Ranking critical activities in process architectures

Research output: Contribution to journalConference article

Abstract

Identification of important activities in a process architecture is a complex task. This paper takes a network perspective approach: Process architectures are represented as activity networks and are modelled as a Discrete-Time Markov Chain (DTMC). Here we propose a DTMC-based component ranking method (Markov chain) to rank important activities in a variety of process architectures of complex system development projects. Analysis of the results show that the proposed model possesses the capability to highly rank critical activities in a process architecture that are capable of either leading to task volatility i.e. increase the probability of rework, have the tendency to cause large iteration cycles, posing a high risk to the process in terms of delay and failure, or leading to unplanned rework. The results are also compared with other widely used network metrics, namely, closeness, betweenness and eigenvector centrality. In our pilot study on four different process architectures, Markov chain outperformed other ranking strategies in highly ranking the significant activities of the process architecture.

Original languageEnglish (US)
Pages (from-to)46-55
Number of pages10
JournalProcedia Computer Science
Volume140
DOIs
StatePublished - Jan 1 2018
EventComplex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 - Chicago, United States
Duration: Nov 5 2018Nov 7 2018

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Markov processes
Eigenvalues and eigenfunctions
Large scale systems

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

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title = "Ranking critical activities in process architectures",
abstract = "Identification of important activities in a process architecture is a complex task. This paper takes a network perspective approach: Process architectures are represented as activity networks and are modelled as a Discrete-Time Markov Chain (DTMC). Here we propose a DTMC-based component ranking method (Markov chain) to rank important activities in a variety of process architectures of complex system development projects. Analysis of the results show that the proposed model possesses the capability to highly rank critical activities in a process architecture that are capable of either leading to task volatility i.e. increase the probability of rework, have the tendency to cause large iteration cycles, posing a high risk to the process in terms of delay and failure, or leading to unplanned rework. The results are also compared with other widely used network metrics, namely, closeness, betweenness and eigenvector centrality. In our pilot study on four different process architectures, Markov chain outperformed other ranking strategies in highly ranking the significant activities of the process architecture.",
author = "Srinivasan, {Satish Mahade Van} and Ergin, {Nil Hande} and Sangwan, {Raghvinder S.} and Neil, {Colin J.}",
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Ranking critical activities in process architectures. / Srinivasan, Satish Mahade Van; Ergin, Nil Hande; Sangwan, Raghvinder S.; Neil, Colin J.

In: Procedia Computer Science, Vol. 140, 01.01.2018, p. 46-55.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Ranking critical activities in process architectures

AU - Srinivasan, Satish Mahade Van

AU - Ergin, Nil Hande

AU - Sangwan, Raghvinder S.

AU - Neil, Colin J.

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N2 - Identification of important activities in a process architecture is a complex task. This paper takes a network perspective approach: Process architectures are represented as activity networks and are modelled as a Discrete-Time Markov Chain (DTMC). Here we propose a DTMC-based component ranking method (Markov chain) to rank important activities in a variety of process architectures of complex system development projects. Analysis of the results show that the proposed model possesses the capability to highly rank critical activities in a process architecture that are capable of either leading to task volatility i.e. increase the probability of rework, have the tendency to cause large iteration cycles, posing a high risk to the process in terms of delay and failure, or leading to unplanned rework. The results are also compared with other widely used network metrics, namely, closeness, betweenness and eigenvector centrality. In our pilot study on four different process architectures, Markov chain outperformed other ranking strategies in highly ranking the significant activities of the process architecture.

AB - Identification of important activities in a process architecture is a complex task. This paper takes a network perspective approach: Process architectures are represented as activity networks and are modelled as a Discrete-Time Markov Chain (DTMC). Here we propose a DTMC-based component ranking method (Markov chain) to rank important activities in a variety of process architectures of complex system development projects. Analysis of the results show that the proposed model possesses the capability to highly rank critical activities in a process architecture that are capable of either leading to task volatility i.e. increase the probability of rework, have the tendency to cause large iteration cycles, posing a high risk to the process in terms of delay and failure, or leading to unplanned rework. The results are also compared with other widely used network metrics, namely, closeness, betweenness and eigenvector centrality. In our pilot study on four different process architectures, Markov chain outperformed other ranking strategies in highly ranking the significant activities of the process architecture.

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