Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies

Abstract The digital factory is a broad computer based concept, which overwhelmingly integrates realworld facilities into high-level information systems. The objective is to represent key characteristics of the entire operational base on a common computer platform. The main pillar of this 'new&...

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Main Author: Rutherford, Oliver Stuart
Format: Dissertation (University of Nottingham only)
Language:English
Published: 2006
Subjects:
Online Access:https://eprints.nottingham.ac.uk/20217/
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author Rutherford, Oliver Stuart
author_facet Rutherford, Oliver Stuart
author_sort Rutherford, Oliver Stuart
building Nottingham Research Data Repository
collection Online Access
description Abstract The digital factory is a broad computer based concept, which overwhelmingly integrates realworld facilities into high-level information systems. The objective is to represent key characteristics of the entire operational base on a common computer platform. The main pillar of this 'new' concept is simulation modelling, which allows planners to reproduce reality through easy to use computer software. Simulation attempts to reveal how existing or ideal systems work or should work and is increasingly applied in managerial decision making. This 'new' planning concept draws upon on two equally important inputs for its models: 1. process knowledge 2. technical planning parameters. Operations expertise in the form of process knowledge and planning parameters, such as cycle time or availability, directly affect the outcomes of simulation modelling. Accuracy of both factors is indispensable for making simulation models as realistic as possible. But this is challenging to accomplish! The difficulty to acquire profound process knowledge, which often takes years of experience, is frequently ignored by advocates of the digital factory. Likewise, little attention is paid to the challenge of retrieving and processing accurate planning parameters to feed into simulation models. Accuracy [or rather inaccuracy] is a latent obstacle for simulation and the digital factory as a whole. Research and industry need to be aware of potential impacts. Only the correct identification of possible shortfalls will make digital planning a precise and trusted tool for management and research in the years to come.
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spelling nottingham-202172017-12-29T15:10:54Z https://eprints.nottingham.ac.uk/20217/ Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies Rutherford, Oliver Stuart Abstract The digital factory is a broad computer based concept, which overwhelmingly integrates realworld facilities into high-level information systems. The objective is to represent key characteristics of the entire operational base on a common computer platform. The main pillar of this 'new' concept is simulation modelling, which allows planners to reproduce reality through easy to use computer software. Simulation attempts to reveal how existing or ideal systems work or should work and is increasingly applied in managerial decision making. This 'new' planning concept draws upon on two equally important inputs for its models: 1. process knowledge 2. technical planning parameters. Operations expertise in the form of process knowledge and planning parameters, such as cycle time or availability, directly affect the outcomes of simulation modelling. Accuracy of both factors is indispensable for making simulation models as realistic as possible. But this is challenging to accomplish! The difficulty to acquire profound process knowledge, which often takes years of experience, is frequently ignored by advocates of the digital factory. Likewise, little attention is paid to the challenge of retrieving and processing accurate planning parameters to feed into simulation models. Accuracy [or rather inaccuracy] is a latent obstacle for simulation and the digital factory as a whole. Research and industry need to be aware of potential impacts. Only the correct identification of possible shortfalls will make digital planning a precise and trusted tool for management and research in the years to come. 2006 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/20217/1/06MSclixosr.pdf Rutherford, Oliver Stuart (2006) Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies. [Dissertation (University of Nottingham only)] (Unpublished) Operations Control Shop Floor Control Digital Factory Simulation Car Body
spellingShingle Operations Control
Shop Floor Control
Digital Factory
Simulation
Car Body
Rutherford, Oliver Stuart
Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title_full Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title_fullStr Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title_full_unstemmed Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title_short Technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
title_sort technical planning parameters for the development of future car models with reference to the optimal works structure for the manufacture of car bodies
topic Operations Control
Shop Floor Control
Digital Factory
Simulation
Car Body
url https://eprints.nottingham.ac.uk/20217/