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Modelling Key Performance Indicators for Improved Performance Assessment in Persistent Maritime Surveillance Projects

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 271))

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Abstract

Correct monitoring and communication of performance throughout the system design life-cycle is of paramount importance in complex safety and security projects. To this end a combination of a NATO Architectural Framework Based Dashboard and an advanced performance assessment model has been proposed. This paper presents the model proposed within the framework of a persistent maritime surveillance project. Specifically, the modelling approach inherited from multi-criteria decision making makes use of knowledge acquisition methods to elicit quantitative weights assigned to Key Performance Indicators. The analysis highlights strengths and weaknesses of two alternative elicitation approaches to be further exploited to improve the overall performance assessment.

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References

  1. Hyysalo, J., Kelanti, M., Markkula, J.: Redefining KPIs with information flow visualisation – practitioners’ view. In: Thirteenth International Conference on Software Engineering Advances (2018)

    Google Scholar 

  2. Shi, L., Newnes, L., Culley, S., Gopsill, J., Jones, S., Snider, C.: Identifying and visualising KPIs for collaborative engineering projects: a knowledge based approach. In: Weber, C., Husung, S., Cantamessa, M. Cascini, G., Marjanovic, D., Montagna, F. (eds.) 20th International Conference on Engineering Design. Organisation and Management, vol. 3, pp. 377–386 (2015)

    Google Scholar 

  3. H2020 PJ 19 Content Integration: D4.1 - PJ19: Performance Framework. Technical report (2017)

    Google Scholar 

  4. Architecture Capability Team: NATO Architecture Framework Version 4. Technical report, North Atlantic Treaty Organisation (2018)

    Google Scholar 

  5. Brundage, M.P., Bernstein, W.Z., Morris, K.C., Horst, J.A.: Using graph-based visualizations to explore key performance indicator relationships for manufacturing production systems. In: Takata, S., Umeda, Y., Kondoh, S. (eds.) Procedia CIRP 2017, vol. 61, pp. 451–456. Elsevier (2017)

    Google Scholar 

  6. Kabir, G., Sadiq, R., Tesfamariam, S.: A review of multi-criteria decision-making methods for infrastructure management. Struct. Infrastruct. Eng. 10(9), 1176–1210 (2014)

    Article  Google Scholar 

  7. Rezaei, J., van Wulfften Palthe, L., Tavasszy, L., Wiegmans, B., van der Laan, F.: Port performance measurement in the context of port choice: an MCDA approach. Manag. Decis. 57(2), 396–417 (2019)

    Article  Google Scholar 

  8. Wu, H., Chen, J., Chen, I.: Performance evaluation of aircraft maintenance staff using a fuzzy MCDM approach. Int. J. Innov. Comput. Inf. Control 8(6), 3919–3937 (2012)

    Google Scholar 

  9. Danesh, D., Ryan, M.J., Abbasi, A.: Multi-criteria decision-making methods for project portfolio management: a literature review. Int. J. Manag. Decis. Mak. 17(1), 75–94 (2017)

    Google Scholar 

  10. Canetta, L., Mattei, G., Guanziroli, A.: Multi criteria analysis applied on value chain definition in c sector. In: International Conference on Engineering, Technology and Innovation (ICE/ITMC) 2017, pp. 1096–1103. IEEE Press, New York (2017)

    Google Scholar 

  11. Saaty, T.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977)

    Article  MathSciNet  Google Scholar 

  12. Roy, B.: Classement et choix en présence de points de vue multiples (la méthode ELECTRE). La Revue d’Informatique et de Recherche Opérationelle 8, 57–75 (1968)

    Google Scholar 

  13. Bana E Costa, C.A., Vansnick, J.C.: The MACBETH approach: Basic ideas, software, and an application. In: Meskens, N., Roubens, M. (eds.) Advances in Decision Analysis. Mathematical Modelling: Theory and Applications, vol. 4, pp. 131–157. Springer, Dordrecht (1999). https://doi.org/10.1007/978-94-017-0647-6_9

    Chapter  MATH  Google Scholar 

  14. Brans, J.P.: L'ingénierie de la décision: élaboration d'instruments d'aide à la décision. La méthode PROMETHEE. Presses de l’Université Laval (1982)

    Google Scholar 

  15. Ishizaka, A., Labib, A.: Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 38(11), 14336–14345 (2011)

    Google Scholar 

  16. Gregory Hunter, D., Emond, E.J.: Analytical support to PMO JSS. Technical report, DRDC – Centre for Operational Research and Analysis (2005)

    Google Scholar 

  17. Dodd, F., Donegan, H.: Comparison of priotization techniques using interhierarchy mappings. J. Oper. Res. Soc. 46, 492–498 (1995)

    Article  Google Scholar 

  18. Bauk, S., Kapidani, N., Lukšić, Ž., Rodrigues, F., Sousa, L.: Autonomous marine vehicles in sea surveillance as one of the COMPASS2020 project concerns. In: Journal of Physics Conference Series, vol. 1357, p. 012045. IOP Science, Durban (2019)

    Google Scholar 

  19. Goepel,, K.D.: Implementing the analytic hierarchy process as a standard method for multi-criteria decision making in corporate enterprises – a new AHP excel template with multiple inputs. In: International Symposium on the Analytic Hierarchy Process 2013, pp. 1–10. Creative Decisions Foundation, Kuala Lumpur (2013)

    Google Scholar 

  20. AHP Excel Template, version (2018). https://bpmsg.com/

  21. Ishizaka, A., Balkenborg, D., Kaplan, T.: Influence of aggregation and measurement scale on ranking a compromise alternative in AHP. J. Oper. Res. Soc. 62, 700 (2011)

    Article  Google Scholar 

  22. Bafahm, A., Sun, M.: Some Conflicting Results in the Analytic Hierarchy Process. Int. J. Inf. Technol. Decis. Mak. 18(02), 419–443 (2019)

    Article  Google Scholar 

  23. Krejci, J., Stoklasa, J.: Aggregation in the analytic hierarchy process: why weighted geometric mean should be used instead of weighted arithmetic mean. Expert Syst. Appl. 114, 97–106 (2018)

    Article  Google Scholar 

  24. Goepel, K.D.: Implementation of an online software tool for the analytic hierarchy process (AHP-OS). Int. J. Anal. Hierarchy Process 10(3), 469–487 (2018)

    Google Scholar 

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Acknowledgments

This work has been partially funded by the EU Research and Innovation program HORIZON 2020, COMPASS2020 project - Grant Agreement No: 833650 and NATO STO Centre for Maritime Research and Experimentation, through the Data Knowledge and Operational Effectiveness programme of work, funded by NATO Allied Command Transformation. The results are reported in accordance with the relevant security and ethical regulations.

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Authors and Affiliations

  1. NATO STO Centre for Maritime Research and Experimentation, 19126, La Spezia, Italy

    Francesca de Rosa, Thomas Mansfield, Anne-Laure Jousselme & Alberto Tremori

Authors
  1. Francesca de Rosa
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  2. Thomas Mansfield
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  3. Anne-Laure Jousselme
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  4. Alberto Tremori
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Corresponding author

Correspondence to Francesca de Rosa .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Z. Ahram

  2. University of Central Florida, Orlando, FL, USA

    Waldemar Karwowski

  3. Department of Pathology, College of Medicine, University of Saskatchewan Royal University Hospital, Saskatoon, SK, Canada

    Jay Kalra

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de Rosa, F., Mansfield, T., Jousselme, AL., Tremori, A. (2021). Modelling Key Performance Indicators for Improved Performance Assessment in Persistent Maritime Surveillance Projects. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_37

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