six-sigma class 1 - 12
TRANSCRIPT
SIX-SIGMA
For Operations
What is Quality?
• Deming: Quality is defined from the customer’s point of view as anything that enhances their satisfaction.
• Juran: Fitness for use. Those product features which meet the needs of customers & thereby provide product satisfaction. Freedom from deficiencies.
• ASQC: The totality of features & characteristics of a product or service that bear on its ability to satisfy stated or implied needs.
• COPC: Quality is defined as knowledge of agents that would enable them to provide accurate & consistent solutions to the customer at the very first attempt.
• ISO: Degree to which a set of inherent characteristics of a product or service, fulfil requirements.
Quality Defined
• Simply stated, Quality comes from meeting customer expectations and occurs as a result of four activities:
- Understanding customer requirements- Designing products & services that satisfy those
customer requirements- Developing processes that are capable of producing
those products & services- Controlling and managing those processes so that they
consistently deliver to their capabilities
Quality Defined
Quality is all about
doing it right the
first time!!!!
Quality Evolution
Quality Traditional View Quality Six-Sigma View“ Errors are inevitable” “ Errors can be eliminated”
Focus on cost Focus on customer requirements
Quality not important Increased awareness of Quality
Culture of Radical change Culture of Continuous Improvement
Little analysis performed Data driven analysis
Cut middle management Includes suppliers
Sporadic labor productivity Continuous productivity
BRAND EROSION! BRAND ENHANCEMENT!!
Cost improvements to Quality improvements to
Result in Net Income result in Market Share
A paradigm shift!!!!
Quality EvolutionT.Q.M. B.P.R. Six-Sigma
Business DriversGlobalisation &
Product Quality
Recession & changing markets Internet
Goals Defect Reduction Streamlining Process Alignment
Tools SPC Chart Process Map DOE/Simulation
Method
Add value to existing processes
Challenge Process Fundamentals
Prioritize by COPQ & Capability
Deployment Bottom-up Top-DownTop, Middle &
Bottom
Key Feature Quality Circles Outside Consultants Internal Experts
Impact Long TermShort & Medium
termShort, Medium &
Long term
Role of Technology Support Enabler Enabler
Risk / Return Low/Low High/Low Medium / High
Continuous Improvement Radical Redesign Align & Maintain
Quality Mindset• Quality focuses on achieving “Operational Excellence”
- Our survival is dependent upon continued growth of the business- Our business growth is largely determined by customer satisfaction- Customer satisfaction is governed by quality, price & delivery- Quality, price & delivery are controlled by process capability- Our process capability is greatly limited by the process variation- Process variation leads to increased defects, costs & cycle times- To eliminate variation, we must apply the right knowledge at work- In order to apply the right knowledge we must first acquire it- To acquire new knowledge means willingness to get trained!
Growth, Customer satisfaction, quality, knowledge and training….all tied together!!!!
Quality Mindset
Excellence is not an act but a habit!
- Aristotle
Six-Sigma History
• Motorola (1987)
• Texas Instruments (1988)
• Digital Equipment (1989)
• IBM Computers (1990)
• Asea Brown Boveri (1993)
• AlliedSignal & Kodak (1994)
• General Electric (1996)
Six-Sigma Results
Profile of an Average Company:• Profitable & growing• Market prices declining• Has a quality assurance program• Spending over 25-35% of sales dollars on correcting, or reworking
product before it ships• Unaware that best in class companies have similar processes that
are greater than 100X more defect free• Believes that a zero-defects goal is neither realistic nor achievable• Has 10X the number of suppliers required to run the business• 5-10% of the firms customers are dissatisfied with product, sales or
service and will not recommend that others purchase products or services
• Competitors are increasing!!
Businesses operate roughly around 3 σ
Six-Sigma Results
• Motorola: Over $16 bn savings and won MBNQA’89
• General Electric: Over $10-15 bn savings!
• AlliedSignal: Over $2.2 bn savings!
Six-Sigma Defined
What is Six-Sigma….
Six-Sigma Defined
“ Past definitions of quality focused on conformance to standards, as companies strived to create products and services that fell within certain specification limits…”
- Mikel Harry & Richard Schroeder
Six-Sigma Defined
“…this Six-Sigma journey will change the paradigm from fixing products so that they are perfect to fixing processes so that they produce nothing but perfection, or close to it”
- Jack Welch
Six-Sigma Defined
VisionMetricPhilosophyMethodologyToolCultureValueSymbolDefinitionGoal
Six
Sigma
Six-Sigma Defined
• Six-Sigma as a symbol – “Brand Enhancement”
6 σ
Measure of Capability – the values Measure of Variation - The
to σ represent the symbol σ represents the
performance and consistency standard deviation of any data
level of any process population
The symbol σ is also the 18th letter in the Greek Alphabet!!!
Six-Sigma Defined
• Six-Sigma as a Philosophy – “Measurements”
Measure
Everything
That
Results
In
Customer
Satisfaction
Six-Sigma Defined
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6
Sigma Value
Imp
rove
men
t F
acto
r
Sigma PPM Factor
1 690000 -
2 308500 2.24
3 66800 4.62
4 6210 10.76
5 233 26.65
6 3.4 68.53
6σ is not 3 times but 90735 times better than 2σ !!!
Six-Sigma Defined
Is 99.00% in
our daily lives
good enough?
Six-Sigma Defined
• Six-Sigma @ Daily Life!
Common ActivityDefects @ 99.0%
(3.80 Sigma)Defects @ 99.9997%
(6.00 Sigma)
Mail Delivery20,000 lost articles of mail
per hour7 lost articles of mail per
hour
Drinking WaterUnsafe drinking water for
15 mins per dayUnsafe drinking water for 2
mins per year
Hospital Surgery5000 incorrect surgery
procedures per week2 incorrect surgery
procedures per week
Air Travel
2 abnormal landings at most airports each day
1 abnormal landing every 5 years
Sometimes 99% is just not good enough!!!
Six-Sigma Defined
• Six-Sigma @ Daily Life!
Defects @ 99.9997%(6.00 Sigma)
500 incorrect surgery procedures per week
22,000 cheques deducted from wrong bank accounts each hour
50 newborn babies dropped at birth by doctors each day
32,000 missed heartbeats per person, per year
One hour of unsafe drinking water every month
Even 99.9% is just not good enough!!!
Six-Sigma Defined
“ Consistently delighting customers with increased Quality & decreased costs through people and technology”
Six-Sigma Defined
• Six Sigma as a Culture – “Metrics”
- We don’t know what we don’t know- If we cant express what we know in the form of numbers,
we really don’t know much about it- If we don’t know much about it, we can’t control it- If we can’t control it, we are at the mercy of chance!
Measure everything that results in customer satisfaction as measurements get attention!!!
Six-Sigma Defined
• Six Sigma as a Culture – “Metrics”
Focus
On
Customer
Understanding &
Satisfaction
Variation may be the spice of life…but for a customer, consistency is the king!!!
Six-Sigma Defined
• Six Sigma as a Methodology
Always look for the low hanging fruits!!
Six-Sigma Defined
A journey, not a destination!
Attitude & Discipline
Attitude & Discipline
Attitude & DisciplineThe winning combination for a Quality
centric Business!
Attitude & Discipline
• Behaviour of any individual is the way in which he responds to situations in his day to day life and is a function of values imbibed over a period of time
• Values being a complex of beliefs, ideals or standards, which characterizes any individual in the society.
Our values drive the behaviour that we demonstrate!!!
B = f (V)
Attitude & Discipline
• Behaviours & Values
Behaviors
Attitudes
Beliefs
Values
Our environment drives everything above
Attitude & Discipline
• Attitude is a complex mental orientation involving values, beliefs, feelings and dispositions to act in certain ways at any given point in time
• Discipline is to get accustomed to a regular and systematic action in order to act together under orders and form a habit of continued obedience
Attitude & Discipline
• Attitude in Quality is all about being focused on the customer. We need to view Quality externally from the customer’s perspective. Customers have all the votes concerning the extent of satisfaction and value!
Attitude & Discipline
• Discipline in Quality deals with the process. It’s about attending to every detail methodically, with no room for error. By meeting challenges everyday, head-on, we strive for continuous improvements in solving whatever issues may arise and close-looping continuous improvements in a systematic, scientific and fact based manner.
Attitude & Discipline
• Attitude & Discipline in Quality is all about being customer centric and meeting customer expectations every time with a process focus and through a continuous improvement cycle which is:
Systematic
Scientific
Fact-Based
Data-driven
The right attitude & discipline are called for on both the management and associate levels!!!
Attitude & Discipline
“ A positive attitude is contagious and like a fire, unless you continue to add fuel, it goes out and stops spreading!”
- Author unknown
Business Statistics
Statistical Thinking
Business Statistics
Measurement tools and statistical analysis necessary to drive a metrics culture!
Statistics• Data, Information and Statistics
- Data is a set of numbers which do not convey any logical meaning
- Information is a set of numbers which when represented correctly conveys meaning and helps us take logical decisions
- Statistics is the science that compiles, analyses and interprets numerical data. Statistics has two branches:
Descriptive Statistics : This branch of statistics organises, summarises and describes the data we want to study (mean, median, mode, range, variance, standard deviation, etc)
Inferential Statistics : This branch of statistics generalises to a whole population the results obtained from a given sample (sampling, confidence and precision levels, hypothesis testing, etc)
Statistics
• Data, Variables and Observations Data as defined by the Dictionary is facts, figures or
values from which a logical conclusion may be drawn as per requirement.
Variable is any quantity that varies; i.e. an aspect or characteristic of a person, object or situation that can assume different values
Observation is any data measurement value for a defined variable; for eg. the scores for boys and girls in a history exam:
Boys 56 67 46 58 57 65 52 67 78 53 45 69 51 52 77 66
Girls 47 43 78 64 49 46 55 70 61 56 63 62 59 51 55 58
Statistics• Observations and Variation
When observations, i.e. measurements for a variable, are repeated for a variable we usually end up getting different answers…which is nothing but variation in the measured data. This variation can be classified into two possible categories:
Common cause variation: Differences in the measurements which are expected and predictable; also known as white noise
Special cause variation: Differences in the measurements which are NOT expected and NOT predictable; also known as black noise
Measurement variation is natural, expected and is the foundation of statistics!!
Statistics
• Data Classification
Data Types
Numeric Attribute
Discrete Continuous Binary Non-Binary
Statistics
• Data Classification – Numeric Data
- Discrete: The data is counted. These counts can be in the form of event occurences (Poisson, e.g. the number of errors in the form, number of calls in the queue, etc.) or can be count-based proportions (binomial, e.g. percentage pass/fail, etc.; pass/fail being binary data). Therefore discrete data can have count-based values and/or count-based proportions as a possibility and involves counting.
- Continuous: The data is measured. For e.g. the measurement of length & breadth of a table in the classroom. This type of measurement can result in an output like 5 feet, or 4 feet 5 inches, etc. Therefore continuous data can have any decimal (decimal sub-division are meaningful) / non-decimal measurement values as a possibility and involves measurement
Statistics• Data Classification – Attribute Data
- Binary: A set of data arising out of a logical decisioning which is either of two possible outcomes
For eg. “…is the door of the classroom open?” (Y/N). Therefore binary data output have only two finite options
- Non-Binary: A set of data arising out of a logical decisioning which is either of multiple possible outcomes (these outcomes can either be ordered or random. Ordered non-binary is called as ordinal data while random non-binary is called as nominal data)
For e.g. “…what is the color of the wall in the classroom? (white, brown, green, ….etc, is nominal; while light, medium, dark is ordinal)”. Therefore non-binary data output can have infinite options.
Statistics• Data Classification
Summary: Data Types
Numeric Attribute
Discrete Continuous Binary Non-Binary
Can take a count based value or a derived proportion value (3, 5, 78, 34, 10 out of 100….)
Eg: Number of people, percentage present, etc.
Can take any numerical value (1, 3.2, 5, 7.35, 9, 15)
Eg: Height, length, temperature, etc.
Can take only two values
Eg:Yes / NoBoy / GirlDay / Night
Can take more than two values
Eg: Colors – Nominal: red, green, blue,…Color intensity – Ordinal: strong, light moderate
Statistics
• Data Representation
Numeric Attribute
Histogram Bar Diagram
Time Plot Pie Chart
Statistics
• Data Representation – Histogram
A Histogram is a graphical representation of numerical data. It is constructed by placing the class intervals on the horizontal axis of a graph and the frequencies on the vertical axis
Age Interval Frequency
21-25 13
26-30 10
31-35 5
36-40 7
02468
101214
21-25 26-30 31-35 36-40
Empl
oyee
s
Statistics
• Data Representation – Time Plot
A time plot is a graphical representation of numerical data. A time plot is a two dimensional graph used to detect trends in time.
Month Tickets
Jan 32
Feb 35
Mar 28
Apr 36
May 33
Jun 40
Tickets Issued
40
28
36
33
3532
01020304050
Jan Feb Mar Apr May Jun
Tick
ets
Statistics
• Data Representation – Bar Diagram
A Bar diagram is a graphical representation of attribute data. It is constructed by placing the attribute values on the horizontal axis of a graph and the counts on the vertical axis.
Social Status No. Of People
High 9
Medium 26
Low 15
9
26
15
0
5
10
15
20
25
30
High Medium Low
Peop
le
No. Of People
18%
52%
30%
High
Medium
Low
Statistics
• Data Representation – Pie Chart
A pie chart is a graphical representation of attribute data. The “pieces” represent proportions of count categories in the overall situation. Pie charts show the relationship among quantities by dividing the whole pie (100%) into wedges or smaller percentages
Social Status No. Of People
High 9
Medium 26
Low 15
Statistics
• Population Attributes
LOCATION
S P R E A D
SHAPE
CONSISTENCY
Statistics
• Population Attributes
Four important process output characteristics that need to be determined to fully profile any process performance, are:
- Location: where the data tends to concentrate or cluster?
- Spread: How much variation exists around the clustering- Shape: What is the frequency pattern in the clustered
data? - Consistency: Is the current snapshot useful for future
decisions?
Statistics
• Descriptive Statistics – Measures of Location & Spread
Measures of Central Tendency
Mean
Mode
Quartiles
Median
Measures of Dispersion
Variance
Range
Span
Standard Deviation
Stability Factor
Statistics
• Measures of Location – Mean
It is the arithmetic average computed by summing all the values in the dataset and dividing the sum by the number of data values
For a finite dataset with measurement values X1, X2, ….., XN (a set of N numbers), it is defined by the formula:
The sample mean is represented by
The population mean is represented by Greek Letter μ
Statistics
• Measures of Location – Median
- If we put all data in rank order (low to high) then the median is the ordered value located at n/2, i.e. in the middle.
- If there are odd number of observations, then median is the (n+1)/2th ordered value
- If there are even number of observations then median is the average of the two middle values i.e. the n/2 and the (n/2)+1 ordered value
Statistics
• Measures of Location – Mode
- It is defined as the most frequently occuring value in the dataset- In a histogram, it is the corresponding value to the highest bar- The mode may not exist; and if it does exist, it may not be unique
Statistics
• Measures of Location – Quartiles
- Quartile 1: (Q1 or P25) is defined as the ordered value below which 25% of the data points fall. If we put all the data in rank order (low to high), then Q1 is the value located at n/4
- Quartile 3:(Q3 or P75) is defined as the ordered value below which 75% of the data points fall. If we put all the data in rank order (low to high), then Q3 is the value located at 3n/4
Statistics• Inferential Statistics- Population: It is the total group of elements we want to study.
Population would mean each and every employee of the organisation.
μ – The mean, or average calculated for a population Σ – The standard deviation calculated for a population
- Sample: It is the subgroup of the population we actually want to study, as to study each and every of the elements of the population is usually not possible. Sample would mean a group of 20 employees chosen randomly from the organisation population.
– The mean, or average calculated for a sample S – The standard deviation calculated for a sample
Statistics
• Measures of Spread – Standard Deviation- Standard Deviation can be interpreted as the average distance of the
individual observations from the group mean; it is a measure of distance with a positive value
- Standard deviation for the population is represented as σ
Statistics
- Standard deviation for a Sample is represented as s
Statistics
Example:• Let's say we wanted to calculate the standard deviation for the
amount of gold coins that the pirates on a pirate ship have.
• There are 100 pirates on the ship. In statistical terms, this means we have a population of 100. If we know the amount of gold coins that each of the 100 pirates has, we use the standard deviation equation for an entire population
• What if we don't know the amount of gold coins that each of the pirates has? For example, we only had enough time to ask 5 pirates how many gold coins they have. In statistical terms this means we have a sample size of 5 and in this case we use the standard deviation equation for a sample of a population:
Statistics• The rest of this example will be done in the case where we have a
sample size of 5 pirates, therefore we will be using the standard deviation equation for a sample of a population.
• Here are the amounts of gold coins the 5 pirates have:4, 2, 5, 8, 6.
• Now, let's calculate the standard deviation:1. Calculate the mean:
Statistics
2. Calculate for each value in the sample:
3. Calculate :
Statistics
4. Calculate the standard deviation:
The standard deviation for the amounts of gold coins the pirates have is 2.24 gold coins.
Statistics
• Measures of Spread – Variance- Variance is defined as the spread of the standard deviation from the
group observations; it is a measure of area with a positive value- Variance for the population is represented as
- Variance for a sample is represented as
Statistics• Measures of Spread – Range
Range is the difference between the smallest value and the largest value in a data set; it is influenced by extreme values
Range for a given dataset is calculated as:Range = Highest Value – Lowest Value
Example: 34 50 61 77 99 26 15 62 44 74 88 is 99 - 15 = 84
- As range is calculated from extreme values it does not tell how much is the scatter of other values around the center; in such scenarios the interquartile range (IQR) is helpful
- Interquartile range for a given dataset is calculated as:IQR = Value Q3 - Value Q1
Statistics
• Example of Range To find the range in 3,5,7,3,11
Step 1: Arrange the numbers in ascending order. 3,3,5,7,11
Step 2: In the above distribution The largest number is 11 The smallest value is 3 Formula = largest number - smallest number Range = 11-3 = 8
Statistics
• Example of Interquartile Range: (temperatures)
Statistics
• Measures of Spread – Span- Span measures how successful we are in meeting our customer
needs and requirements; and is NOT influenced by extreme values
On Time
- Span for a given dataset is calculated as under:
Span = P95 value – P5 value
But how early?
But how late?
Statistics
• Measures of Spread – Stability Factor
- Stability Factor captures the spread or the variation of the output- Stability factor is calculated as:
SF = Q1
Q3
Statistics
Advanced Statistics
Statistics
• Distributions – Measures of Shape
Probability Frequency
Statistics
• Distributions – Measures of Shape
The two possible distribution models for our statistical study are:
- Probability distribution – A theoritical data distribution which represents futuristic data; values are between -1 & 1
- Frequency distribution – An actual data distribution which represents historical data; values are actual measurements
Probability Distribution Frequency Distribution
Futuristic data Historical data
Values are between -1 & 1 Values are actual measurements
Theoritical distribution Actual distribution
Statistics
• Probability Distributions – Measures of Shape
Discrete Continuous
Poisson
Binomial Gaussian
Exponential
Statistics
• Probability Distributions – Measures of Shape
- A probability distribution of a random variable is an assignment of probabilities to each of the possible values the random variable might assume at any given point of time
- Each probability is a value between zero and one and the sum of all probabilities must equal 1
- The bins of a histogram keep getting smaller and smaller as the number of data points gets larger and larger
- With enough data a discrete distribution also approaches as continuous distribution
Statistics
• Probability Distributions – Gaussian- A gaussian distribution also known as the normal distribution
gives us a bell shaped curve for the frequency distribution which is symmetrical about the mean
- This continuous distribution is used to determine the probability that a transaction would take between x & y range
Examples: Used to represent distribution of the time it takes to process a certain type of application measured in seconds; etc.
Normal distribution results in a symmetrical bell shaped curve!!!
Statistics
• Probability Distributions – Gaussian
- This statistically most important probability distribution exhibits the following vital characteristics:
- The probability curve indicates random or chance variation- The mean, median, mode of the probability curve are the same- The probability curve peak represents the center of the process- The probability curve theoretically does not reach towards zero- The probability curve can be divided in half with equal pieces falling
either side of the most frequently occurring value- The total area under the curve represents virtually 100% of the
product or service that the process is capable of producing
Statistics
• Measures of frequency – Normality
• Normality is a measure of distribution of frequently occurring values around the average, and the other values tailing off symmetrically in both directions, from -∞ to +∞
- Anderson-Darling test is used to compare the actual frequency distribution with a theoretical normal distribution calculated by using sample estimates for μ and σ of the frequency distribution
The test calculates a Anderson-Darling statistic (AD) and a Critical-value statistic (CV) and if AD < CV then we assume the frequency distribution to be exhibiting characteristics of a normal distribution
Statistics
• Measures of Consistency – Stability- Stability is a measure of differences in the frequency distribution
which are expected and predictable for and over a period of time- For the frequency distribution to be stable the probability of
differences being un-expected and un-predictable is less than 0.05
Stability presumes only common cause variation!!!
Statistics• Inferential Statistics
- Estimation: Estimation is the process by which sample data are used to indicate the value of an unknown quantity in a population
- Results of estimation can be expressed as: A single value known as a point estimate, or A range of values known as a confidence interval
- How well the sample represents the population is gauged by two important statistics – the sampling confidence level (confidence coefficient) and precision level (margin of error). They tell us how well the samples represent the entire population!!
- The confidence coefficient statistic is a variable dependent of confidence intervals and confidence limits
Statistics
• Inferential Statistics – Confidence Interval
- Confidence Interval: A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data; these intervals are useful in assessing the practical significance of an given result
- The width of a confidence interval is related to sample size and measurement variability in the observations
The width is decreased by increasing the sample size, but is increased with the increasing variability in our processes
Statistics
• Inferential Statistics – Confidence Limit
- Confidence Limits: The lower and upper limits of a confidence interval that define the interval within which a population parameter being estimated presumably lies. These limits are computed from sample data and have a known probability that the unknown parameter of interest is contained between them
- Confidence limits, which define the range of a confidence interval, are usually annotated as:
LCL – Lower confidence limitsUCL - Upper confidence limits
Statistics
• Inferential Statistics – Confidence Level
- Confidence Coefficient: The confidence coefficient of a confidence interval for a parameter is the probability that the interval will contain the value of the parameter of interest. It is the percentage of intervals that can be expected to include the actual value of the parameter being estimated
- The confidence level (CL) is the probability value associated with a confidence interval and is often expressed as percentage
Statistical experiments are often carried out at a confidence level of 95%!!!
Statistics
• Inferential Statistics – Confidence Level
- Confidence coefficient for running statistical experiments can take different values depending on the confidence level required for the estimation of results
- Coefficient has a value of 1.64 for tests conducted at 90%-CL- Coefficient has a value of 1.96 for tests conducted at 95%-CL- Coefficient has a value of 2.58 for tests conducted at 99%-CL
The most commonly used confidence coefficient values for many statistical experiments!!
Statistics• Inferential Statistics – Confidence Level
Example: Confidence level in sampling for survey respondents of ABC firm from the entire consumer base in a metropolitan city
A 95% level of confidence means that 5% of the surveys will be off the wall with numbers that don’t make sense. Therefore for example, if 100 surveys are conducted using the same customer service question, five of them will provide results that are somewhat wacky. Normally researchers do not bother about this 5% because they are not repeating the same question over and over so the odds are that they will obtain results from among the 95%.
Statistics
• Inferential Statistics – Precision Level
- Margin of Error: The precision level refers to the spread of an estimate of a parameter, and/or the quality associated with a set of measurements by which repeated observations approximate to the true value of a parameter
- A precise measurement may not be accurate because of the unrecognised bias or the other errors in the process of sampling
Statistical experiments are typically carried out at a “precision level” of 5%!!!
Statistics
• Inferential Statistics – Precision Level
Example: Precision level in sampling for survey respondents of ABC firm from the entire consumer base in a metropolitan city
A survey may have a margin of error of plus or minus 5% at a 95% level of confidence. These terms (treat it as precision) simply mean that if the survey were conducted 100 times, the data would be within a certain number of percentage points above or below the percentage reported in 95 of the 100 surveys.
Statistics
• Inferential Statistics – Confidence & Precision Levels
- Summary: Confidence & Precision Levels in sampling for survey respondents of ABC firm from the entire consumer base in a metropolitan city
A customer research firm surveys customers and finds that 50% of the respondents say that its customer service is “very good”. The confidence level is cited as 95% plus or minus 5%. This information means that if the survey were conducted 100 times, the percentage who say service is “very good” will range between 45% & 55% most of the time at a 95% confidence level.
Normal Curve
A theoretical probability distribution which represents variation in any
data futuristically on a bell shaped curve!!
Normal Curve
• Normal Curve
– A probability distribution where the most frequently occuring value is in the middle and the other probabilities tail off symmetrically in both directions
from -∞ to +∞
-6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6
68.26 Percent
95.46 Percent
99.73 Percent
99.9937 Percent
99.999943 Percent
99.9999998 Percent
Normal Curve
68.26% of the area falls within +/- 1 sigma95.5 % of the area falls within +/- 2 sigma99.73% of the area falls within +/- 3 sigma
Normal Curve
Normal Curve
Normal Curve2 Sigma Process
308,500 Defects per million
High inspection required
More correction and re-work
Clumsy and delayed process
High cost per transaction
Normal Curve
6 Sigma Process 3.4 Defects per million
No inspection required
No correction and re-work
Agile and On-time process
Optimum transaction cost
Normal Curve
Six-Sigma indicates how much of the data falls under a normal curve within
the customers’ requirements!!!
Process Focus
The business Process
A Process is a collection of activities that takes one or more kind of inputs and creates output that is of value to the
customer!
Process Focus
• Levels of maturity of a process
- Any business process at a given point in time, in its life cycle, can be operating at any one of the following maturity levels:
Level 1 - Anecdotal (ancient) Level 2 - Informal Level 3 - Formal Level 4 - Managed Level 5 - Optimised
Understanding the level of maturity for our business process(es) is vital to our project
success!!
Process Focus• Defining Process and Process Capability
- A process focused business constantly realigns processes to optimised levels in order to remain capable of meeting changing market demands. Only by gaining predictability, can an enterprise truly maintain capable processes to changing customer demands
- Three key terms that help us define process capability are: Process Baseline Process Entitlement Process Benchmark
Six Sigma focuses on understanding variation in our business processes!!!
Process Focus
• Process Baseline
- Process Baseline is the average, long term performance level of a process when all the input variables in the process are running in an unconstrained fashion
- It doesn’t mean that we let our processes run out of control. Instead, it means that we let all input variables vary across their entire range of expected values
- The result of allowing all possible variables to vary across their full range helps us determine our average, long term performance
Process Focus
• Process Entitlement
• Process entitlement is the best case, short term performance level of a process when all the input variables in the process are centered and in control
• It is effectively the opposite of the process baseline• The result of process entitlement helps us determine
what we are really capable of. This allows us to set realistic goals, not wishes
Process Focus
• Process Benchmark
- Process benchmark is the performance level of the process deemed by comparison to the best process possible
- It takes us to the best that anyone has ever done. In practical terms this means researching and finding the best that has ever been done in the industry
- Knowing the benchmark is very valuable because it allows us to establish competitive and realistic objectives
Process Focus
• Secret truths about Processes
• Processes are everywhere• We often don’t know that we are part of a process until something
goes wrong!• Processes grow and change without anybody realising it!• Left alone, processes tend to get more complex!
DMAIC
DMAIC
Methodology
DMAIC
DMAICA continuous improvement
methodology under the Six-Sigma Framework!
DMAIC
• A data based Improvement methodology
- You need baseline data to understand the process- Don’t trust historical data! If it’s all that’s
available….question it!- Analysis and improvement plans must be based on data- Improvements must be validated with data- Monitoring and control plans require data
DMAIC
• The Approach
DMAIC
IMPROVE
DEFINE
MEASURE
ANALYZE
CONTROL
Plan
DoStudy
(Check)
Act
PDCA
DMAIC
• DEFINE
This is the first step that refers to defining the goals of the project. Process improvement goals may be aimed at increasing market share, the output of a particular department, bringing about improved employee satisfaction as well as customer satisfaction and so on.
The goal has to align the customer demands and the strategic goals of the organization. Data mining methods can be used to find prospective ideas for project implementation.
In other words, businesses are designing a road map for achieving the targets and goals of the organization.
DMAIC
• Measure
This phase refers to the analysis of the existing system with various measurement techniques for the defects and levels of perfection that exist. In this step, accurate metrics have to be used to define a baseline for further improvements.
This helps Six Sigma team leaders understand if progress has been achieved when process improvements are implemented.
DMAIC
• Analyze
This phase is extremely important in order to determine any disparity that may exist in the goals set and the current performance levels achieved. Various statistical tools are available to undertake such an analysis.
The understanding of the relationship between cause and effect is necessary to bring about any improvements, if needed.
DMAIC
• Improve
Improvements in existing systems are necessary to bring the organization towards achievement of the organization goals. Creative development of processes and tools brings about a new lease on life for the organization’s processes and takes them nearer to organizational objectives.
Various project management and planning tools can be used to implement these new techniques and processes. Appropriate usage of statistical tools is important to measure the data, which is necessary to understand improvements done and any shortcomings that may exist.
DMAIC
• Control
The last phase of DMAIC is the Control phase. It helps ensure that variations in the processes are rectified before they have a negative effect. Controls can be used to ensure sustained improvements in new processes and operating procedures.
The new project components should become a part and parcel of existing processes. Once all the factors are performing to satisfaction, transfer of ownership should be done to process owners and process teams.
The DMAIC model is extremely beneficial in bringing about the change which the Six Sigma project aims at and has the ability to show good business results
DMAIC
• Define – Identify the business problem that needs to be fixed and the scope that will be addressed by the project team in a stipulated time frame
- Step 1 – Customer and Project CTQ Identification
- Step 2 – Project chartering and story boarding
- Step 3 – Project acceptability and success factors
- Step 4 – As-Is process mapping and SIPOC
Project alignment!!!
DMAIC
• Measure – Quantify the problem at hand as it currently exists and gather data that will help you understand the issues completely
- Step 5 – Define performance Measurements
- Step 6 – Identify and prioritize possible Project X’s
- Step 7 – Develop data collection strategy
- Step 8 – Calculate Process Capability
Establish baseline!!
DMAIC
• Analyse – Use the gathered data to understand the problem statistically and identify the area of focus for improvement
- Step 9 – Define Performance Objectives
- Step 10 – Identify and validate Vital X’s
Determine Y = f(X)
DMAIC
• Improve – Find possible solutions, plan for new measurements and implementation strategies for fixing the quantified problem
- Step 11 - Generate possible solutions
- Step 12 - Select and pilot solutions
Optimise Y = f(X)
DMAIC
• Control – verify that implemented improvements have fixed the problem and put procedure in place to continue the verification on a periodic basis
- Step 13 – Process Control & Risk Analysis
- Step 14 – Document and execute Control Plan
- Step 15 – Institutionalize and Sign-Off
Sustain long term!!
DMAIC
• The DMAIC Philosophy
Define Project Alignment
Measure Establish Baseline
Analyse Determine Y = f(X)
Improve Optimise Y = f(X)
Control Sustain Long term
DMAIC
DMAIC Projects
DMAIC
Improvement Strategy:
Cost Empowerment
Cycle Behaviour
Quality Enhanced skills
Competitive Price
Timely Delivery
High Quality
Customers
Pro
cess
es
Peo
ple
DMAIC
Improvement Strategy:
Project
ResultQuality Cycle Cost
CostLower inspection & rework costs
Higher productivity, Less expediting Lower input costs
Cycle Faster Throughput
Faster Throughput None
QualityHigher Quality & Customer satisfaction None None
DMAIC• Cost, Quality or Cycle?
- Goal – I want to reduce the cycle timeMost projects start this way because our view is that this is the
problem and it is impacting customer satisfaction!- Whats the defect – “The process takes too long”
Which leads to an erroneous conclusion that the defect is …too much of time. Therefore the tendency is to spend our energy in reducing it by
going faster! - Where is the re-work?
If we focus more on identifying the rework loops, bottlenecks, etc., we get closer to the real cause of “why does this take so long?”
- What is causing it?If we now take the approach that an incorrect procedure (for example)
is the real cause, then we focus more on the process and drive quality!- Is your project centered around cost, quality and cycle?
Improving quality in the process will eliminate rework loops/bottlenecks that improve cycle and drive costs down!
DMAIC
• Variation – Discrete vs. Continuous Thinking
- Discrete Thinking: e.g.; Payment posted on time? (yes / no)
- Continuous Thinking: e.g.; When was payment posted? (number of hours); how much payment was posted (value of transaction)
Projects with Continuous data are preferred as more data
to measure!!
DMAIC
• Variation – the prediction Equation Thinking
Variation in product characteristics (Output Variation) = Function of variation in process parameters (Input and process variation)
Y = f (X)
DMAIC
• The prediction Equation and the Project Philosophy
Define The Problem
Measure 20 -25 Inputs
Analyse 10 – 12 X’s
Improve 4 – 8 Vital X’s
Control 3 – 6 Vital X’s
DMAIC
• Lifecycle of a Project – The DMAIC Philosophy
Define
What is important to the customer?(Voice of customer / Surveys & Interviews / Transaction Behaviours / ….)
Measure
What is the frequency of defects?(Measurement system / Process mapping / FMEA / Output Measure baseline / ….)
Analyse
When, Where and why do defects occur?(Descriptive statistics / Pareto / Fishbone / Hypothesis testing / FMEA / ….)
Improve
How can we improve the process?(Expert brainstorming / benchmarking / modeling / Design of experiments…)
ControlHow can we sustain the improvement?
(Measurement feedback & control / Procedural transparency / ….)
Define Phase
Define
• Key deliverables
- Step 1 – Customer and Project CTQ IdentificationIdentify the customers; identify customer needs & requirements;
identify customers CTQs & interpret Project CTQs; define Defects- Step 2 – Project chartering and story boarding
Define the business case; develop the problem statement; develop the goal statement; assess the project scope; decide project milestones; project team selection; define team roles & responsibilities
- Step 3 – Project acceptability and success factorsConduct threat vs. opportunity Matrix; identify critical success
factors; conduct stakeholder analysis- Step 4 – As-Is process mapping and SIPOC
Identify key suppliers, inputs & outputs to the process; Conduct As-Is Process mapping; Document the SIPOC
Define
Step 1
DMAIC
Define
• Key deliverables- Step 1 – Customer and Project CTQ Identification
Identify the customers;Identify customer needs & requirements;Identify customers CTQs & interpret Project CTQs;Define Defects
Define
Customer Identification
Define
• A word from your customer
- I am your customer…..and if you satisfy my wants with personal attention and a friendly touch, I will become a walking advertisement for your products & services. Ignore my wants, show carelessness, inattention and poor manners, and I will simply cease to exist – as far as you are concerned
- I am sophisticated…much more than I was a few years ago. I have become accustomed to better things. I have better money to spend.
- I am an egotist…and I am sensitive. My ego needs the nourishment of a friendly, personal greeting from you. It is important to me that you appreciate my business. After all, when I buy your products & services, my money is feeding you.
Define• A word from your customer
- I am a perfectionist …and I want the best that I can get for the money that I spend. When I criticize your products or services – and I will, to anyone who will listen – take heed. The source of my discontent lies in something you or the products that you sell have failed to do. Find the source and eliminate it or you will lose my business and that of my friends as well.
- I am fickle …and other businesses continually beckon me with offers for my money. To keep my business, you must offer something better than they. I am your customer now, but you must prove to me again and again that I have made a wise choice in selecting you, your products and services, above all others!
Define
• Customer Identification
- Customer is one who buys or uses your products and/or services
- Customer is one who receives the process output time-to-time
- We broadly classify customers into two categories: Internal Customer – Management, employees, any
functional department in your organisation, internal shareholders, etc
External Customer – Clients, end-customers, external shareholders, etc.
Define
• Customer Identification
Internal External
VOICES
Needs and Requirements
Define
Needs and
Requirements
Define
• Customer Needs & Requirements
- Need is a desire or an expectation of a customer from a given product or service; Customers have many stated needs which are often vague and generally are the ‘wants’ from a product or service
- Requirement is an attribute of the product or service which fulfills the needs of a customer; customer defines these requirements and are the “musts” of a product or service
Needs are stated while requirements are defined!!Needs are stated while requirements are defined!!
Define
• Understanding Key Customer Needs- In order to understand key customer requirements it is
important to obtain information on varied customer needs from customer voices
- There are various ways of customer research to obtain information on customer needs:
InterviewsCustomer ObservationBe a CustomerCustomer ComplaintsFocus GroupsMarket ResearchSurveys
Define
Voice
Of Customer
Define• Voice of Customer
- Voice of customer methodology is used as a framework to capture and translate the varied customer needs – both current and latent
- VOC methodology helps capture the needs of a customer through verbatim comments i.e. voices
- VOC helps translate verbatim comments (customer voices) into wants (customer needs) to product or service output characteristics that are musts (customer requirements)
- VOC does not rewrite the customer need, but merely translates it!
Voices are infinite, needs are many while requirements are few!!
Define• Voice of Customer – The Framework
At an organisation level we broadly classify the Voice of Customer framework into four different and distinct categories:
Voice of Customer: The feedback from our customers on fulfillment
Voice of Business: The feedback from our Management; what is fundamentally required to be done in a business. Has to go with profitability, growth, strategies, etc.
Voice of Process: The feedback from our CTPs & our CTQs. Has to go with what we control as an input while the customer may not see the same; adjectives like capable, stable, deteriorating, etc
Voice of Employee: The feedback from our employees. Has to do with freedom to speak, opportunity to grow, supporting environment, etc.
Define
• Voice of Customer – The Framework
Voice of Customer: VOC
Voice of Business: VOB
Voice of Process: VOP
Voice of Employee: VOE
Define
• Voice of Customer and Customer Segmentation
- Not all of your customers have the same needs, requirements or priorities. Segmenting your customers allows you to more clearly understand the requirements of different groups and focus your improvement efforts accordingly
If you have many types of customers, breaking them down into different subgroups allows you to develop targeted measurement indicators and strategies for each group
All customers are important, but some customers will use your services more frequently, or could be more critical than others and hence would require more time and resources being devoted!
Define
• Voice of Customer and Customer Segmentation- A segmentation of the customer base is recommended
to focus customer research on the most important customers as different customer segments often have: Different functions Different causes
Segments can be based on many different variables like: Geography Customer relationship tenure Type of business or industry, etc
Define• Voice of Customer and Problem containment
- A critical problem is a problem that’s revealed through VOC capture and requires immediate attention for an urgent corrective action
Critical problems require containment to avoid severe, costly damage to your customer and business need. If a critical problem is not contained, your team misses the opportunity to help the business avoid unnecessary risks
- An identified problem requires immediate containment if it: Could cause harm to the customer concerned Impacts the customers ability to deliver the output Results in a significant financial loss to business Results in significant risk of loosing the customer Has controllership and compliance issues
Define
• Problem containment – The MethodThe steps for the containment of a critical problem are: Assess financial impact of a critical problem containment Identify what action will result in an immediate fix and stop
bleed Ensure that customer and business will be benefiting from
the action Identify consequences of the action on the project,
business and customer Define how effectiveness of the containment action will be
measured Establish target date for conclusion of the containment
strategy
Define
• Voice of Customer – The Method
- In order to manage customer expectations throughout VOC data collection, we need to: Select the target customer audience carefully Explain your intent for gathering the information Clarify your ability to act on the information gathered Communicate next steps to the customer in advance
Asking for information does not translate to a promise to act!!!
Define
• Voice of Customer – The Method
- Three key methods that can be developed to capture VOC are: Customer focus groups Customer Interviews Customer surveys
- One key method that can be deployed to classify VOC is: Affinity Diagram
Define
• VOC Capturing method – Customer Focus Groups - Customer focus groups is a technique in which we capture or
organise perceptions i.e. the collective viewpoint, of a group of customers on a pre-defined parameter or a particular need segment Participants share their ideas, concerns, likes or dislikes Focus groups have an environment which is conducive Focus groups are moderated by a facilitator
- The objective or use of a focus group during customer research is: To clarify and define the customer needs Gain insights into the prioritization of needs To test for and get feedback on concepts
Define• Focus Groups – The method
- A focus group being a carefully planned discussion , designed to obtain perceptions on a defined area of interest in a non-threatening environment, needs to meet the following rules: Compose a team of 7- 14 participants who share characteristics
that relate to the focus group topic (a pre-defined parameter or a particular segment)
Often ask only two to three vital questions during the focus group discussion
Participants should be asked to thoroughly discuss on the vital questions
A typical focus group should be planned to last for at least two or four hours
Usually try to offer an incentive for better team participation
Define
• Focus Groups – The method
Planning Tips Common mistakesPull together existing data (e.g. verbatim complaints, comments) Not enough "raw voices" collectedAdminister the unstructured questionaire with your key or lead customers Segments of customers are ignored
Spend time evaluatiing VOC to understand the underlying, objective issues
Not enough time or resources dedicated to preparation work (e.g 100 voices will take 2 people 2 days to translate!)
Be clear on theme, scope and constraints of the focus group
Constraints not identified up front during pre-planning
Define
• Focus Groups – The methodExecuting tips Common mistakes
Move straight to understanding requirements of the customers Preparation not sufficientDemonstrate by examples to provide good clarity and ask for examples to clarify meaning
Segments of customers not invited for discussion
Keep participants focused on a single discussion; try to avoid having multiple conversations
service providers not allowed to ask clarifying questions
Let customers begin the data reduction; show by an example how to group and title data Voices left too abstract to understand
Goals not set in agenda during kick-off
Define• VOC Capturing method – Customer Interviews
- Customer interviews are forums in which we capture or learn about a customer’s perception on a specific product or service The interview process can be either informal or structured The interviewer can use both open-ended and close ended questions to
gather information
- The objective or use of an interview during customer research is: At the beginning: To learn what is important to customers, which
supports the development of hypotheses about customer needs and priorities
In the middle: To clarify points or to better understand why a particular issue is important to customers
At the end: To clarify findings, to get ideas and suggestions, or to test ideas with customers
Define
• Customer Interviews – The method
- Some of the important interviewing requirements are: When listening to customers record exactly what they say
as you hear and what they do as you see!When asking questions try and get maximum facts and not
just the opinionsDon’t come head-on, but approach the interviewees from
different angles using open ended questioning and relevant examples
Define
• VOC Capturing Method – Customer Surveys
- Customer surveys is a technique in which we take the feedback from a customer sample representing the entire population to measure the importance and/or performance of customer needs.
- The Survey focuses on collecting prompt and accurate information on:
Customer preferences, needs & behaviorsPerformance against their needs
- Different question types that can be used in a survey are:Open ended: TextLikert Scale: Rating from 1 – 10Binary: Yes/No
Define
• Customer Surveys – The Method
- A customer survey as a technique should be used to analyse goals, processes and possible cause & effect relationships upfront
- Widely scaled rating questions should be used as they allow lots of analytics on the results and help keep survey sample sizes low
- We should have only few written questions so as to address any unforeseen situations or problems
Surveys should have an action oriented focus to generate Surveys should have an action oriented focus to generate solutions, not more questions!!!solutions, not more questions!!!
Define
• Customer Surveys – The Method
A good survey questionnaire has the following three sections:
o The demographic section – Captures the categories into which the respondents need to be split along with their basic particulars
o The numerical section – captures the ranking of root causes using a continuous rating scale, like “agreement’ or “satisfaction”
o The written question section – captures root causes that did not come out of the stated questions, providing some clues, in case none of the expected rot causes could hit home. It should be short & focused!
Define
• Customer Surveys – The Method• A rating scale is an important factor towards the success of
any survey:
• A poor rating scale – Discrete scales: 1 to 5 or Disagree to Agree. This provides limited analysis and requires big sample sizes for administering
• A good rating scale – Continuous scales: 1 to 9 or Strongly Agree to Strongly Disagree. This provides a strong analysis and require small sample sizes
Define
• Customer Surveys – The DMAIC linkage
DefineLinking the Business Case / CTQs to the ProcessSimple surveys here, get the Voice of customer
MeasureLinking the Process to the Big "Y"
AnalyseLinking the "Y" to the family of "Xs"
ImproveLinking the "Y" to vital "Xs" (Root causes)Surveys here drill down from demographic Xs to the root cause level
Control Linking the vital "Xs" to Solutions
Define
• Comparison of VOC Data SourcesFocus Groups Interviews Surveys
Group Interaction helps us to generate better information
Can help tackle complex issues with relative ease & comfort Lower cost/ time approach
More in-depth responses can be solicited from participants
Facilitates use of visual aids towards data capture Higher rate of response
Excellent for defining CTQs
Good when people are not wanting to respond willingly or accurately via phone/ mail
Require least amount of trained & experienced resourcses to administer
Inputs can be easily sought on vital & complex issues
Results can be produced much faster & quicker
Learnings only apply to those who get asked & we cant just generalise for the rest
Requires trained & well experienced interviwers to get the desired information
We can get incomplete results from the respondents
We get only anecdotal dataLong cycle time to cover all respondents & to complete data collection exercise
Mail based response rate is generally 20%-30%
data collected is qualitative v/s quantitative requirement
Interviewer can influencethe interviewee in a tele-survey
Define
• VOC Capturing Methods – Key Considerations
- How could the collectors bias affect what is heard?
- What contact or relationship do you have with the customer?
- What are your time constraints to complete data capture?
- What budget is available to facilitate data gathering?
- How much certainty do you need to move forward with the project?
- How will you ensure that customer expectations are aligned with your expectations or actions?
Define
• VOC Classification Method – Affinity Diagrams
- The Affinity diagram tool used to organise & present large amounts of data (ideas, issues, solutions, problems, etc) into logical categories based on user perceived relationships and conceptual frame-working
- The freely collected ideas, one written on cards or sticky notes, are lined up & organised to show mutual, exclusive or natural relationships, i.e. their affinity.
Define
• Affinity Diagrams – The Objective
We should use Affinity Diagrams in situations like:
Where a breakthrough to new ideas is desired Where a group consensus is necessary When issues seem too large & complex to grasp Where facts or thoughts are in chaos When the teams creativity & intuition is to be tapped
Define
• Affinity Diagrams – The Method
The key steps towards making an affinity diagram are: Capture all the voices i.e. ideas, as they come Clarify on ideas discussed if so be the requirement Let ideas emerge & do not have predefined groups Have each team member write one idea per Post-it note and post
on a wall randomly. Group ideas that seem to belong together or “affinities” See if smaller idea sets belong to a larger group Build consensus on the grouping of ideas Create a 'header' card for each general category of ideas below it.
Define• Affinity Diagram: Example
The personnel department of a food supermarket chain store identified a high resignation rate of good checkout staff. There was information available from exit interviews about their reasons for leaving, but this was disorganized and there was no clear area that they felt they could address. They decided to use an Affinity Diagram to try to better understand why these people were leaving (see as illustrated).
As a result, the checkout process was investigated further and eventually completely redesigned. This included a redesigned booth and hourly breaks for operators. Consequently, there were significantly fewer leavers (and as a bonus, customer satisfaction increased).
Diagram……
Define
Affinity
Diagram
Define
• Translating Voices to needs – The objective
The objective of translating infinite voices to the customer needs is to meet the following:
Differentiate the needs, or the wants, from infinite voices From the voices establish the service or product requirements To capture the stated needs, or wants of the customers To help get the final customer requirement parameters
Define
• Translating Voices to needs – The Method
Some of the guidelines for documenting “needs” are:
Must be written from the customers perspective Write the need and NOT the potential solution Write the need in complete sentences; examples help Use measurable terms & be concise Write from a positive perspective Validate the need with the customer
Define
• Translating Needs to Requirements – The Method
Some of the guidelines for establishing “requirements” are: Ask questions & clarify on need verbatim Probe for deeper understanding & better clarity Translate into terms that make sense to you & to your process Review with customers & process participants Refine & re-state customer requirements
Define
• Prioritising Key Customer Requirements
Basic Requirements One-dimensional Requirements Delighter Requirements Indifferent Requirements Reverse Requirements
Define
• Basic Requirements
Are “must-be’s” Are required & expected to be there Are needs the customer assumes will be met When are fulfilled, the customer is greatly satisfied When fulfilled, the customer is neutral (i.e. they do not produce additional
satisfaction)
For example, if a restaurant is very clean, it will not bring additional satisfaction to the customer because cleanliness is regarded as a must-be requirement. If a restaurant does not meet the minimum requirement for cleanliness, customers will not go to that restaurant at all. Customers usually do not explicitly demand must-be requirements.
Define
• One-dimensional Requirements
A direct positive co-relation exists b/w satisfaction levels & the degree of presence
The more One-dimensional elements needs are met, the better
For example, for a given model of car, the higher the mileage per gallon, the higher the customer satisfaction. If the mileage per gallon is under a certain level, customers will be dissatisfied. We can say that the lower the mileage per gallon, the higher the dissatisfaction regarding this requirement. In this example, the level of neutral satisfaction is the industry average for that class of car. Usually, customers explicitly demand one-dimensional requirements.
Define
• Delighter Requirements
Are “Attracters” Their presence in a product or process is unexpected & fulfills the latent
needs of a customer Leads to great satisfaction if found present When delighters are absent, the customer still is neutral (& not dissatisfied)
For example, if at the end of a dinner a restaurant gives a complimentary gift to its customers, it will bring satisfaction. If the gift is not offered, it will not bring dissatisfaction to customers. Attractive requirements are neither explicitly expressed nor expected by the customer.
Define
• Indifferent Requirements
Indifferent elements are needs that result in neither satisfaction nor dissatisfaction whether they are present / met or not
Examples of neutral/ indifferent characteristics are those product features that are never or rarely used by the customer
Define
• Reverse Requirements
Reverse elements are needs that result in: Dissatisfaction when they are fulfilled Satisfaction even when they are not fulfilled
They may indicate that the perception of that question in the marketplace is the opposite of the perception of the survey’s creator.
Define
CTQ Identification
and Interpretation
Define
• Translating Requirements to Project CTQs
A Critical-to-Quality (CTQ) parameter is a specific attribute or quality of the output that is a key requirement for customer satisfactionOnce the key requirements have been determined & classified into basic, performance & excitement needs, the same need to be converted into product or service CTQsTwo commonly used tools to convert customer requirements into project CTQs are: Quality Function Deployment CTQ Drilldown tree
Define
• Quality Function deployment Quality Function deployment is a structured approach to: translate customer voices into requirements (CTQs) translate specific wants into service requirements translate service requirements to service processes translate service processes to process controls (CTPs)
Many Six Sigma practitioners use the Quality Function Deployment (QFD) tool to translate the voice of the customer (VOC) into product specifications. QFD offers a customer-oriented approach, supporting design teams in developing new products based on an assessment of customer needs. Customer needs are translated into design attributes, which are then deployed in process and quality requirements.
Define
Benefits derived from using the QFD tool include:
• The creation of work teams that include multiple skills and experiences
• The determination of specific work aims • A display of a wide variety of important design information in
one place in a compact form • Reduced overall costs from realizing a reduction in design
changes • Reduced production costs by eliminating redundant features
and over-design
Define
What Is QFD?
QFD is composed of four stages:• Complete the house of quality (HOQ). • Design the product – Determine tolerance of each part of the
product so that it satisfies the target value identified from the HOQ.
• Design the process – Determine the necessary production process that will satisfy tolerances established during product design.
• Control the process – Determine quality standards for the new product design.
Define
• House of Quality - HOQ Room 2 of the HOQ, is sometimes completed by the marketing department because of its relationship with customers, although other areas of the company with customer touch points also may contribute. The information contained in this section represents the VOC and is obtained from various sources such as:Searching the market for industry standards / Customer surveys /Analyzing customer complaints, etc
Define
• Kano Model
Created in the 1980's by Professor Noriaki Kano, it's main objective is to help teams uncover, classify, and integrate 3 categories of Customer Needs and Attributes into the Products or Services they are developing. Missing any of these needs will jeopardize the success of the offering.
The Benefits Provides a systematic, data-based method for deeper
understanding of customer needs by prioritising them Helps focus efforts on meeting the vital few needs
Define
• Kano Model –
Define
• Kano Results and the HOQ
An example of designing a pencil is used to illustrate the process of completing the HOQ. Step 1 for this example is shown below, where results from the Kano survey are entered into the customer requirements and needs section of the HOQ Customer Needs and Requirements
Define
• Kano Results and the HOQ
Step 2 involves asking customers to rate the importance of each requirement using a numerical ranking (from 1 to 10). The ranking is then entered into the HOQ as shown:
Ranked
Requireme
nts
Define
• Kano Results and the HOQThe benefit of Step 3 is viewing situations where a company’s product is weaker than its competitors products as assessed by customers. This step also uncovers which specific requirements to focus on for improvement. To display this comparison, A represents the company and X, Y and Z the competitors: Compared Requirements
Define
• Kano Results and the HOQ
Step 4: This section of the HOQ is typically completed by the product design or engineering department. This information represents the design elements that correspond to customer-stated needs. This process transforms customer requirements into specific characteristics to be designed into the product Engineering
Characteristics
Define
• Kano Results and the HOQStep 5: Determining the Relationship Between Customer Requirements and Engineering Characteristics
Different indicators are used to depict the level of relationship between the customer requirements and the design requirements. In the example below, the following indicators are used:
• Strong relationship = 9 points
(reflected on HOQ as =)
• Moderate relationship = 3 points
(reflected on HOQ as O)
• Weak relationship = 1 point
(reflected on HOQ as X)
Define
• Kano Results and the HOQ
Step 6: Comparing Competitor Characteristics with Design Engineering Characteristics
In this step the engineering design
characteristics are compared
with those of the competitor
products.
Define
• Kano Results and the HOQStep 7: Completing the HOQ RoofSometimes two product characteristics have a negative influence on each other. For example, when one is increased the other is decreased. This is recorded by placing an X in the roof of the HOQ chart where influence is anticipated
Expected Negative Influence Marked in HOQ Roof
Define
• Kano Results and the HOQStep 8: Weighing the Engineering Characteristics
• It is important to weigh the characteristics in order to identify the amount of importance each characteristic has on the level of customer satisfaction desired. The weight of each characteristic is derived using the following formula:
Wj = sum(Wi x dij), where• Wj = weight of characteristic j, • Wi = rate of importance of requirement • i (determined in Step 2) • dij = point of relationship between
characteristic j and requirement i
Define• Kano Results and the HOQ
Step 9: Determining New Target Values for CharacteristicsAt this point in the completion of the HOQ, it is possible to set new target values for the product. These values are determined based on the following items:
• Relative importance (Step 2) • Ranking of competitors relative to
the satisfaction level of customers (Step 3)
• Competitor product value (Step 6) • Component influences at the roof
of house (Step 7)
Define
Kano Results and the HOQ
• The new target values in the last row of the chart provide direction to set a strategy that matches the best competitor on the most important customer requirements. To achieve the goal, the design must consider the following challenges:
• Technical difficulty of designing and deploying the new product
• Cost of changing to a new design • Negative relationship shown in the roof of the chart indicating
potential difficulty in implementing specific requirements
Define
• The Kano Model of customer satisfaction is a useful tool to help determine attractive or must-be customer requirements. Results from the Kano survey feed the customer requirements and needs section of the HOQ matrix.
Define
• CTQ Drilldown Tree- Superficial CTQs (e.g. “easy business) are too vague to be made
actionable- QFD requires that improvement take place at the most fundamental
level whereas CTQ drilldown requires in-depth analysis of customer CTQs & as a tool can be used effectively to:
Convert customer needs & requirements to measurable product or service characteristics
Establish linkage between Project Y & Business Y Make the Project manageable
Define
• CTQ Drilldown Tree – The Need
Situations in which we can make use of a CTQ Drilldown Tree are:
CTQ Drilldown Tree is used to identify Process CTQs v/s Project CTQs
Frequently used in the Define Phase of the Project & helps to integrate Project CTQs with business strategy
Often helps us identify what we need to work & measure in order to meet our CTQs consistently
Using the CTQ Drilldown Tree we first measure where we are & then identify targets to where we want to be
Define
• Defect Definition
- A Defect is defined as any variation of a required characteristic in a product or its parts; or of a service, which is far enough away/ removed from its target value
- Any variation of a required characteristic within a product or service, that prevents that product or service from fulfilling the specific requirements of the customer or internal quality standards
- Anything requiring rework!
Define
• Defect Definition
Now we need to understand what an opportunity for making defects is.
Lets look at the possible ‘opportunities’ on a Credit Card Statement: The Credit Card Statement Transactions made on the card Customer Information Every word / letter / number
Potential definitions for a defect are: At least one error (any error) on a Statement Any error on the transactions made on the Card Any piece of incorrect customer information Any incorrect word / letter / number
Define
• Defect Definition
Some important guidelines for defining an opportunity are: It should be defined as how the customer is judging you Track what the customer actually feels throughout the process Determine whether you are measuring DPU or DPMO
Defect & Opportunity should be clearly
defined in the Problem Statement!!
Define
• Defect – Various ExamplesPurchase Order Revisions Maintenance errors
Discrepancy reports Quality errors
Late job order releases Shortages
Ledger entry errors Wrong parts
Time card errors
Reworks
Audit findings
Safety errors
Late responses to customer requests
Define
• Key deliverablesStep 2 – Project chartering and story boarding
Define the business case Develop the problem statement Develop the goal statement Assess the project scope Decide project milestones Project team selection Define team roles & responsibilities
Define• Project Charter
Project Charter is one of the most important things necessary to get a project team started on a solid footing. It helps to:
Set expectations & obtain buy-in on scope, goal & resources (It is documented, so that there is no misunderstanding)
Accelerate the on-boarding of new resources (It is documented, you don’t need as much time explaining your project)
Avoid future scope from creeping into a Project (Refer back to the Project Charter at all times to ensure that you are on the right track)
Define
• Project Charter Elements
Business Case Problem Statement Goal Statement Project Scope Milestones Team Roles & Responsibilities
Define
• Business Case
A business Case helps the Management understand how the Project is linked with the overall business objectives. It also helps them make a decision about investing in the Project.
It should be able to address a few vital questions
Define
• Problem Statement
Quantitatively describes the current pain in the process & answers the following questions: What is the pain? When & where is the pain? How much is the pain? What has the pain led to?
However it should not: Assign a cause or blame Include a solution
Define
• Goal Statement
The goal statement specifies the target that the Project team is seeking to achieve collectively. It must: Start with a verb Have focus of the Project Have a measurable target (e.g. by 25%, by 70%) Have a completion date
It should not: Assign a cause or blame Include a solution
Define
. Project Scope
- One of the characteristics of a good Project is that it is manageable & can be finished within 60-90 days
- While selecting a Project keep in mind the start & end point / resource availability / “regular jobs” / other constraints
Define
• Milestones
- The project Charter should clearly mention the dates on which each phase of the DMAIC cycle will be completed
- Should be aggressive
- Must include a detailed project plan along with a documented communication plan
Define
• Project Team & Roles Sponsor and/ or Champion Project CoachDrive “vision” by sharing needs Drive “mission” by deploying teamsDefine project need & scope Prioritise & strategise projectsEngage MBB for initiating Project Facilitate project managementProvide strategy & roadmap Train & mentor project teamsOrient teams towards support Coach on successful closuresTimely project progress reviews
Project Leader Team MemberDrive “results” by project execution Drive “tasks” to logical closuresTrain & mentor teams on tools Provide subject matter expertise
applicability & usage Participate in project meetingsPlan & conduct team meetings help with administrative tasksEnsure meeting of deadlinesDrive change & create buy-in
Define
• Project Team & Roles – ARMI
Used to assess each persons role in the Project during various phases of the Project
Approver
Resource
Member
Informed
Define
• Project Storyboarding- Identify the customer
- Define customers needs & requirements
- Specify deliverables tied to those expectations
- Identify CTQs for those deliverables
- Map the process
- Determine where in the process the CTQs can be most seriously affected
- Evaluate which CTQs have the greatest opportunity for improvement
- Define the defect to be attacked
Define
• Project Storyboarding & Risk Management
Must be written with care & precision to avoid misinterpretation by any third party
Must reflect the effort of improving a good business Avoid blame of the current status & focus on improvement Don’t speculate…let facts / data speak
Define
• Key deliverables
Step 3 – Project acceptability and success factors
Conduct threat vs. opportunity MatrixIdentify critical success factorsConduct stakeholder analysis
Define
• Project acceptability
Effectiveness of a solution =
Quality of the solution * Acceptability of the Solution
E = Q * A
Define
• Project acceptability
- Improvements bring about change & it is a commonly known fact that change is always resisted
- It is important for all of us to realise that just finding a solution is not good enough
- The identified solution should be understood & implemented
WIIFM!!
Define
• Project acceptability – Key Benefits
- Enable projects to be started & completed more quickly- Help ensure that solutions are supported- Helps ensure that customers & suppliers are getting involved
appropriately- Team involvement in ensuring change sustenance- Reinforcing change- Helps drive change on a global basis
Define
• Project acceptability
Three commonly used tools to help create a shared need are: Threat vs. Opportunity Matrix Critical Success Factors Stakeholder analysis
Define
• Threat vs. Opportunity Matrix
Threats Opportunities
Short
Term
Long
Term
The Recession
Or
Commonwealth Games Scandal
Define
• Critical Success Factors- These are factors that are critical for the success of the
project & needs to be considered & tracked. Some of them are:
Appropriate metric Data availability Resource availability Proximity to Champion Degree of difficulty Scalabilty Benefits
Define
• Stakeholder Analysis
This enables the team to answer questions like: Who are the key stakeholders? Where do they currently stand on issues associated with this
change initiative? Where do we need them to be in terms of their level of support?
Define
• Stakeholder Analysis
Stakeholders
Name
Strongly
Against
Moderately
Against Neutral
Moderately
supportive
Strongly
Supportive
Define
• Key deliverables
Step 4 - As-Is Process Mapping and SIPOC
Identify key suppliers, inputs & outputs to the process; Conduct As-Is Process mapping; Document the SIPOC
Define
• Process Mapping
Process mapping is a graphic display of steps, events & operations that constitute a process
A pictorial illustration which identifies the steps, inputs & outputs and other related details of a process by providing a step-by-step picture of the process “as-is”
It is a team effort & is documented by everyone A process map is a picture of the current steps in the process
targeted for improvement.
Define
• Process Mapping – Key Benefits• Provides a structure for thinking through a complex process
• Creates a means for communicating knowledge across the business
• Helps understand communication disconnects
• Helps clarify process reality from perceived / documented plan
• Helps reveal unnecessary / redundant steps
• Magnifies “insignificant” / “overlooked” areas
Define
• Process Mapping – SIPOC
SIPOC as a tool displays a cross-functional set of activities in a single & simple diagram which:
helps us identify process inputs (Xs) & outputs (Ys) helps us identify the process owner, customers & suppliers helps us identify & establish boundaries for the process
Define
• SIPOC
The five key elements of this tool are:
Supplier – Whoever provides the input to your process
Input – The product / data that a process delivers
Process – Activities that you must perform to satisfy your customers requirements
Output – Product / data that results from the successful operation of a process
Customer – Whoever receives the output of your process
Define
• SIPOC Example:
S(Supplier)
I(Input)
P(Process)
O(Output)
C(Customer)
1. Client1. Scanned documents
1. Open the document /E-mail / queue
1. Completed transaction 1. Client
2. Management 2. E-mail2. Tag the document 2. Management
3. Update on queue
3. Process the document
4. Staff4. QC the document
5. Systems5. Update the document
Define
• SIPOC• A process map has five major categories of work from the
identification of the suppliers of the process, the inputs the suppliers provide, the name of the process, the output of the process, and the customers of the process. Each of these steps is summarized as SIPOC to indicate to the team the steps that must be conducted to complete a process map.
Measure
Measure
• The role of Measurement:
- A Measure describes the dimension, quantity, performance, capacity or characteristic of a population. In our context, it would now represent organised data.
Measure
Key questions at this Stage:
• What is the Process How does it function?
• Which Outputs affect CTQ’s most?
• Which Inputs affect Outputs (CTQ’s) most?
• Is our ability to measure/detect sufficient?
• How is our current process performing?
• What is the best that the process was designed to do?
Measure• What is the Measure phase ?
The Measure phase is the second phase of DMAIC methodology. The measure phase defines the defect(s), gathers baseline information about the product or process, and establish improvement goals.
• Objective
Assure that the current situation is understood in detail from a variety of perspectives so that strategies can be developed to address it.
Measure
• Why is the Measure phase important ?
The Measure phase allows you to understand the present condition of the process before you attempt to identify improvements. Because the Measure phase is based upon valid data, it eliminates guess work about how well your process is working.
Measure• A Project should address some or all of the
following on Measures:
What measures are used to assess the state of the problem & the success of the Project?
Are these measures already in place or they now being defined / established?
Is the measurement system understood in terms of its accuracy, repeatability & reproducibility?
What is the baseline data for these measures? What is the extent of the problem? What is the gap between actual & desired performance?
Has the process been mapped, identifying key measurable outputs & inputs per step?
Measure• Types of Output Measures
Effectiveness Measures- The degree to which customer needs & requirements are met & exceeded- E.g. “You need to complete a transaction in 10 minutes and the overall
accuracy must be 95%”. In this case effectiveness is: Completing each transaction in 10 minutes with 95% overall accuracy”
Efficiency Measures- The amount of resources allocated in meeting & exceeding customer
requirements- E.g. “You need to complete a transaction in 10 minutes and the overall
accuracy must be 95%”. In this case efficiency is: Completing each transaction in less than 10 minutes with 95% overall accuracy”
If the TAT remains 10 minutes but your accuracy goes up; it is not efficiency!!
Measure
DefineCollect (1) facts about a problem or opportunity (2)VOC information
MeasureEstablish a baseline performance for Project Y to understand how well we meet customer expectations
AnalyseIdentify the root cause of a problem & find the key to solving the problem
Improve
Evaluate competing solutions based on their impact on performance; degree & direction of change; to compare process performance before & after a solution is implemented
ControlQuantify the change in process performance to ensure improvement gains are sustained
Relation of Measure to DMAIC
Measure
• Key deliverables- Step 5 – Define performance Measurements
Define Unit, Opportunity & Defect for the Process; Set Performance Targets & identify specification limits; determine the Project Y
- Step 6 – Identify and prioritize possible Project X’sIdentify & Prioritise possible X’s driving defect occurrences
- Step 7 – Develop data collection strategyUnderstand data types; develop data collection plan; develop data sampling strategy; validate measurement system; execute the data collection plan
- Step 8 – Calculate Process Capability Understand variation to figure out Special & Common causes; interpret &
analyse the data graphically; establish Defect rate & Process Yield; Establish process capability & calculate Baseline process Sigma
Measure
• Key deliverables
- Step 5 – Define performance Measurements Define Unit, Opportunity & Defect for the Process; Set Performance Targets & identify specification limits; Determine the Project Y
Measure
• Unit, Opportunity & Defect Unit
The item produced or processed relative to the Project Y as reviewed by the customer
DefectAny Project Y measurement value that does not meet the “Y” performance standard
OpportunityAny Y measurement event which provides a chance of not meeting the performance standard
DefectiveA unit with one or more defects
Measure
• Performance Targets & Specifications
- A performance target is the requirement (s) and specification is the requirement range, imposed by the customer on a specific CTQ. It addresses the following:What does the customer want?What is a good process / product?What is a customer defect?
Measure
• Performance Targets & Specifications
The goal of a performance Target & Specification is to translate the customer requirements into a measurable characteristic which has a defined:Operational definitionTarget valueSpecification limitDefect definition
Measure
• Performance Targets & Specifications
Operational definition An operational definition is a precise description that tells
how to get a value for the characteristic (CTQ) you are trying to measure
It includes “what something is” & “how to measure it”
Target value A target value is the level or output characteristic of a CTQ
where the customer satisfaction is maximised in terms of his requirements being met by the product or process
Measure
• Performance Targets & Specifications
Specification limitA specification limit is the range on the target for output
characteristic of a CTQ within which the customer is satisfied satisfactorily
Is defined by the customer & is also called as “performance standards” and/or “tolerance limits”
Defect definitionA defect is a customer experience that results in an
unacceptable level of customer satisfaction by the usage of the product / process
Measure
• Determine the Project Y
Project Y is defined as An output characteristic of a process, as is felt by the
customer Determines how well the process meets the CTQs Is dependent on the inputs (Xs) to the process
Measure
• Performance metrics can be gauged from:– Material shortage
– Downtime
– Absenteeism
– Quality level
– Setup time
– Transport time
– Delivery on time
Measure
. Key deliverables
Step 6 – Identify and prioritize possible Project X’s Identify & Prioritise possible X’s driving defect occurrences
Measure
• Three key process analysis tools that are used to identify the casual Xs are:Cause & Effect DiagramFailure Modes & Effects AnalysisProcess Map Analysis
• Two key tools that are used to prioritise the casual project Xs areCause & Effect MatrixControl Impact Matrix
Measure
The Five Whys
Measure
• The Five Why’s
The standard rule of thumb is that if you ask why 5 times you will usually get to the root cause of the problem
Why? Why? Why? Why? Why?
Problem Root cause
Measure
• The Five Why’s
Problem Statement: You are on your way home from work and your car stops in the middle of the road.1. Why did your car stop? - Because it ran out of gas.2. Why did it run out of gas? - Because I didn't buy any gas on my way to work.3. Why didn't you buy any gas this morning? - Because I didn't have any money.4. Why didn't you have any money? - Because I lost it all last night in a poker game.5. Why did you lose your money in last night's poker game? - Because I'm not very good at "bluffing" when I don't have a good hand.
Measure
Cause & Effect Diagram
Measure
• Cause & Effect Diagram
• A method of systematically identifying all of the potential causes that may be contributing to the problem (i.e. Effect)
• A Cause & Effect Diagram also known as a fishbone or an ishikawa diagram, classifies all the sources of variation into 6 categories (5M / 1P) Machine Material Mother Nature Measure Method People
Measure
• Cause & Effect Diagram – The method
• Call the team for a fishbone brainstorming session into a room & provide the members with adequate Post-its
• Create a “Fishbone” structure on a white board
• The head of the fish contains the “problem”; & the bones of the fish contain the possible “causes”
• The 5 M’s & 1P is a commonly used nomenclature
• Use the Affinity Model to get all the discussion findings into similar groups
• Every member sticks post-its onto the relevant 5M’s & 1P
Measure•Cause & Effect Diagram – The method
•Get all the Post its together & remove the duplicates. Discuss for any conflicts of placing the post-it in wrong categories
•Check if the points identified are measurable or not; identify measures for each of the factors (X’s) identified; relate the factors (X’s) to the Project Y.
Effect “Y”
Mother Nature
Measurement
Methods
Materials
Machine
People
Potential
Causes (X’s)
Measure
• Cause & Effect Diagram – Key Benefits
• It helps summarise potential causes for the problem
• Helps provide a visual display of possible potential causes
• Helps stimulate the identification of other potential causes
Measure
• Failure Modes & Effects Analysis• An FMEA is a structured analysis tool for identifying ways &
methods in which a product or process can fail and then attempts to plan an action to prevent those failures
Failure Mode – It is a manner in which a part or a process can fail to meet customer specifications. It is usually associated with defects or non-conformities
Cause – Causes are sources of variation which are associated with key process inputs. Cause can be best defined as a deficiency which results in a failure mode
Effect – Effect is the impact on the customer if the failure mode is not prevented or corrected
Measure• FMEA Timing
The FMEA is a living document. Throughout the product development cycle change and updates are made to the product and process. These changes can and often do introduce new failure modes. It is therefore important to review and/or update the FMEA when:
A new product or process is being initiated (at the beginning of the cycle).
Changes are made to the operating conditions the product or process is expected to function in.
A change is made to either the product or process design. The product and process are inter-related. When the product design is changed the process is impacted and vice-versa.
New regulations are instituted. Customer feedback indicates problems in the product or process
Measure• Benefits of FMEA
FMEA is designed to assist in improving the quality and reliability of design. Properly used, the FMEA provides us with several benefits. Among others, these benefits include:
• Improve product/process reliability and quality • Increase customer satisfaction • Early identification and elimination of potential product/process
failure modes • Prioritize product/process deficiencies • Capture engineering/organization knowledge • Emphasizes problem prevention • Documents risk and actions taken to reduce risk • Provide focus for improved testing and development • Minimizes late changes and associated cost • Catalyst for teamwork and idea exchange between functions
Measure
• Failure Modes & Effects Analysis – The Method• Select the process FMEA team & call them for a brainstorming• Refer to the process map & list down all the process steps• List all the value added process requirements that the process adds• For each process step, list process inputs & rank the inputs
according to importance (C&E Matrix)• Start the process FMEA; ask how each process step can fail (i.e.
the potential failure mode)• For each process input, list ways that it can vary (causes) & identify
associated failure modes & effects• List all other cause & associated failure modes & effects
Measure
Measure• Identify Failure Modes. A failure mode is defined as the manner in
which a component, subsystem, system, process, etc. could potentially fail to meet the design intent .
• A failure mode in one component can serve as the cause of a failure mode in another component. Each failure should be listed in technical terms. Failure modes should be listed for function of each component or process step. At this point the failure mode should be identified whether or not the failure is likely to occur. Looking at similar products or processes and the failures that have been documented for them is an excellent starting point.
• Describe the effects of those failure modes. For each failure mode identified the engineer should determine what the ultimate effect will be. A failure effect is defined as the result of a failure mode on the function of the product/process as perceived by the customer. They should be described in terms of what the customer might see or experience should the identified failure mode occur. Keep in mind the internal as well as the external customer
Measure• Establish a numerical ranking for the severity of the effect. A
common industry standard scale uses 1 to represent no effect and 10 to indicate very severe with failure affecting system operation and safety without warning. The intent of the ranking is to help the analyst determine whether a failure would be a minor nuisance or a catastrophic occurrence to the customer. This enables us to prioritize the failures and address the real big issues first.
• Identify the causes for each failure mode. A failure cause is defined as a design weakness that may result in a failure. The potential causes for each failure mode should be identified and documented
• Identify the causes for each failure mode. A failure cause is defined as a design weakness that may result in a failure. The potential causes for each failure mode should be identified and documented
Measure• Identify Current Controls (design or process). Current Controls
(design or process) are the mechanisms that prevent the cause of the failure mode from occurring or which detect the failure before it reaches the Customer. We have to now identify testing, analysis, monitoring, and other techniques that can or have been used on the same or similar products/processes to detect failures. Each of these controls should be assessed to determine how well it is expected to identify or detect failure modes. After a new product or process has been in use previously undetected or unidentified failure modes may appear. The FMEA should then be updated and plans made to address those failures to eliminate them from the product/process
• Determine the likelihood of Detection. Detection is an assessment of the likelihood that the Current Controls (design and process) will detect the Cause of the Failure Mode or the Failure Mode itself, thus preventing it from reaching the Customer.
Measure• Review Risk Priority Numbers (RPN). The Risk Priority Number is a
mathematical product of the numerical Severity, Probability, and Detection ratings: RPN = (Severity) x (Probability) x (Detection)The RPN is used to prioritize items than require additional quality planning or action.
• Determine Recommended Action(s) to address potential failures that have a high RPN
• Assign Responsibility and a Target Completion Date for these actions. This makes responsibility clear-cut and facilitates tracking
• Indicate Actions Taken. After these actions have been taken, re-assess the severity, probability and detection and review the revised RPN's. Are any further actions required?
• Indicate Actions Taken. After these actions have been taken, re-assess the severity, probability and detection and review the revised RPN's. Are any further actions required?
Measure
• Process Map Analysis
With the SIPOC & as-is process maps in place, we conduct a process map analysis to identify casual X’s.
Four key tools that can be used are: Nature of Work Moments of Truth The flow of work Disconnects Matrix
Measure
• Nature of Work Nature of Work evaluates each and every step in the process map in terms of it being a :
Value Added – Steps that are considered essential to produce and deliver the product or service to meet the customer’s needs & requirements. Customer is willing to pay for this.
Non Value Added - Steps that are considered non-essential to produce and deliver the product or service to meet the customer’s needs & requirements. Customer is not willing to pay for this.
Value Enabling - Steps that are not essential, but that allow the value adding tasks to be done better / faster.
Measure
• Nature of WorkThe main focus in Nature of Work analysis is on non value added work & one must be on the watch out for all kinds of process steps beginning with the prefix “Re”
Revise
Recheck
Return
Redesign
Remeasure
Reship
Remake
Rework
Reject
Reissue
Retype
Reevaluate
Redo
Repeat
Retest
Recall
Measure
Moments of Truth
This is defined as the critical judgement of a customer about the service delivery and can be drawn anytime based upon a service experience (or lack thereof)
Measure
• The Flow of Work
o Helps us understand & identify what in our process s causing a service delay that results in a customer wait
o Evaluates the cycle time of the process under two broad categories: Process time Delay time
Process time
+Delay time
Cycle Time
Measure
• Disconnects MatrixSpeaks of identifying different types of disconnects which are an integral part of the process at a given point in time & helps prioritise disconnects for initiating improvement.
Speaks of identifying process disconnects in the form of: Process Gaps Conflicting objectives Redundancies Unclear requirements Tricky hand-offs Common problem areas
Measure
• Cause & Effect Matrix
Is a method of systematically prioritising all of the potential causes that may be contributing to the problem (possible project X’s)
It is a grid structure, also known as C&E matrix, which numerically relates X’s to the Y’s
Measure
• Cause & Effect Matrix
2799999Order entry forms
491131Legal information
1971399Sales person knowledge
2493999Customer Needs
1971399Technical Information
311111Sales LeadsNegotiate order
Process InputProcess Step
Total
4321
57910Rating of Importance to customer
2799999Order entry forms
491131Legal information
1971399Sales person knowledge
2493999Customer Needs
1971399Technical Information
311111Sales LeadsNegotiate order
Process InputProcess Step
Total
4321
57910Rating of Importance to customer
Co
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ith
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Acc
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Hig
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Measure
• Control Impact Matrix
Helps Prioritise Possible X’s
High Impact Medium Impact Low Impact
In Control
Out of
Control
Measure
• Key deliverablesStep 7 – Develop data collection strategy
Understand data types Develop data collection plan Develop data sampling strategy Validate measurement system Execute the data collection plan
Measure
Data collection plan:Pre-Data Collection Steps1. Clearly define the goals and objectives of the data collection2. Reach understanding and agreement on operational definitions and methodology for the data collection plan3. Ensure data collection (and measurement) repeatability, reproducibility, accuracy, and stabilityDuring Collection Steps4. Follow through with the data collection processPost-Data Collection Steps5. Follow through with the results
Measure
Step 1: Define Goals And ObjectivesA good data collection plan should include:
• A brief description of the project • The specific data that is needed • The rationale for collecting the data • What insight the data might provide (to a process being
studied) and how it will help the improvement team • What will be done with the data once it has been
collected• Being clear on these elements will facilitate the accurate
and efficient collection of data.
Measure
Step 2: Define Operational Definitions and Methodology
• How many observations are needed • What time interval should be part of the study • Whether past, present, and future data will be collected • The methodologies that will be employed to record all
the data
MeasureStep 3: Ensuring Repeatability, Reproducibility, Accuracy and StabilityThe data being collected (and measured) will be repeatable if the same operator is able to reach essentially the same outcome multiple times on one particular item with the same equipment. The data will be reproducible if all the operators who are measuring the same items with the same equipment are reaching essentially the same outcomes. In addition, the degree to which the measurement system is accurate will generally be the difference between an observed average measurement and the associated known standard value. The degree to which the measurement system is stable is generally expressed by the variation resulting from the same operator measuring the same item, with the same equipment, over an extended period.
Measure
Step 4: The Data Collection Process• Once the data collection process has been planned and
defined, it is best to follow through with the process from start to finish, ensuring that the plan is being executed consistently and accurately. Assuming the Black Belt or project lead has communicated to all the data collectors and participants what is to be collected and the rationale behind it, he or she might need to do additional preparation by reviewing with the team all the applicable definitions, procedures, and guidelines, etc., and checking for universal agreement. This could be followed up with some form of training or demonstration that will further enhance a common understanding of the data collection process as defined in the plan.
Measure
• Step 5: After The Data Collection Process• Referring back to the question of whether or not the data
collection and measurement systems are reproducible, repeatable, accurate, and stable, the Black Belt or project lead should check to see that the results (data and measurements) are reasonable and that they meet the criteria. If the results are not meeting the criteria, then the Black Belt or project lead should determine where any breakdowns exist and what to do with any data and/or measurements that are suspect. Reviewing the operational definitions and methodology with the participants should help to clear up any misunderstandings or misinterpretations that may have caused the breakdowns.
Measure
• Sampling Strategy
• Population is the total group of elements that we want to study
• Sample is the subgroup of the population that we actually study, as to study each & every element is usually not possible,
Measure
• Population & Sampling – The PurposeSampling is carried out to estimate & to test characteristics of distributions and to develop explanatory or predictive models showing the relationship between variables of the distribution.
Estimates can be categorised as:• Point Estimates – Single numbers, unlikely to be exactly
correct of the parameter being estimated• Confidence Intervals – ranges of values with a known
likelihood of containing the correct value of the parameter being estimated
Measure
• Sampling Approach
Four key techniques that can be used to sample are:
• Random Sampling
• Stratified random sampling
• Systematic sampling
• Sub group sampling
Measure• Random Sampling• Here each unit of the population has the same chance of being selected
while drawing out a sample• Random samples are used in population sampling situations when
reviewing historical or batch data. The key to random sampling is that each unit in the population has an equal probability of being selected in the sample. Using random sampling protects against bias being introduced in the sampling process, and hence, it helps in obtaining a representative sample.
• In general, random samples are taken by assigning a number to each unit in the population and using a random number table or Minitab to generate the sample list. Absent knowledge about the factors for stratification for a population, a random sample is a useful first step in obtaining samples.
• For example, an improvement team in a human resources department wanted an accurate estimate of what proportion of employees had completed a personal development plan and reviewed it with their managers. The team used its database to obtain a list of all associates. Each associate on the list was assigned a number. Statistical software was used to generate a list of numbers to be sampled, and an estimate was made from the sample.
Measure• Stratified Random Sampling• Like random samples, stratified random samples are used in
population sampling situations when reviewing historical or batch data. Stratified random sampling is used when the population has different groups (strata) and the analyst needs to ensure that those groups are fairly represented in the sample. In stratified random sampling, independent samples are drawn from each group. The size of each sample is proportional to the relative size of the group.
• For example, the manager of a lending business wanted to estimate the average cycle time for a loan application process. She knows there are three types (strata) of loans (large, medium and small). Therefore, she wanted the sample to have the same proportion of large, medium and small loans as the population. She first separated the loan population data into three groups and then pulled a random sample from each group.
Measure• Systematic Sampling• Systematic sampling is typically used in process sampling situations
when data is collected in real time during process operation. Unlike population sampling, a frequency for sampling must be selected. It also can be used for a population study if care is taken that the frequency is not biased.
• Systematic sampling involves taking samples according to some systematic rule - e.g., every fourth unit, the first five units every hour, etc. One danger of using systematic sampling is that the systematic rule may match some underlying structure and bias the sample.
• For example, the manager of a billing center is using systematic sampling to monitor processing rates. At random times around each hour, five consecutive bills are selected and the processing time is measured.
Measure• Rational Sub-Grouping• Rational sub-grouping is the process of putting measurements into
meaningful groups to better understand the important sources of variation. Rational sub-grouping is typically used in process sampling situations when data is collected in real time during process operations. It involves grouping measurements produced under similar conditions, sometimes called short-term variation. This type of grouping assists in understanding the sources of variation between subgroups, sometimes called long-term variation.
• The goal should be to minimize the chance of special causes in variation in the subgroup and maximize the chance for special causes between subgroups. Sub-grouping over time is the most common approach; sub-grouping can be done by other suspected sources of variation (e.g., location, customer, supplier, etc.)
• For example, an equipment leasing business was trying to improve equipment turnaround time. They selected five samples per day from each of three processing centers. Each processing center was formed into a subgroup.
• When using sub-grouping, form subgroups with items produced under similar conditions. To ensure items in a subgroup were produced under similar conditions, select items produced close together in time.
Measure
• Sampling Bias• Bias – Systematic differences between sample & the
population; occurs when these differences are introduced into the sample as a result of the selection process resulting in a non-representative sample and the subsequent results will not be reliable.
Bias can enter at two levels:
• At a strategic level: while developing the sampling plan – e.g. environmental sampling bias, convenience sampling bias, systematic sampling bias, etc
• At a tactical level: while carrying out the sampling plan e.g. measurement bias, non-response bias, etc
Measure
• Measurement System Analysis• In order to ensure that the measurement system is sufficient
we formulate a plan to test for measurement system variability:
• Data Repeatability – Variability exclusive of time, people differences or other changing conditions
• Reproducibility – Variability inclusive of time, people differences or other changing conditions
Measure
• Measurement System Analysis
Key parameters: Bias Repeatability Reproducibility Stability Linearity Correlation Tolerance Distinct categories
Measure• Bias – If a standard scale measures the distance b/w point A
& point B as 10 ft, then the scale that the appraiser is using must also measure the distance b/w point A & point B as 10 ft
• Repeatability – If an appraiser measures the distance b/w point A & point B as 10 ft with a scale; given the same scale to the same appraiser, he/she should measure the distance b/w A & B as 10-ft again
• Reproducibility - If an appraiser measures the distance b/w point A & point B as 10 ft with a scale; given the same scale to another appraiser, he/she should measure the distance b/w A & B as 10-ft again
• Stability - If an appraiser measures the distance b/w point A & point B as 10 ft with a scale; given the same scale to the same appraiser after a long interval of time, he/she should measure the distance b/w A & B as 10-ft again
Measure
• Gauge Repeatability & Reproducibility (GR&R)
Is used to analyze the variation of components of measurement systems to minimize any variation in the measurement systems.
Measure
• Key deliverables
Step 8 – Calculate Process Capability
Understand variation to figure out Special & Common causes Interpret & analyse the data graphically; Establish Defect rate & Process Yield; Establish process capability & calculate Baseline process
Sigma
Measure
• Process Variation – The types
• Common Cause variation is free of any assignable causes and the observed variation represents the effect of only random causes
• Special Cause variation has assignable causes present and the observed variation represents the effect of non-random causes
Measure
• Data Interpretation
Stability (Run Charts) Normality (Anderson-Darling test) Shape (Skewed data) Spread Centering
Measure
• Stability is a measure of differences in the frequency distribution which are expected & predictable for and over a long period of time.
• Run Chart is an important tool for understanding data stability and simple time ordered plots of process output data
• Their patterns indicate special causes of variation
Measure
• Run Chart – Special Cause patterns
Same Value Plot Clustering or too few runs Plot Mixtures or too many runs Plot Oscillations Plot Trends Plot Shifts Plot
Measure
Measure
• Centering – Spread, Shape & Normality
Symmetrical Distribution Modal (Bi & Multi) Distribution Exponential (Skewed & Long Tailed) Distribution
Anderson Darling test is performed on the data to understand its normality characteristics.
Measure
Negatively skewed
Measure
• Process Capability
Process capability is the inherent variability of a customer quality characteristic that the process is capable of maintaining, when in a state of statistical control, under a given set of conditions. Of 2 types:
Short term capability Long term capability
Difference b/w both these is known as the Process Shift.
MeasureNormal Distribution with Curve
-3 -2 -1 0 1 2 3
0
5
10
15
20
25
30
35
40
45
Norm al
Frequency
Normal Distribution, with Curve
Measure• Measures provide information on
– current process performance.– impact of changes to the process.– signals of potential problems.
• Without measures, it is difficult to – establish priorities and set realistic goals.– assess whether changes in a process result in
improvement.– identify causes of problems.– determine if changes in the process are maintained over
time.
Examine the current situation
Objectives:
Examine the process from the problem’s perspective by detailing how the overall process works.
Analyze process data to find major sources of variation.
Questions:
• What can we learn from a detailed process flow ?
• Are there obvious weaknesses ?
• When does the problem occur ?
• Where does the problem occur ?
• Who is involved when the problem occurs ?
• What are the symptoms of the problem ?
• How will the data be gathered ?
Describe the current
situation with data
View the process flow
in detail
MeasureMeasure
A
ANALYSE
Analyse
• Use the gathered data to understand the problem statistically and identify the area of focus for improvement
• Key Deliverables
- Step 9 – Define Performance Objectives
Baselining, Entitlement & Benchmarking
- Step 10 – Identify and validate Vital X’s
Establish Segmentation & Stratification Plan; Conduct process door analysis; prioritise vital few Xs; conduct data door analysis; quantify opportunity; conduct hypothesis testing; summarise significant Xs
Analyse
• Key deliverablesStep 9 – Define Performance Objectives
Baselining, Entitlement & Benchmarking of a Process
Analyse
• Baselining is quantifying the goodness (or badness) of the current process before ANY improvements can be made, using a sample data.
(Average long term performance of a process)
• Know how the best do it
• Benchmarking – "World class in process“
• Competitive comparison
• Best practices
Analyse
• Key deliverablesStep 10 – Identify and validate Vital X’s Establish Segmentation & Stratification Plan Conduct process door analysis Prioritise vital few Xs Conduct data door analysis Quantify opportunity Conduct hypothesis testing Summarise significant Xs
Analyse
Total Variation
BetweenVariation
WithinVariation
Understand Don’t understand
Cancontrol
Cannotcontrol
Technologylimitation
Fullcontrol
Partialcontrol
Naturalvariation
Total Variation
BetweenVariation
WithinVariation
Understand Don’t understand
Cancontrol
Cannotcontrol
Technologylimitation
Fullcontrol
Partialcontrol
Naturalvariation
Analyse• Quantify Opportunity
– Pareto Histogram is useful for continuous data same way when the data is
discrete, most teams create a Pareto chart. Discrete data is counted data - go/no-go, off/on, yes/no, and defect/no defect type data.
An Italian economist Vilfredo Pareto,in the sixteenth century proved mathematically that 80 percent of the world's wealth was controlled by 20 percent of the population. This 80-20 rule eventually proved applicable in arenas other than economics.
When dealing with discrete data, the project team should create reason codes for why a defect occurs and count and categorize the data into these reason codes and a pareto chart should be prepared.
Analyse• How To Construct A Pareto Chart
A pareto chart can be constructed by segmenting the range of the data into groups (also called segments, bins or categories). For example, if your business was investigating the delay associated with processing credit card applications, you could group the data into the following categories:
• No signature • Residential address not valid • Non-legible handwriting • Already a customer • Other • The left-side vertical axis of the pareto chart is labeled Frequency (the number of counts
for each category), the right-side vertical axis of the pareto chart is the cumulative percentage, and the horizontal axis of the pareto chart is labeled with the group names of your response variables.
• You then determine the number of data points that reside within each group and construct the pareto chart, but unlike the bar chart, the pareto chart is ordered in descending frequency magnitude. The groups are defined by the user.
• What Questions The Pareto Chart Answers• What are the largest issues facing our team or business? • What 20% of sources are causing 80% of the problems (80/20 Rule)? • Where should we focus our efforts to achieve the greatest improvements?
Analyse
• Box PlotA Box Plot is a graphical representation of a five number summary. The ends of the "box" are the first and third quartiles, with the median inside the "box". Points are used to represent the maximum and minimum value, with lines (called whiskers) connecting the points to the box.
Analyse
• Scatter Plot
Y
Xr = 0
Y
Xr = 1.0
Y
Xr = -1.0
Y
X r = 0.6
Y
Xr = 0.8
Y
X r = 0.0
Analyse
• Hypothesis Testing Discuss and clarify the potential root causes. Clean-up the potential root causes by removing duplicates. Prioritize the cleaned-up potential root causes by using a
voting or ranking process. The team should reach consensus on the final list of root causes.
Develop a formal hypothesis for each root cause on the final list.
Analyse
• Conclusion:• Analysis can take several forms. Some Six Sigma
programs like to use a lot of diagrams and worksheets, and others prefer discussion and list making. There are many tools which can be used to perform analysis like Box Plot, Cause and Effect Diagram, Progressive Analysis, Ranking, Pareto Analysis, Prioritization Matrix, Value Analysis etc. The proper procedure is the one that works best for your team, provided that the end result is successful.
IMPROVE
Improve• Improve – Find possible solutions, plan for new measurements
and implementation strategies for fixing the quantified problem
Key Deliverables- Step 11 - Generate possible solutions
Define Solution parameters; Generate possible solutions & refine solutions; screen possible solutions against musts & wants; conduct cost benefit analysis for potential solutions; assess risks for potential solutions
- Step 12 - Select and pilot solutions
Select implementable solutions; implementation of solutions through a pilot; validate measurement system; calculate improved process sigma; set new interval team targets; conduct new process mapping
Improve
Key Deliverables
- Step 11 - Generate possible solutions Define Solution parameters Generate possible solutions & refine solutions Screen possible solutions against musts & wants Conduct cost benefit analysis for potential solutions Assess risks for potential solutions
Improve
• Solution Generation:
(1)Re-Express from Someone Else's Perspective - say, with the mentality of a five-year-old or of an alien from another planet. With this technique, team members drop all pre-conceived notions, experience and knowledge about the problem and simply ask: "What would I expect of a new mobile phone?" or "How would I overcome long waiting hours at the medical clinic on Monday mornings?" It might take the team some time to warm up to this approach, but wait and see. By allowing everybody to say "stupid" things, a project leader may start some great solution generation. As Albert Einstein said, "If at first an idea is not absurd, then there is no hope for it."
Improve• (2) Related Worlds - Never assume that no one has ever faced an
issue like the one the team is facing now. And never assume there is nothing valuable to learn from the immediate world.To harness related worlds, the project leader should encourage every member of the team to go out and see what can be learned from others. The creative act occurs when someone applies what they discover to the team's own challenge, but in a unique way.
• Roll-on deodorant illustrates this technique. The related world was the ballpoint pen. Inventors of the roll-on deodorant "discovered" the similarity between two situations where a liquid had to be spread equally thin across a surface.
• Related worlds is a great technique for demystifying the creative process. All one has to do is ask, "Where in the world has my challenge been faced before?" and "What can I learn and steal from that?" Yes, steal. "Originality is nothing but judicious imitation," according to Voltaire.
Improve(3) Revolution - Revolution is creativity at its most provocative. Revolution is a
deliberate challenge of existing rules and assumptions. Some of the great creative leaps of our time - "What if we could fly?" - have come from revolution. To use this technique, start by writing down all the "rules." What is the shape, use, feel, touch, application, process, etc.? Somebody, somewhere started with the rule that "Human beings cannot fly." And then other people - among them, Leonardo da Vinci and the Wright brothers - imagined breaking that rule.
• Take, for example, shampoo:
• As illustrated in Table 1, once the rules are written down, the team can start to play around with them: Try exaggerating, opposing, reducing and reversing as many of the assumptions as possible.
Table 1: Rules Are Made to Be Broken
Rule Rule Breaker
Shampoo is a liquid. Shampoo is a solid, a mousse, a milk.
Shampoo comes in plastic bottles.
Shampoo comes in a beautiful glass bottle, a fabric container.
Shampoo is used with water. Shampoo is used with a hairdryer, a gas, a comb.
Improve(4) Random Links - Though no more effective than the other techniques,
random links may feel like the most creative one. It is the simple art of selecting a random piece of stimulus that has nothing to do with the creative challenge and then deliberately forcing a connection.
• This technique was used in a brainstorming session some years ago with engineers in the mobile phone industry. Participants collected ideas on new features, wrote them on cards and put them on a pin wall. After some time the team realized it was stuck. To get ideas flowing again, the session facilitators put the pin wall out of sight, set up another one and asked the team to brainstorm ideas on an advertisement randomly chosen from a magazine.
• The brainstorming produced words like "friendship," "eternity," and "vacation." After a couple of minutes, the facilitators stopped this process, brought back the first pin wall and asked the team to make a connection between the ideas on the advertisement regarding the original topic - new features for mobile phones. Some of the "crazy" ideas that appeared on the pin wall in that session became reality!!
Improve
Key Deliverables- Step 12 - Select and pilot solutions
Select implementable solutions Implementation of solutions through a pilot Validate measurement system; Calculate improved process sigma Set new interval team targets; Conduct new process mapping
Improve• Improve
Improvements in existing systems are necessary to bring the organization towards achievement of the organization goals. Creative development of processes and tools brings about a new lease on life for the organization’s processes and takes them nearer to organizational objectives.
Various project management and planning tools can be used to implement these new techniques and processes. Appropriate usage of statistical tools is important to measure the data, which is necessary to understand improvements done and any shortcomings that may exist.
Improve
• If the project team does a thorough job in the root causation phase of Analysis, the Improve phase of DMAIC can be quick, easy, and satisfying work.
• The objective of Improve Phase is to identify improvement breakthroughs, identify high gain alternatives, select preferred approach, design the future state, determine the new Sigma level, perform cost/benefit analysis, design dashboards/ scorecards, and create a preliminary implementation plan.
CONTROL
Control
• Control – Verify that implemented improvements have fixed the problem and put procedure in place to continue the verification on a periodic basis
Key Deliverables- Step 13 – Process Control & Risk Analysis
Control Charts Deployment & Monitoring; Risk assessment & mistake proofing
- Step 14 – Document and execute Control Plan- Step 15 – Institutionalise and Sign-Off
Control• (1) Quality control:• The ultimate purpose in control is overall assurance that a high
standard of quality is met. The customer's expectations depend on this, so control is inherently associated with quality.
• Since the purpose to Six Sigma is to improve overall process by reducing defects, quality control is the essential method for keeping the whole process on track; for enabling us to spot trouble and fix it; and for judging how effectively the project was executed and implemented.
• Quality is at the heart of the Six Sigma philosophy. Reducing defects has everything to do with striving for perfection. Whether we reach perfection or not, the effort defines our attitude toward quality itself.
Control
• (2) Standardization:
• One feature of smooth processing is to enable processes to go as smoothly as possible. This usually means standardization. In a manufacturing environment, the value of standardization has been proven over and over.
• We need to devise a control feature to processes so that the majority of work is managed in a standardized manner.
Control
• (3) Control methods and alternatives:• The development of a new process of any change to an
existing process requires the development of procedures to control work flow.
• When a process cannot be managed in the normal manner, we need to come up with alternatives short of forcing compliance to the standardized method.
Control• (4) Responding when defects occur:• The final step in a control process is knowing how to respond
once a defect is discovered. The weak links in the procedure where defects are most likely to occur, can and should be monitored carefully so that defects can be spotted and fixed before the process continues.
• The response to a defect may be to prevent a discovered flaw from becoming a defect at all. In the best designed systems, defects can be reduced to near zero, so that we may actually believe that Six Sigma can be attained.
Control
• Conclusion:• The project team determines how to technically control
the newly improved process and creates a response plan to ensure the new process maintains the improved sigma performance.
MinitabNew Minitab techniques: Opening Minitab, navigating, entering data, generating random data, naming columns, displaying descriptive statistics, time series plots, control charts and options, dot plots, histograms, box plots, tiling graphics.
Calc > Random Data > NormalGenerate 100 rows of data store in C1, mean of 100, standard deviation of 3.
Name C1”Normal 100-3”Window > InfoStat > Basic Statistics > Display Descriptive > Select C1Stat > Basic Statistics > Display Descriptive > Select Graphs > Select “Histogram of data, with normal curve” > Close GraphGraph> time series plot> select C1
Project Selection Guidelines
Clarify the Big Picture
In sync with business objectives, who is accountable, who will execute, etc
Strategic alignment through Projects
Look at improving customer satisfaction, cost, capacity & top end growth
Identify & improve key performance metrics
Defects / cost savings / Quality / Performance / TAT