Identifying the Impact of Incomplete Datasets on Process Cycle Time Prediction in an Aerospace Assembly Line

David Allen, Joe Butterfield, Stephen Cowan, Adrian Murphy, Mullan Matthew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Supply Chain Simulation (SCS) is applied to acquire information to support outsourcing decisions but obtaining enough detail in key parameters can often be a barrier to making well informed decisions.
One aspect of SCS that has been relatively unexplored is the impact of inaccurate data around delays within the SC. The impact of the magnitude and variability of process cycle time on typical performance indicators in a SC context is studied.
System cycle time, WIP levels and throughput are more sensitive to the magnitude of deterministic deviations in process cycle time than variable deviations. Manufacturing costs are not very sensitive to these deviations.
Future opportunities include investigating the impact of process failure or product defects, including logistics and transportation between SC members and using alternative costing methodologies.
Original languageEnglish
Title of host publicationInternational Manufacturing Conference (IMC33)
Publication statusPublished - 01 Sep 2016
EventInternational Manufacturing Conference - University of Limerick, Limerick, Ireland
Duration: 31 Aug 201601 Sep 2016
Conference number: 33rd


ConferenceInternational Manufacturing Conference
Abbreviated titleIMC33
Internet address


  • Supply Chain Simulation, Incomplete Datasets, Variable Cycle Times


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