“Milk-Run” Simulation
Members:
Advisor:
Daniel Dawson
Jesus Jimenez
Reid Pierson
Richard McEvoy-Kemp
Industry Advisor:
Eleazar Zavala
Sarah Chowdhury
Background
• Philips Lighting in San Marcos currently lacks a steady
manufacturing line to assemble products
 Largely due to custom orders
• Philip’s hopes that implementing an optimized assembly line
will allow their facility to be the top manufacturing facility
worldwide
• Implementation of a Kanban Supermarket will provide an
increase in production and efficiency within their custom
facility
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Reduced bottlenecks
Increased throughput
Increased inventory control
Increased profits
“Milk Run”
“The milkman would not only deliver fresh
milk in the morning, but would collect empty
bottles also to eliminate unnecessary trips”
• A round trip delivery method which
facilitates collection and distribution
• In this project, we will determine the
frequency at which components should be
replenished and the “milk-runner” will fetch
the components from the warehouse
• This idea will have an effect on cycle time,
throughput, and overall efficiency
2-Bin System / Kanban System
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Will help reduce waste due to under and over production
Use of Safety stock
Card replacing
Work inside out to control inventory
Deliverables/Goals
• Increase efficiency and throughput in the Philips model line 741
• Configure and optimize Kanban Supermarket variables such as bin
capacities, reorder points, and frequency of milk runs
• Develop detailed model of the ordering, picking, supermarket and
workstation assembly process given the yearly demand
Methodology
• Microsoft Excel
 Data collection and analysis
• WITNESS Simulation
 Modeling the assembly line and the throughput of the
parts
 2-bin Kanban Supermarket
 “Milk-Run”
Data collected
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Parts to use within the supermarket
Designated workstation for each of the parts
Part usage rate
Lead time
Classification of parts based on lighting models
Bill of materials for each product made within the assembly line
Finished Goods PureForm
Demand forecast
• Use Holt’s Method to
produce forecast of
PureForm product
• The parameters alpha
and beta were set to
0.2
Model Methodology
• Our model simulates the effectiveness and efficiency of a “MilkRun” process to achieve the forecasted monthly demand
• Simulated 6 workstations and 7 workstations within the modelline; each simulated for 140 hours (4 working weeks)
• Inputs needed to construct the model
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Milk Run
Reorder points
Bin sizes
Component inter-arrival times
Each scenario evaluated with “Experimenter” within WITNESS
Model Methodology
• 6 workstations utilized a triangular distribution for the stations
process times:
• Min = 7 minutes
• Mean = 10 minutes
• Max = 13 minutes
• 7 workstations utilized a triangular distribution for the stations
process times
• Min = 5 minutes
• Mean = 8.5 minutes
• Max = 10 minutes
The Model
The model will be running at our booth for those interested in seeing it work!
6 stations
o Six Stations-Baseline
a. Inter-arrival time: 0.185 hours
b. Max bin capacity: 50
c. Reorder point: 25
o Six Stations-Experimenter
7 Stations
• Seven Stations
a. Inter-arrival time: 0.159 hours
b. Max bin capacity: 50
c. Reorder point: 25
• Seven Stations-Experimenter
Outcomes/Conclusions
• Preferred number of workstations
• 7 stations produced the highest throughput, although the demand was still
not able to be met
• A total of 8 stations is ultimately needed to meet the forecasted demand
without having backorders
Acknowledgments
• Philips Lighting-San Marcos
• Dr. Jesus Jimenez (Advisor)
• Sarah Chowdhury (Industry Advisor)
• Psyche Bryant (Industry Supervisor)