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.

International Conference FAIM2012, Helsinki

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

BUSINESS CHANGES: Less time for production and process planning Faster inovations and product development Cooperation inside organization on every level Efficient flow of information

DEMANDS ON PRODUCTS: Increased complexity in more variants Better quality for same or less price Flexible production processes Strengthening of competitors

PLM SOLUTION

Business approach, strategy Product lifecycle management

PLM

One of tools are simulations...

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

Starting model (without optimization)

CASE STUDY

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

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

DEVELOPING GENETIC ALGORITHM

Processing time reduced for 28 days (~ 9%)Costs reduction over 20 000 dollars (~ 2%)

RESULT ANALYSISOptimizated costs per machinining tools

Distribution of production time per machining tools

Sanky diagram of material flow for initial model