Gen-X: Manufacturing Analysis

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


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



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



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



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


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


Manufacturing Systems: EMP-5179

Module #6: Manufacturing Metrics

Dr. Ken AndrewsHigh Impact Facilitation

Fall 2010


EMP-5179: Module #6

Sigma, Variance, SPC etc. Revisited

Factory Physics

Balanced Scorecard


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)


Nu

mb

er o

f S

amp

les

Process Spread/Variability

Mean

Process variability is determined by US


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


Tolerance Limits


Variation in Process Output Due to Random Causes


Low Process Capability


High Process Capability


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


Process Capability

Cpk = min

USL – μ3σ

μ - LSL3σ

14 20 26 15 24

24 – 203(2)

= =.667

20 – 153(2)

= =.833


Cpk measures “Process Capability”

Good quality:defects are rare (Cpk>1)

μtarget


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.


EMP-5179: Module #6

Sigma, Variance, SPC etc. Revisited

Factory Physics

Balanced Scorecard


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


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


Production Throughput


“Decrease Inventories”

A factor of variability

Lower WIP = Less Throughput = Not Good


“Reduce Variability AND Inventories”

Reduced variability

Lower WIP + Reduced variability = Higher Throughput = Good


Self-Paced Study

Review and research the following material relating to:

SCV

Availability

Factory Physics

Confirm your understanding by following the examples provided.


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


Availability

Consider a workstation that operates an average of 70 hoursbefore it must be shut down for maintenance, lasting 10 hours.


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?


0.028




scv = squared coefficient of variationmr = mean time to repairA = availabilityt0 = original processing time



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


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


Cycle Time, Utilization & Variability

High Variability

Low Variability

20% 50% 100%

CycleTime

Capacity Utilization

ModerateVariability

Standard & Davis: “Running Today’s Factory”


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.


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


Preparation for Next Week

Watch for new articles/links on the website

Download material for module #7

Documents

Gen-X: Manufacturing Analysis