identifying cost reduction and performance improvement ... · identifying cost reduction and...
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![Page 1: Identifying Cost Reduction and Performance Improvement ... · Identifying Cost Reduction and Performance Improvement Opportunities Through Simulation December 14 th, 2009 Ethan Brown](https://reader030.vdocuments.net/reader030/viewer/2022040122/5d5a997288c993b04f8bb1ac/html5/thumbnails/1.jpg)
Identifying Cost Reduction and Performance
Improvement Opportunities Through SimulationImprovement Opportunities Through Simulation
December 14th, 2009
Ethan Brown
Deloitte Consulting LLP
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� Client Dynamics
� Project Team Constraints
� Manufacturing process overview
� Process complexities requiring modeling
� Defined key hypotheses for modeling
Agenda
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� Defined key hypotheses for modeling
� Demonstration of the model
� Results of the simulation
� Benefits of using Simio
� Modeling challenges within consulting engagements
� Lessons learned
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� 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
Client dynamics
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� 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
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� New manufacturing environment and industry
� Four week duration from start-to-finish to:
o Understand the business
o Identify opportunities to improve the process
Project team constraints
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o Learn a new simulation package and model the current
process and potential improvements
� An “alpha” version of the Simio software package
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Manufacturing process overview
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� Multiple production schedules are utilized
� Kitting units
� Assembly line moved at the pace of the slowest unit
� Some resources were shared between equipment
Process complexities requiring modeling
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� Differing processing times
� Varying hours of operation
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Hypotheses to improve throughput and reduce WIP:
1. Operations baseline: developing a functional model that
mimics the process
2. Schedule Integration: aligning / streamlining the schedule
3. Kitting Availability: increased kitting frequency to reduce
Defined key hypotheses for modeling
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3. Kitting Availability: increased kitting frequency to reduce
WIP on the line and to reduce the frequency of kits being
delayed or delivered incomplete
4. Part Presentation: improving how parts were kitted to the
line to improve the speed of picking / using the part
5. Workforce reduction: that fewer people were required
than were currently utilized on the line
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Demonstration of the model
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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 202 N/A 566 N/A
Results of the simulation
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Baseline
performance202 N/A 566 N/A
Schedule
Integration247 ↑ 22% 358 ↓ 37%
Kitting
Availability271 ↑ 10% 361 ↑ 1%
Part
Presentation285 ↑ 5% 363 ↑ 1%
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� Free-form structure of Simio
� Powerful Standard Library
� Customization with Add-on processes
� “No code” object enhancements
Benefits of using Simio
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� Capability to define any mathematical KPIs
� Advanced Ribbon” GUI
� Experiments with multi-processor support
� Model within model functionality
� Integrated 3D Animation
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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
Modeling challenges within consulting engagements
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� Identifying / modeling improvement initiatives within the
shortened window of consulting engagements
� Developing a model that is impenetrable to inquisitive
review
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We identified several actions that could increase the speed /
probability of acceptance:
� White-board the model and walk client participants through
the approach
� Incorporate local programming talent
Lessons learned
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� Incorporate local programming talent
� Utilize formerly constructed client simulation models
� Complex is not always better
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� Questions?
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� Questions?