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Job Shop Optimization. December 8, 2005 Dave Singletary Mark Ronski. Introduction. Problem Statement. Open Ended Optimize a job shop Utilize Pro Model software to optimize Cost Model SimRuner Module. Problem Statement (Cont.). Optimized Model For… Delivery Schedule Q Size Takt Time

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job shop optimization

Job Shop Optimization

December 8, 2005

Dave Singletary

Mark Ronski

problem statement
Problem Statement
  • Open Ended
    • Optimize a job shop
  • Utilize Pro Model software to optimize
    • Cost Model
    • SimRuner Module
problem statement cont
Problem Statement (Cont.)
  • Optimized Model For…
    • Delivery Schedule
    • Q Size
    • Takt Time
    • Number of Workers
outline
Outline
  • Overview Pro Model
  • Job Shop Model
  • Optimization Terms
  • Results
pro model
Pro Model
  • Process optimization and decision support software model
    • Serving:
      • Pharmaceutical
      • Healthcare
      • Manufacturing industries.
    • Helps companies:
      • Maximize throughput
      • Decrease cycle time
      • Increase productivity
      • Manage costs.
pro model cont
Pro Model Cont…
  • Pro Model technology enables users to:
    • Visualize
    • Analyze
    • Optimize
  • Helps make better decisions and realized performance and process optimization objectives.
what pro model is
What Pro Model Is…
  • Create 3-D Simulation of Shop Space
    • Machines X-Y Coordinates
    • Time
  • Alter Machine, Worker, and Cost Parameters to Simulate Outcome
  • Tools to Optimize Shop Model
default shop layout

MILL

Cap.: 1

TURN

Cap.: 1

2 ft

2 ft

DRILL

Cap.: 1

MILL Q

Cap.: 90

TURN Q

Cap.: 20

0 ft

0 ft

GRIND

Cap.: 1

RECEIVING

Cap.: 150

OUTPUT

GRIND Q

Cap.: 20

5 ft

15 ft

15 ft

15 ft

2 ft

5 ft

10 ft

DEBURR Q

Cap.: 80

0 ft

DEBURR

Cap.1

Key

Cap. = Maximum Capacity

Default Shop Layout
parts to be manufactured
Parts to Be Manufactured
  • 3 Parts to be Manufactured
  • 5 Machining Processes
  • 4 Process Per Part
machining processes

DEBURR

2 min

RECEIVE

DRILL

7 min

MILL

3.66 min

DEBURR

2 min

OUTPUT

GRIND

5.4 min

Machining Processes

Part N101

machining process cont

RECEIVE

DRILL

3.6 min

TURN

4 min

DEBURR

5 min

OUTPUT

GRIND

2.6 min

Machining Process (Cont.)

Part N201

DEBURR

7 min

machining process cont16

DEBURR

2 min

RECEIVE

MILL

3.8 min

TURN

4 min

DEBURR

5 min

OUTPUT

GRIND

1.2 min

Machining Process (Cont.)

Part N301

process variability
Process Variability
  • Default Job Shop Model
    • Constant Setup Time
    • Constant Machining Time
    • No Machine Failure
  • Introduce Variability to Mimic Actual Conditions
process variability cont
Process Variability (Cont.)
  • Normally Distributed…
    • Setup Time
    • Machining Time
    • Machine Failure
  • Average Time = Default Value
  • Standard Deviation = ¼ Average Time
normal distribution
Normal Distribution
  • In a normal distribution:
    • 50% of samples fall between ±0.75 SD
    • 68.27% of samples fall between ±1 SD
    • 95.45% of samples fall between ±2 SD
    • 99.73% of samples fall between ±3 SD

Xbar = Mean

slide21
COST

Machine Cost and Life

slide22
COST

Man Power Cost and Initial Part Cost

slide23
COST

Tool Cost, Tool Life, and Hours Down to Change Part

workers
Workers
  • Speed 120 feet per minute
    • With or Without Carrying a Part
  • Pick Up or Place Object in 2 seconds
  • Logic
    • Stay at Machine Until Q is Empty
    • Go to Closest Unoccupied Machine
    • Go to Break Area When Idol
takt time
Takt Time
  • Takt Time = ratio of available time per period to customer demand.
  • Longest operation must not exceed Takt time.
  • If Takt time exceeded customer demand is not met.
kanban capacity
Kanban Capacity
  • Kanban = Maximum number of parts allowed between stations
    • Size of Deburr Q, Mill Q, Drill Q
  • When Q is full machine prior to Q must shut down
  • Pull manufacturing controlled by Kanban
    • Open slot in the Q causes the previous machine to make a part.
kanban capacity cont
Kanban Capacity (Cont.)
  • Each part in Q has value added
    • Parts in Q are not earning the company money
  • Increase in Kanban capacity increases production rate.
    • Upper limit exists
just in time jit production
Just In Time (JIT) Production
  • Receive supplies just in time to be used.
  • Produce parts just in time to be made into subassemblies.
  • Produce subassemblies just in time to be assembled into finished products.
  • Produce and deliver finished products just in time to be sold.
takt time optimization
Takt Time Optimization
  • Slowest process must be faster than required Takt time.
  • Checked if job shop can meet demand of 229 parts per week.
  • Determines if…
    • More Machines Required
    • Faster Machines Required
takt time calculations
Takt Time for job shop

Longest Operation = 7 minutes

Drill N101 and Deburr N201

Conclusions:

Current machine process times less than Takt time

Margin provided for variability and failure.

Takt Time Calculations
kanban capacity optimization
Kanban Capacity Optimization
  • Default Simulation
    • Run to Detect Inadequate Kanban Capacity
  • Optimized Simulation
    • Smallest Allowable Kanban Capacity Resulted in Q 0% Full Over 1 Month of Production
    • Run for Default Receiving Delivery Schedule
delivery schedule optimization
Delivery Schedule Optimization
  • Delivery Schedule
    • The Timed Arrival of Raw Material to Receiving.
  • Default Simulation
    • Run to Determine the Effect of Delivery Schedule on Production
default production rate
Default Production Rate

Waiting For Parts to Arrive

158 Hours to Make All Parts

delivery schedule optimization38
Delivery Schedule Optimization
  • Optimized Simulation
    • Delivery Schedule Altered to Simulate Just in Time Production
    • All Parts for 4 Weeks Received at Start of Week
optimized production rate
Optimized Production Rate

No Breaks in Production Due to No Parts in Receiving

136 Hours to Make All Parts

delivery schedule conclusions
Delivery Schedule Conclusions
  • Option 1: 3 Full Time Employees Not Required for Part Demand
    • Cost Savings
  • Option 2: Increase Production
    • Only if Market Demand Will Meet Increased Production
resource optimization for max production
Resource Optimization for Max Production
  • Default Model Setup
    • 3 Workers
  • Optimized Model
    • Maximize Production
    • Minimize Worker Down Time
      • Get Maximum Value Out of Workers
      • During Worker Down Time No Value Added
resource optimization model
Resource Optimization Model
  • Pro Model Sim Runner
    • Optimizes Macro
    • Varies Number of Workers 1:10
    • Maximizes Weighted Optimization Function F
      • A and B are Weighting Constants
      • N101, N201, N301is Average Time in System for Each Part
      • Pworkers = Percent Utilization of Workers (%)
resource optimization model cont
Resource Optimization Model (Cont.)
  • Values of Constants
    • A = Ave. Time in Sys. Constant
      • Set Equal to 1
    • B = Percent Utilization of Workers Const.
      • Equal in Importance to Ave. Time in Sys.
    • Calculating B Through Default Values
resource optimization results
Resource Optimization Results
  • Sim Runner Calculated 3 Workers to Optimize Job Shop
    • Current Default Value
    • Important Result
  • Increasing Workers Will Increase Production But Decrease Return on Worker Cost
  • Must Buy New Machines to Stay Optimized and Increase Production
job shop optimization46
Job Shop Optimization
  • Optimize for Currant Demand
    • Alter Q Size
      • Increase Deburr and Mill, Decrease Turning and Grinding
      • Remove Bottle Necks
      • Decrease Lost Profits Due to Parts Sitting in System
    • Switch to Just In Time Production
      • Decrease Shop Downtime Due to Waiting for Parts
job shop optimization cont
Job Shop Optimization (Cont.)
  • Optimize for Increased Demand
    • Purchase New Machines
      • Increase Production Not at the Expense of Worker Utilization
    • Switch to Just In Time Production
      • Decrease Shop Downtime Due to Waiting for Parts
    • Revaluate Takt Time
      • Ensure Demand Will Be Met
pro model recommendation
Pro Model Recommendation
  • Sim Runner Difficult to Use
    • Non Robust Optimization Technique
    • Difficult to Compare Parameters that have Different Units
  • Good At Modeling Shop Layout and Work Flow
    • Easy to Find Bottle Necks
references
References
  • Schroer, Bernard J. Simulation as a Tool in Understanding the Concepts of Lean Manufacturing. University of Alabama: Huntsville.
  • Gershwin, Stanley B. Manufacturing Systems Engineering. Prentice Hall: New Jersey, 1941.
  • Kalpakjian, S. and Schmid, R. Manufacturing Engineering and Technology. Fourth Edition, Prentice Hall: New Jersey, 2001.