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A User Requirement-driven Approach Incorporating TRIZ and QFD for Designing a Smart Vessel Alarm System to Reduce Alarm Fatigue

Published online by Cambridge University Press:  02 July 2019

Fan Li
Affiliation:
(School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore)
Chun-Hsien Chen
Affiliation:
(School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore)
Ching-Hung Lee*
Affiliation:
(School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China)
Li-Pheng Khoo
Affiliation:
(School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore)
*

Abstract

Alarm fatigue is a critical safety issue, as it can increase workload and impair operators' situational awareness. This paper proposes a design methodology to enhance the interaction between alarm systems and operators. Through input from VTS personnel as the fundamental design requirements, a user requirement-driven design framework is proposed. It integrates quality function deployment, the theory of inventive problem solving, and software quality characteristics into three design phases. In Phase I, user requirements are obtained from the analysis of current working processes. Phase II investigates the specific non-functional design requirements of vessel alarm systems and the contradictions. In Phase III, the innovative principles generated with the contradiction matrix were analysed. A case study was conducted to verify and illustrate this framework, resulting in a conceptualisation design of a smart vessel alarm system.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Altshuller, G. S., Shulyak, L. and Rodman, S. (1999). The innovation algorithm: TRIZ, systematic innovation and technical creativity, Technical Innovation Center, Inc.Google Scholar
Babu, A. and Ketkar, W. (1996). Strategic Planning of Vessel Traffic Services using ABS Analysis and Optimization. The Journal of Navigation, 49, 235252.Google Scholar
Bustamante, E., Bliss, J. and Anderson, B. (2007). Effects of varying the threshold of alarm systems and workload on human performance. Ergonomics, 50, 11271147.Google Scholar
Chang, H. and Lu, P. (2009). Using a TRIZ-based method to design innovative service quality: a case study on insurance industry. Journal of Quality, 16, 179193.Google Scholar
Coelho, D. A. (2009). Matching TRIZ engineering parameters to human factors issues in manufacturing. Wseas Transactions on Business and Economics, 6, 547556.Google Scholar
Dieste, O., Juristo, N. and Shull, F. (2008). Understanding the customer: what do we know about requirements elicitation?. IEEE Software, 25(2), pp. 1113.Google Scholar
Engineering Equipment and Materials Users Association (EEMUA). (1999). Alarm Systems: A Guide to Design, Management and Procurement, Engineering Equipment and Materials Users Association London.Google Scholar
Filipowicz, W. (2004). Vessel traffic control problems. The Journal of Navigation, 57, 1524.Google Scholar
Filippi, S. and Barattin, D. (2015). Exploiting TRIZ tools in interaction design. Procedia Engineering, 131, 7185.Google Scholar
Hollifield, B., Habibi, E. and Oliver, D., (2013). The high performance HMI handbook. Plant Automation Services, Incorporated.Google Scholar
Hughes, C. T. (2009). When is a VTS not a VTS? The Journal of Navigation, 62, 439442.Google Scholar
Hughes, T. (1998). Vessel Traffic Services (VTS): Are We Ready For The New Millenium? The Journal of Navigation,51, 404420.Google Scholar
ISO. (2007). ISO/IEC 25001:2007, Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Planning and management. (https://www.iso.org/obp/ui)Google Scholar
Izadi, I., Shah, S. L., Shook, D. S., Kondaveeti, S. R. and Chen, T. (2009). A framework for optimal design of alarm systems. IFAC Proceedings Volumes, 42, 651656.Google Scholar
International Maritime Organization (IMO). (2015). Guideline on software quality assurance and human-centred design for e-Navigation, MSC.1/Circ. 1512.Google Scholar
Kao, S.L., Lee, K.T., Chang, K.Y. and Ko, M.D. (2007). A fuzzy logic method for collision avoidance in vessel traffic service. The Journal of Navigation, 60, 1731.Google Scholar
Kluender, D. (2011). TRIZ for software architecture. Procedia Engineering, 9, 708713.Google Scholar
Lam, J. S. L. and Lai, K.H. 2015. Developing environmental sustainability by ANP-QFD approach: the case of shipping operations. Journal of Cleaner Production, 105, 275284.Google Scholar
Lee, C.H., Wang, Y.H. and Trappey, A. J. (2015). Service design for intelligent parking based on theory of inventive problem solving and service blueprint. Advanced Engineering Informatics, 29, 295306.Google Scholar
Lee, C.H., Chen, C.H., and Trappey, A.J. (2019). A structural service innovation approach for designing smart product service systems: Case study of smart beauty service. Advanced Engineering Informatics, 40, 154167.Google Scholar
Lee, B., Park, N., and Kim, J. (2014). A Requirement Analysis of Awareness-Based Vessel Traffic Service System for Maritime Safety. Advances in Computer Science and its Applications, 279, 379384.Google Scholar
Li, F., Lee, C. H., Xu, G., Chen, C. H., and Khoo, L. P. (2017). A QFD-Enabled Conceptualization for Reducing Alarm Fatigue in Vessel Traffic Service Centre. Transdisciplinary Engineering: A Paradigm Shift: Proceedings of the 24th ISPE Inc. International Conference on Transdisciplinary Engineering, July 10–14 2017., Vol. 5.Google Scholar
Li, S., Zhou, J. and Zhang, Y. (2015). Research of vessel traffic safety in ship routeing precautionary areas based on navigational traffic conflict technique. The Journal of Navigation, 68, 589601.Google Scholar
Mann, D. (2004). TRIZ for software. TRIZ Journal, Oct.Google Scholar
Mann, D. and Dewulf, S. (2003). Updating the contradiction matrix. TRIZCON12003, Philadelphia, March.Google Scholar
Mansson, J. T., Lutzhoft, M. and Brooks, B. (2017). Joint Activity in the Maritime Traffic System: Perceptions of Ship Masters, Maritime Pilots, Tug Masters, and Vessel Traffic Service Operators. The Journal of Navigation, 70, 547560.Google Scholar
Moore, J. M. and Shipman, F. M. (2000). A comparison of questionnaire-based and GUI-based requirements gathering. Automated Software Engineering, the Fifteenth IEEE International Conference on. 35–43.Google Scholar
Praetorius, G. and Lützhöft, M. (2012). Decision support for vessel traffic service (VTS): user needs for dynamic risk management in the VTS. Work, 41, 48664872.Google Scholar
Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 4577.Google Scholar
Rayo, M. F. and Moffatt-Bruce, S. D. (2015). Alarm system management: evidence-based guidance encouraging direct measurement of informativeness to improve alarm response. BMJ Quality and Safety, bmjqs-2014-003373.Google Scholar
Rea, K. C. (2001). TRIZ and software-40 principle analogies, part 1. The TRIZ Journal.Google Scholar
Şen, C. G. and Baraçlı, H. (2010). Fuzzy quality function deployment based methodology for acquiring enterprise software selection requirements. Expert Systems with Applications, 37, 34153426.Google Scholar
SOLAS. (2002). the International Convention for the Safety of Life at Sea (SOLAS V) Chapter V Regulation 12. (https://mcanet.mcga.gov.uk/public/c4/solas/solas_v/Regulations/regulation12.htm).Google Scholar
Su, C.-M., Chang, K.-Y. and Cheng, C.-Y. (2012). Fuzzy decision on optimal collision avoidance measures for ships in vessel traffic service. Journal of Marine Science and Technology, 20, 3848.Google Scholar
Wang, L., Shen, W., Xie, H., Neelamkavil, J. and Pardasani, A. (2002). Collaborative conceptual design—state of the art and future trends. Computer-Aided Design, 34, 981996.Google Scholar
Wang, Y.H., Lee, C.H. and Trappey, A. J. (2017). Service design blueprint approach incorporating TRIZ and service QFD for a meal ordering system: A case study. Computers and Industrial Engineering, 107, 388400.Google Scholar
Wang, Y. H., Hsieh, C. C. (2018). Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology. Technological Forecasting and Social Change, 127, 281290.Google Scholar
Winters, B. D., Cvach, M. M., Bonafide, C. P., Hu, X., Konkani, A., O'Connor, M. F., Rothschild, J. M., Selby, N. M., Pelter, M. M. and McLean, B. (2018). Technological distractions (part 2): a summary of approaches to manage clinical alarms with intent to reduce alarm fatigue. Critical Care Medicine, 46, 130137.Google Scholar
Xu, G., Li, F., Chen, C.-H., Lee, C.-H. and Lee, Y.-C. (2015). Toward Resilient Vessel Traffic Service: A Sociotechnical Perspective. Transdisciplinary Engineering: A Paradigm Shift, 829.Google Scholar
Yamashina, H., Ito, T. and Kawada, H. (2002). Innovative product development process by integrating QFD and TRIZ. International Journal of Production Research, 40, 10311050.Google Scholar
Yan, W., Khoo, L. P. and Chen, C.-H. (2005). A QFD-enabled product conceptualisation approach via design knowledge hierarchy and RCE neural network. Knowledge-Based Systems, 18, 279293.Google Scholar
Yeh, C., Huang, J. C. and Yu, C. (2011). Integration of four-phase QFD and TRIZ in product RandD: a notebook case study. Research in Engineering Design, 22, 125141.Google Scholar
Zhang, F., Yang, M. and Liu, W. (2014). Using integrated quality function deployment and theory of innovation problem solving approach for ergonomic product design. Computers and Industrial Engineering, 76, 6074.Google Scholar