Jim McKiernan

Operational Excellence in Biopharmaceuticals3. Introduction to Lean, The Measure Phase

October 2014

IntroductionsCourse Outline and TimingsDate Event Content Timing

16 Sept 11-1 Face to Face

Intro, Business Context, Operational Excellence Overview, LSS Structure, DMAIC, Project selection and initiation

2 hours

2 Oct 11-12 Face to Face

The Define phase, Benefit definition 1 hour

2 Oct 12-13 Face to Face

Measure phase, Process mapping, data types and measurement 1 hour

21 Oct 11-13 Face to Face

Intro to statistics, Lean; 5 lean principles, 8 wastes, Kaizen, 5S 2 hours

27 Oct 11-12 Webinar Analysis Phase, Six Sigma, Root cause analysis 1 hour

10 Nov 11-12 Webinar Data analysis, Design of experiments 1 hour

17 Nov 11-12 Webinar Improve phase

25 Nov 11-13 Face to Face

Change management, team dynamics and team leadership, Control phase, project follow-up and ensuring sustainability

2 hours

28 Nov 9-10 Webinar Test preparation, course review and open questions 1 hour

16 Dec tbc Test Multiple choice, open-book 2 hours

Content

3

Measure Phase

Process Mapping

Data Types

Measure – The Second of the DMAIC Phases

4

Understand and Characterize the Process

Process Maps:SIPOCValue StreamsSwim LanesSpaghetti Diagrams

Initial Ideas:BrainstormingFishbone Diagrams

Graphical Analysis:Charts and GraphsVisualization

Tools:Measurement SystemsCharts and GraphsVisualization

Process

0

5

10

15

20

25

30

35

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

Month

# R

equi

red

Cha

nges

Might already have some of these

Control Plan

ProceduresImprovement

Activities

Preliminary

Analysis

Measurement

System

Process

Maps

C&E

Matrix

FMEA

Measurement

System Analysis

%R&R, %P/T

Calibration

Requirements

Sampling &

Testing Plans

SPC Plans

Operating

Windows

DOE's

Maintenance

Schedules

Training

Materials

SOP's

Customer

Requirements

Short Term

Capability

Long Term

Capability

Noise Variable

Compensation

Reaction

Plans

Trouble

Shooting

Aids

Initial Control

Plan Assessment

These need to be

updated before

proceeding

Measure – Key Elements

5

Understand the Customer and Demand patterns

Takt Time

Map the As-Is Process

Understand the Process

Collect Process Metrics -> Data Collection Plan

Measure the Process Baseline

Visualize the Process and Data

Measure Phase – Deliverables

6

Process Characterized

Data Collected

Initial Analysis

Quick Wins

Plan for going forward

Process Mapping

7

SIPOC

Value Stream Mapping

Swim Lane Diagrams

Spaghetti Diagrams

Process Mapping succeeds because it allows the team to see the process through the eyes of the customer

Benefits of process mapping

Clear understanding of the ‘as is’ process

Inefficiencies, duplications, time delays are highlighted

Cross functional handovers visible – level of collaboration ?

Increased employee participation & morale

Greater ownership of the process

Smarter ways of thinking & working

Reduced costs

Process Mapping - SIPOC

9

Suppliers Inputs Process Outputs Customers

•Sister Companies•3rd Party Suppliers•Engineering Cos•HR Agencies

•Intermediates•Raw materials•Equipment•Temps

Company X•Intermediates•Finished Goods•Product Info

•Wholesalers•Sister companies•Doctors•Regulators

Suppliers Inputs Process Outputs Customers

•Upstream processing•Warehouse• RM Suppliers•HR Agencies

•Bulk Actives•Excipients•Packaging material•Temps

Fill/Finish•Finished Goods•Batch records•Retain samples

•Warehouse•Stability Testing•QA•Regulators

High Level:

Lower Level:

10

SIPOC example

Process cross functions

Greater opportunities for disconnects and waste creation

Business process improvement

Objective of waste reduction is to create smooth, uninterrupted, flow in the process & reduce non value adding activities

The practicalities of mapping

Large dedicated room for process map, discussion, brainstorming Brown paper rolls A variety of coloured markers Post-its, different colours Blue tack Board/flip chart Documents associated with the process (forms, completion sheets,

procedures etc.) Cross section of workers from the process area & areas affected by the

process (e.g. internal suppliers & customers)

14

Sample Swim Lane Diagram

15

Sample Value Stream Map

15

16

Sample Spaghetti Diagram

16

• Physically walk the process – can be used for materials, documentation etc

• Allows quick and easy visualization of poor layouts and redundant activities

Complete the map

Connect the activities by pencil

Review for flow & completeness

All inputs & outputs accounted for?

Revise activity sequence, add activities as necessary

Invite review from external personnel

Connect activities with arrows

Analyse the map /1

Identify activities which are Value Add and those that are not

Seek to eliminate non-value added work, simplify steps, refine the process

Start again

Analyse the map /2

Measurements

Identify what family of measures should be used to quantify the process e.g.

Quality

Cost

Time

Are these relevant to the customer??

Measure the process performance not an individual employee’s output

Analyse the map /3

Seek opportunities to:

Eliminate causes of errors

Eliminate duplication

Combine operations

Bring checking & inspection to the start of the process

reduce or eliminate paperwork,

e-mails, meetings

Process mapping software

Software is available which is specifically suited to mapping:

Visio

Smartdraw

Essential for large and complex process maps

Excel is also useful for visualization

Types of Data

1. Continuous (Variable) data

2. Discrete (Attribute) data

Working with Data

Types of Measurement Data

Continuous (variable) Data: Continuous scale Standard, defined units

Measurement Data

What is average height of Population ?

Discrete (attribute) Data: Observed or counted Pass or Fail Low information content No scale Need a large sample size

Types of Measurement Data

NO-GO GO

Discrete versus Continuous

Quantity of samples required to understand process

ContinuousDiscrete

$ $

SparseInformation

Rich WithInformation

Categories of Scales

Category of Scale Description Example

Nominal Data Classification No ordering

Eye Colour

Ordinal Data Ordered Differences between values can not be determined

Satisfaction scale 1 .. 5 your degree of satisfaction

Interval Data OrderedDifferences between values can be measuredRatios not meaningfulNo natural start point

Temperature (C,F) – 20o is not twice as hot as 10o

Ratio DataOrderedConstant scale Natural zeroRatios are meaningful

Height, weight, age, length, cost.20cm is twice as long as 10cm

Conclusions

No practical difference between interval and ratio data in terms of quantity of data required

Discrete (ordinal/ nominal) will require much bigger sample size than Continuous data (interval/ratio).

How to collect data

The Process:

Clarify the data collection goals

Develop a data collection procedure

Validate the Measurement System

Collect the Data

Modify type and quantity of data collected, if

necessary

1. Clarify data collection goals

Why are you collecting data ?

What questions will be answered ?

What patterns should be explored ?

How will the data help?

What type of data do I need ?

Should the data be stratified ?

Drill down to find root causes

••

••

•

A B C D E Other

20

40

60

80100%

50%

75%

25%

Business Metric (e.g. downtime)Business Metric

Defect A

y1

Dri

ll d

ow

n

y2 y3

Defect B

y4 y5

Data stratification

Who – Departments, groups, shifts, sections

What – Machines, equipment, products, services

Where – The physical location of defect/problem

When – Day of the week, time of the day etc.

33

How to measure ?

Who should collect the data?

What instrument should be used?

Sample interval or every item?

2. Develop procedures

34

3. Validate measurement system

Measurement system must be validated

What gets measured, gets improved !

Data/measurement accuracy

Finished files are the result of years of scientific study

combined with the experience of years

How many times does the letter F appear in the sentence ?

4. Collect Data

Train the data collectors

Pilot the data collection System

Error Proof the collection system

Present the data

Some questions ?

Do you currently collect / receive any data that helps you drive improvements?

Is there any data that you should be collecting / receiving but currently don’t ?

Do you currently collect / receive any data that does not help you drive improvements?

What data will you collect for your project ?

39

Pareto chart – Why use it ?

Identifies areas that offer the greatest potential for improvements

Shows relative descending frequency in a bar graph.

Focuses effort

Pareto Chart

40

Pareto chart

What does it do?

Applies the Pareto principle: “80/20” rule.

Selects the starting point for problem

solving.

Compares progress before and after

improvement activity.

80%

80%

20%

20%

Causes Problems

Cause

Cause

41

Pareto example

Reasons for failed mortgage applicationEx

ceed

gui

de...

No

ID

Page

mis

sing

No

sign

atur

e

Stat

us c

hang

e

Empl

oym

ent .

..

Too

old

Too

youn

g

Clai

m o

n ho

use

Faile

d m

edic

al

Oth

ers

0

40

80

120

160

200 90

80

70

60

50

40

30

20

10

33%45%

55%

64%72%

80%85% 89% 92%

93% 100%

No.

of r

epor

ted

occu

rren

ces

Contractdisputes

0 -

120 -110 -100 -

90 -80 -70 -60 -50 -40 -30 -20 -10 -

Instructor Content Date mix up

Misc. Failed Recon-called

Debt Bynd.

# of unpaid Bills

Contract disputes50 -

40 -

30 -

20 -

10 -

0 -No PO

No Spec Error on PO

No Proposal.

AFD. Misc. No record

OA

% ofContractDisputes

Pareto chart stratification

43

Run chart – Why use it ?

To allow a team to study a process for

trends over a specified period of time.

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5

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30

35

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

Month

# R

equi

red

Cha

nges

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Run chart

What does it do? Monitors the processes over time.

Identifies trends or patterns over time

Triggers process improvement efforts

Defines timing and trend of problem

Compares performance before and after process improvement.

0

5

10

15

20

25

30

35

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

Month

# R

eq

uir

ed

Ch

an

ges

Plot the data

(x axis)Time or sequence

Average

(y a

xis)

Mea

sure

men

t

Run chart essentials

Key success behaviours

Continue to ask questions

Appreciate the ‘aha’s’ revealed through time

Don’t jump to conclusions

Histogram

Why use it ?

Displays the center, variation and shape of the data

Displays data is relative to the specification

Displays process variability (spread)

Benefits

Highlights unusual or unexpected results

Compares before and after change

48

Histogram – What does it do ?

Helps answer the question: “Is the process capable of meeting my customers requirements?”

Displays large amounts of data that are difficult to interpret in tabular form

Histograms

The following data represents measurements for a CTQ with upper and lower specification limits of .14 and .06 respectively. How are you doing at meeting your customer’s expectations?

.13

.11

.11

.13

.12

.14

.16

.16

.14

.12

.10

.09

.17

.13

.13

.10

.10

.16

.12

.13

.15

.15

.14

.13

.12

.13

.12

.11

.14

.15

.13

.10

.12

.15

.11

.13

.11

.14

.11

.15

.15

.12

.13

.13

.12

.14

.13

.13

.16

.12

Constructing a histogram

Step 1: Decide what to measure

Step 2: Gather the data

Step 3: Decide on the class interval

Step 4: Determine the class size

Step 5: Assign the data to each class

Step 6: Plot the data

Step 7: Interpret the chart

Histograms

To build a picture first we have to:

Determine class intervals:

The square root of the number of data points

Calculate the range across all the data:

The range = Max – Min

Estimate the size of each class:

Class size = The range/ (number of classes -1)

Histograms

Using the data from previous slide…

Determine class intervals

The square root of 50 is roughly 7

Calculate the range across all the data

The range = 0.17 – 0.09 = 0.08

Estimate the size of each

Class size = 0.08/6 = 0.013

However, with this data it makes more sense to use nine class intervals

Class size = 0.08/8 = 0.01

53

Histograms

• Fig. 1 - Tally chart • Fig. 2 - Histogram

CTQ Tally

0.17 |

0.16 ||||

0.15 |||| |

0.14 |||| |

0.13 |||| |||| |||

0.12 |||| ||||

0.11 |||| |

0.10 ||||

0.09 |

0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.170

2

4

6

8

10

12

14

CTQ

Fre

qu

ency

54

Histograms

Histogram for a sample of

sixty recorded mortgage

cycle times

The histogram shows:

Spread

Location

Shape of the distribution2 3 4 5 6 7 8 9 10

0

10

20

Mortgage Cycle Time

Time

Std. Dev. = 1.63Mean = 6.0

N = 61

55

Histograms

240 observations of

mortgage cycle time

Notice how the bumps in the

previous graph disappear

with an increased sample

size2 3 4 5 6 7 8 9 10

0

10

20

30

40

50

60

70

80

Mortgage Cycle Time

Time

Std. Dev. = 1.49Mean = 5.9

N = 241

Normal Type Positively Skew Type

Left-Hand Precipice Type Comb Type

Twin Peak Type

Isolated Peak Type

Types of Histogram Distributions

57

Box plot – Why use it ?

To give you a graphical summary of values

Identify extreme values.

1 2 3 4

1415161718192021222324

Moulding machine

Dim

ensi

on X

58

4321

64

62

60

58

56

54

52

50

Press

We

ight

1

60

59

58

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56

55

54

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52

51

Press,

We

ight

1

Box plot example

A box plot is another way to

visualise variation

It marries categoric data and

variable data (In this case the

weights produced on several

machines)

Box plot

A Box Plot: depicts measures of the distribution that allows comparison of various distributions

1 2 3 4

14

15

16

17

18

19

20

21

22

23

24

Filling machine

Fill

Vol

ume

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Box plot – What does it do ?

A Box Plot consists of:

A rectangular box that represents roughly the middle 50% of the data

Lines or ‘whiskers’ extending from either side that represent the general extent of the data

Marks for outliers (observations far from the rest of the data)

Box plot analysis

*

+

Maximum observation

75th Percentile (Third Quartile)

Median (50th Percentile)

25th Percentile (First Quartile)

Minimum observation

Outlier

Mean

http://www.six-sigma-material.com/

http://asq.org/index.aspx

http://www.lean.org/

Useful Resources

Recommended Texts

Statistics without tears

Derek Rowntree

Penguin

Lean Thinking

James Womack & Daniel T Jones

Free Press

Understanding A3 Thinking

Durward K Sobek II & Art Smalley

CRC Press