Input data management in simulation - Industrial practices and future trends

Discrete Event Simulation has been acknowledged as a strategically important tool in the development and improvement of production systems. However, it appears that companies are failing to reap full benefits of this powerful technology as the maintenance of simulation models has become very time-consuming, particularly due to vast amounts of data to be handled. Hence, an increased level of automation of input data handling is highly desirable. This paper presents the current practices relating to input data management and identifies further research and development required to achieve high levels of automation. A survey of simulation users shows that there has been a progress in the use of automated solutions compared to a similar study presented by Robertson and Perera in 2002. The results, however, reveal that around 80% of the users still rely on highly manual work procedures in input data management.

[1]  Björn Johansson,et al.  Mapping of Time-Consumption During Input Data Management Activities , 2009, Simul. Notes Eur..

[2]  Janet M. Twomey,et al.  Unit Process Life Cycle Inventory for Product Manufacturing Operations , 2009 .

[3]  Terrence D. Perera,et al.  Methodology for rapid identification and collection of input data in the simulation of manufacturing systems , 2000, Simul. Pract. Theory.

[4]  Stewart Robinson,et al.  Simulation: The Practice of Model Development and Use , 2004 .

[5]  Kai Mertins,et al.  Integration of Factory Planning and ERP/MES Systems: Adaptive Simulation Models , 2006, APMS.

[6]  Gunnar Bolmsjö,et al.  Reducing bottle‐necks in a manufacturing system with automatic data collection and discrete‐event simulation , 2005 .

[7]  Sang Do Noh,et al.  Digital Factory Wizard: an integrated system for concurrent digital engineering in product lifecycle management , 2010, Int. J. Comput. Integr. Manuf..

[8]  Young B. Moon,et al.  Enhancing ERP system's functionality with discrete event simulation , 2005, Ind. Manag. Data Syst..

[9]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[10]  Gunnar Bolmsjö,et al.  Database driven factory simulation: a proof-of-concept demonstrator , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[11]  Cathal Heavey,et al.  Proposed visual wiki system for gathering knowledge about discrete event systems , 2010, Proceedings of the 2010 Winter Simulation Conference.

[12]  Thomas H. Davenport,et al.  Book review:Working knowledge: How organizations manage what they know. Thomas H. Davenport and Laurence Prusak. Harvard Business School Press, 1998. $29.95US. ISBN 0‐87584‐655‐6 , 1998 .

[13]  Christoph Laroque,et al.  Design and implementation of an MDA interface for flexible data capturing , 2010, J. Simulation.

[14]  Averill M. Law,et al.  How the Expertfit distribution-fitting software can make your simulation models more valid , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[15]  Terrence D. Perera,et al.  Automated data collection for simulation? , 2002, Simul. Pract. Theory.

[16]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[17]  Wolfgang Kuehn,et al.  Digital Factory - Simulation Enhancing the Product and Production Engineering Process , 2006, Proceedings of the 2006 Winter Simulation Conference.

[18]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..

[19]  Daniel Diep,et al.  Integration of agents and RFID in a manufacturing simulation , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[20]  John W. Fowler,et al.  Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems , 2004, Simul..

[21]  Sigrid Wenzel,et al.  A new procedure model for verification and validation in production and logistics simulation , 2008, 2008 Winter Simulation Conference.

[22]  Anca Draghici,et al.  Collaborative Product Development in PLM Multisite Platform , 2009 .

[23]  Frank Riddick,et al.  Core Manufacturing Simulation Data – a manufacturing simulation integration standard: overview and case studies , 2011, Int. J. Comput. Integr. Manuf..