An Event-driven Approach for Modeling R&D Project Selection Process

Ou Liu, Duanning Zhou

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

R&D project selection is a critical area of capital budgeting for technology-oriented enterprises or government funding agencies, for which researchers have proposed many decision models as well as supporting tools. However, existing literature seldom takes it as an organizational process in which manpower and other resources collaborate. This makes the organizational planning for risk management unavailable. It seems also difficult to carry out a further analysis of such process, especially a quantitative one. In this paper, R&D project selection process is identified as a discrete-event dynamic process which can be modeled with high-level Petri net. With advantages like explicit expression of concurrency, conflicts etc., Petri net can well describe R&D project selection as an organizational process. In addition, the availability of different analysis methods makes it possible to evaluate and compare Petri net models, as well as to predict threats and manage emergency. Therefore, business processes can be improved and risk can be controlled.

Original languageEnglish
Title of host publication19th Americas Conference on Information Systems, AMCIS 2013 - Hyperconnected World
Subtitle of host publicationAnything, Anywhere, Anytime
PublisherAIS/ICIS Administrative Office
ISBN (Print)9781629933948
Publication statusPublished - 2013
Externally publishedYes
Event19th Americas Conference on Information Systems, AMCIS 2013 - Chicago, IL, United States
Duration: 15 Aug 201317 Aug 2013

Publication series

Name19th Americas Conference on Information Systems, AMCIS 2013 - Hyperconnected World: Anything, Anywhere, Anytime

Conference

Conference19th Americas Conference on Information Systems, AMCIS 2013
Country/TerritoryUnited States
CityChicago, IL
Period15/08/1317/08/13

Keywords

  • Petri net
  • R&D project selection
  • business process modeling
  • risk management

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