ERICSSON MEETS SMID Improve

ERICSSON MEETS SMIDImproveApril, 2011

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Ericsson @ SMID - Improve | Commercial in confidence | © Ericsson AB 2011 | 2011-04-04 | Page 2 (40)

Agenda› 4th April–Ericsson presentation–Statistical tools in manufacturing–DMAIC/IDDOV

› 7th April–Define–Measure

› 11th April–Analyze

› 14th April– Implement–Control


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DMAIC ChartC

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DefineUnderstand the task and its financial impact.

• Task selection matrix • SMART review • Stakeholder map• Risk Management

• SWOT analysis• Process map• VOC and break down to CTQs• 7MT• Affinity diagram

MeasureDevelop and execute an appropriate data collection method.

• Process map• Data collection table •Pareto diagram

• 7QCT• Measurement system analysis• Sampling technique• SIPOC• Gauge R&R, Gauge attribute• Capability analysis• Benchmark • Tagushi loss functions

AnalyzeFind the root causes.

• Fishbone diagram• Correlation analysis

• 7QCT• Hypothesis testing• Regression-analysis• DOE• Anova• 7MT• Data transformations• Simulations

ImproveGenerate and implement solutions.

• FMEA risk analysis• Process map

• Poka-Yoke• Hypothesis testing• Loss functions• Cost/Benefit selection•Pugh Concept Selection

ControlEnsure that the results will last.

• Documentation, standardization and training

• 7QCT• SPC• Business case verification

FMEA

FAILURE MODE AND EFFECTS ANALYSIS


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FMEA

Focus on observable behaviors


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FMEA

› Why?– “If it can go wrong - it will go wrong!”– “Prevention is better than cure”

› Where?–Can be applied to any process

› Who?–A team of people connected with the process

"A large safety factor does not necessarily translate into a reliable product. Instead, it often leads to an overdesigned product with reliability problems.” Failure Analysis Beats Murphy's LawMechanical Engineering , September 1993


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FMEA worksheet Microsoft Excel Worksheet


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FMEA severity (SEV) rating

› For each failure mode, decide on the impact on the product or operation when it occurs.

› Rate this impact in the column labeled SEV (severity).

› Establish your baseline for the analysis, i.e. SEV=10 means death of a human or machine failure.

› A SEV rating cannot change when improvement actions are put in place unless the design has changed.

Potential Failure Mode Potential Failure Effects

SEV

Reciever & Output data loss, track loss 3Output loss from pre-amp

Effect of failure mode

Potential Failure modes

Loss of signal from 2nd RF amp Loss of position, velocity & time. 5

Severity Rating

for the Failure Effect.


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FMEA occurrence (OCC) rating

› For each potential failure mode come up with one or more potential causes.

› Rate the probability of each potential cause occurring and place the rating in the column labeled OCC (occurrence).

Potential Failure Effects

SEV Potential Causes

OCC

C1 short 1 U21 function 10

Severity Rating for the Failure Effect.

Effect of the failure mode

Receiver & Output data loss, track loss

Loss of position, velocity & time

3

5

Potential cause of failure mode

Probability

of Occurrence


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FMEA detection (DET) rating

› For each potential cause, identify the current controls which are in place to prevent or detect the failure mode. Rate the ability of each current control to prevent or detect the failure mode once it occurs. Place the rating in the column labeled DET (detection).

Potential Causes

OCC Current Controls

DET

RPN

C1 short2

Test PR-20 & HW-52 32

U21 function 6None

6 288

Current Controls for each failure mode.

Ability to prevent or detect the failure mode.


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RPN calculation => action plan

› Multiply the SEV, OCC and DET ratings together and place the value in the RPN column. The largest RPN numbers should get the greatest focus. Any SEV which has a value 10 should have attention regardless of the OCC and DET values. For those RPN numbers which warrant corrective action, recommended actions and the person responsible for implementation should be listed.

SEV * OCC * DET = RPN

(3 *1*6 = 18)

SEV * OCC * DET = RPN

(3 *1*6 = 18)

PotentialFailure Effects

SEV

Potential Causes OCC

Current Controls DET

RPN

Actions

Recommended Resp.

Operator skill/training

Motor frame is unstable 3 1 6 18

Recommended actions and person responsible for implementation.

Operator Knowledge experience Ticket specifies - but coded

Coding chart to be issued & displayed by machine

DarrenWooler

Risk Priority Number

(RPN)


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Impact of corrective actions

› When making corrective actions use the available data to secure the best corrective action at the time.

› Correct SEV most probably needs a design change.

› Correct OCC may need a design and process change.

› Correct DET may need a design or process change.


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Action results

› After corrective action has been taken, place a brief summary of the results in the “Actions Taken” block. A new value should be assessed for the severity, occurrence and detection of the failure mode and root cause with the recommended action implemented. Place these values in the SEV, OCC and DET columns and calculate the new RPN.

Coding chart to be issued and displayed

Chart displayed &operator informed

Actions Recommended Resp. Actions Taken

SEV

OCC

DET

RPN

. machine all viking asno feet - add to works spec

1

5 1 4 20

33 9

DarrenWooler

Clarify effect of with feet & no feet measure to drg spec

Summary of actions completed.

New SEV, OCC and DET values.

New RPN

(risk priority number)


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Focus on the critical few high RPNs in your Pareto diagram and do changes in the operations to decrease SEVERITY or OCCURANCE alt. increase opportunity of DETECTION

Example of a Pareto diagram FMEA analysis

POKA-YOKE

MISTAKE PROOFING


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History

› Poka-yoke was invented by Shigeo Shingo in the 1960s. › The term "poka-yoke" comes from the Japanese words

"poka" (inadvertent mistake) and "yoke" (prevent). › The essential idea of poka-yoke is to design your process

so that mistakes are impossible or at least easily detected and corrected.


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Poka-yoke major categories

› A prevention device engineers the process so that it is impossible to make a mistake at all. Prevention devices remove the need to correct a mistake, since the user cannot make the mistake in the first place.

› A detection device signals the user when a mistake has been made, so that the user can quickly correct the problem. Detection devices typically warn the user of a problem, but they do not enforce the correction.


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What Causes Defects?

› Process Variation From–Poor procedures or standards–Machines–Non-conforming material–Worn tooling–Human Mistakes

› Except for human mistakes these conditions can be predicted and corrective action can be implemented to eliminate the cause of defects


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Ten types of human mistakes

› Forgetfulness (Not Concentrating)› Misunderstanding (Jump to Conclusions)› Wrong identification (View Incorrectly...Too Far Away)› Lack of experience› Willful (ignoring rules or procedure)› Inadvertent or sloppiness (Distraction, Fatigue)› Slowness (Delay in Judgment)› Lack of standardization (Written & Visual)› Surprise (unexpected machine operation, etc.)› Intentional (sabotage)


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Methods for using Poka-yoke

› Poka-yoke systems consist of three primary methods:–Contact–Counting–Motion-Sequence

› Each method can be used in a control system or a warning system.

› Each method uses a different process prevention approach for dealing with irregularities.

COST/BENEFIT ANALYSIS


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Costs and benefitsExample – screening of ASICs subject to failure risk related to process factor

Too many ASICs screened out

Too high

failure risk

0,00%

0,50%

1,00%

1,50%

2,00%

2,50%

260

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255

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252

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Process factor screening limit

Screened ASICs

Max failure risk with screen

Cost of reduced volumes Benefit of low failure fallout

Cost of high failure fallout Benefit of maintained volumes


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0,00%

0,50%

1,00%

1,50%

2,00%

2,50%

260

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255

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Process factor screening limit

Screened ASICs

Max failure risk with screen

Costs and benefits Finding the optimum

0

100 kSEK

200 kSEK

300 kSEK

400 kSEK

500 kSEK

$ cost of reduced volumes $ cost of failure fallout

$ total cost

SAMPLE SIZE


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Student Curve - “t” Distribution

MeanNormal Curve

t Curvesn

Smaller

n Bigger

t curves vary with sample size -they get wider and flatter than normal as sample size is reduced

In a normal curve 95% of sampling distribution is contained within 1.96 se

In a t distribution 95% is within 2.131 se and 2.776 se for sample size of 16 and 5 respectively

As sample size approaches n = 30, the t- distribution approaches the normal distribution


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Hypothesis Testing - Options and Errors

TruthTruth

DecisionDecision

Type IErrorrisk

Type IIErrorrisk

CorrectDecision

CorrectDecision

Ho

Ho Ha

Ha

T-test F-testH0: my = mx sy = sx

Ha: my mx sy sx

my < mx sy < sx

my > mx sy > sx

-risk: We reject a null hypothesis that is in fact true

-risk: We fail to reject a null hypothesis that is in fact false


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Power and Sample Size

› The Power of a test is the probability that it will allow you to reject Ho when Ho is wrong (Ha is really true). (power = 1 - )

› The following has a direct bearing on ”power” as do the following;– the alpha () level increases, the power increases– the variability of the population () increases, the power decreases – the difference (effect size) increases, the power increases– the sample size increases the power increases


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Front Panel Manufacturer

› A front panel manufacturer wants to detect significant changes in front panel lengths. They sample thousands of them because it is cheap and quick to do. But this huge sample makes the test too sensitive: the blue line shows it will sound the alarm if the average length differs by a trivial amount (0.05).This Power Curve shows they are wasting resources on excessive precision. A sample size of just 100 will detect meaningful differences (0.25) without "crying wolf" at every negligible blip.

› You also want the confidence in your results that's appropriate for your situation (testing seat belts demands a greater degree of certainty than testing shampoo). We measure this certainty with statistical power – the probability your test will detect an effect that truly exists


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Power and Sample size

› "We've always done it this way." That's why a PCB manufacturer would sample 10 units to test whether their strength meets the target.

› According to the Power Curve, this small sample size made their test incapable of detecting important effects. They must sample 34 PCBs to detect meaningful differences (0.50).


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Acceptance sampling


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Acceptance Sampling: how to reduce inspection costs

› Quality inspection department receives a shipment of 540 capacitors every week. You need to develop a sampling plan to make decisions regarding the lot without having to inspect all of the capacitors.

› Because some defects are inevitable, you and your supplier decide on quality levels and risks that allow some defects while maintaining profitability for both of you.


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Acceptance Sampling (2)

› You and the supplier in common agreement decide that the worst quality you are willing to accept on a regular basis is 2% defective (AQL) and the quality that you want to reject most of the time is 8% defective (RQL).


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Acceptance Sampling (3)

› It is important to notice that:

› The sampling plan that that has been suggested is a good starting point.

› Sometimes people involved in the sampling procedure want you to adjust the sample size and acceptance number. In cases like these we should try to generate multiple plans at the same time and compare OC Curves to find the best plan. The following scenarios might have to be considered:

–More convenient sample size: The inspectors find it most convenient to inspect 10 capacitors from each of the nine boxes in the shipment. They want you to change the sample size from 98 to 90. – Smaller sample size: Looking to save time, your supervisor suggests taking a much

smaller sample. He wants you to reduce the sample size from 98 to 50. – Larger acceptance number: Your supplier is nervous that his shipments will be

unfairly rejected. He wants you to raise the acceptance number and accept at least 10 defective capacitors before returning an entire lot.


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Acceptance Sampling (4)› The black line represents the original sample

plan with sample size of 98 (n) and acceptance number of 4 (c).The red line represents a relatively small departure from the original plan, showing a negligible reduction in the producer’s risk and a slight increase in the consumer’s risk. You are willing to change your sample size to a more convenient one to keep your inspectors happy.

› The green and blue lines represent more significant changes to the sampling plan which result in more risk than you are willing to accept.

› Show your supplier that the resulting consumer risk is much too high for you to consider raising your acceptance number to 10. Perhaps you will evaluate other acceptance numbers between 4 and 10


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ERICSSON MEETS SMID Improve