Six Sigma Method for evaluating Energy Conservation Portfolio


Six Sigma is often promoted as framework for organizational excellence. The Six Sigma concept started around 1987 at Motorola manufacturing division, where millions of parts were made using the same standardized process repeatedly. Eventually Six Sigma evolved as a concept and currently is applied to other non-manufacturing processes. Today Six Sigma Methodology and tools are successfully used in many fields such as Services, Medical, Insurance, Call Centers, Government and Utilities. The following article presents a practical application of Sigma Levels to managing Energy Conservation Program Portfolio.


What is Six Sigma?

Six Sigma [6σ] is a methodology that improves any existing business process by constantly re-evaluating and optimizing the business processes or the program as a whole, while striving to achieve a virtually error free process running at the yield of 99.9997% or 3.4 defects per million opportunities. Six Sigma Methodology can be applied to Energy Conservation in Utilities Industry for evaluating the overall program performance in order to put in place measures that will increase its overall benefits.

What is Sigma [σ] Level ? It is a statistical term that measures how much a process varies from its target and can be used as measure of assessing the process performance.The opportunities for error are predefined customer specifications in order to satisfy the customer who wants a defect free product. DPMO is a measurable number that stands for Defects per Million opportunities for error. DPMO is calculated using the following equation:

In process improvement efforts, the process capability index (Cpk) is a statistical measure used to identify the ability of selected process to produce output within the specification limits. The following formula is used to calculate Cpk:

The following is the minimum recommended Process Capability index based on the type of process being examined:

Existing Process                   Cpk = 1.33

New Process                      Cpk =1.50

Existing critical parameter            Cpk=1.50

New critical parameter               Cpk=1.67

Six Sigma quality process            Cpk=2.00


A capable process normally falls between 4 to 5 Sigma Level. Some industries have standardized required Process Capability and demand capable processed to be put in place in order to produce quality product.

 Calculating the Sigma level for Energy Conservation Program Portfolio 

The Energy Conservation Program Portfolio (ECPP) Management can be viewed as a process managing a program portfolio that includes a number “Energy Conservation Projects” as its process output or product. Conservation Projects are different in nature, size, complexity, intended real users, and degrees of opportunities that must go right. Therefore, it appears very difficult or impossible to apply the simple concept of Sigma level in the case of the overall Program Portfolio management . The question is how to determine the set of standardized measures and specifications, or in other words the things that must go right in order to satisfy the program anticipated outcome. However, if we assume that the basic intent of the Energy Conservation Program Management is improving its processes and reducing the variation, then we can follow simple steps to apply Six Sigma Process Metrics that can be used as a performance assessment tool for the overall program capability performance. We can further use Plan–Do-Check-Act (PDCA) technique to drive process improvement for this process.

 Define: Define the process. The Energy Conservation Program Portfolio Management (ECPPM) Office is responsible for the management of energy conservation projects which may include initiative from simple energy efficient lighting replacement to very complex retrofits of large heavy equipment. Projects must be evaluated based on performance and financing in order to determine the overall program portfolio capability and benefits. The ECPPM Office has to ensure also that managed processes are repeatable and reproducible and at the end to meet their objectives.

Identify the Opportunities for Error. In order to be successful, an Energy Conservation Project as any project must satisfy “the vicious triangle” of Time, Cost, and Scope. In other words this means that a project is considered satisfactory completed when it is completed On-time, On-budget, and agreed Scope (or within the regulatory schedule requirements). Therefore, for our calculation of Sigma level, we can use these three values as measurable opportunities for error.

Identify the Tolerance Target. The target of the selected measures of opportunities for error must be defined in order to allow identifying defects. It is very unlikely that projects can be completed exactly on their defined target or exactly on-time, on budget, and on-scope. Therefore, realistically the Program Management Office will be satisfied if the projects are completed within a pre-established tolerance for each of these opportunities for error. So, for example, a target for the “On-Time” measure can be defined as “a project is satisfactory when it is completed within +/- 10% of its initial agreed time”. It is important to select a tolerance target that can be achieved and that is below the current process performance level.

Define the Defect. According to Taguchi, who is known as the father of Quality Engineering in Japan, any deviation from the target results in cost to society. Thus, for our purpose whether a process is producing below or above the target should be irrelevant to calculating Sigma level because at this phase of the process improvement we are only concerned with the variation. Therefore, the defect for our purpose is defined as “any measure above the target”. The measure then is calculated as follows:

Measure: Set up a Spreadsheet in Excel:

Set up a spreadsheet in Excel to capture the result of project performance and determine process Sigma calculations. Suppose we can start with 10 projects as shown in figure 1.

Figure 1- Sample Spreadsheet

Fill out the spreadsheet. Identify the projects and populate the spreadsheet. Suppose we have 10 projects that we have completed. We fill in the Planned and Actual values for each of the three Opportunities of Error. Observe the Process Sigma level of our process (figure 2)

Figure 2: Sample ECPPM Process Sigma Level


In this example, the current Process Sigma is 1.58 (Cpk 0.52) or 466,667 DPMO which is a long way from 6σ level of 3.4 defects per million opportunities. For conversion you can use the table on Figure 3 or various calculators.

Figure 3: Sigma Level Conversion Table

Analyse: Monitor the Process. As it can be seen at the start of this process the process Sigma is low; Management should put various plans in place to improve the process.

Improve and Control: Reduce the Tolerance Target. Once the desired level of Sigma is achieved, reduce the target tolerance and continue monitoring and improving the process. Process sigma is 2.78 (Cpk = 0.92)

Figure 4: Sample ECPPM Process Sigma Level


Six Sigma principles can be applied to most processes. This article provided a methodology for evaluation of the performance of an Energy Conservation Program Portfolio. Time, Budget, and Scope were selected as opportunities for error and target tolerance as a means of identifying defect was defined. Process Sigma is a way to benchmark and track the overall program optimization.

I will be happy to hear more about your performance evaluations of project portfolios and other analytic. Sample excel spreadsheet is available upon request.


Energy Conservation Business Plan

Executive Summary

Green Power Consultancy is a start-up organization in Burlington, VT that offers designs and advice to architects and consumers regarding environmentally sensitive buildings as well as energy consumption recommendations. Green Power has identified three keys to its success. The first is the need to only offer solutions which are based on market demand. The second is to ensure that all of its offerings are based on economic justifications; the solution should make sense beyond the environmental considerations because it has long-term economic value.

Green Power will be targeting architects and individual consumers. Green Power will work closely with several architects providing them the ability to offer environmental solutions to their customers. This group is growing at 7% and there are 23 potential customers in the area. The second customer group is individual consumers; an environmentally conscious group that have sought out a service provider to help them implement their personal ethics into the design of their new or existing structure.

Green Power is an environmental energy consultancy that offers a wide range of services: advice regarding passive heating, grey water usage recommendations, renewable energy considerations and employee transportation options.

Green Power will be led by the seasoned management team of Dan and Sue Lang. Dan received a degree in environmental studies, business, and a Masters in architecture. Dan has several years of work experience within the industry. The second part of the team is Sue Lang. Sue has an MBA and work experience with the Bonneville Power Administration in their renewable energy department. Through a combination of excellent education and good work experience, Green Power’s management team will be able to successfully execute on its business plan.

Green Power has conservatively forecasted sales of $202,343 for year two, rising to $238,402 for year three. Net profit will be reached in the second year. Through a combination of a proven business model, a strong management team, and this comprehensive energy business plan to guide the organization, Green Power will be long lasting, profitable business.


It is Green Power Consultancy’s mission to provide the finest green energy solutions for new constructions as well as existing building owners/lessors. Through careful analysis, attentive customer support, and cost effective solutions, Green Power will become a stable business serving the Burlington community.

Keys to Success

Green Power has identified several keys to success that will be instrumental in creating a sustainable business.  If these keys are followed, the likelihood of success will significantly increase.

  1. Offer solutions that are demanded by customers.
  2. Ensure all of the solutions have economic considerations built into the respective models.
  3. Only provide 100% customer satisfaction.  All customers must have their expectations exceeded.


Green Power has identified three objectives that it will pursue for the long-term success of the business:

  • Proven cost-benefit analysis environmental approaches to structure building, maintenance and energy consumption.
  • Become the premier environmental energy consultancy in the state within five years.
  • Reach profitability within three years.

Energy Management Consultant

Corporations and homeowners combined spend billions of dollars annually on energy to light, heat and air-condition their homes and buildings. Imagine how much healthier the environment would be, as well how much money each of us could save every year, if we could all reduce our energy consumption by a mere 10 percent? The aforementioned is the focus of this incredible business opportunity. Working as an energy management consultant from a homebased office you can teach homeowners and business owners practical and useful energy management tips about reducing consumption and waste. Successfully activating this business will require a great deal of research, planning and perhaps training. However, with energy cost continuing to soar, the need to take care of the environment and save money is becoming a major concern for most people. This type of venture should have a very favorable future.

The Market

People who want to learn how to save money on their monthly utility bills

5 Green Businesses You Can Start at Home

If anything’s hot in today’s economy, it’s saving money, including a broad range of green businesses helping people save energy, water and other resources. For those seeking flexible hours and low startup costs, these five green businesses to start at home may be the best way to join the green business wave:

  1. Green Irene Eco-Consultant
    Founded by PJ Stafford and Rosamaria Caballero Stafford, Green Irene trains people from all walks of life to earn part-time income as eco-consultants. Green Irene has more than 150 eco-consultants in 35 states that take pride in being a force for positive change in their communities. As part of the $99 Green Home Makeover, a trained eco-consultant walks through a person’s home with the homeowner and proposes specific changes and products that can save energy, water and money. Green Irene has screened an array of green products, helping customers and consultants avoid the confusion of sorting through these on their own. Calculators show the return on many purchases, such as $7,000 (over seven years) for a $133 two-bathroom water conservation kit. Eco-consultants also offer a green office makeover for offices of two to ten people ($250) and 11 to 50 people ($450).

    Using a direct sales model, Green Irene is like the Avon of green, a business model that provides flexible hours, low costs and the ability to work out of your own home. The $450 startup cost includes a large collection of products sold by Green Irene, customized marketing materials, hours of online training and ongoing support to build business. While becoming an eco-consultant requires commitment and energy, it doesn’t require extensive prior knowledge.

    Startup cost: $450
    For more information:

  2. Zola Goods Home Party Coordinator
    Founded by Beth Remmes, Zola Goods enables people to work from their homes arranging Tupperware-like parties but for green products.

    “There are many people who are interested in the environmental movement, but don’t really know where to start or how they can make a difference,” Remmes says.

    Zola coordinators educate people about eco-friendly products and help bridge the gap between hearing the information and taking action. Zola uses a single-level direct sales model and there are no recruiting or sales requirements. The startup cost for a coordinator is only $149 for a kit containing green products that can be used daily and for demonstrations at parties. Coordinators are often moms seeking income and flexible hours, and people who want to get involved in the green business revolution without turning their lives upside down.

    “The only requirement is that someone wants to make a positive difference,” Remmes says.

    Coordinators inspire others to help make the world a better place and often become a hub for green information in their communities.

    Startup cost: $149 for a startup kit
    For more information:

  3. Green Internet Store
    Though running an internet store out of your home allows you to reach a broad market with lower startup costs than a physical storefront, it can still take a great deal of time, energy and money to get started. You need to get your website ready for e-commerce, create a billing system, market your site, ship products and track deliveries. Another solution for eco-entrepreneurs is to work with a pre-packaged internet eco-store created by OnlyGreen4Me. OnlyGreen4Me delivers a turnkey online eco-store with more than 6,600 green products, and it takes care of inventory, shipping, billing and collection. OnlyGreen4Me provides a way to hit the ground running with a store months earlier than doing it all on your own. There is a $2,500 setup fee, which includes the first-year hosting and maintenance fees.

    Startup cost: $2,500 for initial setup fee
    For more information:

  4. Energy Efficiency Auditing–Pro Energy Consultants
    We waste billions of dollars worth of energy in the United States, but this problem is finally getting the attention it deserves. Pro Energy Consultants, created by Mark Canella, Kris Simonich and Derek Sola, is selling franchises across the U.S. for entrepreneurs to build their own businesses as energy auditors. Canella has run his energy auditing business in Cleveland for 13 years, and is now using this as the foundation of the franchise business. To help homeowners, Consultants use specific technology such as an infrared camera that lets homeowners see where their homes are losing energy.

    “The visit includes consulting–listening and talking with the homeowner–and the technical component–the audit,” Simonich says.

    To purchase a franchise, you need to go through a qualification process to meet the founders and start what will be a long-term partnership. Franchisees receive a territory, sign the agreement, go through training, pay the initial franchise fee and are ready to get started.

    Initial franchise fee: $29,900
    For more information:

  5. Making Gold Out of Garbage
    We throw away mountains of garbage every day, but much of this garbage can be converted into new products and given another life. TerraCycle may provide a model for the kind of business others can build by making useful things out of what would otherwise be garbage.

    “Garbage is one of the few things we pay people to take away from us,” says Tom Szaky, founder of TerraCycle. The company started packaging worm poop fertilizer in used plastic soda bottles, and today it’s working with big companies like Target. Target disposable bags are transformed into reusable shopping totes, juice pouches are made into lunch boxes, and granola bags into shower curtains. If they do it, they can show you how, too. This business can start out small at home in your garage, but there’s plenty of room for growth.

    Startup costs: $100s to $1,000s for overhead costs
    For more information:
    See examples at

    Getting involved can be more than donating to a charity or starting a nonprofit. With these double bottom-line business models, you can endorse your environmental activism while making money to be self-sufficient.

Market Characterization

Determining the size of the opportunity for energy efficiency and identifying where it exists – customer sectors, end uses and geography – provides us with guidance in the design and delivery of conservation strategies, initiatives and programs.

Technical Reference Guides

These guides were published by CEATI (the Centre for Energy Advancement through Technological Innovation) with the OPA contributing funding.
  • This guidebook addresses typical compressed air systems common to most small and medium manufacturing facilities.
  • This primer explains what demand response is, how businesses can benefit from participation, and various participation strategies.
  • This guide provides an overview of the major types of electric motors available today, including advanced motor technologies.
  • The Electrotechnologies Energy Efficiency Guide provides brief descriptions, characteristics, and advantages and disadvantages of various industrial processes used in small- to medium-size industrial plants.
  • This guide is aimed at new construction and renovation projects. It outlines key technologies, methods and systems to make the house more energy efficient.
  • This guide highlights basic considerations for determining the energy and demand savings arising from an energy efficiency project.
  • This guidebook is intended to provide the fundamental information required to make informed and educated decisions about the use and energy efficient operation of fan and blower systems.
  • The guide explains how a heat pump works, important considerations in buying a heat pump, its components and operating cycles,  efficiency definitions, performance standards used by industry, and what influences energy savings.
  • The Power Quality Reference Guide is written to be a useful and practical guide to assist end-use customers and covers material from concepts to solving power quality issues.
  • This guide is aimed at helping you implement energy efficiency methods and practices involving pumping systems at your location. It will also help you to make informed decisions about operating, maintaining or modifying your existing pump system.
  • This guide is aimed at helping you implement energy efficiency methods and practices involving refrigeration systems at your location. The main emphasis is on small to medium systems that operate with refrigerants other than ammonia.
  • This guide has been developed as an overview of Variable Frequency Drive (VFD) technology to assist in the effective understanding, selection, application, and operation of VFDs. In this guide, the word “drive” refers to the electronic VFD.

Measuring Complexity

John David Kendrick

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A method for measuring the level of defects effort and development time.

Complexity can be thought of as the level of difficulty in solving mathematically presented problems. Six Sigma practitioners and operations research professionals are often asked to predict the complexity of a hardware or software product by predicting (in man-hours or full-time equivalents) the expected development time, the expected number of customer-facing defects, the expected number of production defects, or the expected level of effort for a new object.

For the purposes of this discussion, the term “object” refers to hardware or a software product. Objects have properties that are called attributes, and the values we assign to an attribute represent the characteristics of the product and are referred to as “instances of the attribute.” When we have a collection of instances, we can refer to the collection of numbers as “data.”

Cluster analysis

Cluster analysis can be thought of as an aggregation of objects based on the concept of distance. A measure of distance that accounts for the similarity between objects and their relative distance is Euclidean distance. If we leverage the concept of Euclidean distance, then we can minimize the mean absolute deviation between a collection of objects from a centroid to cluster a collection of objects into groups that have common properties.

Principal component analysis

Principal component analysis is a data-reduction technique. If we describe an object based on a number of attributes (or descriptors), then we can use principal component analysis to reduce the number of attributes. Instances of the descriptors are a set of data. For example, an object that has three attributes (e.g. a person’s height, width, and girth) would have three dimensions of data. Principal component analysis gives us a way to describe that same set of characteristics using a smaller number of attributes. In the example that was just mentioned, an index value that we could call “size” might be used to characterize the height, width, and girth. Therefore, we are describing the same object in terms of one attribute rather than three.


Mathematically, principal component analysis is a transformation. The direction of the transformation is based on the maximum variation in the data. If we have three-dimensional data then we can use principal component analysis to reduce the number of dimensions. The number of dimensions will depend on the number of directions of variation in the data, and the degree of variation in that direction.

There is one important advantage that the practitioner should be aware of when using principal component analysis: If the original data set is correlated, then the transformed data set will be uncorrelated. If the units of each attribute are different, then the transformation should be based on the correlation matrix and not the covariance matrix.

The approach

The goal is to develop a specified number of clusters in a collection of objects, understand the properties of each cluster, and then have a way of assigning a new object to one of these clusters so that we can make an educated prediction about the behavior of the new object. We can quickly make these calculations using commercial off-the-shelf software. Like any good model-building approach, we will clearly understand the question we need to answer, develop a model, and then verify and validate that the model is accurate.

The examples that follow illustrate our approach:

• Generate a predetermined number of clusters based on the similarity of the objects under consideration

• Establish an index using principal component analysis

• Establish the bounds of the index for each cluster

• Verify and validate the model

• Use the model to make predictions


Example No. 1: Estimating the time to develop a software release

For years, the software development community has tried to develop accurate models that can predict the development time of software releases. One of the many challenges is employing data that are relevant in an environment where the software development tools, process, and personnel change more rapidly than the software development time. However, if we examine smaller software releases, updates, or the production of software patches that have a relatively shorter software development time, then we can apply this technique to estimate the level of effort because the data are relevant to the problem under examination.

Software can be characterized by the number of lines of software code that can be assembled and executed, the number of calls to functions or subroutines, and the number of requirements met in a portion of code. This example in figure 1 uses fictitious data to illustrate the application of this technique to estimate the time to develop a small software release.


Figure 1: Graphical representation of low, medium, and high levels of effort

Figure 2: Principal component calculations

The principal component index “PC1Effort” illustrated in figure 2 is associated with the clusters in the following way:
If -3021 < PC1Effort < -2219 then the Level of Effort is associated with Cluster 1–Low Level of Effort

If -7085 < PC1Effort < -6063 then the Level of Effort is associated with Cluster 2–Medium Level of Effort

If -11155 < PC1Effort < -9940 then the Level of Effort is associated with Cluster 3–High Level of Effort

In this case, 97 percent of the cumulative value of the eigenvalues are represented by the first eigenvalue. Therefore, the three dimensions of data can be transformed into one dimension as represented in the equation above.

Figure 3: View of transformed data

After the model is verified and validated against a test set of data (see figure 3), the practitioner can predict the expected level of effort in hours to develop a software release using estimates of the number of lines of code, the number of requirements, and the expected number of calls. If a calculation of PC1 was -7000, then we would estimate the level of effort to be between 160 and 190 hours of work.

Example No. 2: Estimating the number of defects from an electronic device

Electronic devices can be characterized based on the number of components, the number of electronic board levels, and the number of solder joints. In this example we will apply this technique to estimating the number of defects that occur during the production of a new electronic device. In this example, a fictitious data set represents the number of defects in the production of 100 units of various electronic products (see figure 4).

Figure 4: Graphical view of electronic product defects

Figure 5: Principal component analysis


In this example, 97 percent of the cumulative value of the eigenvalues are represented in the first two eigenvalues. In figure 5, we transform the data from three dimensions into two dimensions and make estimates of the expected level of defects using the first two principal components. If PC1 was -1150 and PC2 was 50, then we would expect 180 to 210 defects in 100 production units. Figure 6 is a graphical representation of the product defects.

Figure 6: View of transformed data


The examples above illustrate that complex relationships can be reduced to simple predictive models that give accurate estimates of product or software complexity. Although these examples are intended to demonstrate basic principles, the approach can be used on more complex data sets having a higher level of dimension. But as these simple examples illustrate, the quality practitioner who employs a combination of cluster analysis and principal component analysis has a powerful approach to develop predictive models that answer some common questions in our field.

For more on this subject, consult Finding Groups in Data by Leonard Kaufman and Peter Rousseeuw (John Wiley & Sons, 1990), chapters 1–5; and Applied Multivariate Statistical Analysis, by Richard Johnson and Dean Wichern (Prentice Hall, 1988) Chapter 8: Principle Components.




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About The Author

John David Kendrick’s picture

John David Kendrick

John David Kendrick is a certified Six Sigma Master Black Belt and a principal with Business Process Management Inc. in the greater Los Angeles area. His professional interests include quality, private equity, and quantitative finance. He holds a master of engineering degree in simulation and modeling from Arizona State University; master of applied statistics from Penn State, and a master of business administration in finance from the University of Pittsburgh. His undergraduate degrees are a bachelor of science in physics from Purdue; a bachelor of science in math/computer science from the University of Pittsburgh; and a bachelor of arts in economics from the University of Pittsburgh. He is a senior member of the American Society of Quality (ASQ) and is ASQ-certified as: CSSBB, CRE, CSQE, CQM/OE. He also holds two lean certifications

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