Survival analysis in supply chains using statistical flowgraph models: Predicting time to supply chain disruption
DOI10.1080/03610926.2014.957856zbMath1365.90029OpenAlexW2509084345MaRDI QIDQ2832618
Gholamreza Shafipour, Abdolvahhab Fetanat
Publication date: 11 November 2016
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2014.957856
survival analysisdecision support systemsupply chain disruptiontime-to-event datastatistical flowgraph model
Transportation, logistics and supply chain management (90B06) Reliability, availability, maintenance, inspection in operations research (90B25)
Cites Work
- Unnamed Item
- Failure rate modeling for reliability and risk
- The Fourier-series method for inverting transforms of probability distributions
- Slice sampling. (With discussions and rejoinder)
- Flowgraph models: a Bayesian case study in construction engineering
- Multistate Models, Flowgraph Models, and Semi-Markov Processes
- Posterior sampling with constructed likelihood functions: an application to flowgraph models
- Flowgraph Models for Generalized Phase Type Distributions Having Non-Exponential Waiting Times
- Bayesian prediction of waiting times in stochastic models
- Flowgraph Models for Multistate Time‐to‐Event Data
- Numerical Inversion of Laplace Transforms of Probability Distributions
- Multi-state models for event history analysis
- Survival analysis. A self-learning text.
This page was built for publication: Survival analysis in supply chains using statistical flowgraph models: Predicting time to supply chain disruption
