whether at check‐in, retail or boarding, gives Changi Airport tremendous insights into how it can serve customers better. For example, the airport electronically received instant feedback from the customers at the rate of 1.8 million per month. The repair and service recovery actions that had been performed provide another large data set to understand how an airport can innovate and improve. Aircraft movements are tracked and statistics and performance measures are also collated for touch points to measure service standards; as is the large data set in terms of retail transactions. Such data sets are being used to gain deep insights so as to help to better plan resource allocation for check‐in and aircraft gates and other resources.
An example of a smart project is called SWIFT (Service Workforce Instant Feedback Transformation). The project is explained below.
37.5 Service Workforce Instant Feedback Transformation (SWIFT)
SWIFT was implemented in September 2010 to improve feedback management and raise performance standards of service personnel across all three terminals. It comprises two components – the instant feedback system (IFS) and e‐Inspection as shown in Fig. 37.1.
Fig. 37.1 Instant Feedback System (IFS) and e‐Inspection
e‐Inspection enables timely responses to facility faults across all terminals through real‐time inspections by service teams. The system also raises productivity levels of frontline staff such as washroom attendants, facilities management officers and other service personnel, with more streamlined workflows and processes. A total of 750 SWIFT instant feedback devices have been installed at nine key customer touch points. Each month, about 1.8 million instant feedback is collected from customers.
Achieving Operational Anticipation & Reaction
In the toilet cleanliness context, while anticipated passenger loads allow to deploy cleaners proactively, it would be too costly to deploy an army of cleaners to ensure that toilets meet cleanliness standards at all hours. By collecting instant feedback from passengers and putting in place the technology and processes to alert the cleaning crew so that they can act on specific feedback promptly, it is possible to greatly reduce the cleaning and maintenance resources required, while keeping cleanliness and maintenance standards high.
Achieving Data‐Enabled Resource Planning
Over time, the data collected in SWIFT guides the allocation of resources as the patterns and correlation between flights and toilet cleanliness are being appreciated. The charts below (Fig. 37.2) are examples of the data analytics that is possible. It shows examples of analysis that can be performed to understand time of day effects on demand and top reasons of service feedback. This empowers the management teams to deep dive using data into areas for improvement.
Fig. 37.2Example analyses of the data collected in SWIFT
Achieving Data‐Driven Collaboration & Problem Solving
The SWIFT platform serves as a platform to share feedback data and patterns directly with partners, but the foundational technology platform itself is insufficient to drive collaboration. The data captured has helped Changi Airport to drive service enhancement conversations with partners, and while the initial collaboration seen in SWIFT is encouraging, the project is still young to create a truly collaborative problem‐solving culture.
37.6 SMART Framework
With the proliferation of technologies and ideas it is imperative to have a frame to think about smart projects. The smart data capability is premised on the SMART framework as shown in Fig. 37.3, w
hich aptly stands for “Service, Safety & Security Management through Analytics and Resource Transformation”.
Fig. 37.3SMART (Service, Safety & Security Management through Analytics and Resource Transformation) Framework
37.6.1 Smart Outcomes
The described smart data capability is more than a technology tool and involves the airport community coming together to share data and solve problems. There are three key outcomes envisioned: 1. Operational Anticipation and Reaction. Using information to anticipate and take pre‐emptive measures if possible; furthermore respond promptly to operational issues across the airport, especially when situations change suddenly. With such data it will be possible to describe many of the situations that expert “intuitively” know. With increasing operational complexity, intuition without a Smart data capability may not be good enough.
2. Data‐enabled Resource Planning. Allocating resources efficiently based on effective collection and analysis of data. There is often a gap between the design capacity and the actual operable capacity, and a data‐enabled resource planning approach will help planners for resources like infrastructure or even manpower planning see trends over time; that cannot be solved or even justified without such a basis.
3. Data‐driven Platform for Collaboration & Problem Solving. Building platforms for data sharing between airport partners that not only enable each other’s outcomes, but also foster a data‐driven approach to find airport‐wide improvements, and institutionalise a culture of collaborative problem‐solving for the benefit of the air hub. The A‐CDM (Airport Collaborative Decision Making) platform mentioned earlier is an excellent example of such a platform.
37.6.2 Smart Enablers
Three key enablers underpin these Smart data capability outcomes. The ability to collect the right data and to make sense of the information will be central to the development of Changi’s Smart Data Capability. To that end, Changi Airport will continue to invest in sensors and data fusion, as well as to begin investing in the rapidly‐maturing machine cognitive capabilities. 1. Sensor Masterplan. Deploy sensors, collecting data via digital touchpoints, and even through travellers’ mobile devices. This is Changi’s own “Internet of Things”. The sensors are used to help tracking the demand and supply of resources, tracking airside movements and status, and monitoring queues. Changi will continue to invest in ‘eyes’ on the ground to obtain real‐time operations information for proactive and even predictive response, as well as longitudinal analysis. The Sensor Masterplan goes beyond data collection and includes data analytics and business intelligence that quantify performance and identify areas for improvement. Underpinning the sensor projects in the Masterplan is an investment in enabling IT infrastructure which provides connectivity for the sensors in Changi’s “Internet of Things”.
2. Data Fusion. Merge collected data into a coherent view, giving planners and operators Changi’s “Comprehensive Awareness”. For example, with the establishment of the Airport Operations Centre (AOC) in 2010, Changi Airport also implemented an AOC System that provided “comprehensive awareness” for everything needed to know to help coordinate and collaborate across the wider airport community, both airside and landside; Direct contractors and staff and partners like ground handlers and government agencies were also involved.
Another example is the fusion of customer data into insights into the needs of customers and passengers in both, online and offline channels, to help serve customers better.
3. Cognitive Capabilities. Use Smart data capability to move from predictive decisions to artificial intelligence (AI)‐enabled prescriptive actions. Machine learning and artificial intelligence are quickly becoming accessible and available through applications (e. g. virtual assistants and chat bots) and more sophisticated predictive models for resource allocation and modelling. These cognitive capabilities have the potential to enhance sense‐making abilities, and are still relatively new. Changi Airport is exploring use cases for such capabilities and will invest in proofs‐of‐concepts.
To illustrate the Smart Framework, the A‐CDM implementation will be used as a case study. Changi Airport was fully operationalised A‐CDM in Nov 2016, after more than 2 years of testing and pilots to ensure a smooth rollout.
37.7 Airport Collaborative Decision Making (A‐CDM) as an Example of Smart Initiative
Changi’s A‐CDM program is a prime example of the Smart data capability and illustrates the SMART Framework well. In 2014, Changi Airport partnered with Air Traffic Control (ATC) on the A‐CDM capacity enhancement programme whose objective is to improve operational efficiency, predictability and punctuality for the air traffic management (ATM) network and airport stakeholders. A‐CDM shifts the concept of operations from “First Come First Serve” to “Best Planned Best Served”, where flights would be sequenced for departure in a planned manner that would ultimately benefit the entire system with reduced variability.
A‐CDM Operational Anticipation & Reaction
With A‐CDM, ground handling agents (GHAs) and airlines work towards a common Target Off‐Block Time (TOBT) for each flight. ATC uses the TOBT to set the best Target Start‐up Approval Time (TSAT) and clear aircraft for departure, significantly optimising the usage of taxiways and runways.
A‐CDM Data‐Enabled Resource Planning
With tactical planning information such as TOBT and TSAT available in advance, airlines, GHAs, ATC, and Changi’s airport operations planners are then capable of optimising the available resources to meet both the above‐wing and below‐wing operational needs.
A‐CDM Data‐Driven Platform for Collaboration & Problem Solving
By aligning airport partners (ATC, Airport Operations, airlines and GHAs) to a common operations framework, A‐CDM becomes a data‐driven collaboration platform for: Improving the operational efficiency of all airport partners by reducing delays;
Increasing the predictability of events of a flight (turnaround; pre‐departure plans; etc.);
Maximising the utilisation of resources (manpower, airport fixed resources like gates, stands, airspace, etc.); and
Paving the way for further benefits in baggage performance and passenger experience.
Changi’s investment in airside initiatives like A‐CDM is paying off in an average of 2 min taxi‐out time savings per departure, and a 10% improvement in daily departure slots punctuality. The A‐CDM framework has also enabled the airport community to recover effectively when runway capacity was disrupted.
To enable these A‐CDM outcomes, Changi Airport invested systematically in: Sensors, as part of the wider Sensor Masterplan. To provide the awareness of aircraft position, docking status and other relevant useful information, several projects to link all the aircraft docking & guidance systems were completed, as well as sensor information from ATC and other sources. In addition to supporting A‐CDM, Changi is leveraging these same sensors in various other service and security related applications.
Data Fusion and Analytics. For A‐CDM, data from various sources have been collected and fused into useful data sets for operational planning and anticipation. Sources include flight data from airlines’ operational systems, arrival passenger load data, and airport gate allocation data. Changi Airport is currently integrating new air traffic information to better predict aircraft on‐block time up to 20 min before arrival.
Cognitive capabilities. Changi Airport is only starting to invest in this emerging area. The use of artificial intelligence and analytics to predict arrival times better are explored. Also, the arrival passenger loads for various arrival touch points such as immigration, arrival trolleys and taxi queues are anticipated. The ability to exploit cognitive capabilities is dependent and builds on the availability of lar
ge datasets that are now becoming available to Changi from internal and external sources.
37.8 Conclusion
Changi’s progress on the Smart Airport initiative has been made possible largely by strong teamwork at all levels within the airport as well as the close collaboration with partners, airlines, government, and tenants. Changi Airport will continue to innovate with the best in the industry to develop the best possible data‐driven collaborative problem‐solving capability that enriches the passengers’ experience, improves productivity for airport partners, and addresses complexities wrought by capacity constraints, while keeping Changi safe and secure in a sustainable manner.
The Smart Airport Framework that has been described has helped to focus clearly on the key outcomes and ensures that Changi continues to invest in the enablers needed to support Smart outcomes.
Just as Singapore is pursuing Smart Nation initiatives to improve the lives of the citizens, Changi Airport has been and will continue to pursue Smart Airport ideas to improve the experience of the customers and to enhance operational efficiency at the airport.