Evidence-based models to support humanitarian operations and crisis management
DOI:
https://doi.org/10.31181/dmame030222100yKeywords:
Operations management, analytic hierarchy process, humanitarian operations management, organizational learning, fault tree analysis.Abstract
Term humanitarian operation (HO) is a concept extracted from the need to perform supply chain operations in special, risky, and critical events. Understanding and implementing operations under such conditions is a strategic responsibility. Due to its importance, we design a framework for organizational learning from major incidents through root cause analysis. The case studies contain a purely industrial disaster at Bhopal and a mixed industrial-natural disaster at Fukushima. An approach is proposed for organizational safety by incorporating techniques related to root cause analysis applied to one case study. Moreover, we employ the analytic hierarchy process, which is applied to the second case study. We incorporate operations management models to analyse data related to two major disasters. The case studies in two organizations are then compared with respect to their causes and effects along with the models adopted to support HO& crisis management (CM). The contribution is the use of hybrid modelling techniques to analyse disasters in terms of humanitarian operations and crisis management.
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Abdi, M.R. & Labib, A. (2017). RMS capacity utilization: product family and supply chain. International Journal of Production Research, 55(7), 1930-1956.
Abdi, M.R. & Labib, A.W. (2011). Performance evaluation of reconfigurable manufacturing systems via holonic architecture and the analytic network process. International Journal of Production Research, 49(5), 1319-1335.
Abdi, M.R. & Sharma, S. (2007). Strategic/tactical information management of flight operations in abnormal conditions through Network Control Centre. International journal of information management, 27(2), 119-138.
Alosta, A., Elmansuri, O., & Badi, I. (2021). Resolving a location selection problem by means of an integrated AHP-RAFSI approach. Reports in Mechanical Engineering, 2(1), 135-142.
Altay, N. & Green III, W.G. (2006). OR/MS research in disaster operations management. European journal of operational research, 175(1), pp.475-493.
Aoki, M. & Rothwell, G. (2013). A comparative institutional analysis of the Fukushima nuclear disaster: Lessons and policy implications. Energy Policy, 53, 240-247.
Aven T. (1992) Basic Reliability and Risk Analysis Methods. In: Reliability and Risk Analysis. Springer, Dordrecht, (Chapter 2).
Balcik, B. & Ak, D. (2014). Supplier selection for framework agreements in humanitarian relief. Production and Operations Management, 23(6), 1028-1041.
Beamon, B. M. (2004). Humanitarian relief chains, issues and challenges. Proceedings of the 34th International Conference on Computers & Industrial Engineering, San Francisco, CA, USA, 77-82.
Cacciabue, P.C. & Vella, G. (2010). Human factors engineering in healthcare systems: The problem of human error and accident management. international journal of medical informatics, 79(4), e1-e17.
Chávez, M.E., Villalobos, E., Baroja, J.L. & Bouzas, A. (2017). Hierarchical Bayesian modeling of intertemporal choice. Judgment & Decision Making, 12(1), 19-28.
Chouhan, T. R. (2005). The unfolding of Bhopal disaster. Journal of Loss Prevention in the Process Industries, 18(4-6), 205-208.
Cook, R.I. & Woods, D.D., (2006). Distancing through differencing: an obstacle to organizational learning following accidents. Resilience engineering: concepts and precepts, 1, 329-338.
Dijkzeul, D., Hilhorst, D. & Walker, P., (2013). Introduction: evidence‐based action in humanitarian crises. Disasters, 37, S1-S19.
Dubey, R. & Gunasekaran, A., (2016). The sustainable humanitarian supply chain design: agility, adaptability and alignment. International Journal of Logistics Research and Applications, 19(1), 62-82.
Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Wamba, S.F. & Song, M., (2016). Towards a theory of sustainable consumption and production: constructs and measurement. Resources, Conservation and Recycling, 106, 78-89.
Eftekhar, M., Masini, A., Robotis, A. & Van Wassenhove, L.N., (2014). Vehicle procurement policy for humanitarian development programs. Production and Operations Management, 23(6), 951-964.
Ergun, Ö., Gui, L., Heier Stamm, J.L., Keskinocak, P. & Swann, J., (2014). Improving humanitarian operations through technology‐enabled collaboration. Production and Operations Management, 23(6), 1002-1014.
Erlandsson, A., Björklund, F. & Bäckström, M., (2017). Choice-justifications after allocating resources in helping dilemmas. Judgment and Decision making, 12(1), 60-80.
Galindo, G. & Batta, R., (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, 230(2), 201-211.
González, A.J., Akashi, M., Boice Jr, J.D., Chino, M., Homma, T., Ishigure, N., Kai, M., Kusumi, S., Lee, J.K., Menzel, H.G. & Niwa, O., (2013). Radiological protection issues arising during and after the Fukushima nuclear reactor accident. Journal of Radiological Protection, 33(3), 497-571.
Gralla, E., Goentzel, J. & Fine, C., (2014). Assessing trade‐offs among multiple objectives for humanitarian aid delivery using expert preferences. Production and Operations Management, 23(6), 978-989.
Gupta, S., Starr, M.K., Farahani, R.Z. & Matinrad, N., (2016). Disaster management from a POM perspective: Mapping a new domain. Production and Operations Management, 25(10), 1611-1637.
Heng, L. & Tao, C., (2014). Multiple attributes decision making method on social stability in nuclear accident scenario. In Proceedings of the 11th International ISCRAM Conference – University Park, Pennsylvania, USA, May.
Hollnagel, E. (2004). Barriers and Accident Prevention. Hampshire, England: Ashgate (Chapter 1).
Ishizaka, A. & Labib, A. (2009). Analytic hierarchy process and expert choice: Benefits and limitations. Or Insight, 22(4), 201-220.
Ishizaka, A. & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert systems with applications, 38(11), 14336-14345.
Ishizaka, A. & Labib, A. (2014). A hybrid and integrated approach to evaluate and prevent disasters. Journal of the Operational Research Society, 65(10), 1475-1489.
Ishizaka, A., Quintano, A., Labib, A. & Apostolakis, A. (2019), "Do five-star hotel managers know their customers’ priorities? An AHP-Prioritised scorecard study", EuroMed Journal of Business, 14(2), 137-167.
Jabbour, C.J.C., Sobreiro, V.A., de Sousa Jabbour, A.B.L., de Souza Campos, L.M., Mariano, E.B. & Renwick, D.W.S. (2019). An analysis of the literature on humanitarian logistics and supply chain management: paving the way for future studies. Annals of Operations Research, 283(1), 289-307.
Knowles, S.G. (2014). Learning from disaster? The history of technology and the future of disaster research. Technology and Culture, 55(4), 773-784.
Kondo, S. (2014). Lessons learned from the severe accident at Fukushima Daiichi-JAEC’s struggle since the accident. Probability and Safety Assessment Management, 12, 22-27, Honolulu, Hawaii, USA.
Kretschmer, A., Spinler, S. & Van Wassenhove, L.N. (2014). A school feeding supply chain framework: Critical factors for sustainable program design. Production and Operations Management, 23(6), 990-1001.
Kurokawa, K. (2012). Kurokawa Investigation Report of the Nuclear Accident Investigation Commission of the Diet of Japan (commissioned October 7, 2011).
Labib, A. & Champaneri, R. (2012). The Bhopal Disaster-learning from failures and evaluating risk. Maintenance Engineering, 27(3), 41-47.
Labib, A. & Harris, M.J. (2015). Learning how to learn from failures: The Fukushima nuclear disaster. Engineering Failure Analysis, 47, 117-128.
Labib, A. & Read, M. (2013). Not just rearranging the deckchairs on the Titanic: Learning from failures through Risk and Reliability Analysis. Safety science, 51(1), 397-413.
Labib, A. (2014). Learning from Failures: Decision Analysis of Major Disasters. Oxford: Butterworth-Heinemann.
Labib, A., (2015). Learning (and unlearning) from failures: 30 years on from Bhopal to Fukushima an analysis through reliability engineering techniques. Process Safety and Environmental Protection, 97, 80-90.
Labib, A., Hadleigh‐Dunn, S., Mahfouz, A. & Gentile, M. (2019). Operationalizing learning from rare events: Framework for middle humanitarian operations managers. Production and Operations Management, 28(9), 2323-2337.
Labib, A.W. (2011). A supplier selection model: a comparison of fuzzy logic and the analytic hierarchy process. International Journal of Production Research, 49(21), 6287-6299.
Labib, A.W., O’Connor, R.F. & Williams, G.B. (1998). An effective maintenance system using the analytic hierarchy process. Integrated Manufacturing Systems, 9(2), 87-98.
Leveson, N., (2004). A new accident model for engineering safer systems. Safety science, 42(4), 237-270.
Leveson, N., Dulac, N., Marais, K. & Carroll, J. (2009). Moving beyond normal accidents and high reliability organizations: A systems approach to safety in complex systems. Organization studies, 30(2-3), 227-249.
Lindell, M.K. & Perry, R.W. (1990). Effects of the Chernobyl accident on public perceptions of nuclear plant accident risks. Risk Analysis, 10(3), 393-399.
McCoy, J.H. & Lee, H.L., (2014). Using fairness models to improve equity in health delivery fleet management. Production and Operations Management, 23(6), 965-977.
Muhammad , L. J., Badi, I., Haruna, A. A., & I.A. Mohammed. (2021). Selecting the Best Municipal Solid Waste Management Techniques in Nigeria Using Multi Criteria Decision Making Techniques. Reports in Mechanical Engineering, 2(1), 180-189.
National Academies of Sciences, Engineering, and Medicine. (2016). Lessons Learned from the Fukushima Nuclear Accident for Improving Safety and Security of U.S. Nuclear Plants: Phase 2. Washington, DC: The National Academies Press, (Chapters 2 & 3).
Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393.
Pamucar, D.S., & Dimitrijevic, S.R. (2021). Multiple-criteria model for optimal antitank ground missile weapon system procurement. Military Technical Courier, 69(4), 792-827.
Pamucar, D.S., & Savin, L.M. (2020). Multiple-criteria model for optimal off-road vehicle selection for passenger transportation: BWM-COPRAS model. Military Technical Courier, 68(1), 28-64.
Pedraza Martinez, A., Stapleton, O. & Van Wassenhove, L.N., (2010). Using OR to support humanitarian operations: Learning from the Haiti earthquake. INSEAD working paper, 1-29.
Perrow, C. (1984). Normal Accidents: Living with High-Risk Technologies. New York: Basic Books.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Ruggiero, A. & Vos, M. (2015). Communication challenges in CBRN terrorism crises: Expert perceptions. Journal of Contingencies and Crisis Management, 23(3), 138-148.
Ruggiero, A., & M. Vos. (2014). Social media monitoring for crisis communication: Process, methods and trends in the scientific literature. Online Journal of Communication and Media Technologies, 4(1), 103-130.
Saaty, T. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill.
Shanthikumar, J.G., & Sargent, R.G. (1983). A unifying view of hybrid simulation/analytic models and modeling. Operations research, 31(6), 1030-1052.
Sinnamon, R.M., & Andrews, J.D., (1997). New approaches to evaluating fault trees. Reliability Engineering & System Safety, 58(2), 89-96.
Slovic, P. (2012). The perception gap: Radiation and risk. Bulletin of the Atomic Scientists, 68(3), 67-75.
Sodhi, M. S., & Tang, C. S. (2014). Buttressing supply chains against floods in Asia for humanitarian relief and economic recovery. Production and operations management, 23(6), 938-950.
Starr, M. K., & Van Wassenhove, L. N. (2014). Introduction to the special issue on humanitarian operations and crisis management. Production and Operations Management, 23(6), 925-937.
Stephen, C., & Labib, A. (2018). A hybrid model for learning from failures. Expert Systems with Applications, 93, 212-222.
Woods, D. D., & Cook, R. I. (2002). Nine steps to move forward from error. Cognition, Technology & Work, 4(2), 137-144.
Woods, D. D., L. J. Johannesen, R. I. Cook, N. B. Sarter. (1994). Behind human error: Cognitive systems, computers, and hindsight. CSERIAC State-of-the-Art-report, Wright-Patterson Air Force Base, OH: Crew System, Ergonomics Information Analysis Centre.
Yadav, D.K. & Barve, A., (2015). Analysis of critical success factors of humanitarian supply chain: An application of Interpretive Structural Modeling. International journal of disaster risk reduction, 12, 213-225.
Yazdani, M., Abdi, M.R., Kumar, N., Keshavarz-Ghorabaee, M. & Chan, F.T., (2019). Improved decision model for evaluating risks in construction projects. Journal of Construction Engineering and Management, 145(5), 04019024.