Client was facing higher costs and large amounts of capital tied up as inventory
Production line was struggling to produce current required volumes and showed signs of being unable to produce targeted levels of projected volume
Operating expenses were increasing due to constant overtime requirements
Client desired a way to reduce cost from the process,
reduce inventory, but increase the production capacity to
eliminate overtime and position the company to attain
future production goals
Project team constraints
New manufacturing environment and industry
Four week duration from start-to-finish to:
Understand the business
Identify opportunities to improve the process
Learn a new simulation package and model the current process and potential improvements
An “alpha” version of the Simio software package
Manufacturing process overview
Process complexities requiring modeling
Multiple production schedules are utilized
Kitting units
Assembly line moved at the pace of the slowest unit
Some resources were shared between equipment
Differing processing times
Varying hours of operation
Defined key hypotheses for modeling
Hypotheses to improve throughput and reduce WIP:
Operations baseline: developing a functional model that
mimics the process
Schedule Integration: aligning / streamlining the schedule
Kitting Availability: increased kitting frequency to reduce WIP on the line and to reduce the frequency of kits being
delayed or delivered incomplete
Part Presentation: improving how parts were kitted to the
line to improve the speed of picking / using the part
Workforce reduction: that fewer people were required than were currently utilized on the line
Demonstration of model
Results of the simulation
The four modeled improvements resulted in ~40% increase in throughput and ~35% decrease in WIP inventory.
Modeled
Improvement
Avg. Throughput
Avg. WIP
Volume
Change
Volume
Change
Baseline Performance
202
N/A
566
N/A
Schedule
Integration
247
↑ 22%
358
↓ 37%
Kitting
Availability
271
↑10%
361
↑1%
Part
Presentation
285
↑5%
363
↑1%
Benefits of using Simio
Free-form structure of Simio
Powerful Standard Library
Customization with Add-on processes
“No code” object enhancements
Capability to define any mathematical KPIs
Advanced Ribbon” GUI
Experiments with multi-processor support
Model within model functionality
Integrated 3D Animation
Modeling challenges within consulting engagements
The use of simulation in a consulting environment posed
several challenges:
Providing an accurate baseline of the process in order to gain confidence in predictive simulations
Identifying / modeling improvement initiatives within the shortened window of consulting engagements
Developing a model that is impenetrable to inquisitive review
Lessons learned
We identified several actions that could increase the speed / probability of acceptance:
White-board the model and walk client participants through the approach
