Lantech Case Study
Presented by: Ray Essig
Natalie Lavergne
Karine Lavoie-Tremblay
Background Information
• Lantech is a Kentucky based wrapping machine
manufacturer
• saved from the brink of bankruptcy by multiple Kaizen
events
• Kaizen’s have resulted in increased productivity and
profitability:
– went from 50 employees turning out 8 machines/day to
20 people turning out 14 machines/day
– order processing time went from 5 wks to 14 hrs
– sales increased by 3 1/2 times
Abstract
This project took information from a real life
manufacturing system for wrapping machines for a
company called Lantech Inc., and applied various
tools described in the Modeling Manufacturing
Systems class to their system. The results from each
of four of these tools were analyzed and then
compared to published results from multiple Kaizen
events at Lantech.
The Kaizen Process
• “Kaizen” means continuous, incremental
improvement in an activity, to create more
value with less waste.
• A “Kaizen Event” is a process that a
company orchestrates to try to improve their
specific manufacturing operations.
• Carried out in a real time enviroment
• Held towards end of improvement process
100% Lean
No Waste
Improvement process
Process
Standardization
and Improvement
Training
and
Planning
Time
Kaizen closes the
gap and applies
the finishing
touches.
• Team should include:
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–
–
workers who work in current system
workers who will work in new system
experienced kaizen facilitators
management who can implement changes
• Steps
–
–
–
–
–
–
–
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problem identification
gather information
analyze information
determine root cause
generate ideas to solve problems
utilize simulation software
implement agreed upon solutions
check results
Assumptions
• In order to be able to perform an analysis of
the Lantech systems, some assumptions had
to be made where actual data was not
available
• the same assumptions were used in all 4
analysis techniques
Queuing Theory
• Utilized following equations to improve
throughput time:
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–
–
–
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P(0) = Probability system is empty = 1- 
Wq = expected time in line = /(1-)
L = time in system = /(1-)
W = Total time in system = 1/(1-)
Where:  = /c ,  = average arrival rate, c = number of servers, and =
average service rate.
• Current Gear drive assembly takes 6 hours:
– 1 machine, 6 hours,
1.5 machine, 9 hours.
• Current Demand is 1.5 / 8 hour day
• Using queuing theory, reduced throughput time to 1 machine, 5
hours
Queuing Theory Results, before Kaizen
M/M/s
Arrival rate
Service rate
Number of servers
Assumes
Poisson
process for
arrivals and services.
0.5
1.33333
1
Utilization
P(0), probability that the system is empty
Lq, expected queue length
L, expected number in system
Wq, expected time in queue
W, expected total time in system
Probability that a customer waits
37.50%
0.6250
0.2250
0.6000
0.4500
1.2000
0.3750
Queuing Theory Results, after Kaizen
M/M/s
Arrival rate
0.5
Assumes Poisson process for
Service rate
1.6
arrivals and services.
Number of servers
1
Utilization
31.25%
P(0), probability that the system is empty
0.6875
Lq, expected queue length
0.1420
L, expected number in system
0.4545
Wq, expected time in queue
0.2841
W, expected total time in system
0.9091
Probability that a customer waits
0.3125
Flow shop sequencing
• Created a precedence structure and
sequence the departments accordingly: 38%
gain in efficiency from original case
• Multi-task resources in addition to the
precedence structure: 69% gain in
efficiency from original case
Facility Layout
• Built a REL chart
• TCR based on new layout: 33% savings on
total flow cost
ProModel Analysis
• Several analyses were run using ProModel
• limiting factor on analysis was model size
• ProModel helped identify areas of
bottleneck where re-allocation of resources
could improve throughput - specifically
areas with high cycle time
Discussion
• 4 different tools: “Queuing Theory”, “Flow shop
sequencing”, “Facility Layout” and “Pro-Model”.
• Each tool improves a different element of the Lantech
manufacturing system:
– total flow cost
– resources
– throughput time
– layout
– sequencing
– efficiency/profitability
Conclusion
• A company initially needs to identify
which elements of their system they
would like to optimize.
• The company should then proceed to
use a combination of the different tools
in order to maximize their respective
strengths and provide an overall system
benefit.