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Continuous Improvement Toolkit

Histogram

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The Continuous Improvement Map

Multivariate

Brainstorming

IDEF0

Document control

Check Sheets**

Flowcharting

Flow Process Charts**

KPIs

SCAMPER***

Pugh Matrix

Lateral Thinking

Reliability

Service Blueprints

Affinity Diagrams

Mind Mapping*

Attribute Analysis

Morphological Analysis

How-How Diagram***

Control Charts

Waste Analysis**

Time Value Map**

Value Stream Mapping**

Value Analysis**

Suggestion Systems

Histogram

Activity NetworksRACI Matrix

Stakeholder Analysis

Improvement Roadmaps

DMAIC

SWOT Analysis

Policy Deployment

Project Charter

PDCA Gantt Charts

MOST

PERT/CPMDaily Planning

Delphi MethodPayoff Matrix

Relations Mapping

Data Mining

Just in Time

Automation

Product Family Matrix Flow

Spaghetti**

Multi-vari Studies

Decision Tree

FMEA

PDPC

RAID Log*

Fault Tree Analysis

Paired Comparison

Traffic Light Assessment

TPN AnalysisRisk Analysis*

Run Charts

Scatter Diagram

A3 Thinking

Importance Urgency Matrix

Four Field Matrix

Critical-to X Portfolio MatrixForce Field Analysis

Decision Balance Sheet

Break-even Analysis

Voting

Quality Function Deployment

Pick Chart

Gap Analysis*

Bottleneck Analysis

Cost Benefit Analysis

Kaizen Events Control Planning

Standard Work

Mistake Proofing

Quick Changeover Visual Management

Simulation

TPM

5S

Health & Safety

Best PracticesPareto Analysis

5 Whys

Prioritization Matrix

Hypothesis

SIPOC*

Matrix Diagram

Fishbone Diagrams

Tree Diagram*

Root Cause Analysis

Correlation

DOEANOVA

NonparametricChi-Square

Regression

Observations

KanoLean Measures

Benchmarking***

Interviews

Graphical Methods

Data collection planner*

Questionnaires

Probability Distributions

MSA

Descriptive Statistics

Cost of Quality*

Sampling

Focus Groups

Capability Indices

Process Yield

Project KPIs

Normal Distribution

DataCollection

UnderstandingPerformance**

ImplementingSolutions***

Planning & Project Management*ManagingRisk

UnderstandingCause & Effect

Designing & Analyzing ProcessesGroup Creativity

Selecting & Decision Making

Five Ws

Process Redesign

Pull

Process Mapping

OEE

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Histogram

One of the best ways to analyze any process is toplot the data

*

XX

X

X

X

X

X X

XX

X

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Histogram

A histogram is a graphical way that summarizes the important aspects of the distribution of continuous data

I t i s a t y p e o f b a r c h a r t

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Histogram

Histograms are sometimes called Frequency Plots as they show the frequency of continuous data values on a graph

While Pareto charts plot the frequency of count data

Nu

mb

er o

f o

ccu

rren

ce

(fre

qu

en

cy)

|––––––––– Value bins –––––––––|

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Histogram

Histograms can be drawn either vertically or horizontally

|–––––– Frequency ––––––|

|–––

–––

Freq

uen

cy –

––––

–|

The height of the column indicates how often that data value occurred

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Histogram

Histograms are widely used in statistics, process improvement, scientific research, economics,

and in social and human sciences

Mainly used to explore data as well as to present the data in an easy and understandable manner.

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Histogram

It allows to visually and quickly assess . . .

The central tendency and the amount of spread in the data

The shape of the distribution

The presence of gaps, outliers or unusual data points

Spread OutliersGap

Center*

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Histogram

Spread Outliers

Center*

Shows where most of

the data exists

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Histogram

Spread Outliers

Center*

Enables to quickly identify the

spread of the data

Creates a picture of the variation in a process

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Histogram

Spread Outliers

Center*

Overall shape shows

how the data is

distributed

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Histogram

Spread Outliers

Center* Helps to find unusual

data points and outliers

that may need further

investigation

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Histogram

Spread Outliers

Center* Helps to find unusual

data points and outliers

that may need further

investigation

Overall shape shows

how the data is

distributed

Enables to quickly identify the

spread of the dataShows where most of

the data exists

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Histogram

Plotting data in a histogram allows to know . . .

Minimum and maximum values Gaps and outliers

The shape of data (symmetric or skewed) Whether it’s unimodal, bimodal or multimodal

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Histogram

Used as the first step to determine the underlying probability distribution of a data set

A way to shape the sample data to make predictions and draw conclusions about an entire population

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Histogram

Histograms are used to identify . . .

Whether you can apply certain statistical tests

Patterns that provide clues to certain types of problems

Whether variability is within specification limits

Whether the process is capable or not

Whether there is a shift in the process

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Histogram

Used to verify that the changes made were a real improvement

Before After

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Histogram

Ideal to represent moderate to large amount of data

In practice, a sample size of at least 30 data values would be sufficient

N = 40 N = 14

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Histogram

It may not accurately display the distribution shape if the data size is too small

Dot plots are preferred over histograms when representing small amount of data

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Histogram

How to Construct a Histogram

Collect the data set and prepare it for the analysis

Data

C r e a t e a s u m m a r y t a b l e o f t h e d a t a

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Histogram

How to Construct a Histogram

T h e t o t a l w i d t h s h o u l d b e e q u a l t o t h e r a n g e o f t h e d a t a

Draw a horizontal line and divide it into equal intervals or bins (between 7 to 10 intervals)

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Histogram

How to Construct a Histogram

Draw bars above each bin to represent the frequencyof the data values within each interval

T h e b a r s s h o u l d b e a d j a c e n t w i t h n o g a p s b e t w e e n t h e m ( t o i n d i c a t e t h e c o n t i n u i t y o f t h e d a t a )

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Histogram

How to Construct a Histogram

Indicate the mean of the data and other important information

S u c h a s t h e s t a n d a r d d e v i a t i o n a n d t h e s p e c i f i c a t i o n l i m i t s

Mean

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Histogram

Example – Cable Diameters

0.600.580.560.540.520.50

20

1 5

1 0

5

0

Mean 0.5465

StDev 0.01934

N 100

Diameter of cable

Fre

qu

en

cy

Data source: Minitab

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Histogram

Example – Cable Diameters

0.600.580.560.540.520.50

20

1 5

1 0

5

0

Mean 0.5465

StDev 0.01934

N 100

Diameter of cable

Fre

qu

en

cy

The result can be summarized usingday to day language such as:

“The distribution looks symmetricaround the cable diameter mean(0.546 cm) and appears to fit theNormal Distribution”.

Data source: Minitab

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Histogram

Example – Presence of Diabetes

Mean 99.65 StDev 36.58N 310

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Histogram

Example – Presence of Diabetes

The distribution of the data is skewed to the right.

The distribution is more like an exponential distributionwhich is normal for this type of data.

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Histogram

Further Information

Histograms, however, can’t see changes and trends over time

Histograms like control charts can be used to assess improvement overtime

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Histogram

Further Information

You can illustrate a stratification factor in histograms

Morning shift Evening shift Night shift

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Histogram

Further Information

There are many applications and online services that allow the creation of histograms quickly and automatically

(such as Minitab, JMP, and SPSS)

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Histogram

Further Information – One of the 7 Basic Tools of Quality