Fundamental Prerequisites of Operational Readiness, Activation, and Transition: Case Study of Istanbul Grand Airport
Abstract
:1. Introduction
2. Operational Readiness, Activation, and Transition
2.1. Literature Review on Previous Operational Readiness, Activation, and Transition Studies
2.2. Previous Operational Readiness, Activation, and Transition Experiences
3. Research Methodology
3.1. Literature Review for the Identification of Operational Readiness, Activation, and Transition Activities
3.2. Pilot Study for the Validation of Identified Activities
3.3. Case Study of İstanbul Grand Airport
3.3.1. Information about the Descriptive Case Study
3.3.2. Data Collection for the Descriptive Case Study
3.3.3. Data Analysis of the Descriptive Case Study
3.4. Pythagorean Fuzzy Analytic Hierarchy Process
3.4.1. Data Collection
3.4.2. Data Analysis of the Pythagorean Fuzzy Analytical Hierarchy Process
4. Findings of The Study
4.1. Findings of Pythagorean Fuzzy Analytical Hierarchy Process Analysis
4.2. Findings of Sensitivity Analysis
5. Discussion of the Findings
6. Conclusions
6.1. Practical Implication of the Study
- Cross-functional collaboration for fostering collaboration among all airport systems: As stated above, there are lots of connected information technologies and systems in the airports. However, these technologies and systems are designed to serve different stakeholders, such as ground services, check-in, security, etc. Therefore, the collaboration should be fundamentally performed by meeting the demands of stakeholders. The management of stakeholders can be the challenging part of this strategy. Stakeholder management and regular meetings will be the key to implement this strategy successfully.
- Implementing regular drills and simulations to assess readiness and identify areas for improvement: Performing drills and simulations are one of the costliest parts of ORAT activities. It requires intensive resource allocation, such as time, personnel, and equipment. Also, while drills are performed, some part of the construction activities is executed in the facility. Therefore, the management of drills is complex and risky. However, simulations and drills will be useful to prevent failures during the operation phase of the facility.
- Developing contingency plans that includes alternative procedures, and communication protocols in case of emergencies or unexpected events to address potential issues or disruptions during the ORAT process: ORAT is a dynamic and complex process. Therefore, as being in every project phase, it contains risks and requires the wise allocation of limited resources. To handle risks and unexpected events, the development of ORAT contingency plans requires intensive collaborations with stakeholders to consider possible scenarios. Communication and stakeholder engagement will be important to implement this strategy. Within this context, the employment of an experienced ORAT team can be the facilitator for this strategy.
- Developing a detailed integration plan that outlines the sequence of activities, dependencies, and timelines “developing a robust data migration strategy” to ensure data integrity and security measures: The information technologies and systems found in the airports requires databases that are managed securely. Moreover, when the operations are transferred to a newly constructed facility, data migrating, and the integration of old and new systems, can be challenging. However, if the ORAT activities involving data mapping and documentation, quality assessment, and trials are performed carefully, this process can be error-free.
- Implementation of basic and advanced trials, including functional testing, interoperability testing, performance testing, and user acceptance testing: The most challenging part of trials/tests will be interoperability issues. However, this issue can be overcome with the use of interoperable systems that are currently used at operating airports.
- Establishing an airport operations control center (AOCC) to manage information systems before, during, and after ORAT relocation: Resource allocation (financial, human, and technological), data integration and monitoring, trainings, hiring experienced personnel, coordination, and communication will be important to establish a successful airport operations control center. Especially, setting up and operating an AOCC can require significant financial resources in terms of acquiring the necessary technology, infrastructure, and personnel. Limited budgets or competing priorities may hinder the establishment of a fully functional AOCC. On the other hand, ensuring regulatory compliance can be another challenge, since an AOCC must comply with various regulations and standards related to data privacy, security, and operational procedures.
- Having well-defined processes and documentation (handbooks, procedures, operation and maintenance plans, human resources planning documents, and education planning documents) that will be compatible with national and international civil aviation institutions’ legislation, procedures, instructions, and handbooks for enhancing success in the ORAT process: Different civil aviation institutions may have various national and international regulations, standards, and guidelines. Complying with these requirements and ensuring compatibility with them can be challenging, as they may involve multiple layers of regulations and frequent updates. However, ensuring effective communication, collaboration, and consensus among regulatory bodies, airport authorities, airlines, and other relevant entities might help to break through this obstacle.
6.2. Theoretical Implication of the Study
6.3. Limitations and Directions for Future Studies
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- IATA. Annual Review. 2019. Available online: https://www.iata.org/contentassets/c81222d96c9a4e0bb4ff6ced0126f0bb/iata-annual-review-2019.pdf (accessed on 17 July 2020).
- IATA. Annual Review. 2022. Available online: https://www.iata.org/contentassets/c81222d96c9a4e0bb4ff6ced0126f0bb/annual-review-2022.pdf (accessed on 5 September 2023).
- Mota, M. Papers Risk management: Where are the greatest risks in the airport development lifecycle and what factors should be front of mind? Airpt. Manag. 2016, 10, 229–244. [Google Scholar]
- Dalkıran, A. Havalimani Yönetimi ve Sürdürülebilirlik. Sürdürülebilir Havacılık Araştırmaları Derg. 2018, 3, 88–109. [Google Scholar]
- Zerjav, V.; Edkins, A.; Davies, A. Project capabilities for operational outcomes in inter-organisational settings: The case of London Heathrow Terminal 2. Int. J. Proj. Manag. 2018, 36, 444–459. [Google Scholar] [CrossRef]
- Alnasseri, N.; Osborne, A.; Steel, G. Managing airports construction projects: Providing an efficient management framework for operators. J. Adv. Manag. Sci. 2013, 1, 3. [Google Scholar]
- Martin, J.M.H.; Martinez, L.M. Madrid airport: A new integration design for a new operational model. In Proceedings of the 23rd Digital Avionics Systems Conference, Salt Lake City, UT, USA, 28 October 2004. [Google Scholar]
- Al-Mazrouie, J.; Bajracharya, A. Study on the Operational Readiness of Mega Construction Project. In Proceedings of the Creative Construction Conference, Budapest, Hungary, 6–9 July 2013; Volume 1994, pp. 1–3. [Google Scholar]
- Saounatsos, G. Implementation Strategies & Methodologies for Airport Openings. Int. Airpt. Rev. 2019, 6, 26–31. [Google Scholar]
- Saounatsos, G. New Larnaca Airport: Opens like Clockwork. Airpt. Int. 2010, 43, 34. [Google Scholar]
- Lee, E. The New Hong Kong International Airport Fiasco: Accountability Failure and the Limits of the New Managerialism. Int. Rev. Adm. Sci. 2000, 66, 57–72. [Google Scholar] [CrossRef]
- Lee, T.; Wong, W.; Yeung, K. Developing a Readiness Self-Assessment Model (RSM) for Six Sigma for China Enterprises. Int. J. Qual. Reliab. Manag. 2011, 28, 169–194. [Google Scholar] [CrossRef]
- Al-Mazrouie, J. An Investigation into the Impact of Operational Readiness Factors on the Success of Airports Projects in the UAE. Ph.D. Thesis, The British University in Dubai, Dubai, United Arab Emirates, 1 May 2017. [Google Scholar]
- Al-Mazrouie, J.; Ojiako, U.; Williams, T.; Chipulu, M.; Marshall, A. An operations readiness typology for mitigating against transitional ‘disastrous openings’ of airport infrastructure projects. Prod. Plan. Control 2020, 32, 283–302. [Google Scholar] [CrossRef]
- Binnekade, F.; Biciocchi, R.; BO’Rourke, E.; Vincent, C. Creating Smarter Airports: An Opportunity to Transform Travel and Trade; IBM: Armonk, NY, USA, 2019. [Google Scholar]
- Croes, H. Suvarnabhumi Airport a Blend of Modern Western Architecture with Traditional Thai Cultural Artwork. Airpt. World 2007, 36, 1–3. [Google Scholar]
- Khalafallah, K. El-Rayes, Minimizing construction-related security risk during airport expansion project. J. Constr. Eng. Manag. 2008, 134, 40–48. [Google Scholar] [CrossRef]
- Sahoo, S.K.; Goswami, S.S. A comprehensive review of multiple criteria decision-making (MCDM) Methods: Advancements, applications, and future directions. Decis. Mak. Adv. 2023, 1, 25–48. [Google Scholar] [CrossRef]
- Aladağ, H.; Işık, Z. The Effect of Stakeholder-Associated Risks in Mega-Engineering Projects: A Case Study of a PPP Airport Project. IEEE Trans. Eng. Manag. 2018, 67, 174–186. [Google Scholar] [CrossRef]
- Babu, J. Suvarnabhumi Airport Development: Operational Readiness & Airport Transfer 1. 2019. Available online: https://idoc.pub/documents/operational-readiness-airport-transfer-1-5143r93qev4j (accessed on 17 July 2020).
- Krauss, E. How Does Operational Readiness Assist in Asset Management: It Surely is an Operations Function? Aust. J. Multi-Discip. Eng. 2014, 11, 1–11. [Google Scholar] [CrossRef]
- Al-Mazrouie, J.R. An Exploration of the Impact of Operational Readiness Factors on the Success of Large Infrastructure Projects in the UAE. Ph. D. Thesis, The British University in Dubai, Dubai, United Arab Emirates, 2017. [Google Scholar]
- McElvaney, M. The many strategic and tactical benefits of Operational Readiness and Transition (ORAT). J. Airpt. Manag. 2020, 14, 115–122. [Google Scholar]
- Angriani, M.R.; Eliyana, A.; Wulandari, D.S. The effect of good corporate governance and leadership in applying operations readiness. Syst. Rev. Pharm. 2020, 11, 466–471. [Google Scholar]
- Mota, M. We learn more from our mistakes than from our successes: Lessons learned on ORAT and failed activation of air-port passenger terminals. J. Airport Manag. 2022, 16, 338–360. [Google Scholar]
- Talbot, S. Understanding ORAT and how ADM’s ORAT programme contributes to sustainability. J. Airpt. Manag. 2022, 16, 244–255. [Google Scholar]
- Järvelä, T.; Nurminen, M. Helsinki Airport’s development programme: Expanding an airport under one roof. J. Airpt. Manag. 2023, 17, 236–247. [Google Scholar]
- Kalakou, S.; Macário, R. An innovative framework for the study and structure of airport business models. Case Stud. Transp. Policy 2013, 1, 2–17. [Google Scholar] [CrossRef]
- Mohan, V. South–South production and knowledge linkages–an exploratory study based on cases from Malaysia. Asian J. Technol. Innov. 2013, 21, 39–63. [Google Scholar] [CrossRef]
- Barrero, I. A new star in the Emirates’ sky: Ineco and Aena involved in start-up. iTransporte 2014, 5, 12–17. [Google Scholar]
- Everett, J.; Charles, R. Reconsidering the airport business model. J. Airpt. Manag. 2014, 8, 351–359. [Google Scholar]
- Shafer, S.M.; Smith, H.J.; Linder, J.C. The power of business models. Bus. Horiz. 2005, 48, 199–207. [Google Scholar] [CrossRef]
- Rotondo, F. An explorative analysis to identify airport business models. Res. Transp. Bus. Manag. 2019, 33, 100417. [Google Scholar] [CrossRef]
- Pereira, B.A.; Caetano, M. A conceptual business model framework applied to air transport. J. Air Transp. Manag. 2015, 44, 70–76. [Google Scholar] [CrossRef]
- Paraschi, E.P.; Georgopoulos, A.; Kaldis, P. Airport Business Excellence Model: A holistic performance management system. Tour. Manag. 2019, 72, 352–372. [Google Scholar] [CrossRef]
- Koc, K.; Pelin Gurgun, A. Assessment of readability risks in contracts causing conflicts in construction projects. J. Constr. Eng. Manag. 2021, 147, 04021041. [Google Scholar] [CrossRef]
- Yin, R.K. Case Study Research and Applications: Design and Methods, 6th ed.; SAGE Publications: Thousand Oaks, CA, USA, 2018. [Google Scholar]
- Baxter, P.; Jack, S. Qualitative case study methodology: Study design and implementation for novice researchers. Qual. Rep. 2008, 13, 544–559. [Google Scholar] [CrossRef]
- Peksatici, Ö.; Küçükönal, H. Impact of Istanbul New Airport on Turkish Carriers’ Strategies. In Proceedings of the 22nd ATRS World Conference, Coex, France, 2–5 July 2018; pp. 1–27. [Google Scholar]
- IGA. 2020. Available online: https://www.igairport.com/en/about-iga/timeline (accessed on 17 July 2020).
- Shete, P.C.; Ansari, Z.N.; Kant, R. A Pythagorean fuzzy AHP approach and its application to evaluate the enablers of sustainable supply chain innovation. Sustain. Prod. Consum. 2020, 23, 77–93. [Google Scholar] [CrossRef]
- Ayyildiz, E.; Taskin Gumus, A. Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: An application in Istanbul. Environ. Sci. Pollut. Res. 2021, 28, 35798–35810. [Google Scholar] [CrossRef]
- Garg, H. A novel correlation coefficient between Pythagorean fuzzy sets and its applications to decision-making processes. Int. J. Intell. Syst. 2016, 31, 1234–1252. [Google Scholar] [CrossRef]
- Başaran, Y.; Aladağ, H.; Işık, Z. Pythagorean Fuzzy AHP Based Dynamic Subcontractor Management Framework. Buildings 2023, 13, 1351. [Google Scholar] [CrossRef]
- Ilbahar, E.; Karaşan, A.; Cebi, S.; Kahraman, C. A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf. Sci. 2018, 103, 124–136. [Google Scholar]
- Karasan, A.; Ilbahar, E.; Kahraman, C. A novel pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Comput. 2019, 23, 10953–10968. [Google Scholar] [CrossRef]
- Yucesan, M.; Kahraman, G. Risk evaluation and prevention in hydropower plant operations: A model based on Pythagorean fuzzy AHP. Energy Policy 2019, 126, 343–351. [Google Scholar] [CrossRef]
- Yucesan, M.; Gul, M. Hospital service quality evaluation: An integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS. Soft Comput. 2020, 24, 3237–3255. [Google Scholar] [CrossRef]
- Çalık, A. A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput. 2021, 25, 2253–2265. [Google Scholar] [CrossRef]
- Yager, R.R.; Abbasov, A.M. Pythagorean membership grades, complex numbers, and decision making. Int. J. Intell. Syst. 2013, 28, 436–452. [Google Scholar] [CrossRef]
- Yager, R.R. Properties and applications of Pythagorean fuzzy sets. In Imprecision and Uncertainty in Information Representation and Processing; Springer: Cham, Switzerland, 2016; pp. 119–136. [Google Scholar]
- Saaty, T.L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 1977, 15, 234–281. [Google Scholar] [CrossRef]
- Goswami, S.S.; Mohanty, S.K.; Behera, D.K. Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Mater. Today: Proc. 2022, 52, 1153–1160. [Google Scholar] [CrossRef]
- Xu, E.; Zhang, H. Spatially-explicit sensitivity analysis for land suitability evaluation. Appl. Geogr. 2013, 45, 1–9. [Google Scholar] [CrossRef]
- Souissi, D.; Zouhri, L.; Hammami, S.; Msaddek, M.H.; Zghibi, A.; Dlala, M. GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int. 2020, 35, 991–1017. [Google Scholar] [CrossRef]
- Ismail, M.; Fathi, M.S. Leadership in construction: Leadership styles practiced in construction project–A review. J. Adv. Res. Bus. Manag. Stud. 2018, 13, 24–30. [Google Scholar]
References | The Pillars of the ORAT Procedure | The Deficits of Studies |
---|---|---|
[6] |
|
|
[7] |
|
|
[8] |
|
|
[9] |
|
|
[13,14] |
|
|
[21] |
|
|
[23] |
|
|
[24] |
|
|
[25] |
|
|
[26] |
|
|
[27] |
|
|
Expert | Position | Experience (Years) |
---|---|---|
DM1 | Manager (airport facility manager) | 25 |
DM2 | Manager (airport facility manager) | 22 |
DM3 | Academician | 16 |
Expert | Position | Experience (Years) |
---|---|---|
DM1 | Manager (airport facility manager) | 28 |
DM2 | Manager (airport facility manager) | 27 |
DM3 | Manager (airport facility manager) | 22 |
DM4 | Industrial engineer (airport facility manager) | 8 |
DM5 | Engineer (manager in airline company) | 14 |
Criterion | Weight | Rank | Sub-Criterion | Weight | Rank | Global Weight | Global Rank |
---|---|---|---|---|---|---|---|
LG | 0.178 | 4 | LG1 | 0.383 | 1 | 0.068 | 4 |
LG2 | 0.230 | 2 | 0.041 | 14 | |||
LG3 | 0.182 | 4 | 0.032 | 18 | |||
LG4 | 0.204 | 3 | 0.036 | 16 | |||
LD | 0.260 | 1 | LD1 | 0.187 | 2 | 0.049 | 7 |
LD2 | 0.190 | 1 | 0.049 | 6 | |||
LD3 | 0.162 | 4 | 0.042 | 11 | |||
LD4 | 0.158 | 5 | 0.041 | 13 | |||
LD5 | 0.170 | 3 | 0.044 | 9 | |||
LD6 | 0.132 | 6 | 0.034 | 17 | |||
ST | 0.153 | 5 | ST1 | 0.252 | 3 | 0.039 | 15 |
ST2 | 0.180 | 4 | 0.027 | 19 | |||
ST3 | 0.274 | 2 | 0.042 | 12 | |||
ST4 | 0.294 | 1 | 0.045 | 8 | |||
F | 0.180 | 3 | F1 | 0.325 | 2 | 0.059 | 5 |
F2 | 0.439 | 1 | 0.079 | 3 | |||
F3 | 0.235 | 3 | 0.042 | 10 | |||
TSI | 0.229 | 2 | TSI1 | 0.542 | 1 | 0.124 | 1 |
TSI2 | 0.458 | 2 | 0.105 | 2 |
LD | 0.26 | 0.23 | 0.21 | 0.18 | 0.16 | 0.13 | 0.10 | 0.08 | 0.05 | 0.05 |
---|---|---|---|---|---|---|---|---|---|---|
LG1 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
LG2 | 14 | 11 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |
LG3 | 18 | 17 | 15 | 12 | 12 | 12 | 12 | 12 | 12 | 12 |
LG4 | 16 | 15 | 12 | 11 | 11 | 11 | 11 | 11 | 11 | 11 |
LD1 | 7 | 9 | 13 | 14 | 15 | 15 | 15 | 15 | 15 | 15 |
LD2 | 6 | 7 | 11 | 13 | 14 | 14 | 14 | 14 | 14 | 14 |
LD3 | 11 | 14 | 16 | 17 | 17 | 17 | 17 | 17 | 17 | 17 |
LD4 | 13 | 16 | 17 | 18 | 18 | 18 | 18 | 18 | 18 | 18 |
LD5 | 9 | 13 | 14 | 16 | 16 | 16 | 16 | 16 | 16 | 16 |
LD6 | 17 | 18 | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 |
ST1 | 15 | 12 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
ST2 | 19 | 19 | 18 | 15 | 13 | 13 | 13 | 13 | 13 | 13 |
ST3 | 12 | 10 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
ST4 | 8 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 |
F1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
F2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
F3 | 10 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 |
TSI1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
TSI2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
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Share and Cite
Aladağ, H.; Demirdöğen, G.; Işık, Z. Fundamental Prerequisites of Operational Readiness, Activation, and Transition: Case Study of Istanbul Grand Airport. Systems 2023, 11, 468. https://doi.org/10.3390/systems11090468
Aladağ H, Demirdöğen G, Işık Z. Fundamental Prerequisites of Operational Readiness, Activation, and Transition: Case Study of Istanbul Grand Airport. Systems. 2023; 11(9):468. https://doi.org/10.3390/systems11090468
Chicago/Turabian StyleAladağ, Hande, Gökhan Demirdöğen, and Zeynep Işık. 2023. "Fundamental Prerequisites of Operational Readiness, Activation, and Transition: Case Study of Istanbul Grand Airport" Systems 11, no. 9: 468. https://doi.org/10.3390/systems11090468
APA StyleAladağ, H., Demirdöğen, G., & Işık, Z. (2023). Fundamental Prerequisites of Operational Readiness, Activation, and Transition: Case Study of Istanbul Grand Airport. Systems, 11(9), 468. https://doi.org/10.3390/systems11090468