Lean Six Sigma Green Belt Training Module 2 ...€¦ · Lean Six Sigma Green Belt Training Module 2 -Measure December2017 2 Lean Six Sigma • Green Belt PwC Today‘sfocus: D Define

5.12.17

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Lean Six SigmaGreen Belt TrainingModule 2 - MeasureDecember 2017

www.pwc.com

2Lean Six Sigma • Green BeltPwC

Today‘s focus:

DefineD

Measure

Analyze

Improve

Control

M

A

I

C

Define the improvement opportunities

Identify the Key Performance Indicators (KPIs)

Identify specific problems

Identify potential solutions

Determine the control plan

Develop the project plan

Develop a data collection plan

Identify and validate the root causes

Select right improvement solution

Detect the abnormal variations

Develop the project team

Validate the measurement system

Identify y = f (x)

Test and validate optimal solutions

Evaluate results of the changes

Define the processes (high level)

Define the baseline process performance

Develop a change management approach

Document the process changes

Identify critical customers requirements (CCR’s).

Deploy and execute solutions

Close project, communicate the know-how

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3Lean Six Sigma • Green BeltPwCPwC

Why do wemeasure things?

Performance!

Analysis!


Performance: Data show whether we do things right

„I’d like to have the product delivered the same day.”

Customer Company

“What is the average delivery time?”

“Did we improve?”

Current state Future

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Analysis: Data show improvement opportunities

People

Processes

Technology

Attitude?Knowledge?Utilization?

Effectivity?Efficiency?Performance?

Purpose?Effectivity?Usability?

“Where is the waste? What can we improve?”


August 2013Lean Six Sigma • Black Belt Training Week 2PwC

How to measure things?

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Export data from a system

“What is the average delivery time”

“How long and how often we have breakdowns?”

1


“How long does the customer wait to be served?”

“How many time does the warehouse guy spend with paperwork?”

Observe – „Genchi Gembutsu“2

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“Was the staff kind to the customer?”

“Would the customer recommend us?”

Ask3


Net promoter score

„How likely is it that you would recommend ourcompany/product/service to a friend or colleague?? “

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PromotersDetractors Passives

Net promoter score = % Promoters - % Detractors

Ask3

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“Is the movement of the people in the office efficient?”

(Manually / GPS)

Spaghetti chart4

“Can we save transport time by optimizing shop floor layout?”


“Where do people click ounour web?”

(Clicks, mouse movements, eyemovements)

“Is our system user friendly?”

Heatmaps5

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”What is the level of people, processess and technoogy in our company compared to the best in class?”

(also called Reference model, Diagnostic report)

Level Bestpractice

Fulfilled?

Under average ... AnoAverage ... AnoOver Average ... NeWorld class ... Ne

Maturity model5


Key message

If we cannot show in numbers, what weknow, we actually don’t know much.

If we don’t know much, we cannot do much about it.

If we cannot do much about it, we need to rely on luck.

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How to create a KPI

1


CTQ Tree

„I want a seamless

experience with my electricity

bill.”

Understand the bill

Phone support available

Can check online

...

Customer servicecalls / million bills

% success of user testing

% of calls with lessthan 20s waiting

First time resolutionrate

System uptime %

Voice of customer Driver Ctritical-To-Quality

Identify KPIs Develop a data collection plan

Validate themeasurementsystem


1 2 3 4Measure

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Typical examples of KPIs in Lean Six Sigma Projects

Type Examples

Quality% of claimsCustomer service calls / product sold.Breakdown rateOTIF = % On time in full Sigma level / DPMO = defects per million opportunities (PPM = Parts per million)

Utilization OEE = Overal equipment effectivenessEmployee utilizationProcess capacity

Productivity Meals served per workerProduction kg per worker

Cost Cost per kg of productionCost per pallet movement

Lead time % of customers wait less then 20 seconds (Service level)Average delivery time

Satisfaction Average online ratingsNet promoter score

Online sales Conversion rate (e.g. sales / total visits)Cost per click




1 2 3 4Measure





1 2 3 4Measure

Online sales KPI Pharma example SSC Example

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4 questions before choosing a Lean 6 Sigma project KPI




1 2 3 4Measure

Does it reflect value for customer?

Is it possible to measure it accurately?

How do I measure baseline vs. future state?

Is it possible to show impact on profit?

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*KPI = Key performance indicator. Baseline = current value of project KPI


Example of project KPI impact map




1 2 3 4Measure

Profit

Revenue

Costs

Customersatisfaction

Productioncapacity

Flowtime

OEE

MachineAvailability

Machineperformance

Productionquality

Changeovertime

Production

ScraprateOvertimerate

Absenteeism

No.ofemployees

Laborproductivity

Materialconsumption Material

purchasingprice

Inventorylevel

Maintenance

Supplychain

Legend

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Key message

If we want to measure impact of what wedo, we need to convert customer need to a

number. We need to explain management how will

our project impact the profit. This increasesbuy-in and project support.




1 2 3 4Measure


KPI tree excercise

5Find drivers to the customer needrelated to a process

“How to collect data?”

”What Kano model type it is?”

“What is the profit impact?”

Airport transfer Fixing homeinternet




1 2 3 4Measure

Mortgage

„I need a great experience with...“

“What KPIs to choose?”

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KPI tree excerciseAirport transfer




1 2 3 4Measure


Driver KPI Collection

Short time between gates - Performance

% journeys under 15 minutes

Observation/ExperienceWifi tracking

Clean toilet - Basic Customer rating Hot Or not kiosks

Short lines in security -Performance

% security checks under 5 minutes

CamerasObservation - sampling

Easy navigation - Basic Customer service requests/ passenger

Data entry at service desks

Fast & Free wifi –Excitement

% Passengers online Wifi tracking


Airport transfer




1 2 3 4Measure


Profit

Revenue

Costs

Passengers

Utilization

Short timebetween gates

Clean toilets

Easynavigation

Fast & Free Wifi

Returning

Infrastrucutre

Customer service

Short securitylines + +

-

+

+

-

+

+

+

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Develop a collection plan

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Data collection plan will help us be clear how and whatwe measure.

Key performance indicator name

Manufacturing productivity

Formula (How it is calculated)

Weight for material produced/ hours worked

How the data is collected

SAP export to Excel

How does the KPI influence EBITDA?

Higher productivity > + production capacity > + revenue, same cost

Who will measure it? John SmithHow often? How long? Will be measured

Once a week

Where it will be measured?

Workplace A, B and C

What?How?Why?Who?

When?Where?

How often?

Example:

Measure Identify KPIs Develop a data collection plan



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Is the Data Measured Accurately?

Real value

Observed value




1 2 3 4


Is the Data Reproducible?

Person 2

Person 1




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Can the Measurement be Repeated?

Measure 3Measure 1

Measure 2




1 2 3 4


Measure - Review

CreateKPIs

Data collectionplan

Checkmeasurementsystem

DefineKPI baseline

1 2 3 4

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Process Flow analysis


For some processes, utilization is the key

Aviaton

Surgery

Metal press

Hairdresser

“What is the % of capacityused?“

“What is the bottleneck?”

“How to increase output?”

+ Utilization + Sales - Costs=Why?

è Process flow gives the answers

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At a Hairdresser‘s


Four basic process measures

Flow unit A basic unit that enters and leaves the process Customer orders, Customers, Sandwiches, Claims, Packages

Flow time Time that the flow unit spends in the process.Time from start to end of hairdressing, check-in at ain airportIn manufacturing, it is called production time.

Capacity Maximal number of flow units that the process can produce in a time period.The synonym of capacity is troughput.

Flow rate Actual number of units per time period. It equals to demand or processcapacity.Example 1: Demand is 5 customer/ hour, Capacity 10 customer/hour. Then, flow rate is 5 customer / hour.Example 2: Demand is 5 customers / hour, Capacity is 2 customers / hour. then, flow rate is 2 customer / hour.

Inventory Number of flow units in the process at a given moment in time

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A sandwich making factory

Preparing the bread (10s)

Packing (25s)

Cutting the tomatoes and lettuce (40s)

Toasting (60s)

Assembly (20s)


Multi-step process measures

Cycle time Time from start to end of one operation.One process (like the toast example) can have multiple steps/operation.

Bottleneck An operation with the smallest capacity.

Process capacity Capacity of the bottleneck

Process utilization Flow rate / Capacity

Value added time Cycle time of value-added operations / Flow time

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Cable production

Drawing Stranding Insulating wires

Wire stranding Coating

3 days 6 hours 9 days 3 hours 2 hours7 days 5 days 5 hours 4 hours5 days

Flow time

Typical flow time for 10 km (1 package) of cable A:


Relations between process flow measures

Speed = Distance

Time

Time = Distance

Speed

Distance = Time * Speed

Elementary school math: Little‘s law is the same:

Flow rate = Inventory

Flow time

Flow time = Inventory

Flow Rate

Inventory = Flow time * Flow rate

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OEE = Overal Equipment Effectiveness


OEE – the real capacity of production

Overal equipmenteffectiveness

OEE = % Availability * % Quality * % Performance

Availability Availability = Time working / Time assigned for work(assigned time can be for example an 8 hour shift, or 24 hours)Time not working are usually breakdowns, breaks, waiting etc.Example: out of an 8 hour shift, the machine worked 7 hours, the rest wasbreakdown nand machine stopped. Availability = 7/8 = 87,5%

Quality 1- defected pieces / total pieces producedExample: 1000 pcs manufactured, 10 of them were deffectives. Quality = 99%

Performance Performance = Actual performance / Standard performanceStandard performance is for example the speed the machine is designed for (10 pieces / hour, or aircraft speed of 9000 km) or the norm that a person has for its work (for example 5 sandwiches a minute).

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Key message

The waste decreasing availability, performance and quality is multipled. OEE

can be suprisingly low.


Customer lead time = Time from order to deliveryTwo situations typically occur:

Make to order:

Customer order Production start

Production finish

Customer delivery

Lead time

Make to stock

Customer orderProduction start

Production finish

Customer delivery

Lead time

Flowtime

Flowtime

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Key message

Customers prefers “make to order“. However, if the flow time is longer thanthe customer wants to wait, we need to

make to stock.



Data analysis

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Time analysis can show unusual patterns

This analysis shows just hypotheses, not problems. The problems can be then confirmed by other means of mesaurement, such as observation.

14

7

10 10 10

0

25

1415

8 8 8 87

9 9

42

19 19

109

6

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Beginning of a morning shift Beginning of a night shift

è Analyze hours, days, weekdays in a month


Histogram analyzes the random effects on a KPI

0-10

10-20

20

-30

30-40

40

-50

50-60

60

-70

70-80

80

-90

90-10

0 10

0-110

11

0-12

0

120-1

30

130-14

0

140-1

50

150-1

60

160-1

70

170-1

80

180-1

90

190-2

00

Četnostiporuchpodlečasu “What is the mean? Are there any unusual shapes/patterns?/

è Analyze frequencies in equal intervals

Breakdown frequenciesBased on their duration in minutes

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What can we read in a histogram?

”Symmetric, stable, predictable process.

”Is it a one process or more? Or, did it change in time?”

”Why is it cut? That can mean some strange behavior.”


What can we read in a histogram?

”What do these outliers mean?"

”Aren’t these two processes in one?”

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Statistical analysis shows probability of a defect

“I selected xsamples. Whatis theprobability thatthey will not meet thecriteria?“


Repeating measurements show more when shown on a timeline (run chart)

Pizza delivery time in minutes:

X X X X X X X X X X X X

X X X X X X X X X 16 17 18 19 20 21 22 23 24

14151617181920212223242526

14151617181920212223242526

14151617181920212223242526

Prague Brno Plzen X X X X X X X X X X X X

X X X X X X X X X 16 17 18 19 20 21 22 23 24

X X X X X X X X X X X X

X X X X X X X X X 16 17 18 19 20 21 22 23 24

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Pareto analysis – helps to find root causes that have the highest influence on the problem.

Frequency

Cummulative%

100

90

80

70

60

50

40

30

20

10

D B F A C E Jiné0

50

100

150

20% root causes is a source of80% of problems

Root causes


Scatter chart shows outliers and anomalies

Shop size in square meters

Averagedailyrevenuein EUR

“Great? How do they do that?”

“There might bea problem“

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Interní benchmarking show the result between best and worst workers/branches/shifts.

2,8

3,4

1,9

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

Observed average norm Worst 15% Best 15%

Proces

sed ite

ms pe

r man-

hour 43,8 %

“How might we allhave thisperformance?“

Lean Six Sigma • Green BeltPwC

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Day in life observation shows waste in a typicalposition.

1. Observe a full working day of a position.

2. Ask how the person perceived their day

3. Ask how the person perceived their day should be

DILO is not a method for firing people, but for improving the processes they work on.

Example

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55

Contact

Jiří Benedikt

Trainer, ConsultantI help people work smarter

Lean Six Sigma | Design thinking | Digital skills

[email protected]

777 253 754

www.jiribenedikt.com



Appendix

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57

Data Collection Plan




1 2 3 4

What? Why? Who? How? When? Where?Metric (What to measure)

Stratification factors

Operational definition

Sample size and when applicable a formula

Objective of the data collection

Person responsible

Collection method

Date/time/frequency

Source and location

Production speed

Factors Amount of cars per hour

Sample size

Day-production/(8 hours – x hours break)

Determining current process performance

Jan van den Berg

Export of production numbers from MIS. Calculating in Excel according to formula

From 16 March until 29 March daily after the completion of daily production

System Auto module production numbers

Creating a data collection plan gives the team the opportunity to collect data about the performance indicators in an unambiguous manner. A description of what, why, who, where and when measurement will take place ensures consistency if data is also collected at a later stage.


Measurement System Analysis

Ø Has your data collection method:- given you dependable data?

- provided consistent information throughout the data collection period?

- provided a reliable set of data?

- given representative data?

Ø If you repeat the data collection will you get similar results?

Ø Does the data collected provide the information you need?




1 2 3 4

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Is the Data Measured Accurately?

Ø The measurement method can contain an error which produces inaccurate values

Ø The question is therefore whether the measurement method measures what it is supposed to measure

Ø If the measurement method is not completely accurate the deviation can be determined

Real value

Observed value




1 2 3 4


Is the Data Reproducible?

Ø Measuring the same sub-section with same instrument can produce different results in different contexts

Ø The question is therefore whether the data is reproducible

Ø There can therefore be an error in how the measurement is conducted

Operator I

Operator III




1 2 3 4

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Can the Measurement be Repeated?

Ø Measuring the same sub-section with same instrument can produce different results when measured by the same person

Ø The question is whether when the measuring is repeated by the same person different results are obtained

Ø An error can therefore lie in the precision of the measurement (test-retest error)

Measure 3

Measure 1

Measure 2




1 2 3 4


Types of Data (1/2)

Qualitative Quantitative

Attribute/ Discrete Variable/ Continuous

My sandwich is not fully covered with cheese

Number of cheese pieces Weight of the cheese

The service is bad! Number of service calls with wait time of >5 minutes

Measurement of a quality characteristic (e.g. degree of customer satisfaction)

This invoice is wrong! Number of defects on an invoice

Measurement of a quality characteristic (e.g. degree of customer satisfaction)




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Types of Data (2/2)

Continuous or variable dataMeasured on a continuum or scale. Usually continuous measures can be divided into parts and still make sense. For example:

• Time can be divided into days, hours, minutes, or seconds (cycle time)

• Money can logically be divided or specified in increments (sales, costs, losses)

• Satisfaction if measured with a continuous scale, (i.e. very dissatisfied, dissatisfied, neither satisfied nor dissatisfied, satisfied, very satisfied) can logically be calculated and expressed in an average level of satisfaction on a scale.

Discrete, categorical or attribute dataMeasured by counting. For example:

• Defects (yes/no, approved/disapproved, pass/fail, met customer requirement/did not meet customer requirement)

• Categories (days of the week, locations, type of customer, type of product, risk - low/medium/high)

• Satisfaction (poor/fair/good/excellent or dissatisfied/satisfied)




1 2 3 4


What is a Histogram?

A histogram is a bar graph that displays the results for a sample of performance data in picture form. This picture is sometimes called a frequency distribution because it shows clearly how frequently each separate value appears in the data.




1 2 3 4Measure

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Histogram Interpretations (1/2)

The shape has a bell shape – it is symmetricSymmetric – If the time plot shows no special causes, data may come from a stable, predictable process.

The shape is flatFlat – A flat distribution occurs when a process drifts over time. It can also appear when what we think of as one process is really a mixture of many processes. For example, it can occur when many people use different procedures for the same job.




1 2 3 4Measure


Histogram Interpretations (2/2)

The shape has a long tail – it is not symmetricNot symmetric – A long tail on one side of the distribution is common when the property being measured is time to complete a job, wait time, or a count of problems. It can also occur when there is an unstable process.

There are one or more – outliers Outliers – When you see one or more data points far away from the rest of the data, first check to make sure they are not the result of clerical error.

The shape has two humps – it is bimodalBimodal – This patterns appears when something that we think of as one process is really two processes. That is, there are two different sets of conditions operating.




1 2 3 4Measure

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Run Chart

Three Different Run Charts with the Same Distribution X X X X X X X X X X X X

X X X X X X X X X 16 17 18 19 20 21 22 23 24

14151617181920212223242526

14151617181920212223242526

14151617181920212223242526

Run charts are used to analyse processes according to time or order. Run charts are useful in discovering patterns that occur over time.




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Documents

Lean Six Sigma Green Belt Training Module 2 ...€¦ · Lean Six Sigma Green Belt Training Module 2 -Measure December2017 2 Lean Six Sigma • Green Belt PwC Today‘sfocus: D Define