TY - CHAP
T1 - Decision Support Using Simulation for Customer-Driven Manufacturing System Design and Operations Planning
AU - Heilala, Juhani
AU - Montonen, Jari
AU - Järvinen, Paula
AU - Kivikunnas, Sauli
N1 - Project code: 32111
The chapter is a summary of following national public research projects: “Integrated dynamic simulation model of enterprise for planning of operations”, (1997-1999); “Integrated dynamic customer driven production network management using operative simulation” (2000-2001) and “Integrated dynamic electronic production and suppliers control and planning of resources” (2000-2001). Development work was later carried out in the projects “Modelling and simulation of manufacturing systems for value networks” (2005-2007) and “Optimisation of autonomous production cell - real time production intelligence”, (2009-2010). The industrial partners, VTT and TEKES (the Finnish Funding Agency for Technology and Innovation) have funded the work carried out by VTT.
PY - 2010
Y1 - 2010
N2 - Manufacturing, engineering and production management
decisions involve the consideration of multiple
parameters. These often complex, interdependent factors
and variables are too many for the human mind to cope
with at one time. Agile production needs a management and
evaluation tool for production changes, manufacturing
system development, configuration and operations
planning. A decision support system based on
manufacturing simulation is one suitable solution.
Discrete Event Simulation (DES) has mainly been used as a
production system analysis tool to evaluate new
production system concepts, layout and control logic.
Recent development has enhanced DES models for use in the
day-to-day operational production planning of
manufacturing facilities. These "as built" models provide
manufacturers with the ability to evaluate the capacity
of the system for new orders, unforeseen events such as
equipment downtime, and changes in operations. After a
simulation model has been built, experiments are
performed by changing the input parameters and predicting
the response. Experimentation is normally carried out by
asking "what-if" questions and using the model to predict
the likely outcome. A simulation-based Decision Support
System (DSS) can be used to augment the tasks of planners
and schedulers to run production more efficiently. This
chapter sheds light on development challenges and current
development efforts to solve these challenges for this
data and model-driven DSS. The major challenges are: 1)
data integration, 2) automated simulation model creation
and updates, and 3) visualisation of results for
interactive and effective decision making.
AB - Manufacturing, engineering and production management
decisions involve the consideration of multiple
parameters. These often complex, interdependent factors
and variables are too many for the human mind to cope
with at one time. Agile production needs a management and
evaluation tool for production changes, manufacturing
system development, configuration and operations
planning. A decision support system based on
manufacturing simulation is one suitable solution.
Discrete Event Simulation (DES) has mainly been used as a
production system analysis tool to evaluate new
production system concepts, layout and control logic.
Recent development has enhanced DES models for use in the
day-to-day operational production planning of
manufacturing facilities. These "as built" models provide
manufacturers with the ability to evaluate the capacity
of the system for new orders, unforeseen events such as
equipment downtime, and changes in operations. After a
simulation model has been built, experiments are
performed by changing the input parameters and predicting
the response. Experimentation is normally carried out by
asking "what-if" questions and using the model to predict
the likely outcome. A simulation-based Decision Support
System (DSS) can be used to augment the tasks of planners
and schedulers to run production more efficiently. This
chapter sheds light on development challenges and current
development efforts to solve these challenges for this
data and model-driven DSS. The major challenges are: 1)
data integration, 2) automated simulation model creation
and updates, and 3) visualisation of results for
interactive and effective decision making.
U2 - 10.5772/39400
DO - 10.5772/39400
M3 - Chapter or book article
SN - 978-953-307-069-8
SP - 235
EP - 260
BT - Decision Support Systems
A2 - Devlin, Ger
PB - InTech
ER -