Improving Efficiency with Milk-Run Simulation for Philips Lighting

“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
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
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
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 “Milk-
Run” process to achieve the forecasted monthly demand
Simulated 6 workstations and 7 workstations within the model-
line; each simulated for 140 hours (4 working weeks)
Inputs needed to construct the model
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)
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Philips Lighting in San Marcos aims to enhance its manufacturing efficiency by implementing an optimized assembly line using a Kanban Supermarket approach. The Milk-Run Simulation project focuses on replenishing components efficiently, reducing bottlenecks, and boosting throughput, leading to increased profits. The methodology involves data collection, analysis in Microsoft Excel, and simulation modeling using WITNESS. Key deliverables include configuring the Kanban Supermarket variables and developing a detailed model of the assembly process to meet yearly demand.


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  1. Milk-Run Simulation Members: Daniel Dawson Reid Pierson Richard McEvoy-Kemp Eleazar Zavala Advisor: Jesus Jimenez Industry Advisor: Sarah Chowdhury

  2. 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 Reduced bottlenecks Increased throughput Increased inventory control Increased profits

  3. 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

  4. 2-Bin System / Kanban System Will help reduce waste due to under and over production Use of Safety stock Card replacing Work inside out to control inventory

  5. 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

  6. 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

  7. Data collected 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

  8. Demand forecast Use Holt s Method to produce forecast of PureForm product The parameters alpha and beta were set to 0.2

  9. Model Methodology Our model simulates the effectiveness and efficiency of a Milk- Run process to achieve the forecasted monthly demand Simulated 6 workstations and 7 workstations within the model- line; each simulated for 140 hours (4 working weeks) Inputs needed to construct the model Milk Run Reorder points Bin sizes Component inter-arrival times Each scenario evaluated with Experimenter within WITNESS

  10. 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

  11. The Model The model will be running at our booth for those interested in seeing it work!

  12. 6 stations o Six Stations-Baseline a. Inter-arrival time: 0.185 hours b. Max bin capacity: 50 c. Reorder point: 25 oSix Stations-Experimenter

  13. 7 Stations Seven Stations a. Inter-arrival time: 0.159 hours b. Max bin capacity: 50 c. Reorder point: 25 Seven Stations-Experimenter

  14. 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

  15. Acknowledgments Philips Lighting-San Marcos Dr. Jesus Jimenez (Advisor) Sarah Chowdhury (Industry Advisor) Psyche Bryant (Industry Supervisor)

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