zbMATH — the first resource for mathematics

Survival analysis in supply chains using statistical flowgraph models: predicting time to supply chain disruption. (English) Zbl 1365.90029
Summary: Random events such as a production machine breakdown in a manufacturing plant, an equipment failure within a transportation system, a security failure of information system, or any number of different problems may cause supply chain disruption. Although several researchers have focused on supply chain disruptions and have discussed the measures that companies should use to design better supply chains, or study the different ways that could help firms to mitigate the consequences of a supply chain disruption, the lack of an appropriate method to predict time to disruptive events is strongly felt. Based on this need, this paper introduces statistical flowgraph models (SFGMs) for survival analysis in supply chains. SFGMs provide an innovative approach to analyze time-to-event data. Time-to-event data analysis focuses on modeling waiting times until events of interest occur. SFGMs are useful for reducing multistate models into an equivalent binary-state model. Analysis from the SFGM gives an entire waiting time distribution as well as the system reliability (survivor) and hazard functions for any total or partial waiting time. The end results from a SFGM helps to identify the supply chain’s strengths, and more importantly, weaknesses. Therefore, the results are a valuable decision support for supply chain managers to predict supply chain behaviors. Examples presented in this paper demonstrate with clarity the applicability of SFGMs to survival analysis in supply chains.
90B06 Transportation, logistics and supply chain management
90B25 Reliability, availability, maintenance, inspection in operations research
Full Text: DOI
[1] Aalen O.O., Stat. Sci. 16 pp 1– (2001)
[2] DOI: 10.1007/BF01158520 · Zbl 0749.60013 · doi:10.1007/BF01158520
[3] DOI: 10.1287/ijoc.7.1.36 · Zbl 0821.65085 · doi:10.1287/ijoc.7.1.36
[4] DOI: 10.4018/jisscm.2009070103 · doi:10.4018/jisscm.2009070103
[5] DOI: 10.1191/0962280202SM276ra · Zbl 1121.62568 · doi:10.1191/0962280202SM276ra
[6] DOI: 10.1016/j.ress.2010.12.011 · doi:10.1016/j.ress.2010.12.011
[7] DOI: 10.1007/978-1-4615-7728-7 · doi:10.1007/978-1-4615-7728-7
[8] DOI: 10.1080/01621459.1997.10473622 · doi:10.1080/01621459.1997.10473622
[9] DOI: 10.2307/3315981 · Zbl 0986.62016 · doi:10.2307/3315981
[10] Cox D.R., Analysis of Survival Data, 1. ed. (1984)
[11] Endrenyi J., Reliability Modeling in Electric Power Systems, 1. ed. (1978)
[12] Finkelstein M., Failure Rate Modelling for Reliability and Risk, 1. ed. (2008) · Zbl 1194.90001
[13] DOI: 10.1016/j.trb.2011.05.020 · doi:10.1016/j.trb.2011.05.020
[14] Grosh D.L., Primer of Reliability Theory, 1. ed. (1989) · Zbl 0697.90022
[15] DOI: 10.1111/1467-9469.00142 · Zbl 0929.60070 · doi:10.1111/1467-9469.00142
[16] DOI: 10.1080/00401706.2000.10486050 · doi:10.1080/00401706.2000.10486050
[17] Huzurbazar A.V., Advances on Methodological and Applied Aspects of Probability and Statistics pp 561– (2000)
[18] DOI: 10.1081/STA-120028678 · Zbl 1066.62103 · doi:10.1081/STA-120028678
[19] Huzurbazar A.V., Handbook of Statistics volume 23: Advances in Survival Analysis pp 729– (2004)
[20] DOI: 10.1016/j.jspi.2004.06.046 · Zbl 1058.62115 · doi:10.1016/j.jspi.2004.06.046
[21] Huzurbazar A.V., Flowgraph Models for Multistate Time-to-Event Data, 1. ed. (2005) · Zbl 1055.62123
[22] DOI: 10.1142/9789812703378_0018 · doi:10.1142/9789812703378_0018
[23] Kleinbaum D.G., Survival Analysis: A Self-Learning Text, 2. ed. (2005) · Zbl 1093.62090
[24] DOI: 10.1109/JRPROC.1953.274449 · doi:10.1109/JRPROC.1953.274449
[25] Neal, R. (1997). Markov chain Monte Carlo methods based on ”slicing” the density function. Technical Report 9722, Department of Statistics, University of Toronto, Ontario, Canada.
[26] DOI: 10.1214/aos/1056562461 · Zbl 1051.65007 · doi:10.1214/aos/1056562461
[27] Sheffi, Y., Rice Jr, J.B., Caniato, F., Fleck, J., Disraelly, D., Lowtan, D., Lensing, R., Pickett, C. (2003). Supply chain response to terrorism: Creating resilient and secure supply chains, Supply Chain Response to Terrorism Project–Interim Report of Progress and Learnings, MIT Center for Transportation and Logistics.
[28] DOI: 10.1111/j.1540-5915.1998.tb01356.x · doi:10.1111/j.1540-5915.1998.tb01356.x
[29] Taghizadeh H., J. Ind. Eng. Int. 22 pp 1– (2012)
[30] Warr, R.L. (2010). Generalizations of the statistical flowgraph model framework, PhD Thesis, University of New Mexico, Albuquerque, NM.
[31] DOI: 10.1002/asmb.623 · Zbl 1114.62027 · doi:10.1002/asmb.623
[32] DOI: 10.1002/sim.1237 · doi:10.1002/sim.1237
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.