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A Lean Six Sigma integrated Multiple-Case Case Study
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Commercial Electrical Distribution: Quality Improvement
Abstract
A business solution for power utility company A in reducing the average technical and
commercial (AT&C) electrical losses for commercial distribution through Lean Six Sigma
integrated Multiple-Case Case Study is imminent. Distribution transformer distance from the
distribution board of the commercial industry and drop electrical cable diameter or size are
crucial elements that determine if such a distribution electrical system incurs losses. The LSS
DMAIC and FMEA approaches will analyze a multiple-case case study that will help top-level
managers and stakeholders alike make informed business decisions. SPSS 25 software will help
in anova analysis and provide a visual exploration of the data collected from the multiple-case
case study. Interpretation of results from the tables generated by the analysis will be coupled
with boxplots and scatterplots to provide firm rationality of the analysis further. A cross-case
synthesis will help provide further scrutiny to all the case study phases.
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Table of Contents
Abstract ......................................................................................................................................2
Table of Contents ........................................................................................................................3
1. Introduction .............................................................................................................................4
2. Case Study Report Audience and Reporting Methodology.......................................................4
3. Visual and Textual Materials of a Case Study Report ..............................................................5
3.1. Electrical Losses ...............................................................................................................5
3.2. Lean Six Sigma (LSS) Start-up .........................................................................................6
4. Reporting Evidence: Striking the Balance................................................................................7
5. Multiple-Case Case Study with Lean Six Sigma ......................................................................7
5.1. Black Belt project: Reducing AT&C electrical losses for commercial distribution ............7
5.1.1. Project Background ....................................................................................................7
5.1.2. Definitions .................................................................................................................7
5.1.3. Measure......................................................................................................................8
5.1.4. Analyze ......................................................................................................................8
5.1.5. Improve .................................................................................................................... 13
5.1.6. Control ..................................................................................................................... 13
6. Conclusion ............................................................................................................................ 14
References ................................................................................................................................ 15
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1. Introduction
A Lean Six Sigma integrated Multiple-Case Case Study's main objective is to improve
commercial electrical distribution quality for power utility company A. According to da Silva et
al. (2018), the goals of productivity, quality, and cost reduction can be realized by improving
operations and processes. The lean six sigma is a methodology that stands out in enhancing
power utility company A's effectiveness, complementarity, and functionality. Electrical losses in
commercial electrical power distribution make the utility company's operating cost increase,
which later translates to increased electricity cost and reduced customer satisfaction (Mehigan et
al., 2018). The purpose of the research is to get the business solution for power utility company
A in reducing the average technical and commercial (AT&C) electrical losses for commercial
distribution through Lean Six Sigma integrated Multiple-Case Case Study.
2. Case Study Report Audience and Reporting Methodology
The case study involves the availability of the prepared report audience and a reporting
methodology considered vital research elements. The report audience consists of the senior
management of power utility company A and other stakeholders, including investors and
commercial business owners. Also, employees such as technicians, clerical staff, and engineers
are part of the audience who will find the case study report beneficial. The audience mentioned
are fundamental in quality control for the utility company, which is the main reason as to why
they are chosen as the primary users of this report. The reporting methodology used for this
study is an analysis using SPSS software tool and report documentation that will utilize data
from measurements of electrical cables sizes and distance of distribution transformer from
commercial industry location. Secondary data will be utilized to draw inferences concerning
electrical cable sizes and distribution transformer location from load location. Middle-level
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managers of utility company A and numerous commercial industries will be asked for detailed
related case documents to enhance effective research and reporting of findings. Measurements of
electrical cable sizes will be done with the help of Vernier calipers that show the diameter of the
cable with the American Wire Gauge used in determining the actual wire size, and the distance
between distribution transformers and the commercial industry was measured using a measuring
tape.
3. Visual and Textual Materials of a Case Study Report
The Lean Six Sigma implementation strategy in this project will build confidence around
the multiple-case case study. The replication strategy will aid in iterating successive tests for a
claim whose purpose is to maximize the chance of finding defects and identifying possible errors
(Sousa et al., 2021). The visual materials of the case study report include tables and figures,
while textual materials are the prepared reports and written suppositions concerning the case
study.
3.1. Electrical Losses
The multiple-case case study was carried out in three industries where cable and
transformer distance measurements were done. According to secondary data and as shown in
table 1, it was found out that the Mean AT&C Losses had been high with industry 1 being 35%
and a standard deviation of 12%, industry 2 had Mean AT&C Losses of 38% with 13.8%
standard deviation, and industry 3 had Mean AT&C Losses of 34% and standard deviation of
12.5%. The utility company is determined to reduce these figures by almost half for improved
quality, efficiency, and consumer experience.
Industry (Efficiency %)
Mean AT&C Losses
Standard deviation %
Industry 1 (84)
0.35
12.0
6
Industry 2 (88)
0.38
13.8
Industry 3 (79)
0.34
12.5
Table 1: Mean AT&C Losses per industry
3.2. Lean Six Sigma (LSS) Start-up
The quality improvement initiative by power utility company A is to reduce
inconsistency, burden, and electrical energy waste. Remarkable success is promised with the
LSS initiative. As customer demand increases, power utility company A will find it effective in
carrying out a quantitative research analysis to reduce AT&C losses (da Silva et al., 2018).
DMAIC (define, measure, analyze, improve, control) approach and tools will be utilized. As
shown in table 2, an FMEA improvement phase will aid in eliminating failure since the proactive
and systematic approach assesses diverse relative failure impact (Subriadi & Najwa, 2020). Type
4 LSS is well demonstrated in table 2 for the multi-case case study concerning AT&C losses
reduction across three commercial industries.
Phases
Tools to be used
Define
Project charter and SIPOC (suppliers, inputs,
process, outputs, and customers)
Measure
Vernier calipers and measuring tape
Analyze
Scatter plot, boxplot, FMEA
Improve
Brainstorm, simulate, prioritize solution
Control
Cost-saving, action plan
Table 2: DMAIC and the tools used for the case study
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4. Reporting Evidence: Striking the Balance
The multiple-case case study analysis requires transparency in performance, principles,
and values for improved sustainability. Therefore, the evidence presented in this report aims to
meet utility company A's information needs. Sustainable reporting will strike a balance between
the utility company and stakeholders, including the consuming industries. As presented in the old
testament, the community concept requires high levels of morality and ethics, which should be
replicated in this case study since the information gathered should be truthful and candid to the
affected stakeholders and the utility company (Testament, 2015). The information about AT&C
losses is very sensitive but engaging all stakeholders is vital since it can benefit the utility
company through improved performance and sustainable development.
5. Multiple-Case Case Study with Lean Six Sigma
5.1. Black Belt project: Reducing AT&C electrical losses for commercial distribution
5.1.1. Project Background
LSS multiple-case case study's purpose is to reduce AT&C electrical losses for
commercial distribution to improve electrical distribution quality for power utility company A
and increase stakeholder confidence. Power utility company A has determined that AT&C
electrical losses disadvantage the company, and a technique to reduce and increase efficiency is
inevitable. The black belt project will drive positive change in the company by deploying lean
principles and allowing for necessary statistical analysis to address the issue of electrical cable
sizes and distance of distribution transformer from the commercial industry premises.
5.1.2. Definitions
The case study took into account three commercial industries, and variable measurements
were done. Through SIPOC analysis, the case study will try to satisfy the power utility company,
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stakeholders, and industrial establishments. High levels of case study ethics are observed by
every team member, including the technicians, chief engineer, and middle-level managers
responsible for providing comparison reports for the case study.
5.1.3. Measure
5.1.3.1. Measurement Analysis
Plastic Vernier calipers measured the diameter of drop electrical cables used for the three
industries (represented by different efficiencies) and a plastic tape measure to measure the
distance between distribution transformers and commercial industry distribution board location.
Only copper electrical cables were used in this case study, and the size was measured in terms of
diameter; and table 3 shows the measurements taken from the three industries.
Table 3: Variable measurements
5.1.3.2. Variable Identification and Process Description
There are two normal independent variables of cable size and distance, which have three
levels, and one dependent variable of efficiency, whose measurement scale is also normal.
Efficiency is affected by cable size and distance. The LSS multiple-case case study analysis will
help improve the system's efficiency and lead to improved quality control and satisfaction.
5.1.4. Analyze
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Analysis of data is crucial when dealing with multiple-case case studies as it allows one
to interpret it more accurately. Since the measurement of both the dependent and independent
variables of efficiency and cable size or distance are scale, the one-way ANOVA test statistic is
used considering the dependent variables have three levels (Morgan et al., 2019). Scatter plots
and boxplots will be used to compare and visualize results distribution respectively and help
inform the audience of the relationship between variables of the study.
5.1.4.1. Effect of cable size on efficiency
The cable diameter affects system efficiency, and there are considerable electrical losses
associated with specific diameters of the drop electrical cable. The hypothesis to be measured is
as follows: H0: high drop electrical cable diameter does not lead to considerable electrical losses
to the system H1: high drop electrical cable diameter leads to considerable electrical losses to the
system. One-way ANOVA test results in table 4 show that the f statistic is 1.251, the numerator
degree of freedom is 7, and the denominator degree of freedom is 1. With a significant level of
0.05, the p-value is 0.599, represented by the significant value in table 4. This means that the
result is not significant at p<0.05; hence we fail to reject the null hypothesis. Therefore, high
drop electrical cable diameter does not lead to considerable electrical losses to the electrical
system. Failing to reject the null hypothesis is further confirmed by the scatter plot in graph 1
when one draws the best fit line, which shows a direct linear relationship between system
efficiency and drop cable diameter. Therefore, smaller diameters are responsible for considerable
AT&C electrical losses than bigger diameters.
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Table 4: One-way ANOVA results for drop cable diameter variable
Graph 1: Scatter plot for efficiency against drop cable diameter
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Graph 2: Box plot for drop cable diameter
5.1.4.2. Effect of distance on efficiency
Distance of distribution transformer from the commercial industry distribution board is
assumed to affect system efficiency that translates to AT&C electrical losses. The hypothesis to
be measured is as follows: H0: greater distance of distribution transformer from commercial
industry's distribution board does not lead to greater electrical losses H1: greater distance of
distribution transformer from commercial industry's distribution board leads to greater electrical
losses. The one-way ANOVA statistic is not defined in table 5, meaning that we cannot rely on it
to determine if we fail to reject the null hypothesis or not. However, when one draws a line of
best fit on the scatterplot in graph 3, there is an indirect relationship between efficiency and cable
distance which means that we can reject the null hypothesis in favor of the alternative
hypothesis.
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Table 5: One-way ANOVA results for drop cable distance variable
Graph 3: Efficiency against drop cable distance scatter plot
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Graph 4: Box plot for drop cable distance from the commercial industry distribution board
5.1.5. Improve
Further simulations and brainstorming were carried out to improve the solution provided
by one-way ANOVA tests performed above. The boxplots and scatterplots helped cement the
claims presented to either fail to reject or reject the null hypothesis. According to Shen et al.
(2018), electrical cable diameter is inversely proportional to electrical losses, while electrical
cable distance or length is proportional to electrical losses. This means that the bigger the
diameter, the lower the electrical losses, and the longer the electrical cable, the higher the
electrical losses in the system. Therefore, the null hypotheses for the two multi-case case studies
were rightly failed to be rejected or vice versa.
5.1.6. Control
The multiple-case case study can help the decision-makers of utility company A to be
informed about the right steps that will reduce losses and increase consumer confidence.
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Installation services of the cables and distribution transformers have been provided with a
knowledgeable guide through this case study. Therefore, whenever technicians install
transformers, they should check that the distance between the distribution board of the industrial
consumer and the transformer is minimized and that the drop cable used has an optimum
diameter to reduce electrical losses. The relative unit for the above control plans is inspection
and technique. They will help guide the chief engineer in deciding on the right team to carry out
such sensitive installations.
6. Conclusion
Commercial electricity consumers are referred to as heavy consumers since the monthly
statistics concerning their grid electrical intake. Therefore, it is expected that power utility
company A will incur significant losses with the substandard installations concerning
distribution transformer location and drop electrical cable size. The LSS integrated multiple-case
case study presented has addressed the possible areas that require improvement. The business
solution presented will benefit the consumer, stakeholders, and power utility company A in the
long term.
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References
da Silva, O. R., Rosini, A. M., Guevara, A. J., Palmisano, A., & Venanzi, D. (2018). Lean Six
Sigma: Multiple Case Study. Journal on Innovation and Sustainability RISUS, 9(1), 74-
84.
Mehigan, L., Deane, J. P., Gallachóir, B. Ó., & Bertsch, V. (2018). A review of the role of
distributed generation (DG) in future electricity systems. Energy, 163, 822-836.
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2019). IBM SPSS for
Introductory Statistics: Use and Interpretation: Use and Interpretation. Routledge.
Shen, B., Coombs, T., & Grilli, F. (2018). Investigation of AC loss in HTS cross-conductor
cables for electrical power transmission. IEEE Transactions on Applied
Superconductivity, 29(2), 1-5.
Sousa, P. R. D., Barbosa, M. W., Tontini, G., Filho, A. R., & Ferreira, M. M. M. P. (2021). A
qualitative analysis of Lean implementation in Brazilian surgical centres: a multiple-case
study. International Journal of Healthcare Technology and Management, 18(3-4), 168-
185.
Subriadi, A. P., & Najwa, N. F. (2020). The consistency analysis of failure mode and effect
analysis (FMEA) in information technology risk assessment. Heliyon, 6(1), e03161.
Testament, O. (2015). Holy Bible.
