Making sense of transient responses in simulation studies

References

• Bausell, C. W., F. W. Rusco, and W. D. Walls. 2001. “Lifting the Alaskan Oil Export Ban: an Intervention Analysis.” Energy Journal 22: 81–94.
   Web of Science ®Google Scholar
• Blackhurst, J., C. W. Craighead, D. Elkins, and R. B. Handfield. 2005. “An Empirically Derived Agenda of Critical Research Issues for Managing Supply-chain Disruptions.” International Journal of Production Research 43(19): 4067–4081.
   Web of Science ®Google Scholar
• Cavinato, J. L. 2004. “Supply Chain Logistics Risks: from the Back Room to the Board Room.” International Journal of Physical Distribution & Logistics Management 34: 383–387.
   Google Scholar
• Chang, I., G. C. Tiao, and C. Chen. 1988. “Estimation of Time Series Parameters in the Presence of Outliers.” Technometrics 30: 193–204.
   Web of Science ®Google Scholar
• Chen, C., and L. M. Liu. 1993. “Forecasting Time Series with Outliers.” Journal of Forecasting 12: 13–35.
   Web of Science ®Google Scholar
• Chopra, S., and M. Sodhi. 2004. “Managing Risk to Avoid Supply-chain Breakdown.” Sloan Management Review 46(1): 53–61.
   Google Scholar
• Craighead, C. W., J. Blackhurst, M. J. Rungtusanatham, and R. B. Handfield. 2007. “The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities.” Decision Sciences 38(1): 131–156.
   Web of Science ®Google Scholar
• Ellis, S. C., J. Shockley, and R. M. Henry. 2011. “Making Sense of Supply Disruption Risk Research: a Conceptual Framework Grounded in Enactment Theory.” Journal of Supply Chain Management 47(2): 65–96.
   Web of Science ®Google Scholar
• Enders, W., and T. Sandler. 2005. “After 9/11: is It All Different Now?” Journal of Conflict Resolution 49: 259–277.
   Web of Science ®Google Scholar
• Falasca, M., C. W. Zobel, and D. F. Cook. 2008. “A Decision Support Framework to Assess Supply Chain Resilience.” ISCRAM (Information Systems for Crisis Response and Management) 2008 - Creating Advanced Systems for Inter-organizational Information Sharing and Collaboration. Washington, DC: May 2008, 596–605.
   Google Scholar
• Forrester, J. W. 1961. Industrial Dynamics. Cambridge, MA: MIT Press.
   Google Scholar
• Greening, P., and C. Rutherford. 2011. “Disruptions and Supply Networks: a Multi-level, Multi-theoretical Relational Perspective.” The International Journal of Logistics Management 22(1): 104–126.
   Web of Science ®Google Scholar
• Größler, A., J. H. Thun, and P. M. Milling. 2008. “System Dynamics as a Structural Theory in Operations Management.” Production and Operations Management 17(3): 373–384.
   Web of Science ®Google Scholar
• Hendricks, K. B., and V. R. Singhal. 2005. “An Empirical Analysis of the Effect of Supply Chain Disruptions on Long Run Stock Price Performance and Equity Risk of the Firm.” Production and Operations Management 14: 35–52.
   Web of Science ®Google Scholar
• Kleijnen, J. P. C. 1975. “Antithetical Variates, Common Random Numbers, and Optimal Computer Time Allocation in Simulation.” Management Science 21(10): 1176–1185.
   Web of Science ®Google Scholar
• Kleijnen, J. P. C., and M. T. Smits. 2003. “Performance Metrics in Supply Chain Management.” Journal of the Operational Research Society 54: 507–514.
   Web of Science ®Google Scholar
• Law, A. M., and W. D. Kelton. 2000. Simulation Modeling and Analysis. 3rd ed. Boston, MA: McGraw Hill.
   Google Scholar
• Liu, L.-M. 2009. Time Series Analysis and Forecasting. Second Edition ed. Chicago, IL: Scientific Computing Associates Corp.
   Google Scholar
• Liu, L.-M., and G. B. Hudak. 2004. Forecasting and Time Series Analysis Using the SCA Statistical System. River Forest, IL: Scientific Computing Associates Corp.
   Google Scholar
• Melnyk, S. A., and C. J. Piper. 1981. “Implementation of Material Requirements Planning: Safety Lead Time.” International Journal of Operations & Production Management 2(1): 52–61.
   Google Scholar
• Nelson, D. 1991. “Conditional Heteroskedasticity in Asset Returns: a New Approach.” Econometrics 52(2): 59–71.
   Google Scholar
• Nelson, J. P. 2000. “Consumer Bankruptcies and the Bankruptcy Reform Act: a Time-series Intervention Analysis, 1960–1997.” Journal of Financial Services Research 17: 181–200.
   Web of Science ®Google Scholar
• Pankratz, A. 1991. Forecasting with Dynamic Regression Models. New York: John Wiley & Sons.
   Google Scholar
• Pettit, T. J., J. Fiksel, and K. L. Croxton. 2010. “Ensuring Supply Chain Resilience: Development of a Conceptual Framework.” Journal of Business Logistics 31(1): 1–22.
   Web of Science ®Google Scholar
• Quah, H. S., and M. U. Zulkifli. 2011. “Supply Chain Management from the Perspective of Value Chain Flexibility: an Exploratory Study: IMS.” Journal of Manufacturing Technology Management 22(4): 506–526.
   Google Scholar
• Rice, J. B. 2011. “Only as Strong as the Weakest Link.” Mechanical Engineering 133(6): 26–31.
   Web of Science ®Google Scholar
• Rong, Y., Z.-J. M. Shen, and L. V. Snyder. 2008. “The Impact of Ordering Behavior on Order-quantity Variability: a Study of Forward and Reverse Bullwhip Effects.” Flexible Service Manufacturing Journal 20: 95–124.
   Web of Science ®Google Scholar
• Schmitt, A., and M. Singh. 2009. “Quantifying Supply Chain Disruption Risk Using Monte Carlo and Discrete-event Simulation.” Proceedings of the 2009 Winter Simulation Conference, 1237–1248.
   Google Scholar
• Shafer, S. M., and T. L. Smunt. 2004. “Empirical Simulation Studies in Operations Management: Context, Trends, and Research Opportunities.” Journal of Operations Management 22: 345–354.
   Web of Science ®Google Scholar
• Sheffi, Y., and J. B. Rice. 2005. “A Supply Chain View of the Resilient Enterprise.” MIT Sloan Management Review 47(1): 41–48.
   Web of Science ®Google Scholar
• Shinozuka, M., S. E. Chang, T.-C. Cheng, M. Feng, T. D. O’Rourke, M. A. Saadeghvaziri, X. Dong, X. Jin, Y. Wang, and P. Shi. 2004. “Resilience of Integrated Power and Water Systems. In: MCEER Research Progress and Accomplishments: 2003–2004 (Ed, MCEER): Buffalo, NY, pp. 65–86.
   Google Scholar
• Sloan, B. J., and A. R. Unwin. 1990. “Common Random Numbers in Multivariate Simulations.” European Journal of Operations Research 48(2): 252–259.
   Web of Science ®Google Scholar
• Stokes, H. H., L.-M. Liu, and W. J. Lattyak. 2005. Generalized Autoregressive Conditional Heteroscedastic (GARCH) Modeling Using the SCAB34S-GARCH and SCA Workbench. River Forest, IL: Scientific Computing Associates Corp.
   Google Scholar
• Sung, H. Y., T. Hu, M. Ong, T. E. Keeler, and M. Sheu. 2005. “A Major State Tobacco Tax Increase, the Master Settlement Agreement, and Cigarette Consumption: the California Experience.” American Journal of Public Health 95: 1030.
   PubMed Web of Science ®Google Scholar
• Swamidass, P. M. 1991. “Empirical Science: New Frontier in Operations Management Research.” Academy of Management Review 16(4): 793–814.
   Web of Science ®Google Scholar
• Tang, C. 2006. “Perspectives in Supply Chain Risk Management.” International Journal of Production Economics 103(2): 451–488.
   Web of Science ®Google Scholar
• Tierney, K., and M. Bruneau. 2007. “Conceptualizing and Measuring Resilience: A Key to Disaster Loss Reduction.” TR News, May-June 2007, 14–17.
   Google Scholar
• Tsay, R. S. 1988. “Outliers, Level Shifts, and Variance Changes in Time Series.” Journal of Forecasting 7: 1–20.
   Web of Science ®Google Scholar
• Tsay, R. S. 2002. Analysis of Financial Time Series. New York: Wiley.
   Google Scholar
• Wright, R. D., and T. E. Ramsay Jr. 1979. “On the Effectiveness of Common Random Numbers.” Management Science 25(7): 649–656.
   Web of Science ®Google Scholar
• Zobel, C. W. 2010. “Comparative Visualization of Predicted Disaster Resilience.” Proceedings of the 7th International ISCRAM Conference. (2010) Seattle, WA.
   Google Scholar
• Zobel, C. W. 2011. “Representing Perceived Tradeoffs in Defining Disaster Resilience.” Decision Support Systems 50(2): 394–403.
   Web of Science ®Google Scholar
• Zobel, C. W., and L. Z. Khansa. 2012. “Quantifying Cyberinfrastructure Resilience against Multi-event Attacks.” Decision Sciences 43(4): 687–710.
   Web of Science ®Google Scholar