Disruption in the effective conduct of operations of an individual, system, organisation or nation. It may result into loss of infrastructure, resources and human lives. Types of Disaster : Required to identify and implement some measures in order to reduce the occurrence or rather impact of disaster, as much as possible. Identification of Critical Success Factors (CSFs) Preparedness : ◦ Before disaster hits. ◦ To avoid the gravest possible consequences of a disaster. Response ◦ ◦ Recovery ◦ ◦ Immediate response sub-phase. Restore sub-phase. Rehabilitation operations for a long-term perspective. Post-Disaster. Mitigation ◦ To retain social and economic conditions. ◦ To reduce destruction level by implementing various laws and mechanisms. Information and Technology Utilization: Crucial as it directly impacts the speed of the response at the time of relief operations and enables better coordination between the actors. Continuous improvement: Process of benchmarking in which processes and performances of relief operations are evaluated and implemented by comparing it with the best practices from the previous relief operations. Effective utilisation of resources: disparate teams with a common goal. Strategic Planning: A long-term approach is adopted which allows an organisation to be prepared for what must be done when an emergency occurs (Long, 1997). Distribution Strategy: Time and cost are major constraints in emergency relief operations. For the same, it is quite important to adopt some of the standard distribution strategies, such as: Direct shipping, Cross-docking, and Centralized warehouse. Minimisation of loss of human lives: The objective of the disaster relief operation is always linked to how quickly and conveniently the resources reach the affected people (Roy et al., 2012). It depends upon the collaborative working of Transport and capacity planning: Transportation is not just about transferring material, it also involves other aspects such as: selecting transport mode, capacity scheduling, maintenance, and intermodality (Pettit, 2009). Disaster Assessment: Pre-warning system helps in providing the information regarding volume and intensity of disaster that could affect the human lives and infrastructure as well. Risk Mitigation: Three levels of risk management: Strategic organisational, systematic operational, and dynamic operational. Organisations should adopt a formalised approach towards risk mitigation. Prompt Response: The challenges include physical destruction, which limits logistical pathways and constrained resources, which limit funding during the disaster. Restoration: Post-disaster recovery planning can be thought of as providing a blueprint for the restoration of a community after a disaster occurs. This can be done through long and short-term strategies. Identification and Modeling 1.Interpretive Structural Modeling (ISM) - Depicts hierarchical relationships between various variables identified and produces structural models from poorly articulated mental models so as to make it more visible and well- defined. Steps: ◦ Development of Structural Self Interaction Matrix (SSIM). ◦ Constructing Initial Reachability Matrix ◦ Constructing Final Reachability Matrix. ◦ Level Partitions. ◦ Creation of ISM based model (Diagraph). 2.MICMAC Analysis - To classify and analyse the critical success factors on the basis of their driving and dependence power. Positioning of factors within four clusters of a graph. Clusters: ◦ First cluster : Weak driver power and weak dependence (autonomous). ◦ Second cluster : Weak driver power but strong dependence (dependent). ◦ Third cluster : Strong driving power and also strong dependence (linkage). ◦ Fourth cluster : Strong driving power but weak dependence (independent). Level partitions I. Outcome of ISM II. Outcome of MICMAC : Graphical representation This research paper has considered only limited number of critical success factors. In real situation, there can be few other critical success factors that may impact the HSCM. Also, the ISM model developed can be statistically validated using Structural Equation Modelling (SEM) which has a capability to test the already developed hypothetical model. The consistency among the expert opinions can also be validated using Kappa technique. Barbarosa G., Arda Y., (2004), “A two-stage stochastic programming framework for transportation planning in disaster response”, The Journal of Operational Research Society, vol. 55, pp 43. Beamon, B.M. (2004), “Humanitarian relief chains: issues and challenges”, paper presented at the 34th International Conference on Computers & Industrial Engineering, San Francisco, CA, November 14-16. Beamon B.M., Balcik B., (2008) “Performance measurement in humanitarian relief chains”, International Journal of Public Sector Management” ,vol. 21, pp4-25. Chopra, S., & Meindl, P., (2004) “Supply Chain Management, Strategy, Planning, and Operations 2nd ed. Pearson Education International, USA. Cozzolino, A. (2012). “Humanitarian Logistics – Cross-Sector Cooperation in Disaster Relief Management”, SpringerBriefs in Business. De la Torre L. E, Dolinskaya I.S, Smilowitz K.R., (2011), “Disaster relief routing: Integrating research and practice”, SocioEconomic Planning Science, pp 1-10. Gooley TB (1999), “In times of crisis, logistics is the job”, Logistics Management and Distribution Report, Vol. 38 (9), pp. 82. Gurnasekaran, A., Ngai, E.W.T (2004), “Information System in Supply Chain Integration and Management”, European Journal of operational research, Vol. 159 pp. 269-295. Huotari, M-L, Wilson, TD (2001), “Determining organisational information needs: the critical success factors approach”, Information Research, Vol. 6 (3), April. Korpela, J. and Tuominen, M. (1996), “Benchmarking logistics performance with an application of the analytic hierarchy process”, IEEE Transactions on Engineering Management, Vol. 43 No. 3, pp. 323-33. Kovacs, G., Spens, K. M. (2007). “Humanitarian logistics in disaster relief operations”, International Journal of Physical Distribution & Logistics Management, Vol. 37, Issue 2, pp. 99-114. Mandal, A., Deshmukh, S.G., (1993), “Vendor Selection Using Interpretive Structural Modelling (ISM)”, International Journal of Operations and Production Management. Vol. 14, No. 6, pp. 52-59. Munzberg, T. et.al (2013). “Decision Support for Critical Infrastructure Disruptions: An Integrated Approach to Secure Food Supply”. 10th International ISCRAM Conference – Baden-Baden, Germany, May 2013. Pettit, S., Beresford A., (2009), “Critical success factors in the context of humanitarian aid supply chains”, International Journal of Physical Distribution & Logistics Management vol. 39 No. 6, pp. 450-468. Roy, P., Albores, P., Brewster, C., (2012), “Logistical Framework for Last Mile Relief Distribution in Humanitarian Supply Chains: Consideration from the Field” Aston university, available at: http://windermere.aston.ac.uk/~kiffer/papers/Roy_ICMR12.pdf. Sushil, (2012), “Interpreting the Interpretive Structural Model”, Global Journal of Flexible Systems Management, vol. 13(2), pp.87-106. Van Wassenhove, L.N. (2006). “Blackett memorial lecture. Humanitarian aid logistics: Supply chain management in high gear”. Journal of Operational Research Society, 57(5), 475-489.