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Continuous Improvement Toolkit

Graphical Analysis


Check Sheets

DataCollection

Process MappingFlowcharting

Flow Process Charts

5S

Value Stream Mapping

Control Charts

Mistake Proofing

Tree Diagram*

UnderstandingPerformance

Fishbone Diagram

Design of Experiment

ImplementingSolutions**

Creating Ideas

Brainstorming Attribute Analysis

Deciding & Selecting

Decision Tree

Force Field Analysis Cost Benefit Analysis

Voting

Planning & Project Management*

Value Analysis

Kaizen Events

Quick Changeover

ManagingRisk

FMEA

PDPC

RAID Log*

Observations

Focus Groups

UnderstandingCause & Effect

Pareto Analysis

IDEF0

5 Whys

Matrix DiagramKano Analysis

KPIs

Lean Measures

Importance-Urgency Mapping

Waste Analysis

Fault Tree Analysis

Relationship Mapping*

Benchmarking**

SCAMPER**

C&E Matrix

Confidence Intervals

Pugh Matrix

SIPOC*

Prioritization Matrix

Stakeholder Analysis

Critical-to Tree

Paired Comparison

Improvement Roadmaps

Interviews

QFD

Graphical Analysis

Lateral Thinking

Hypothesis Testing

Visual Management

Ergonomics

Reliability Analysis

Cross Training

How-How Diagram**

Flow

Time Value Map

ANOVA

Gap Analysis*

Traffic Light Assessment

TPN Analysis

Decision Balance Sheet

Suggestion systems

Risk Assessment*

AutomationSimulation

Break-even Analysis

Service Blueprints

DMAIC

Process Redesign

Run Charts

TPM

Control Planning

Chi-Square

SWOT Analysis

Capability Indices

Policy Deployment

Data collection planner*

Affinity DiagramQuestionnaires

Probability Distributions

Bottleneck Analysis**

MSA

Descriptive Statistics

Cost of Quality*

Process Yield

Histograms & Boxplots

Just in Time

Pick Chart

Portfolio Matrix

Four Field Matrix

Root Cause Analysis Data Snooping

Morphological Analysis Sampling

Spaghetti Diagram

Pull

OEE

Mind Mapping*

Project Charter

PDCA

Designing & Analyzing Processes

CorrelationScatter PlotsRegression

Gantt Charts

Activity NetworksRACI Matrix

PERT/CPMDaily Planning

MOST

Standard work Document controlA3 Thinking

The Continuous Improvement Map

Multi vari Studies


Statistic is the science of describing,interpreting and analyzing data.

Statistics may be: • Graphical:

Makes the numbers visible.

• Inferential:Makes inferences about populations from sample data.

• Analytical:Uses math to model and predict variation.

• Descriptive:Describes characteristics of the data (location and spread).

- Graphical Analysis


Graphs truly show that a picture is worth a thousand of words.

A long list of data is usually not practical for conveying information about a process.

One of the best ways to analyzeany process is to plot the data.

Many graphical tools are available which can generate graphs quicklyand easily.


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Benefits:

Allows to learn about the nature of the process.

Enables clarity of communication.

Helps understanding sources of variation in the data.

Provides focus for further analysis.



Different graphs can reveal different characteristics of your data:• Central tendency.

• Dispersion.

• The general shape for thedistribution.

Conclusions drawn from graphs may require verification through advanced statistical techniques such as significance testing and experimentation.



Graphing the data can be utilized for both historical data and live data collection activities.

You need to pick the right graphical tool as there are a lot of different ways to plot your data.

If one graph fails to reveal anythinguseful, try another one.



Line Charts:

One of the simplest form of charts.

Useful for showing trends in quality, cost or other process performance measures.

They represent the data by connecting the data points by straight lines to highlight trends in the data.

A standard or a goal line may also be drawn to verify actual performance against identified targets.

Time series plots, run charts, SPCcharts and radar charts are all linecharts.



Time Series Plots:

Line charts used to evaluate behavior in data over a time interval.

They can be used to determine if a process is stable by visually spotting trends, patterns or shift in the data.

If any of these are observed, then we can say that the process is probably unstable.

It requires the data to be in the order which actually happened.

More advanced charts for assessing the stability of a process over time are run charts and SPC charts.



Time Series Plots:

Time Series Analysis is the analysis of the plotted data in order to get meaningful information.

Different behaviors of the data can be observed such as:• Upward and downward trends.

• Shifts in the mean.

• Changes in the amount of variation.

• Patterns and cycles

• Anything not random.

Time Series Forecasting is theuse of a model to predict futurevalues based on observed values.



Example – The average time it needed to change a label:


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A time series plot for evaluating continuous data


Example – The number of unanswered calls in a call center:


A time series plot for evaluating count data

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Example – The number of scrapped products generated from three machines:


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Pie Charts:

Circular charts that make it easy to compare proportions.

Widely used in the business and media worlds for their simplicity and ease of interpretation.

They represent each category as a slice of the pie.

They display the proportion of each category relative to the whole data set.



Pie Charts:

A Doughnut Chart is a variation of the pie chart with a blank center.

It allows for additional information to be included about the data.

Pie and doughnut charts work well with few categories.

They are suitable for presenting data for around seven groups or fewer.



Bar Charts:

Used to display frequencies of attribute data.

They focus on the absolute value of the data.

The bars on the chart are presented horizontally or vertically.

When a bar chart presents the categories in descending order of frequency, this is called a Pareto Chart.



Bar Charts:

Grouped Bar Charts display bars clustered in groups.

Staked Bar Charts stack bars of each group on top of each other to show the cumulative effect.

A 100% Staked Bar Chart is used for demonstrating the difference in proportion between categories.



Example – A grouped bar chart displaying the number of occupied beds in a hospital in two consecutive years.


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Example – A stacked bar chart displaying the number of occupied beds in a hospital in two consecutive years.


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Dotplots:

A Dotplot is a graphical representation ofdata using dots plotted on a simple scale.

A form of frequency distribution.

It is suitable for displaying small tomoderate data sets.

The X-axis is divided into many small intervals called bins.

The data values falling within each bin are represented by dots (one or more dots per data point).

The end result is a set of vertical lines of dots.



Dotplots:

It is generally used when the data is discrete.

It can also be used to present continuous data.

It shows where the data are clustered, where the gaps are located and can help identify outliers.

Dotplots are also useful for comparing distributions in terms of their shape, location and spread.



Example – A dotplot that displays the number of complaints made by customers in a given period of time.


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Number of Complaints

A dotplot for evaluating count data


Example – A dotplot showing the GPA scores of all students in a business college.


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GPA

Each symbol represents up to 4 observations.

A dotplot for evaluating continuous data


Example – A dotplot is created to compare the teachers who had been on sick leave between two types of schools.


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Example – An analysis that was conducted for diagnosing the presence of diabetes at a workplace.


Describes the shape of the data


Example – An analysis that was conducted for diagnosing the presence of diabetes at a workplace.


New diagnosis

High levels

Diabetes under control

Low level


Individual Value Plots:

Graphs that are useful to give an overall picture of the individual values that make up a data set.

Often used for comparing distributions that have small number of data.

They enable to see all the values of a dataset even if there are similar data points.

They give an idea of the distribution shapesand whether outliers are present.



Example – An individual value plot showing the responses of a particular marketing campaign that uses multiple advertising methods.


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Individual Value Plots:

What can you conclude from this Individual Value Plot?



Radar Charts:

Used to display and compare multiple data sets over a range of characteristics or over a specific period of time.

It comes in the form of a two-dimensional chart.

It has a radial axis and an angular axis.

After plotting the data, a point close to the center indicates a low value and a point near the edge indicatesa high value.

A line is normally drawn connectingthe data values for each data set.



Example – A radar chart that displays the daily mean temperatures in four different cities over the year.


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Multi-Vari Charts:

Variation in the data may come from multiple sources.

A Multi-Vari Chart is a graphical tool that allows to visually show where the major variation is coming from.

Multiple variables are plotted together on a single chart.

Often used when studying the variation within:• A subgroup.

• Between subgroups.

• Over time.



Example – A multi-vari chart showing how the type and composition affect the durability of a carpet.



Scatter Plots:

Many problems require theestimation of the relationshipbetween two or more variables.

Scatter plots are used to study therelationship between two variables.

They are used to determine whathappens to one variable whenanother variable changes value.



Probability Plots:

Graphical techniques that provide a more decisive approach for determining if your data follows a particular distribution.

Constructed in a way that the pointswill fall in a straight line if they fit thedistribution in question.

They are an improvement from justassessing visually.


Normal Probability Plots


Graph Selection:

The graphs you choose depends on:• The type of data you have.

• The objective you are trying to achieve.

There are graphs for continuous data andgraphs for count and attribute data.

Remember that you need to perform additionalstatistical analysis before drawing any conclusion.



Graph Selection:


Continuous Data

Dotplots

Histograms*

Individual Value Plots

Boxplots*

Time Series Plots

Determine the shape of the data

Scatter Plot

Multi-Vari ChartsUnderstanding thesource of variation

Study relationshipsbetween variables

Understanding process stability

Understandingthe differencesbetween groups

* Larger amount of data

Count/Attribute Data

Bar Charts

Pareto Charts

Pie Charts

Dotplots

Comparing betweengroups

Determine the shape of the data

Time Series PlotsUnderstanding process stability

Scatter PlotStudy relationshipsbetween variables

continuous improvement toolkit...continuous improvement toolkit . truly show that a picture is worth...

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