Decision Support System: Aviation Industry Challenges and Benefits

This report delves into the application of Decision Support Systems (DSS) within the aviation industry, emphasizing their importance in a technically challenging environment. It explores the shift towards DSS, driven by the need for enhanced operator performance and reduced decision errors. The report discusses the benefits of DSS, such as improved information processing, real-time data utilization, and enhanced aviation management through historical data analysis. It also acknowledges the challenges in implementation, including the difficulty in quantifying all data and the reliance on computerized systems. The report examines the application of DSS in various areas, including airlines, air transport systems, airports, and air traffic management. The report also provides a comprehensive analysis of the challenges faced during DSS implementation. It concludes by recommending further research and development in this field to fully realize the potential of DSS in aviation, ensuring safety, efficiency, and sustainability.

Decision Support System in Aviation Industry
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Table of Contents
1. Introduction............................................................................................................................3
1.1 Importance of decision making in challenging technical environment such as aviation.....3
1.2 Challenges in aviation industry when it comes to performance of operators......................4
2. Discussion..............................................................................................................................5
2.1 Shift of aviation towards decision support systems (DSS)..................................................5
2.2 Need of such systems in aviation industry...........................................................................7
2.3 Challenges in implementing the DSS..................................................................................9
2.4 Possible benefits in implementation of such systems for better performance...................10
3. Conclusion & Recommendations.........................................................................................13
References................................................................................................................................15
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1. Introduction
1.1 Importance of decision making in challenging technical environment such as
aviation.
Decision making in the technical environment of aviation sector was defined by Federal
Aviation Authority (FAA) in 1991, as an organized approach to a cognitive process which is
used by aircraft companies including pilots to determine the suitable course of action based
on situation. The real time decision making in highly technical environment of aviation is
considered as a psychomotor performance skill. It involves awareness of the situation, along
with the selection of the best alternatives and related risk assessments within a short span of
time. The pilots are responsible to perform an array of jobs or functions, in order to; maintain
the technicalities of the aircraft (Janssen et al., 2017). The decision-making ability related to
the mission performance enables them to do so under critical situations. Traditionally, the
decision making in aviation sector was viewed as an innate quality in flying experience. The
decision-making process in aviation sector, involves two elements namely situation
assessment, which is considered as pre-cursor, in order to generate possible course of action
and mental simulation which refers to, the assessment of the course of actions for managing
the risks associated with the technical environment. For instance, if the pilot recognizes that
there is ample time for making the considerations, then he would evaluate the course of
action by conducting the simulation assessment. This would ensure the lower possibility of
errors in decision. The fatal and non-fatal accidents in aviation sector takes place due to the
errors in the decision making, hence; the management should understand the importance of
decision making in such technical situation and train their personnel accordingly.
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1.2 Challenges in aviation industry when it comes to performance of operators
The aviation industry has a contribution in the growth and development of global economy
but still when it comes to performance of operators the industry faces three major challenges
namely safety, convenience and sustainability (Albers et al., 2017).
a. Safety: Though flying is safe today as compared to early days, but accidents do occur now
also. According to a report in the year of 2017 there were 37 million flights and 24 fatal
accidents across the world, in that study a statistic revealed that there are chances of accidents
on aircraft were one in two thousand. The main reason behind maximum accidents were error
in decision making in technical issues.
b. Convenience: The travellers are experiencing convenient and hassle-free journey,
compared to early days in terms of the quality of the services of airlines, huge availability of
choices - from basic seats in economy to luxury seats and many more. Some challenges that
the aviation industry still faces in terms of convenience are the security and checking
approach that all the travellers had to face and missing framework in order to share crucial
data relating to the safety measures.
c. Sustainability: When it comes to sustainability, the vector is profitability; which is
dependent on the performance of the operators as, there is high level of competition in this
sector. The aviation companies are spending billion dollars in implementing technology to
ensure fuel efficiency and maintaining environmental balance. This is anyway affecting the
profit margin of the companies. Despite of strong demand in travel industry, there are
economic uncertainties in this industry as, the stocks are volatile. The operators are
continuously challenging each other, in terms of sustainability, by providing friendly
surroundings to the travellers.
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2. Discussion
2.1 Shift of aviation towards decision support systems (DSS)
Decision Support Systems, acronymic as DSS, is a computer program application that aims at
recognizing the business-related information and data and presents that to the stakeholders of
the business enabling them to take decisions effectively. It is regarded as information
application that collects, summarizes and presents data for making the business operations
smooth. The aviation industry is also shifting towards decision support system due to the
complexity in the technology and enhancement of the performance of the operators with
minimum cost and risk (Bruce, 2016). The rich set of assessment, simulation and
optimization activities helps the decision makers in reducing the decision errors. The reasons
behind the shift of aviation industry towards the implementation of DSS are discussed in the
following section.
 Information processing: The DSS for maintaining the aviation sector requires the
support of real time data and historical data in order to judge the decisions.
 Real time data processing: The information technology applied in modern aircrafts are
controlled and maintained by central computer system which enables the operators to
maintain the aircraft parameters (Bazargan, 2016). The parameters are shared to the
aircraft online fault diagnosis system which assess the faults in the aircrafts by using the
diagnosis model and thereafter share the results to the ground system in order to provide
decision support for the efficiency of the performance.
 Aviation management: The application of DSS enhances the aviation maintenance
management as, it ensures the safety and security and reduces the decision errors by
supporting it in the technical environment.
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 Historical data processing: The aviation maintenance process accumulates large amount
of historical data which is divided into three types:
a. Flight state parameters: The information related to state parameters are linked with
the failure of the aircraft and is recorded by quick accesses recorder. The online airplane
fault diagnosis model is created by using the relationship between the failure symptoms of
the system and malfunction cause. This model aims at supporting the fault diagnosis via
online.
b. Fault maintenance data: The records of aircraft maintenance provides the depiction
of development of aircraft systems. The data is used to determine the depth of the
relationship between the symptoms of system failure and the reasons behind malfunctions.
The in-depth diagnosis provides the support to the decision of ground maintenance.
c. Air material loss data: In order to enhance the performance of the modern operators
of aviation industry several types of aircraft materials and systems are used. The price
range of these materials and systems are high and thus due to lack of maintenance these
materials can become obsolete resulting in the huge loss for the company. The proper
selection of aircraft material along with the good and appropriate use of the same
increases the efficiency of the enterprise.
The DSS has benefited the aviation industry by processing the real time and historical data
but recent study reveals that one of the major uncertainty related with the information
processing is the difficulty in quantifying all the data and information because in reality it is
challenging to define the intangible and specific data. As a result of that one cannot fully rely
on DSS for decision making (Ivanov and Netjasov, 2014).
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 Maintaining the technical support: The aviation sector needs the DSS in their system in
order to maintain the technical support. There are two aspects of maintaining the technical
support:
The fault diagnosis model: The fault diagnosis model is segmented into two different parts
online diagnosis model and offline diagnosis model. The online diagnosis model collects the
history of state parameters from the historical data processing model and from there the
model using the expert system identifies the fault and update the database accordingly to keep
the entire system informed about the diagnosis. The offline diagnosis model is based aircraft
maintenance record module wherein there is an in-depth analysis of fault related to the
system.
Maintenance of technical documentation: DSS plays an important role in maintain the
aviation technical support as this is an integration of advance technology including the
manufacturing, process and information. There are several technical documents which are
regarded as important tools to support the maintenance, for instance MEL, TM, RPM, OS and
many more. Traditionally these documents were maintained in papers which created a
problem in decision making at the time of urgency but at present the documents are recorded
in a system of MIS which enhance the decision making.
2.2 Need of such systems in aviation industry
The growing need of implementing the decision support systems in aviation industries is due
to certain reasons. Decision making in technical environment of aviation sector is challenging
and the need of making good decisions are continuously increasing. There is a need of fast
decision making in aviation industry at the time of urgency using the relevant set of
information from the available set of information. Distortion of information in the society is a
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problem for all the organizations, DSS emphasizes on fact-based decision making which
supports the organizations in taking relevant decisions.
DSS in Airlines
According to Goeters (2017), usage of DSS for what if analysis in order to ascertain the
required number of engineers along with their qualifications in order to maintain the aircrafts.
It also determines the effect of KLM contracts with other companies based on the size of
KLM maintenance workforce along with organization. DSS is used in developing the
strategic manpower planning of the airline companies. There is a need of interaction of high
planner computer wherein the planner is capable of drawing conclusion from the DSS output.
The DSS output provides appropriate information along with the alternatives and their scores.
The model of DSS cannot directly solve the problem of scheduling category that is the
problem relating to the optimal allocation of time resources act as a constraint.
DSS in Air Transport system
In the field of Air Transport System, the application of DSS is limited due to certain reasons
like the availability of unstructured or semi structured information, the nature of the human-
machine interface which supports the human decision makers and the usage of data dialogue
model system.
DSS in Airports
DSS in airports uses what if analysis along with the modelling tools based on the time
horizon. The DSS of airports can be classified as strategic, tactical and operational.
According to a study conducted by Stamatopolos et al. (2004) the short term or operational
DSS in airports are conferred in detail in “total airport management concept” and long term
DSS includes strategic and tactical planning which is based on a model named Stochastic
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Analytical model used in the estimation of runway capacity and simulation. DSS is used in
analysing the performance of airports by increasing the capacity utilization using the
optimum scheduling.
DSS in Air Traffic Management
The usage of DSS and its applications are high in Air Traffic Management for managing the
air traffic properly. The airports that are having high volume of traffic creates a problem for
the airlines, airports and for passengers. The paper presented by Janic (2014), address this
problem. The authors collected to data related to LTI for analyzing and developing models
suggesting the proper sequence of arrival in order to manage the traffic properly. As opined
by Desai and Prakash (2016), DSS also provides arrival planner to the airports through
effective communication between the airlines and air traffic controllers.
2.3 Challenges in implementing the DSS
The implementation of DSS has benefited the aviation industry in many ways but
implementing the same, faces many challenges. DSS enables the aviation companies in
regulating the management and taking decisions in a technical environment but, the
implementation of DSS in an organization involves huge amount of investment because of
the advancement in analyzing data, statistics, information system and econometrics. The
focus of computerized decision support system is to make and support decisions but the
implementation of DSS overemphasizes the decision making. The managers of the
organizations rely upon this system too much and as a result of that, they cannot take
decisions on broader context. DSS uses real time and historical data for data processing in an
organization but, it is near about impossible in collecting all the required data and relevant
information and as a result of that the value presented by the system is not fully reliable. The
models of DSS applied in aviation industry assume certain things based on the situation and
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problems (Galliers and Leidner, 2014). The decision maker might not be fully aware of the
assumptions made previously and while making decisions did not considered the factors, in
that case the results or values presented by DSS would become irrelevant. DSS is a user-
friendly application but still it requires technological knowledge for operating it successfully.
The operator’s lack of knowledge is one of the major challenges in implementing DSS. The
application of DSS sometimes have unanticipated effects because the application overloads
the managers of aviation industry with load of information and as a result of that the decision
becomes ineffective.
2.4 Possible benefits in implementation of such systems for better performance.
Implementation of DSS is beneficial for the organization in several ways in terms of
performance which is the point of discussion in this following section (Chiu et al., 2014).
 Improved effectiveness: The first category of benefits of DSS examined in terms of
performance is enhanced performance of the organization due to effective decision
making. The decision quality and the effectiveness of decision making are interrelated
with each other. For instance, the managers opt for DSS implementation in the
organization in order to obtain set of information from model driven DSS application.
 Enhance communication: DSS aims at improving the communication and cooperation
among the decision makers in the organization. The model based DSS aims at sharing the
assumptions along with the facts whereas the data based DSS aims at making similar
version of truths regarding the operations of the company and therefore encourage the
decision making based on facts. Improved accessibility of information is considered as
motivation behind the data based DSS.
 Competitive advantage: The application of DSS in the organization ensures the
competitive advantage to the business by utilizing the model driven and data driven DSS.
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The managers with the help of the application can effectively take decisions at the time of
urgency moreover DSS provides bulk of information to the managers from where the
performance of the organization can be enhanced. Web based DSS is a part of
performance management and business intelligence systems as it provides computerized
decision support.
 Promote learning: DSS in an organization promotes two types of learning namely
learning upcoming concepts and learning how to develop better understanding in the
business in order to make factual decision. “De facto” is considered as a training tool
served by DSS for new employees in order to make them train and learn about the
decision making.
 Increase in control: DSS especially data driven make the transaction data available in
order to monitor the performance of the organization. Implementation of such system in
the organization enhance the performance and provides better understanding about the
business. DSS also provides a summary regarding the decisions made, usage rate of the
systems along with the recommendation, hence it allows the organization to have full
control over the operations.
According to some research studies DSS also reduces the cost related to the labour but the
main goal of having the DSS in an organization is to enhance the performance by effective
means. Some examples of DSS that has benefited the aviation industries are as follows:
 Pentagon shield: It is considered as a prediction system which aims at protecting people
around the Pentagon by assessing the airborne hazard.
 MDSS: The tool which aims at supporting the decision for winter road maintenance.
 LLWAS: The tool which is used to alert the microbursts which act as threat to the landing
and departure of aircraft.
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 4DWX: The decision-making tool used at the army test ranges in order to provide
guidance to the army.
 WSDDM: The tool which depicts appropriate rate of snowfall along with the weather
conditions that is used by the pilots for de- icing and by the airport operators as well.
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3. Conclusion & Recommendations
This study provides an insight into the implementation of decision support systems in
aviation industry. It includes the areas in which DSS can be applied that is, DSS in airports,
airlines, air traffic management and air transport system. The study also sheds light on the
possible benefits of implementing DSS in an organization along with the challenges and
pitfalls of DSS application. The reason behind the shift of aviation industry towards DSS has
also been the point of discussion in the above study. The main purpose of the study was to
focus on the major issues of the airline companies which can be resolved by implementing
the DSS system.
There are certain reasons behind the shift of aviation industry towards decision support
systems. DSS process the information based on real time basis or historical basis. This
provides a set of relevant information to the aircraft companies, aircrafts and the operators
based on which relevant decisions could be made. The extensive usage of DSS results in the
overloading of information and hence can create confusion while making decisions. DSS also
aims at maintaining the technical support to the aviation companies through fault diagnosis
model. Though there are assumptions made in the model which can distort the decision-
making process. Technical support is also provided by DSS through the proper integration of
technology. The technical documents which were maintained in paper format traditionally,
now it is maintained or recorded in the MIS system. DSS enhances the management of the
aviation industry by providing safety measures.
There is a need of DSS application in different department of aviation sector such as in
airlines DSS is required to determine the requirements of engineers, impact of KLM and
many more. The requirement of DSS in Air transport system is limited as compared to other
departments. In airports DSS determines the modelling tools depending on the time horizon.
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The air traffic management immensely need the application of DSS in order to plan the
arrival and departure schedule to reduce the traffic. The possible benefits of implementing the
DSS system in an organization are the enhanced effectiveness in the performance, effective
communication, promotion of learning and many more. In short, it can be concluded that, the
application of DSS in any organization where there is challenging technical environment such
as aviation, is effective in terms of performance.
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References
Albers, S., Baum, H., Auerbach, S. and Delfmann, W., 2017. Strategic management in the
aviation industry. Routledge.
Bazargan, M., 2016. Airline operations and scheduling. Routledge.
Bruce, P.J., 2016. Understanding decision-making processes in airline operations control.
Routledge.
Chiu, C.M., Liang, T.P. and Turban, E., 2014. What can crowdsourcing do for decision
support?. Decision Support Systems, 65, pp.40-49.
Desai, J. and Prakash, R., 2016. An optimization framework for terminal sequencing and
scheduling: The single runway case. In Complex Systems Design & Management Asia (pp.
195-207). Springer, Cham.
Galliers, R.D. and Leidner, D.E., 2014. Strategic information management: challenges and
strategies in managing information systems. Routledge.
Goeters, K.M., 2017. Aviation psychology: practice and research. Routledge.
Ivanov, N. and Netjasov, F., Review of decision support systems in air transport system.
Janic, M., 2014. Air transport system analysis and modelling. CRC Press.
Janssen, M., van der Voort, H. and Wahyudi, A., 2017. Factors influencing big data decision-
making quality. Journal of Business Research, 70, pp.338-345.
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