Continuous Improvement Toolkit Graphical Analysis Continuous - - PowerPoint PPT Presentation

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Continuous Improvement Toolkit Graphical Analysis Continuous Improvement Toolkit . www.citoolkit.com The Continuous Improvement Map Managing Deciding & Selecting Planning & Project Management* Risk PDPC Decision Balance Sheet

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Continuous Improvement Toolkit . www.citoolkit.com

Continuous Improvement Toolkit Graphical Analysis

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Check Sheets

Data Collection

Process Mapping Flowcharting Flow Process Charts 5S Value Stream Mapping Control Charts Mistake Proofing Tree Diagram*

Understanding Performance

Fishbone Diagram Design of Experiment

Implementing Solutions** 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

Managing Risk

FMEA PDPC RAID Log* Observations Focus Groups

Understanding Cause & Effect

Pareto Analysis IDEF0 5 Whys Matrix Diagram Kano 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* Automation Simulation 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 Diagram Questionnaires 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

Correlation Scatter Plots Regression Gantt Charts Activity Networks RACI Matrix PERT/CPM Daily Planning MOST Standard work Document control A3 Thinking

The Continuous Improvement Map

Multi vari Studies

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 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
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 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 analyze

any process is to plot the data.

 Many graphical tools are available

which can generate graphs quickly and easily.

  • Graphical Analysis

*

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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.

  • Graphical Analysis
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 Different graphs can reveal different characteristics of your

data:

  • Central tendency.
  • Dispersion.
  • The general shape for the

distribution.

 Conclusions drawn from graphs may require verification

through advanced statistical techniques such as significance testing and experimentation.

  • Graphical Analysis
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 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 anything

useful, try another one.

  • Graphical Analysis
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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, SPC

charts and radar charts are all line charts.

  • Graphical Analysis
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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

  • ver time are run charts and SPC charts.
  • Graphical Analysis
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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 the

use of a model to predict future values based on observed values.

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

  • Graphical Analysis

37.5 35.0 42.9 39.8 38.6 47.2 36.6 34.3 30.8 35.2 32.7 29.0

20 25 30 35 40 45 50 55

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

A time series plot for evaluating continuous data

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

  • Graphical Analysis

A time series plot for evaluating count data

25 27 27 27 28 22 21 30 24 27 15 20 24 31 22 21 22 21 20 30 27

10 15 20 25 30 35

Wk 1 Wk 3 Wk 5 Wk 7 Wk 9 Wk 11 Wk 13 Wk 15 Wk 17 Wk 19 Wk 21

Unanswered Calls

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

  • Graphical Analysis

2,000 4,000 6,000 8,000 10,000 12,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Scrapped Products Machine 1 Machine 2 Machine 3

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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.

  • Graphical Analysis
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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

  • r fewer.
  • Graphical Analysis
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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

  • rder of frequency, this is called a Pareto Chart.
  • Graphical Analysis
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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.

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

  • Graphical Analysis

450 420 432 363 320 340 309 350 320 340 389 440 440 426 420 380 342 359 352 360 350 325 399 480 100 200 300 400 500 600

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Number of Occupied Beds

Year 1 Year 2

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

  • Graphical Analysis

100 200 300 400 500 600 700 800 900 1000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Number of Occupied Beds

Year 1 Year 2

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Dotplots:

 A Dotplot is a graphical representation of

data using dots plotted on a simple scale.

 A form of frequency distribution.  It is suitable for displaying small to

moderate 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.

  • Graphical Analysis
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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.

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

  • Graphical Analysis

26 24 22 20 1 8 1 6

Number of Complaints

A dotplot for evaluating count data

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

  • Graphical Analysis

3.6 3.0 2.4 1 .8 1 .2 0.6

GPA

Each symbol represents up to 4 observations.

A dotplot for evaluating continuous data

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

  • Graphical Analysis

100.0% 80.0% 60.0% 40.0% 20.0% 0.0%

Sick %

Public Private

School Type

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

  • Graphical Analysis

Describes the shape of the data

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

  • Graphical Analysis

New diagnosis High levels Diabetes under control Low level

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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

  • f data.

 They enable to see all the values of a data

set even if there are similar data points.

 They give an idea of the distribution shapes

and whether outliers are present.

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

  • Graphical Analysis

Newspaper Mail Magazine

20 1 5 1 0 5

Type of Advertising Response

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Individual Value Plots:

 What can you conclude from this Individual Value Plot?

  • Graphical Analysis
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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 indicates a high value.

 A line is normally drawn connecting

the data values for each data set.

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

  • Graphical Analysis
  • 10

10 20 30 40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Abu Dhabi Istanbul Helsinki Cape Town

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

  • Graphical Analysis
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Scatter Plots:

 Many problems require the

estimation of the relationship between two or more variables.

 Scatter plots are used to study the

relationship between two variables.

 They are used to determine what

happens to one variable when another variable changes value.

  • Graphical Analysis
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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 points

will fall in a straight line if they fit the distribution in question.

 They are an improvement from just

assessing visually.

  • Graphical Analysis

Normal Probability Plots

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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 and

graphs for count and attribute data.

 Remember that you need to perform additional

statistical analysis before drawing any conclusion.

  • Graphical Analysis
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Graph Selection:

  • Graphical Analysis

Continuous Data

Dotplots Histograms* Individual Value Plots Boxplots* Time Series Plots

Determine the shape of the data

Scatter Plot Multi-Vari Charts

Understanding the source of variation Study relationships between variables Understanding process stability Understanding the differences between groups

* Larger amount of data

Count/Attribute Data

Bar Charts Pareto Charts Pie Charts Dotplots

Comparing between groups Determine the shape of the data

Time Series Plots

Understanding process stability

Scatter Plot

Study relationships between variables