kenneth j. andrews emp-5179-6-1 gen-x: manufacturing analysis what is the process?build & test...
TRANSCRIPT
Kenneth J. Andrews EMP-5179-6-1
Gen-X: Manufacturing Analysis
What is the process? Build & test of AXIS machine for a specific Customer
Who is the customer? MegaPower - product quality
- install time- on-time
delivery- ship what
ordered- good
training
Installation - complete shipment
- documentation
- tested, working
- acceptance test OK
- early notification
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Gen-X: Manufacturing Analysis – Flowchart (1)
1. Order is logged in2. Scheduled by the Manufacturing Manager (remote board)3. Order sent to Manufacturing Engineer4. Wait for drawings – always 5 days late5. Initiate system build (before designs arrive)6. Designs are checked, mistakes noted – no direct feedback7. Problems with designs – try to reach designer WAIT8. Mfg. Engineer modifies the designs (inventory-driven)9. Supervisor takes the new designs10.Systems are re-worked to account for actual designs11.Parts are requested from Stores WAIT12.Problems during build Mfg. Eng Mfg. Mgr Eng. Mgr
13.System hardware completed14.System moved to Test
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Gen-X: Manufacturing Analysis – Flowchart (2)
15.Chase software from Design WAIT16.Software arrives (late)17.Hardware functional check – problems fixed – no
feedback18.Software check – patches for bugs – documentation?19.No time for Acceptance Test20.System moved to shipping dock21.Install Coordinator advised about imminent ship
Kenneth J. Andrews EMP-5179-6-4
Gen-X: Manufacturing Analysis – Flowchart (1)
1. Order is logged in2. Scheduled by the Manufacturing Manager (remote board)3. Order sent to Manufacturing Engineer4. Wait for drawings – always 5 days late5. Initiate system build (before designs arrive)6. Designs are checked, mistakes noted – no direct feedback7. Problems with designs – try to reach designer WAIT8. Mfg. Engineer modifies the designs (inventory-driven)9. Supervisor takes the new designs10.Systems are re-worked to account for actual designs11.Parts are requested from Stores WAIT12.Problems during build Mfg. Eng Mfg. Mgr Eng. Mgr
13.System hardware completed14.System moved to Test
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Gen-X: Manufacturing Analysis – Flowchart (2)
15.Chase software from Design WAIT16.Software arrives (late)17.Hardware functional check – problems fixed – no
feedback18.Software check – patches for bugs – documentation?19.No time for Acceptance Test20.System moved to shipping dock21.Install Coordinator advised about imminent ship
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Process Improvement
What process?
Customer +requirements
Map currentprocess
Identifyhot-spots
Root-causeanalysis
Improvements toa) fix root causes b) meet C requirements
Metrics (1-3 months)
Communicate plan
Implement,measure,fine-tune
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Manufacturing Systems: EMP-5179
Module #6: Manufacturing Metrics
Dr. Ken AndrewsHigh Impact Facilitation
Fall 2010
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EMP-5179: Module #6
Sigma, Variance, SPC etc. Revisited
Factory Physics
Balanced Scorecard
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Variability
The world tends to be bell-shaped
Most outcomes
occur in the middle
Fewer in the “tails”
(lower)
Fewer in the “tails” (upper)
Even very rare outcomes are
possible(probability > 0)
Even very rare outcomes are
possible(probability > 0)
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Nu
mb
er o
f S
amp
les
Process Spread/Variability
Mean
Process variability is determined by US
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Nu
mb
er o
f S
amp
les
Specification Tolerance
MeanUpper
Specification Limit (USL)
Lower Specification
Limit (LSL)
Specification tolerance is defined by the Customer
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We can be much more specific about process capability by measuring the process variability and comparing it directly to the required tolerance. Common measures are called Process
Capability Indices (PCIs)
6LSLUSL
C p
3
),min( LSLUSLC pk
μ= meanσ= std. deviationUSL= Upper Spec. LimitLSL= Lower Spec. Limit
Process Capability Indices
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Process Capability
Cpk = min
USL – μ3σ
μ - LSL3σ
14 20 26 15 24
24 – 203(2)
= =.667
20 – 153(2)
= =.833
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Cpk measures “Process Capability”
Good quality:defects are rare (Cpk>1)
μtarget
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Cpk measures “Process Capability”
Poor quality: defects are common (Cpk<1)
μtarget
If process limits and control limits
are at the same location, Cpk = 1
Cpk ≥ 2 is exceptional.
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EMP-5179: Module #6
Sigma, Variance, SPC etc. Revisited
Factory Physics
Balanced Scorecard
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Factory Dynamics: Batch ProductionConsider a simple 4-station production line, where the
processing time at each station is exactly 1 minute
Batch Size(WIP)
Cycle Time(minutes)
Throughput(pieces/minute)
Throughput(pieces/hour)
10 40 0.25 15
9 36 0.25 15
8 32 0.25 15
7 28 0.25 15
6 24 0.25 15
5 20 0.25 15
4 16 0.25 15
3 12 0.25 15
2 8 0.25 15
1 4 0.25 15
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Factory Dynamics: Single-Piece FlowConsider a simple 4-station production line, where the
processing time at each station is exactly 1 minute
Batch Size(WIP)
Cycle Time(minutes)
Throughput(pieces/minute)
Throughput(pieces/hour)
1 4 0.25 15
2 4 0.50 30
3 4 0.75 45
4 4 1.00 60
5 5 1.00 60
6 6 1.00 60
7 7 1.00 60
8 8 1.00 60
9 9 1.00 60
10 10 1.00 60
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“Decrease Inventories”
A factor of variability
Lower WIP = Less Throughput = Not Good
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“Reduce Variability AND Inventories”
Reduced variability
Lower WIP + Reduced variability = Higher Throughput = Good
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Self-Paced Study
Review and research the following material relating to:
SCV
Availability
Factory Physics
Confirm your understanding by following the examples provided.
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Objective Measure of Variability
For example, an assembly operation with an average process timeof 20 minutes and a standard deviation of 1 minute:
scv = (1/20) 2 = 0.0025
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Availability
Consider a workstation that operates an average of 70 hoursbefore it must be shut down for maintenance, lasting 10 hours.
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Optimal Maintenance Intervals?
Infrequent maintenance:70 hours on, 10 hours off
Frequent maintenance:3.5 hours on, 0.5 hours off
What about variability? Isn’t that important too?
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Optimal Maintenance Intervals?
scv = squared coefficient of variationmr = mean time to repairA = availabilityt0 = original processing time
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Optimal Maintenance Intervals?
Infrequent maintenance: 70 hours on, 10 hours off
Frequent maintenance: 3.5 hours on, 0.5 hours off
For the same equipment availability,shorter repair times lead to lower variability
i.e. they are better
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Utilization: High or Low?
One way to improve Return on Investment (ROI) is to maximize the revenue generated by utilizing production resources to the fullest extent possible = high capacity utilization.
Is a 24/7/52 factory a good strategy?
It depends on whether you are striving for shorter cycle times
It also depends on whether you are living in a:
deterministic (ideal) world = very low variability
stochastic (real) world = moderate/high variability
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Cycle Time, Utilization & Variability
High Variability
Low Variability
20% 50% 100%
CycleTime
Capacity Utilization
ModerateVariability
Standard & Davis: “Running Today’s Factory”
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Causes of Variability Equipment downtime
Excessive set-up time
Uneven production demand
Batch material movement
Non-standard processes
Human factors
Supplier problems
Unexpected outages (e.g. power)
1. Reduce variability wherever possible throughout the production process.2. Do not strive for 100% capacity utilization.
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Balanced Scorecard Perspectives
Learning and Growth
Are we able to sustaininnovation, change, and
continuous improvement?
Internal Business Process
How well do we perform at keyinternal business processes?
Customer
How well do we satisfy ourinternal and externalcustomer’s needs?
Financial
How do we look to ourstakeholders?
Primary Driver of Performance Secondary Influence on Performance