itSMF Finland Conference 2015It Feels Good!

Phil GreenIn God We Trust Everyone Else Bring Data

When people are pressured to meet a target value, how might they proceed?1

They can work to improve the system

They can distort the system

They can distort the data

1 Brian Joiner, Fourth Generation Management, McGraw-Hill

These are examples of a small number of data points or observations without proper context.

February deficit 1.4 billion larger than January things are worse!February deficit 1.6 billion smaller than previous February things are better!2

The table tops do not look alike but they are!1 Good or bad?

1 - Source: Roger N Shepard, Mind Sights: Original Visual Illusions, Ambiguities and Other Anomalies, WH Freeman and Company2 - Derived from Understanding Variation: The Key to Managing Chaos, Donald J. Wheeler, SPC Press Inc.

Things arent always what they seem!

INVENTORY IN DEPARTMENT 50A Case in Point

Derived from Understanding Variation: The Key to Managing Chaos, Donald J. Wheeler, SPC Press Inc.

Inventory in has fallen to the lowest level recorded in three years!

The Manager rewards everyone with a celebration!

But then inventory rises again

The Manager regrets giving the award time to have a serious talk with the team!

Inventory levels fall again

The Manager thinks tough talk gets results!

But then inventory rises to the highest level recorded in two years!

Manager throws a tantrum, demands improvement plans.The team keep a low profile, hoping things get better, hiding inventory in dark corners, under their desks, etc.

And once again Inventory falls

Once again, the manager thinks tough talk works!

But then inventory rises yet again

And around and around we go. Lather, rinse, repeat!

So what went wrong?

Award Given

Team needs talking to!

Tough talking works!

Tantrum!

Theyre finally getting the message!

Here we go again!

Weve been reacting to data points, comparing numbers to specifications the Voice of the Customer (VoC) which hasnt worked.To understand, we need to listen to the Voice of the Process (VoP)

VoP The Control Chart Approach

CL

LCL

UCL

So what is wrong with this process?Statistically, nothing! The process is in control not one of the data points is a signal to be concerned about.The process shows only common cause variationA change in process requires a structured improvement such as Plan-Do-Check-Act (PDCA), not reacting to normal data points (known as tampering!)

Anything noticeable here?

A signal an assignable cause

And here?

A set of consecutive points below the centre line.A change in the process has occurred

Control Limits can be changed

Centre line has movedProcess variation has been reduced

What have we learned? Without proper context, data is meaningless! Comparing data points to specifications (VoC)

and reacting is merely tampering and wont drive sustainable improvement

The VoC defines what you want from a system, whereas the VoP defines what you will actually get

A control chart will shows us the VoP The VoP will filter out the noise (common

cause variation) so we can clearly see the signals (assignable cause variation)

A SUMMARY OF THE THEORYSix Sigma Overview

Introduction to Six Sigma Pioneered by Motorola in the

1980s, providing $16 Billion in savings

Widespread adoption since, e.g., GE, Ford, Honeywell, GSK, Nokia

Proven to yield dramatic improvements in business results

Lean (Six) Sigma drives process improvement by reducing waste

Six Sigma drives improvement by reducing variation

Variation why does it bother us?Some things varying can be ok, but not others: Quality of food or service at your

favourite restaurant? Unreliable buses or trains? Call wait time telephoning your bank? Produce freshness at the food store? Cycle time from order to delivery of

items needed to manufacture goods?

Sigma as a Quality MeasureDPMO = defects per million opportunities for errorA Case in Point: Concert tickets printed

5 opportunities for error (venue, date, act, time, seat)

1000 tickets issued, 50 defects DPO = 50/(5 x 1000) = 0.01 Yield = (1 - 0.01) x 100 = 99% DPMO = 0.01 x 106 = 10,000 (3.8 achieved1)

Rule of thumb: You need 4 or higher to satisfy most customers2

1 Research has shown that the centre of a processes can naturally shift over time by up to 1.52 Joe Parito, iSixSigma

Control Chart Basics Centre line is the arithmetic mean of

the data points Limits are 3 (3 standard

deviations1) either side of CL UCL/LCL are not specification limits The Empirical Rule

About 60-75% of the data points will be 1 from the CL

About 90-98% of the data points will be 2 from the CL

About 99-100% of the data points will be 3 from the CL

Control limit calculations:

1 Standard deviation = mean of (variation of each point from CL)2

Upper Control Limit (UCL)

Lower Control Limit (LCL)

Centre Line (CL)

Assignable Cause Area

Assignable Cause Area

Voice of the Customer

Voice of the Process

Control Chart: Case In Point Example

0

10

20

30

40

50

60

70

80

90

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

UCL = 88.3

LCL = 32.2

CL = 60.3

Looking for Assignable Causes

One or more points outside the control limits

Looking for Assignable Causes

Eight or more consecutive points the same side of the Centre Line

Looking for Assignable Causes

Sixteen points alternating up and down (rare)Other repeating patterns should also be investigatedA rule of thumb: a pattern repeating 8 times

Learning Models DMAIC & DMADV

DMADV is also known as Design for Six SigmaThis presentation will discuss the DMAIC model.

The DMAIC Improvement Cycle Define articulate the problem quantifiably, the

underlying process and the goal for improvement. Measure collect data to establish current baseline,

and to identify & monitor the gap between current and required performance.

Analyze identify and prioritise potential root causes for elimination to close the gap. Use statistical tools to guide the analysis.

Improve identify and implement (creative) solutions; use statistical methods to validate the improvement.

Control identify and implement sustainment strategies that ensure the improvement is embedded and sustainable.

Tools & Techniques

Analyse

Select Priorities: Affinity Diagrams System / Process Map Benchmarking Project Charter Pareto

Learn About Process: Run Chart Process Map (e.g., flow chart) Histogram SIPOC VoC tools (surveys, focus

groups, etc.)

Investigate Sources of Variation: Run Chart Ishikawa Diagram Pareto Scatter

Test Theories: Ishikawa Diagram Process Maps Control Charts / SPC Brainstorming Matrix Design of Experiments Simulations

Study Results: Control Charts /

SPC Pareto FMEA

Implement: Proto type / Pilot studies Gantt Chart Matrix

Standardise: Control Chart /

SPC FMEA Reporting

Systems

SERVICE OPERATIONS CASE STUDYIncident Resolution Improvements Application Support Service

DMAIC Define StageSelect Priorities Shared Application Support Service Several hundred applications,

supporting 90K+ business users across 100+ countries

Target agreed with business to drive incident resolution 90th percentile down to 40 hours or below.

Learn About Process Baseline compliance to incident

resolution target 79% Baseline 90th percentile 263 hours

SIPOC

Project Title: Incident Resolution 90th Percentile Reduction

Project Leader: Phil Green

Suppliers:

Users (incident details)

IT Help Desk

Offshore Suppliers

Inputs

User supplier information

Remedy Incident ticket

Open Problem tickets

IT Knowledge base

Process:

Resolution of Service Incidents

Owner:

Phil Green, Service Owner

Purpose:

To resolve service incidents for supported applications in a consistent, satisfactory and timely manner.

Outputs:

Resolution provided to customer

Remedy ticket closed

Communication to Problem Mgmt on underlying issues

Customers:

Primary:

BU IT Service Owners

Secondary:

Users (of the process)

BU & IT Management

Process Steps:

(Help Desk resolves incident or forwards to a Resolving Agency) (Business user experiences an IT incident) (Business user submits Incident ticket (e.g., on self-help portal or calling help desk)) (Ticket closed) (Resolving Agency resolves Incident) (Resolving Agency responds to Incident)

Results Measures:

Percentage of incidents resolved within SLA

Incident Resolution time 90th Percentile

Customer Needs:

1. Fast response to Incidents raised

2. Incidents resolved to customer satisfaction

3. Incidents resolved with a sense of urgency

4. Frequent, courteous communication throughout process

Process Measures:

Incident Response time; Incident Resolution time (measured from time ticket reaches RA through to resolve time).

Present Data:

263 hours to resolve 90% of Incident tickets.

Opportunities for Improvement

Improve IT Knowledge Base (especially during Transition and 1st 3 months of early life support)

Better policy/usage of Resolving Agencies

Improve notifications prior to tickets breaching SLA

Work with Help Desk and Service Owners to eliminate opportunities for ticket misrouting

Improve / accelerate Problem Management capability, especially during early life support

Improved reporting capability (e.g. mechanism to exclude apps in transition from resolution metric)

Goal Performance:

Resolve 90% of all Incident tickets within 40 hours for all supported applications that have been in steady state for three months or greater.

Sources of Variation

Portfolio of supported applications continually expanding; skills/capabilities of support analysts; gaps in application knowledge base; tickets misrouted by Help Desk; sharing of RA with non-supported apps; user not available to progress; incorrect sense of urgency during resolution; changes releasing bugs into Production; changes in customer demand; non-compliance with correct process.

Impact on Performance:

Incident not resolved within required SLA escalating impact; dissatisfied customers.

DMAIC Measure StageInvestigate Sources of VariationTechniques selected

Brainstorming Cause & Effect (Ishikawa) diagram Pareto charts

Cause & Effect (Ishikawa) Diagram

Sheet1

Effect: Variation in Incident Resolution Times

Tools

Process

Measurement

Help Desk

Customers

People (RA)

RFCs logged as Incidents

Service Requests logged as Incidents

Incorrect handling of 'hold' times

Unable to measure at source (Remedy)

Incorrect SLA being followed

Sharing of RAs

Incorrect case_type usage

Gaps in IT Knowedgebase

Incorrect urgency to response

Incorrect urgency to resolve

Changes processed as Incidents

Different resolutionprocesses in use

Incidents not in Remedy

No oversight of ticket queue

Too many lowpriority tickets

Processing RFCs as incidents

Incorrect downgradingof priority

Not following correct resolutionsteps per good ITSM practice

Insufficient senseof urgency

Incomplete knowledgetransition to offshore

Using support throughinformal channels

User not available to progress ticket

User not available to confirm resolution

Change in customer demand (service growth)

Assigning ticket to wrong RA

Taking too long to assignticket to an RA

Setting incorrectticket priority

Not logging ticket in Remedy when contacted informally

Sheet2

Sheet3

Pareto Chart

DMAIC Analyse StageForm & Test TheoriesNot following good ITSM practice

Closing incidents on the basis that a corresponding Problem record was raised

Incorrect hold times calculations Clock was being stopped

Changes logged as Incidents! Categorisations in system not clear

Root Causes Selected to Address

Sheet1

Effect: Variation in Incident Resolution Times

Measurement

Help Desk

Customers

People (RA)

RFCs logged as Incidents

Incorrect handling of 'hold' times

Incorrect SLA being followed

Not following correct resolutionsteps per good ITSM practice

User not available to progress ticket

Assigning our tickets toanother group's RA

Sheet2

Sheet3

DMAIC Improve StageImplement Re-training in good ITSM practice Change on hold time calculations System usability adjustmentsStudy Results Incident resolution control chart 90th percentile control chart Reactivation rates control chart

(Balancing measure)

Incident Resolution Control Chart

UCL=13.068

LCL=0.0

CEN=4.0

UCL=7.9341

LCL=0.0

CEN=2.4286

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Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07

Moving R Chart

UCL=89.973

LCL=68.693

CEN=79.333

UCL=97.26

LCL=84.34

CEN=90.8

65

70

75

80

85

90

95

100

105Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07

Percent Incidents Resolved in SLA

Signal: 8 consecutive points above CL

Control Chart Limits Re-drawn

UCL=89.973

LCL=68.693

CEN=79.333

UCL=98.881

LCL=80.261

CEN=89.571

UCL=95.295

LCL=88.455CEN=91.875

65

70

75

80

85

90

95

100

105Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07

Percent Incidents Resolved in SLA

UCL=13.068

LCL=0.0

CEN=4.0

UCL=11.435

LCL=0.0

CEN=3.5 UCL=4.2004

LCL=0.0CEN=1.2857

-4-202468

101214

Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07

Moving R Chart

90th Percentile Control Chart

Reactivation Rates Control Chart

DMAIC Control StageImprovements 90th percentile improved 82% from 263 to 45 hrs Incident Resolution SLT compliance raised from

79% to 92% Reactivation Rates reduced from 8% to 3%

Sustainment Actions New practices formalised within procedures,

knowledge base, training materials, etc. Training and communication ITSM tool changes made permanent

Additional ResourcesLinkedIn GroupsLean Six Sigma (and sub-groups)Lean Six Sigma Worldwide

Computing ResourcesSPC XL Software (Excel plug-in, Windows only)QI Macros (SPC & Statistical software for Mac)Excel (calculates mean and standard deviation)Graphical CalculatorsUseful Websites

http://www.isixsigma.com http://iso-qms.blogspot.com.au http://statstuff.com http://www.qualitydigest.com/sixsigma/index.lasso

Training/Certifications http://www.processexcellencenetwork.com/lean/videos/online-six-sigma-training-with-pex-

institute/ http://www.6sigmastudy.com http://www.lsssp.org

Books Donald J Wheeler, Understanding Variation, The Key to Managing Chaos, SPC Press Thomas Pyzdek, The Six Sigma Handbook, McGraw-Hill George/Rowlands/Price/Maxey, The Lean Six Sigma Pocket Toolbook, McGraw-Hill Quentin Brook, Lean Six Sigma and Minitab: The Complete Toolbox Guide for All Lean Six Sigma

Practitioners, OPEX Resources http://www.spcpress.com/index.php

http://www.isixsigma.comhttp://iso-qms.blogspot.com.auhttp://statstuff.comhttp://www.qualitydigest.com/sixsigma/index.lassohttp://www.processexcellencenetwork.com/lean/videos/online-six-sigma-training-with-pex-institute/http://www.6sigmastudy.comhttp://www.lsssp.orghttp://www.spcpress.com/index.php

Questions?

Phil GreenDirector, G3 Service Solutions LimitedEmail: phil.green@g3servicesolutions.comLinkedIn: www.linkedin.com/in/philxgreen

mailto:phil.green@g3servicesolutions.com?subject=itSMF%20Finlandhttp://www.linkedin.com/in/philxgreen

Thank You!

Slide Number 1When people are pressured to meet a target value, how might they proceed?1Things arent always what they seem!Inventory in Department 50Inventory in has fallen to the lowest level recorded in three years!But then inventory rises againInventory levels fall againBut then inventory rises to the highest level recorded in two years!And once again Inventory fallsBut then inventory rises yet againSo what went wrong?VoP The Control Chart ApproachAnything noticeable here?And here?Control Limits can be changedWhat have we learned?A Summary of the theoryIntroduction to Six SigmaVariation why does it bother us?Sigma as a Quality MeasureControl Chart BasicsControl Chart: Case In Point ExampleLooking for Assignable CausesLooking for Assignable CausesLooking for Assignable CausesLearning Models DMAIC & DMADVThe DMAIC Improvement CycleTools & TechniquesService Operations Case StudyDMAIC Define StageSIPOCDMAIC Measure StageCause & Effect (Ishikawa) DiagramPareto ChartDMAIC Analyse StageRoot Causes Selected to AddressDMAIC Improve StageIncident Resolution Control ChartControl Chart Limits Re-drawn90th Percentile Control ChartReactivation Rates Control ChartDMAIC Control StageAdditional ResourcesQuestions?Slide Number 45