Synergy of Process and Production Planning
by Discrete Simulation in Manufacturing
Zavod za idustrijsko inženjerstvo
Katedra za projektiranje proizvodnje
Department of Industrial Engineering
Chair of Production Design
DEMANDS ON PRODUCTS:
Predrag COSIC, Davor
PIROVIC
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SUMMARY:
Faster innovation processes and
increase in number of new
products developed and
successfully placed on the market
presents a great challenge for
most companies. The main
reason is their traditionally serial,
distributed, manual guidance of
the process whereby the most
frequent product is unnecessary
paperwork. Thanks to its
capability to link all information
about products and processes of
the organization, PLM systems
can significantly reduce the
activity that adds no value and
create a foundation for the
collaboration of all departments
within the organization in real
time using all the necessary
information about the product
throughout its lifecycle.
CONCLUSION:
Although the simulation model
has been made for a possible
scenario of production, all input
values are obtained from real
observed processes and so the
model is usable in real production
systems for tactical and strategic
planning. Simulation displayed
different behavior of system
according to variable production
data (different cost production per
machine, variable delivery times
and working shifts). As the most
important task to optimize the
system, a genetic algorithm was
developed and it showed very
good results and improvement in
the production system regarding
production costs. Production time
for all products in one year was
28 days less.
International Conference
FAIM2012, Helsinki
PLM SOLUTION
Increased complexity in more variants
Better quality for same or less price
Flexible production processes
Strengthening of competitors
PLM
BUSINESS CHANGES:
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 Business approach, strategy
 Product lifecycle management
Less time for production and process planning
Faster inovations and product development
Cooperation inside organization on every level
Efficient flow of information
CASE STUDY
Plant Simulation (discrete simulations
 MODEL:
 10 different products in different series, quantities and
 One of tools are simulations...
delivery times
 Technological processes known
DEVELOPING
GENETIC
ALGORITHM
 Means of production known
 Optimization with developted genetic algorithm to
 optimization direction : MINIMUM
achieve minimal production costs
 number of generation: 12
 number of indiviuals: 50
CASE STUDY
 observations per individuals: 2
 optimization parameter : defined by programming
Starting model (without optimization)
methods
 number of available machines considering the type of
operation
 machine availability changes from 70% to 95% with
increment factor of 5%
 fitness function : defined by programming methods
 total cost for all products in order table with weighting
factor 0.7
 delivery time for each series of product, all with
weighting factor 1.0
Sanky diagram of material flow for initial model
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work in two shifts
all machines with buffers and defined cost per
minute
production by self defined table of orders
model has its own sql database with internet
access
Distribution of production time per
machining tools
RESULT ANALYSIS
Optimizated costs per machinining tools
Processing time reduced for 28 days (~ 9%)
Costs reduction over 20 000 dollars (~ 2%)
References:
[1]
A. Saaksvuori, “Product Lifecycle Management”, Springer-Verlag, 2008.
[2]
J. Stark, “PLM: 21st century Paradigm for Product Realisation”, Springer-Verlag, 2004.
[3]
J.Teresko, “The PLM Revolution”, IndustryWeek, 2004.
[4]
1S. Bangsow, “Manufacturing Simulation with Plant Simulation and SimTalk”, Springer, 2010.
[51
Tecnomatix Siemens, “Tecnomatix Plant Simulation 9 User Guide 2008”, 2008.