1. 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:
Increased complexity in more variants
Better quality for same or less price
Flexible production processes
Strengthening of competitors
PLM SOLUTION
Predrag COSIC, Davor PIROVIC
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.
PLM
Business approach, strategy
Product lifecycle management
BUSINESS CHANGES:
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 delivery times
Technological processes known
Means of production known
Optimization with developted genetic algorithm to achieve minimal production costs
One of tools are simulations...
DEVELOPING GENETIC ALGORITHM
optimization direction : MINIMUM
number of generation: 12
number of indiviuals: 50
observations per individuals: 2
optimization parameter : defined by programming 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
CASE STUDY
Starting model (without optimization)
Sanky diagram of material flow for initial model
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 dollars (~ 2%)
International Conference FAIM2012, Helsinki
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.