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Learn the basics of modeling manufacturing systems, from problem definition to analysis of results, with a focus on material handling systems. Discover key principles, goals, issues, and performance measures in manufacturing modeling projects.
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Simulation Project Steps a.- Problem Definition b.- Statement of Objectives c.- Model Formulation and Planning d.- Model Development and Data Collection e.- Verification f.- Validation g.-Experimentation h.- Analysis of Results i.- Reporting and Implementation
Basic Principles of Modeling • To conceptualize a model use • System knowledge • Engineering judgement • Model-building tools • Remodel as needed • Regard modeling as an evolutionary process
Manufacturing Systems • Material Flow Systems • Assembly lines and Transfer lines • Flow shops and Job shops • Flexible Manufacturing Systems and Group Technology • Supporting Components • Setup and sequencing • Handling systems • Warehousing
Production Control Supplies Storage Packing and Shipping Characteristics ofManufacturing Systems • Physical layout • Labor • Equipment • Maintenance • Work centers • Product • Production Schedules
Modeling Material Handling Systems • Up to 85% of the time of an item on the manufacturing floor is spent in material handling. • Subsystems • Conveyors • Transporters • Storage Systems
Goals and Performance Measures • Some relevant questions • How a new/modified system will work? • Will throughput be met? • What is the response time? • How resilient is the system? • How is congestion resolved? • What staffing is required? • What is the system capacity?
Goals of Manufacturing Modeling • Manufacturing Systems • Identify problem areas • Quantify system performance • Supporting Systems • Effects of changes in order profiles • Truck/trailer queueing • Effectiveness of materials handling • Recovery from surges
Performance Measuresin Manufacturing Modeling • Throughput under average and peak loads • Utilization of resources, labor and machines • Bottlenecks • Queueing • WIP storage needs • Staffing requirements • Effectiveness of scheduling and control
Some Key Modeling Issues • Alternatives for Modeling Downtimes and Failures • Ignore them • Do not model directly but adjust processing time accordingly • Use constant values for failure and repair times • Use statistical distributions
Key Modeling Issues -contd • Time to failure • By wall clock time • By busy time • By number of cycles • By number of widgets • Time to repair • As a pure time delay • As wait time for a resource
Key Modeling Issues -contd • What to do with an item in the machine when machine downtime occurs? • Scrap • Rework • Resume processing after downtime • Complete processing before downtime
Example • Single server resource with processing time exponential (mean = 7.5 minutes) • Interarrival time also exponential (mean = 10 minutes) • Time to failure, exponential (mean=100 min) • Repair time, exponential (mean 50 min)
Example 5.1 -contd • Queue lengths for various cases • Breakdowns ignored • Service time increased to 8 min • Everything random • Random processing, deterministic breakdowns • Everything deterministic • Deterministic processing, random breakdowns
Trace Driven Models • Models driven by actual historical data • Examples • Actual orders for a sample of days • Actual product mix, quantities and sequencing • Actual time to failure and downtimes • Actual truck arrival times
A sampler of manufacturing models from WSC’98 • Automotive • Final assembly conveyor systems • Mercedes-Benz AAV Production Facility • Machine controls for frame turnover system
A sampler of manufacturing models from WSC’98 -contd • Assembly • Operational capacity planning: daily labor assignment in a customer-driven line at Ericsson • Optimal design of a final engine drop assembly station • Worker simulation
A sampler of manufacturing models from WSC’98 -contd • Scheduling • Batch loading and scheduling in heat treat furnace operations • Schedule evaluation in coffee manufacture • Manufacturing cell design
A sampler of manufacturing models from WSC’98 -contd • Semiconductor Manufacturing • Generic models of automated material handling systems at PRI Automation • Cycle time reduction schemes at Siemens • Bottleneck analysis and theory of constraints at Advanced Micro Devices • Work in process evolution after a breakdown • Targeted cycle time reduction and capital planning process at Seagate
A sampler of manufacturing models from WSC’98 -contd • Semiconductor Manufacturing - contd • Local modeling of trouble spots in a Siemens production facility • Validation and verification in a photolithography process model at Cirent • Environmental issues in filament winding composite manufacture • Order sequencing
A sampler of manufacturing models from WSC’98 -contd • Materials Handling • Controlled conveyor network with merging configuration at Seagate • Warehouse design at Intel • Transfer from warehouse to packing with Rapistan control system • Optimization of maintenance policies
SimSource Deneb Valisys (Tecnomatix) Open Virtual Factory EON Simul8 Manufacturing Simulators • ProModel • Witness • Taylor II • AutoMod • Arena • ModSim and Simprocess