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White Belt Training Entry Level Quality and Continuous Improvement

Concepts and Practices

Introductions

• Name

• Company & Role

• Expectations from class

AGENDA

• Exits (emergency), Restrooms, Breaks

• What is LSS

• Quality – How is it defined and measured

• Metrics - Variation and Central Location (Averages)

• Six Sigma and DMAIC Methodology

• Lean Fundementals

• Team Dynamics

• Quality Tools

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LSS: Lean Six Sigma = Problem Solving

BETTER

FASTER

CHEAPER

Lean And 6 Sigma Toolboxes

What is LSS? • LSS = Lean Six Sigma

• Two Methodologies for continuous improvement with a variety of quality tools to help drive the processes

• Lean – Focus on efficiency and eliminating waste in the process • Derived from Toyota Production Methods

• Six Sigma – Focus on quality and eliminating defects • Started at Motorola, refined at GE

Both methodologies work together to drive process improvement & $UCCE$$

MAKING YOUR PROCESSES BETTER, FASTER & CHEAPER

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What is Quality?

• Two types of “quality”

Qualitative - “The works of DaVinci are beautiful.”

qualitative tends to be subjective

Quantitative – “My Maserati does 185”

quantitative is objective

• Posited for industry per quality gurus: • Joseph Juran - “Fitness for use.”

• Philip Crosby - “Conformance to requirements.”

Who determines the need?

• In all cases, the customer determines what quality “is.”

• Quality can have “value” as a consideration • While we usually “think” of “quality as the very best”, we tend to “live” with

“quality is the best at the level we can afford”

Critical To Quality

• CTQs are the internal critical quality parameters that relate to the wants and needs of the customer. They are not the same as CTCs (Critical to Customer), and the two are often confused.

• CTCs are what is important to the customer; CTQs are what’s important to the quality of the process or service to ensure the things that are important to the customer.

http://www.isixsigma.com/dictionary/critical-to-quality-ctq/

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Stated Another Way…

• Critical to Customer is an output • Consistent, even, shiny coat of metallic flake paint on their vehicle

• Critical to Quality is an input • The right color paint • Correct and consistent pressure in paint system

• Well functioning, clean paint spray nozzles

Does Quality = Inspection?

• This is a misperception.

• Inspection may be necessary in some instances; however: • Inspection does not make the product better – it only identifies bad product

to remove • Inspection is costly – it takes time and effort to inspect product

• If inspection is done by someone other than the person who made the product or provided the service, the focus is in the wrong place • People should have an interest in their work, and be proud of good work

• Inspection by another person can promote a punitive system

You can’t inspect quality Into the product

Systems that Rely on Inspection

• This style of quality management is called “Quality Control” • Usually there are many inspectors

• It becomes more difficult to fix problems or find them (because the inspection may occur long after the fact)

• Has a higher level of defects in the process

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Quality Assurance

• People are responsible for their own work

• Very few inspectors – quality helps and supports people doing their own inspections, and audits the process, not the part or service

• Greater sense of ownership and pride of work

• Problems found and resolved more quickly

Cost of Quality, Continued…

• Three areas of cost:

• Prevention costs (good design) • Appraisal costs (a quality system, tools to measure product or service)

• Failure costs (warrantees, replacement etc.)

Prevention costs the least Failure costs the most

Now, Cost Of Poor Quality

http://www.free-six-sigma.com/images/COPQ.png

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Taken Together…

http://www.isixsigma.com/implementation/financial-analysis/cost-quality-not-only-failure-costs/

So How Do we Improve Quality without raising cost?

• We need to know what to improve, first.

• You can’t know what to improve unless you measure it.

• In order to measure it, you have to establish metrics.

What is a Metric?

• A measurement of performance. It can be: • Product or service related (% non-conforming product, % dropped calls)

• Monetarily related (gross sales last quarter)

• Ability to meet performance standard (attainment to schedule, comparison to a standard)

• Some metrics track “real time” (what did we produce in the last hour)

• Some track to historical performance

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Basic Idea Behind Metrics

• “You can’t control what you don’t measure”

• If you don’t measure, you can’t compare performance: • To a known standard • To an expectation of performance

• To itself to see if it’s improving or worsening

Measurement of Data

• Two types of data • Attribute

• Variable

• Attribute data is generally measured as being present or absent (therefore is a count) – there are no degrees of attribute data. However: • This can be difficult if instead of a scale of absent or present, the cut-off is beyond a

certain point (which cannot be measured on a scale). For example, “poor finish” may be defined by representative sample beyond which is considered bad, less than is good.

Attribute Data, Cont’d

• Attribute data is less desirable than variable data, but we can still work with it • P charts (percentage of defectives, lot size varies)

• C charts (count of defectives when lot size constant) • Np Charts (number of defectives)

• U chart (number of defects per unit)

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Variable Data

Variable data

Continuous – can have an “infinite” number of points, even though we round to smaller intervals (1.36 seconds, pi, 3.337 miles)

So Do We Have To Measure Everything?

• No! and that’s a good thing • Costly

• Time consuming

• 100% inspection does not mean 100% accuracy

Instead, We Do Random Sampling

• The proportion of the population taken in the sample is usually representative • The key is that the sample most often must be random

• This gives good cross representation in regard to time, position, materials and operator/shift

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Cont’d

• With variable data, we don’t have to measure just the outputs

• We can measure the inputs that affect the outputs.

• For example, if we know that too high of an oven temperature causes shrink wrap to bubble, why monitor the shrink wrap alone and wait until it is bad? If we monitor the oven temperature, if it starts to climb we can cool it down before it passes the critical point that causes failure

• In a call center, we might measure the wait time that causes the caller to hang up, or the correlation

Variation

• In the examples we just discussed, we had values for height and drive time – none were exactly the same? There was variation in the values. There are two types of variation we are concerned with: • Common cause variation

• Special cause variation

Common Cause Variation

• In an environment where you are producing goods, common cause variation is typically related to machines, design and the process.

• An oven which has temperature variation, causing variation in heat treat outcome

• A drill press, which from wear has some issues holding concentricity due to wobble. Hole ranges from nominal to slightly oversized

• One operator uses high pre-heat on a cutting operation, another does not (standardization/operation issue). Result is slightly different dimensions on cut parts

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Common Cause in a Service Industry

• In an insurance industry, two adjusters are using two separate criteria for claims resulting in differences in payouts

• A more experienced CSR can look up the information more quickly, which gives a significantly shorter time on hold for customer

• Less proficient data entry clerks with higher levels of typing errors resulting in non-delivery to intended addressee (transposition of numbers)

Specially Assigned Cause

• Specially assigned cause is due to unusual circumstances. It’s generally harder to find simply because it doesn’t occur that often.

• An new operator, who is not aware of procedure, fills the fountain solution tank in a printing operation with city water, not from the RO valve. The printing press crew fights ink/water balance issues all night

• Purchasing picks a new supplier of steel based on pricing. The old supplier ran to high side of industry tolerance, new supplier runs low. Fit up in assembly is now off

• A bent drill bit which should have been discarded is chucked up in the drill. An oversized hole results

Specially Assigned Cause, Service Industry

• The computer is down, so CSR has to submit manual order tickets, making call times longer

• Dr.’s handwritten prescription is hard to read, wrong medicine administered (this is extremely serious, so had better be specially assigned cause and not a routine occurrence)

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Variation and Conformance to Specifications

• It would be impossible to eliminate all variation, but most of it can be controlled.

• So how much is an acceptable amount?

• Let’s consider this from the perspective of specification limits.

• A specification limit is the upper and lower tolerance that a customer will accept for a product or service.

• If you were buying 2” x 6” x 7’9” (precut) lumber for framing a house, you would probably have an expectation that the length be very close to 7’9”, and with good reason (cont’d)

Cont’d

• First, you want the lengths as uniform as possible so that the evenness allows for a flat, even sill. Too long or short and you will lose strength as well.

• Second, you want the length correct so that your 8’ tall sheet rock goes up without over or underlapping the frame.

• So, you might have an expectation of 7’9” plus or minus 1/16”. We’re going to say that since this is your expectation, the home do it yourself store is your advocate and they check incoming lumber for compliance.

Cont’d 7’ 8-15/16” 7’ 9-1/16”

7’ 9” nominal

Upper specification limit

lower specification limit

Purple bell curve represents variation

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Cont’d

• So in the previous slide, we can see that the variation is less than or within the specification limits. The “tails” theoretically extend to infinity, but the chance of occurrence is infinitesimally small much beyond the spec limits in this instance.

New Example

7’ 8-15/16” 7’ 9-1/16”

7’ 9” nominal

Upper specification limit

lower specification limit

Cont’d

• Now you can see that the process variation extends beyond the specification limits.

• This produces some fall-out in the tail sections which are non-conforming parts.

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Another Example

7’ 8-15/16” 7’ 9-1/16”

7’ 9” nominal

Upper specification limit

lower specification limit

What’s going on here?

• The process is not centered (running at nominal).

• As you can see, we are inducing defects in some of the parts from a lack of centering.

• This is a common mistake in many processes.

• Using the exact same graphics, check out the next slide

After Adjustment

7’ 8-15/16” 7’ 9-1/16”

7’ 9” nominal

Upper specification limit

lower specification limit

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Improving Processes

• So we basically make improvements two ways: • Shifting the mean (process average)

• Reducing the variation

• Our ability to conform to specifications is expressed as capability.

• We measure capability mathematically (beyond the scope of the course to instruct this). However: • A score of 1.0 is considered minimum process capability

• A score of 2.0 is a six sigma process

Six Sigma 6σ

What Does “Six Sigma” Mean?

• Sigma is “standard deviation” • Measure of dispersion

• Motorola developed concept of six sigma • If you can fit 6 “sigmas” between the mean and your spec limit, your defect rate is only

3.4 parts per million (ppm) There are some slides which show this graphically shortly…

42 I.A.

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Standard Deviation (sigma)

Notice that as the shape changes, standard deviation (width) changes with it. The value of 1 standard deviation is still 34.1% of the data.

Six Sigma

There are six “sigmas” From the nominal (centerline) to the

Spec limit!

Value of Six Sigma

• Cost reductions

• Defect reductions

• Cycle time reductions

• Increased market share

• Customer confidence

45 I.A.

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DMAIC

• So we understand a little about variation, and the concept of six sigma. How do we achieve these results?

• Six Sigma relies on a project management technique known as DMAIC.

• Each of the letters in this acronym stand for a particular phase of the program: • Define • Measure • Analyze • Improve • Control

DMAIC

• Define - Select the outputs to be improved and therefore measured (known as Y’s)

• Measure - data gathering to measure the response variable

• Analyze - identify the significant factors which control the outputs

• Improve - eliminate or reduce the source of variation

• Control - the plan to prevent slipping back into former modes of operation

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One of the First Steps

• Map your process.

• Helps you understand it

• Is particularly revealing in the flow when something doesn’t go as planned (alternate flow)

• Makes it easier to spot waste in the process, opportunities for mistakes and other of the 7 wastes (we’ll discuss later)

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Process Mapping (flowchart)

• Should be fairly detailed – straight “box to box” map is not detailed enough

• Needs to show what happens when the normal process doesn’t occur – that’s were we tend to find underlying problems and waste • Exceptions

• Non-conforming items

• Show decision diamonds – when does someone working in the process have to make a determination?

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User’s computer is not working

User places call to help desk

User goes through menu options

User describes symptoms

Technician has user do simple tests

(reboot, etc)

Problem solved?

Technician writes ticket w/ urgent

Roving technician goes to printer

Urgent? Technician writes ticket w/ routine

User returns to work

Grabs priority

Visits user

Fix?

Needs supply?

Goes back to IT to retrieve supplies or

cart to take old computer

Wait time

average 5 minutes Muda - waiting

yes

no

yes

no

Muda travel Back from last

assignment

Muda over-processing. Was

prioritized before going to printer

Muda travel

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Tech writes follow up ticket