M.S.ENG. Lean/Six-Sigma Project Technical Documentation

| MS ISE Project Documentation
Application of Lean/Six-Sigma + Human Factors Principles
To Power Distribution Network Optimization Efforts:
Master of Science in Industrial Systems Engineering Thesis Project
Nick Boyd
Dr. Carolyn Sommerich
Dr. Jerald Brevick
April 24th, 2013

| MS ISE Project Documentation2
> Project Overview
Statement of Individual Completion for Included MS
ISE Project Components
• All elements related to the following presentation elements have been
completed individually by Nick Boyd for the purpose of fulfilling the MS
ISE Degree Exam Requirements:
› I. Technical Documentation
› II. Project Poster
› III. Project PowerPoint
› IV. Project Process Binder
• These items go above and beyond the requirements of the project
elements created for the completion of the Fisher Lean/Six-Sigma
Foundations and Projects Course.
• Figures, graphical element and text have been developed individually by
Nick Boyd and exclusive of other project member’s efforts towards
project completion.
Project Timeframe
Start Date: 11/30/12
End Date: 03/15/13

> Project Overview
Acknowledgement to Project Contributors
• The author of this body of work would like to acknowledge the input
from the following parties and their support in the completion of this
MS ISE Project
• I. OSU Advisers and Faculty:
› Dr. Jerald Brevick
› Dr. Carolyn Sommerich
› Prof. Peg Pennington
› Prof. Terry Klinker
• II. AEP Senior Leadership and Champions:
› Michael Childs
› Gloria Feliciano
› Omar Hellalat
• III. Fisher Lean/Six-Sigma Project Student Team:
› Joseph Antonelli
› Justin Dalton
› Jeff McKernan
› Madhu Nonavinakere-Muruli
› Mike Hammersmith

> Project Overview
1. Define
a. Identify Customer Needs
b. Qualify and quantify the problem or
improvement requirements
c. Incorporate Customer Perspective
2. Measure
a. Establish Performance Baselines
b. Develop Measurement System
c. Create method of Gauge/Trend Tracking
3. Analyze
a. Quantify Customer Requirements
b. Employ Statistical Analysis for the
identification of Process Inputs of
significant effect 4. Improve
a. Identify key areas for Process
Improvement
b. Link Analyze data with Customer Needs in
quantifiable manner
c. Develop Method for Implementation5 Control
a. Establish Long-Term Measurement System
for trend tracking
b. Implement “Proactive Approach” towards
issue mitigation and process improvement
DMAIC Roadmap / Presentation Outline

> Project Overview
• A large number of tools are available in the completion of a Lean/Six-Sigma
project; those listed above are a mere sampling
• A specific tool can be useful in multiple phases and iterative use affords a
strengthened problem-solving process
• Combination utilized is dependent on the nature of the problem being solved
(product vs. process / quantitative vs. qualitative)
Overview of Lean/Six-Sigma Tools

I. Define Phase
AEP-OSU Lean/Six-Sigma Project

> Define Phase
Project Charter

> Define Phase
Overview of Lean Six-Sigma Tools
• Provides a structured approached towards “division of labor” and
ensures that the tasks are balanced
• Utilized to great effect throughout all parts of the DMAIC process
• Structure for project organization and clear communication between all
parties

> Define Phase
Project Scope / “Problem Space”
• Project scope was largely determined by AEP – focus was to be made
on the Distribution Network components
• The literal “Distributed” nature of the system affords a significant
degree of complexity
• OSU group recognized that the nature of the system would offer
considerable challenges and obstacles to overcome

> Define Phase
Distribution Substation
> Circuit Breakers + > Transformers
• Distribution Substations contained the following elements:
› I. Circuit Breakers
› II. Transformers
• Consists of a myriad of components outside of just these two substation
elements
• Varying degree of “Distributed Nature” between Substations (Circuit
Breakers + Transformers) and Distribution Lines

> Define Phase
Visualizations Developed for “Multi-Level”
Communication
• Further depiction of network hierarchy and inherent differences
between each component
• Utilized for “multi-level” communication to technical/non-technical
project stakeholders
• Diagrams such as the one above allow for a refined overview of the
materials reviewed during multiple DMAIC stages
II. Circuit
Breakers
III. Transformers

> Define Phase
> Voice-Of-Customer (VOC) Hierarchy of Business Success Factors
1. Repair Process Cost Reduction
2. Faster Repair Process
3. Reduced Process Times
5. Increased Marketshare
4. Improve Customer Satisfaction
Primary
Focus
Secondary objectives will follow
successful implementation of
primary drivers
Voice of Customer (VOC) Toolset
• Discussions with AEP representatives lead to the development of
Business Success Factors
• Further refines the focus of the project and the “arena” in which
improvements efforts will be prioritized
• Determination of Primary and Secondary focus areas

> Define Phase
Voice of Customer (VOC) Toolset
• Brainstorming and discussion sessions with AEP representatives lead to the
identification of potential forms of waste within system
• Indicative of the ability for implementation of Lean principles to improve
the overall process flow and efficiency

> Define Phase
Critical to Quality Characteristics (CTQCs) Tree
• An iterative tool utilized to further “drive down” the core characteristics
that are indicative of a quality process
• Provides further refinement of the project focus to ensure that an
appropriate scope is arrived upon
• Developed in conjunction with input from AEP stakeholders

> Define Phase
Affinity Diagram and Element Correlation
• Affords the ability to visualize connections between “major drivers”
related to Network Reliability
• In addition an excellent “multi-level” communication tool

> Define Phase
Network Reliability + Project Scope Prioritizer
• Additional measure related to develop a highly refined vision of the
project definition and focus
• Allows for balance between numerous important elements:
› I. Perceived Importance
› II. Cost of Implementation
› III. Feasibility / Probability of Success
› IV. Cost Reduction
› V. Leverage / Positive Effect on Correlated Processes

> Define Phase
Project Aim Balanced Scorecard
• Correlates the following three components related to the Problem Scope –
further defines feasibility and “health” of each option:
› I. Objective
› II. Proposed Measure
› III. Initiative for Progress

II. Measure Phase

> Measure Phase
AEP Operating Regions of Focus
• Datasets provided by AEP included all operating regions, the
intent of the project is to gauge the status of the entirety of the
company’s presence in all included states:
› I. Kentucky
› II. Ohio
› III. Texas
› IV. Virginia
› V. West Virginia

> Measure Phase
Sources of Data for Measurement + Analysis
• Two different types of databases were utilized for the recording of work
order related to equipment
• Division of databases is correlated to the structure of the network, with
Circuit Breakers and Transformers both within Substations
• The more distributed nature of Transmission Lines affords the need for a
dedicated system due to increased complexity

> Measure Phase
ISIS and TORS Database Output Files
• Overall structure and organization where relatively the same on a macro-level
and allowed for exploration of measurement approaches
• Details such as metric identifiers (Region, Manufacturer, Equipment ID)
however were unique to each database

> Measure Phase
Fishbone Diagram: Measurement Planning
• The utilization of this visualization device allows for the depiction of
those metric categories recorded in ISIS and TORS databases
• Provides a “roadmap” from which to develop an appropriate approach
for measurement and subsequent analysis

> Measure Phase
Visualization of Metric Distribution
• The exploration of several metric measurement visualizations was vital to
the development of an appropriate approach
• The identification of a highly skewed dataset was apparent through this
process
• Even with efforts to eliminate these outliers, a highly non-normal
distribution of data was present in terms of Cost, as well as other metrics
such as Outage Duration

> Measure Phase
Metric Stratification + Fitted Line Plots
• This means of data visualization was helpful in identifying distributions inherent
in both continuous (equipment age) and discrete (rated amps)
• Although no overall “trends” could be established, this is further indication of
the non-normaility of the data
• Relations to the highly distributed nature of the network may be of
considerable influence and warrant further study

> Measure Phase
Trend Tracking + Interval Plots
• The utilization of interval plots allow tracking over the period that data
was available for analysis
• High degree of variability was present across metrics
• Approach proved useful in the comparison of these metrics for possible
correlations present

> Measure Phase
Year of Occurrence + Region of Operation Distribution/Trends
• Elements such as Year of Occurrence and Region of Operation
• A specific tool can be useful in several phases and iterative use affords a
strengthened problem-solving process
• High values related to Standard Deviation across all categories holds
implications for measurement ability

> Measure Phase
Two-Dimensional Stratification
• Stratification of data across two metric categories provides further
definition related to trends and possible measurement techniques
• For many datasets, there are only a handful of intervals worth of data
present which makes overall appraisal of the metric’s appropriateness
somewhat challenging

> Measure Phase
Industrial Standards (Outage Duration + Outage Rate)
• Industry-standard metrics were also considered for inclusion
› I. Outage Duration
(Count of hours out of service / Count of Failures)
› II. Outage Rate
(Count of Failures In Group / Total Population)
• AEP expressed heightened interest in these metrics for incorporation in
the Measure and Analysis phases

> Measure Phase
Metric Median + Standard Deviation + Count Visualization
• The incorporation of the following elements where highly informative in
the identification of “fitness” related to the quantitative and qualitative
metrics recorded
› I. Mean
› II. Standard Deviation
› III. Count
• This specific method of visualization was utilized across numerous
metrics to derive a consistent “baseline” for comparison of the various
metrics within each database

III. Analyze Phase

> Analyze Phase
Overview of Approach Structure

> Analyze Phase
A. Data Validation/Screening Analysis (Source + System Reliability)
I. Circuit Breakers II. Transformers III. Transmission Lines
Details of Approach Structure
• High degree of incomplete and “invalid” data present within all three equipment
categories – inherent of issues with the design of metric database

> Analyze Phase
B. Industrial Standards Analysis (Failure Duration + Failure Rate)
I. Circuit Breakers II. Transformers III. Transmission Lines
• Industry-standard proved promising for analysis
› I. Outage Duration
(Count of hours out of service / Count of Failures)
› II. Outage Rate
(Count of Failures In Group / Total Population)
• Mild correlations were present between Outage Duration and Rate,
however varying relations present from regions
• Extended interval of historical data could afford higher power

> Analyze Phase
C. Two-Dimensional Categorical Analysis (Quantitative + Qualitative)
I. Circuit Breakers II. Transformers III. Transmission Lnes
• Two-dimensional analysis allows for continued exploration of trends and
correlation between metrics including
› I. Quantitative Metrics
› II. Qualitative Metrics
• Allows for association between qualitative and quantitative metrics
• Augmenting the time interval over which the data is depicted could
afford a higher degree of resolution in future efforts

I. Circuit Breakers
36
> Analyze Phase
D. Linear Regression Analysis

I. Circuit Breakers
37
> Analyze Phase

I. Circuit Breakers
38
> Analyze Phase

IV. Improve/Control Phase

> Improve/Control Phase
Brainstorming + Ideation Refinement
• Several rounds of brainstorming and ideation related to improvement
efforts were conducted
• Inclusion and participation of AEP representatives allowed for an
enhanced ability to include Customer Perspective and develop Quality-
centric recommendations

Affinity Diagram for Improvement Drivers
• Improvement drivers were identified, with corresponding core area of
incorporation identified:
› I. Accuracy + Completeness – Metric Databases
› II. Additional Fields – Metric Databases
› III. Clear Definition of Concepts - Operational Definition
› IV. Data Consistency - Metric Databases
› V. Reporting – Field Technicians / System Engineers

Finalized Set of Improvement Drivers
• Further resolution of specific “Improvement Efforts” that have the
potential of improving the overall quality of the system and reducing
potential for errors associated with:
› I. Accuracy + Completeness – Metric Databases
› II. Additional Fields – Metric Databases
› III. Clear Definition of Concepts - Operational Definition
› IV. Data Consistency - Metric Databases
› V. Reporting – Field Technicians / System Engineers

Workflow Improvement: System Design and Human Factors
• The most significant issue present in
the current system is lack of
accountability for fully completing
Work Order entries
• Improved workflow with “feedback”
affords an enhanced ability to close
out each entry and enhance the
statistical power of the collected data
• Establish means for effective
measurement and analysis phases in
future iterations

Refinement of Cause Code Classifications
• Enhanced outage cause coding system
• “Meta-Categories” can be implemented in order to drive a higher degree
of analysis not just between individual categories but within and
between these classifications

Cause Code System Improvement
• Continuation of the improved cause code system
• A pool of pre-established causes are presented to the repair technician,
from which the most three relevant are selected
• Eliminates the subjectivity present within the current system and manual
entry of cause
• Moves beyond just one cause and can allow for advanced analysis
between multiple potential causes – trend tracking, predictive behavior
within the network

Metric QR Code Implementation
• Utilization of QR technology off-loads the monotonous task of metric
recording from technician
• Offloads the error-prone task of manual equipment metrics entry from the
technician to an established automated system
• Affords the ability for the technician to focus on proper cause code
determination
• Ability to utilize new entry system with “feedback” more effectively
through the offloading of metric entry

Outage Cause + Persistence Tracking
• Current system only tracks one element, an improved system can afford
the ability to develop models of the following:
› I. Outage Cause – Performance of the Distribution Network
› II. Outage Persistence – Performance of the Repair Process

Proposal for Improved Measure/Analysis Process
• Through the completion of the Analysis Phase, the team iteratively revised
the approach towards processing the data
• To sustain the process, this flow diagram was developed in order to
establish a standardized means for completion of this task
• Aim is to improve efficiency of analysis and turnaround of results

Outage Visual Control: Frequency + Duration + Tracking
• Network visualization affords a tool for communication and tracking of
overall system status and “flagging” of trends.
• Consolidated and visualized metrics related to a specific outage offer
powerful communication and tracking tools to AEP
• High degree of utility in terms of Inter-departmental and Multi-level
communication due to visual nature and scalable complexity
• Eventual incorporation into “Dashboard” program could provide further
consistency in communication and network tracking

> Referenced Works
Amaro, Vincent A., Jr. Evolver - A Practitioner's Guide to Lean Manufacturing - 5S Edition. 2nd ed. San Juan
Capistrano, California: Lean Manufacturing Consulting & Vincent A. Amaro Jr, 2006. Print.
American Electric Power Co., Inc. AEP Operating Regions Per State. Digital image. AEP - Rates and Tarrifs.
American Electric Power Co., 2010. Web. <https://www.aepnationalaccounts.com/account/bills/
rates/RatesAndTariffs.aspx>.
The Apache Software Foundation. Visual Control - Individual Repair Progress Tracking. Digital image.
Express Dashboard | Q.6. Maps. The Apache Software Foundation, 2009. Web. <http://data.quad
base.com/Docs/edab/help/manual/M>.
Brook, Quentin, and Quentin Brook. Lean Six Sigma & Minitab: The Complete Toolbox Guide for All Lean Six
Sigma Practitioners. [S.l.]: OPEX Resources, 2010. Print.
Desktop to Mobile. QR Code Implementation for Equipment Metric Tracking. Digital image. QR Codes –
Quick Response | What Is a QR Code? Desktop to Mobile -Web Design & Mobile Web Design in Dorse,
2009. Web. <http://www.desktop-mobile.co.uk/qr-codes-quick-response/>.
DTE Energy Company. Illustration of System Components within Scope. Digital image. DTE Energy: About
Electric Service. DTE Energy Company, 2012. Web. <http://www.dteenergy.com/residentialCusto
mers/productsPrograms/electric/aboutElectricService.html>.
Excelarator LLC. Overview of Six-Sigma Statistical Analysis Methods. Digital image. Excelarator News. N.p.,
2012. Web. <http://excelator.org/introduction-to-lean-six-sigma/>.
Global Energy Network Institute. Representation of Power Distribution Network Infrastructure. Digital
image. GENI Archives. Global Energy Network Institute, 2006. Web. <http://www.geni.org/globalen
ergy/library/national_energy_grid/united-states-of-america/ americannationalelectricitygrid.shtml>.
Gupta, Bhisham C., and H. Fred Walker. Statistical Quality Control for the Six Sigma Green Belt. Milwaukee,
Wisc.: ASQ Quality, 2007. Print.
Harry, Mikel J. The Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements.
Hoboken: Wiley-Blackwell, 2010. Print.

> Referenced Works Cont.
IHS GlobalSpec. National AEP Operating Region. Digital image. Power Generation & Distribution News.
IHS GlobalSpec Group, 2007. Web. <http://www.globalspec.com/newsletter/pub/65/power-
generation-distribution?vol=2&issue=10&isPastIssue=1>.
MicroStrategy. Visual Control - “Bird’s Eye” Outage Dashboard. Digital image. Feature Summary for the
MicroStrategy GIS Connector for Google Map. MicroStrategy, 2012. Web. <http://www.microstra
tegy.com/producthelp/9.3/GISHelp/topics/gis_integration.htm>.
Moresteam University Inc. Lean/Six-Sigma Black Belt Course Training: Coursebook Companion. 5th ed.
Columbus, OH: Moresteam University, 2010. Print.
Ōno, Taiichi. Toyota Production System: Beyond Large-scale Production. Cambridge, MA: Productivity,
1988. Print.
Oregon DHS. Eight Forms of Waste Associated With Lean Methods. Digital image. DHS | OHA
Transformation. Oregon.gov, 2009. Web. <http://www.oregon.gov/DHS/transformation/Pages/
lean.aspx>.
QFuse Network Inc. QR Code System Operation and Network Integration. Digital image. QFuse | What
Are QR Codes? QFuse Network Inc., 2010. Web. <http://qfuse.com/learning/what-are-qr-codes>.
WasteSyn Inc. Representation of Power Generation Process. Digital image. WasteSyn Inc.- Technical
Documentation. WasteSyn Inc., 2009. Web. <http://www.wastesyn.com/tech_ft.html>.
Williams Learning Network. Introduction to Distribution Systems: Introduction to Transmission and
Distribution Systems - Workbook. Rockville: Williams Learning Network, 2007. Print

Application of Lean/Six-Sigma + Human Factors Principles
To Power Distribution Network Optimization Efforts:
Master of Science in Industrial Systems Engineering Project
Nick Boyd
Dr. Carolyn Sommerich
Dr. Jerald Brevick
April 24th, 2013
Thank you!
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M.S.ENG. Lean/Six-Sigma Project Technical Documentation

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