a framework for the selection of six sigma projects in...

CASE STUDY

A framework for the selection of Six Sigma projectsin services: case studies of banking and health careservices in Taiwan

Ying-Jiun Hsieh • Lan-Ying Huang • Chi-Tai Wang

Received: 9 August 2011 / Accepted: 24 January 2012 / Published online: 4 February 2012

� Springer-Verlag 2012

Abstract This study develops a framework for effectively implementing service

Six Sigma projects. The framework is composed of four phases: (1) initial project

identification, which deploys candidate projects in accordance with a firm’s stra-

tegic goals, (2) project value assessment, which evaluates project’s value based on

the financial return, cost, and its impact on employee behavior, (3) project com-

plexity assessment, which examines scope, data availability, and risk associated

with the project, and (4) project prioritization, which identifies Six Sigma projects

and categorizes them into black belt and green belt categories. Two cases in banking

and health care services are discussed to demonstrate the proposed framework.

Keywords Six Sigma � Services � Project selection

1 Introduction

Improving service quality is a top priority for firms that aim to differentiate their

services in today’s highly competitive business environment. Researchers and

practitioners attribute this emerging trend to two salient driving forces (Nakhai and

Neves 2009). First, the service sector has become the dominant part of the economy,

Y.-J. Hsieh (&)

Institute of Technology Management, National Chung Hsing University, 250 Kuo Kuang Rd.,

Taichung 402, Taiwan, ROC

e-mail: [email protected]

L.-Y. Huang

Department of Business Administration, National Changhua University of Education,

Changhua 500, Taiwan, ROC

C.-T. Wang

Graduate Institute of Industrial Management, National Central University, Taoyuan 320,

Taiwan, ROC

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Serv Bus (2012) 6:243–264

DOI 10.1007/s11628-012-0134-1

particularly in developed or industrialized nations. Second, products are being

offered more and more bundled with services in response to customer needs (Geum

et al. 2011). To cope with these changes, an increasing number of businesses are

adopting the quality improvement programs originated in manufacturing, such as

total quality management (TQM), Six Sigma, reengineering, benchmarking, quality

function deployment, etc., to enhance service quality (Hoerl and Snee 2002).

Intrinsically, the underlying principles of these quality improvement methodologies

can be applied in a service-oriented business environment (Feigenbaum 1983;

Ishikawa 1985; Deming 1986; Hensley and Dobie 2005). In particular, Six Sigma,

which makes use of a series of well-defined steps (define, measure, analyze,

improve, and control), has received a growing attention and interest from service

firms owing to its customer-centric philosophy that produces satisfactory results for

many world-class companies (Taghaboni-Dutta and Moreland 2004).

In fact, Six Sigma manifests itself in many ways and, as such, is a multifaceted

conceptualization (Tjahjono et al. 2010). For example, Six Sigma may refer to a set

of statistical tools for process improvement (Goh and Xie 2004), or an analysis

methodology that utilizes scientific methods (Kumar et al. 2007). Some researchers

further define Six Sigma as an operational philosophy of management that is

beneficial to customers, shareholders, employees, etc. (Chakrabarty and Tan 2007),

and a business culture, an organized structure that uses process improvement

specialists aiming to achieve strategic objectives (Schroeder et al. 2008). This

project-driven approach which utilizes the problem solver’s expertise and the

support of collected data to form a structured way for improving the quality of

targeted processes or products has been adopted by numerous leading companies in

the world since its introduction in the late 1970s (Goh 2002). In the 1990s, it

migrated to analysis of transactions within the manufacturing sector. Since then, it

saw a shift to applying those concepts to the transactional activities in non-

manufacturing industries.

The success of Six Sigma implementation, however, is contingent on several

factors such as top management support, clear performance metrics, organizational

understanding of work processes, etc. (Chakrabarty and Tan 2007). Among these

critical success factors, selecting the right Six Sigma projects is essential to the early

success and long-term acceptance within an organization (Adams et al. 2003).

A body of research probes Six Sigma project selection and generates fruitful

insights (Antony 2006; Su and Chou 2008; Yang and Hsieh 2009). Particularly,

Antony (2006) and Su and Chou (2008) maintain that the project selection process

should be systematic and respond to three important voices: the voice of the process,

the voice of the customer, and the voice of the strategic business goals. Following

this line of logic, Yang and Hsieh (2009) propose a project selection mechanism

based on Taiwan national quality award criteria and Delphi fuzzy multiple criteria

decision-making method. In contrast, many firms merely consider voices of the

process and customer in project selection and regard Six Sigma as ‘‘operational’’,

ignoring its link to corporate strategies (Kwak and Anbari 2006). While Six Sigma

research suggests a number of project selection approaches, these approaches

generally provide only general guidelines for project selection (Antony 2006), or

primarily for the manufacturing context (Su and Chou 2008; Yang and Hsieh 2009).

244 Y.-J. Hsieh et al.

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Research on the service Six Sigma project selection using a systematic approach

remains deficient (Heckl et al. 2010). In view of the rapid growth of service

industries, this deficiency in service Six Sigma project selection research apparently

warrants research attention.

This study thus aims to fill the research gap by developing a systematic approach

to select Six Sigma projects in the service industries. This research work is

contributive as it integrates various project selection scenarios and tools, and

thereby establishes a framework to facilitate the project selection process. This

study is organized as follows. First, the literature pertaining to current implemen-

tation of Six Sigma in service industries is reviewed. Second, the study proposes a

framework to select service Six Sigma projects. Third, the study uses two case

studies in banking and health care industries to demonstrate the implementation of

the proposed framework. Finally, the study draws conclusions and suggests future

research topics.

2 Six Sigma in services

Occasionally referred to as the ‘‘transactional Six Sigma,’’ service Six Sigma offers

firms a disciplined approach to improve service efficiency (i.e., saving time and

cost) and effectiveness (i.e., meeting the desirable attributes of a service) in the

business processes (Antony et al. 2007). For example, the waiting time to be

admitted into an emergency room is a measure of service efficiency, whereas

cleanliness is a measure of service effectiveness in the hospital environment.

Namely, for firms implementing service Six Sigma, the emphasis in process

improvement lies on the above timeliness characteristics and service non-

conformity characteristics (Antony 2004a). Service-oriented firms adopting Six

Sigma gain benefits essentially from creating more consistent processes for service

delivery that lead to satisfied customers and lower cost (Bisgaard and Freiesleben

2004). As is the case in manufacturing, Six Sigma proves to be a useful tool to

promote quality in a broad range of services (Chakrabarty and Tan 2007; Nakhai

and Neves 2009). Specifically, researchers acknowledge Six Sigma’s benefits in

material and facility management (Holtz and Campbell 2004), education (Jenicke

et al. 2008; Utecht and Jenicke 2009), innovation (Byrne et al. 2007; Cho et al.

2011), payroll and human resource (Hayen 2008), software development (Grant and

Mergen 2009), etc.

Particularly, Six Sigma applications in health care and financial services have

attracted much attention from both academics and practitioners (Antony 2004a;

Heckl et al. 2010). The first health care organization to fully implement Six Sigma

into its culture was Kentucky’s Commonwealth Health Corp., USA in partnership

with General Electric. They jointly performed a Six Sigma project to improve

radiology throughput and reduce the cost per radiology procedure, which generated

a financial return of over $1.2 million (Thomerson 2001). Later, a similar Six Sigma

project was accomplished in the film library of the radiology department of the

University of Texas M.D. Anderson Cancer Center (Benedetto 2003). Overall, Six

Sigma in health care has been adopted primarily to reduce medical errors and

A framework for the selection of Six Sigma projects in services 245

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improve process efficiency (Johnstone et al. 2003; Drenckpohl et al. 2007; Gras and

Philippe 2007; Printezis and Gopalakrishnan 2007; Gowen et al. 2008; Corn 2009).

By analogy, the focus of Six Sigma in financial services can be anything ranging

from shortening transaction times at a particular branch to addressing a particular

customer service issue at the call center. For example, Citibank lessened its service

failure rate by more than 10% in its banking operations in 3 years, resulting in

reduced waste, errors, and customer response time (Rucker 2000). Likewise,

Fidelity Investments launched Six Sigma in 2002 as part of the initiative to move

process analysis efforts to lean/Six Sigma (Nourse and Hays 2004); the goal was to

improve customer satisfaction by ‘‘reducing variation caused by defects and waste

or non-value added activities.’’ Continuing the above theme, Six Sigma research

produces fruitful discussions on a range of issues in financial services (e.g., Jones

2004; Jiantong and Wenchi 2007; Uprety 2009).

2.1 Challenges for Six Sigma in services

Six Sigma research reveals a number of challenges in applying this methodology to

service operations (Biolos 2002; Hensley and Dobie 2005; Antony 2006;

Chakrabarty and Tan 2007; Nakhai and Neves 2009). In contrast to most

manufacturing settings, these challenges in service Six Sigma mainly arise from

the general lack of tangibility and measurability in a service process (Chakrabarty

and Tan 2007), more human involvement (Benedetto 2003), difficulty in data

collection (Nakhai and Neves 2009), and the lack of project selection paradigm

(Heckl et al. 2010). For example, clearly defining the how and what of service

failure in a service process can be arduous (Biolos 2002; Does et al. 2002; Smith

2003). Notably, a mere agreement on what constitutes a defect is potentially

debatable in that it is problematic to reconcile both service provider’s and

stakeholder’s perspectives and clarify who the customers are (e.g., clinicians versus

patients in health care).

Other challenges for service Six Sigma include the ability to identify well-

defined deliverables of the project, to pinpoint the beginning and ending of a service

process, and to measure the performance of the process (Lanser 2000). Particularly,

the measurement of process performance receives inadequate attention from many

service-oriented businesses (Antony 2004b; Hensley and Dobie 2005). It is quite

common to have some sort of measuring mechanism in place in a manufacturing

context (e.g., number of defects per million parts produced), which provides an

indicator of process performance and product quality. This system and practice,

however, do not always translate into the service industries (Antony 2006). While

the steps in the measurement phase are explicitly defined in manufacturing settings,

whereas in services, measuring the process to satisfy customers’ needs is often a

more general problem of data collection, quality, and integrity (Does et al. 2002;

Hensley and Dobie 2005; Antony 2006; Heckl et al. 2010).

Service firms also find it challenging in establishing a systematic process to

identify the sources of errors and drive them down. For example, firms usually

develop process maps before initiating Six Sigma projects in manufacturing.

Nonetheless, the use of flowcharts and process maps remains rare in many service


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processes (Antony et al. 2007). Additionally, service companies are often dependent

on people processes. In this regard, service processes are more subject to noise or

uncontrollable factors compared to manufacturing processes (Does et al. 2002),

explaining partially why Six Sigma came slowly to health care and initially was met

with some skepticism (Benedetto 2003). Intrinsically, this hesitancy results from

disparities between processes driven by humans versus automated or engineered

processes.

In manufacturing, it is likely to eliminate most of human variability through

automation, creating precise measurement of assignable causes of variation.

Conversely, in services, especially in health care, the delivery of service such as

patient care is largely a human process, and the causes of variability are often

subtle and difficult to quantify. Patient variability too represents a key source of

human variability; an acceptable medical care to one patient might be perceived

as unsatisfactory by another patient. Additional sources of human behavioral

characteristics engendering variability in services include friendliness, eagerness

to help, honesty, etc., which are difficult to manage per se, and thus undermine the

implementation of service Six Sigma (Antony 2004a). Furthermore, service-based

companies may struggle with Six Sigma due to its intense data focus, difficulty in

creating cultural changes for empowering Six Sigma leaders, and the low

likelihood to capture the benefits of Six Sigma application immediately (Lanser

2000; Sehwail and DeYong 2003). Particularly, cost benefits generated from

service Six Sigma projects may take time to realize, prompting managers to

concede early (Sehwail and DeYong 2003). Above all, researchers consider

project selection as a universal challenge for Six Sigma in services (Antony et al.

2007; Heckl et al. 2010).

2.2 Project selection for Six Sigma in services

Project selection has drawn notable attention in service Six Sigma due to its decisive

role in the project success (Adams et al. 2003). Antony (2004a, 2006) indicates that

project selection is one of the most critical success factors for the effective

deployment of a Six Sigma program in service industries. Project selection refers to

the process of choosing the best among alternative proposals on the basis of cost-

benefit analysis so that the objectives of the organization will be achieved. In

practice, the selection of Six Sigma projects in many service-oriented organizations

is still based on pure subjective judgment (Raisinghani 2005; Antony 2006).

Management has difficulty making project go/no go decisions and projects are

generally initiated because management thinks they will make a contribution to

quality (Antony 2006).

Research exposes several criteria for service Six Sigma project selection.

Fundamentally, firms should choose projects in accordance with the firm’s goals

and objectives (Gijo and Rao 2005; Antony et al. 2007) that tackle their business

and customer problems (Does et al. 2002). As such, good Six Sigma projects

possess characteristics that connect to business priorities, major importance to the

organization, reasonable scope, etc. (Snee 2002; Antony 2006). Specifically, firms

may rank potential projects based on five strategic imperatives: human resources,


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information technology, finance, quality and market growth, and expansion

(Beaver 2004). Firms may also identify projects according to criteria such as

financial return, customer satisfaction, resource required, risks, and alignment of

strategic business goals and objectives (Antony 2004a; Chakrabarty and Tan

2007).

Chakrabarty and Tan (2008) suggest the following criteria for selecting service

Six Sigma projects: measurable financial benefits, impact on business, linking to

company’s business strategy, high probability of success, and far reaching impact.

Most importantly, potential projects must be capable of reducing the defects or

rework and have a high likelihood of being successfully completed on a tight time

schedule (e.g., within 5 months) (Antony 2006). Furthermore, each project delivers

a bottom-line financial result of $150,000 on an annual basis. In addition to these

common criteria across service contexts, researchers propose several context-

specific guidelines. For example, cost and frequency of problems represent the two

major project selection criteria in banking services (Krupar 2003), whereas data

availablility, clearly defined goals, milestones, timelines, and budgets serve as the

key project selection guidelines in general financial services (Heckl et al. 2010).

Likewise, service level, service cost, customer satisfaction, and clinical excellence

constitute the four project selection criteria in health care (Sehwail and DeYong

Table 1 Summary of service Six Sigma project selection criteria

Author(s) Context Six Sigma project selection criteria

Does et al. (2002) Services Relation to business and customer problems

Snee (2002) Services Business priority, importance to firms, and scope

Krupar (2003) Banking Cost and frequency of problems

Sehwail and

DeYong (2003)

Health care Service level, service cost, customer satisfaction, and clinical

excellence

Antony(2004a) Services Financial return, customer satisfaction, resource required, risks, and

alignment of strategic business goals and objectives

Beaver (2004) Services Human resources, information technology, finance, quality and

market growth, and expansion

Gijo and Rao

(2005)

Services Alignment with firm’s goals and objectives

Antony (2006) Services Impact on customer needs, financial impact, project duration,

required resources, required expertise and skills to implement

projects, probability of success of projects, and risks involved in

projects

Antony et al.

(2007)

Services Cost of poor-quality, risk, expertise required for project, project

alignment to business goals in strategic terms

Chakrabarty and

Tan (2007)

Services Financial benefits, customer satisfaction, employee satisfaction,

service quality, and reduced variation

Chakrabarty and

Tan (2008)

Services Measurable financial benefits, impact on business, linking to

company’s business strategy, high probability of success, and far

reaching impact

Heckl et al. (2010) Financial

services

Data availablility, clearly defined goals, milestones, timelines, and

budgets


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2003). Table 1 summarizes the frequently endorsed service Six Sigma project

selection criteria.

Quite a few tools are available for service Six Sigma project selection. For

example, service-based companies may adopt the process map to identify those

potential projects (Wyper and Harrison 2000; Krupar 2003; Antony 2004a;

Raisinghani 2005; Antony et al. 2007; Nakhai and Neves 2009; Heckl et al. 2010).

Likewise, the Failure Mode and Effects Analysis (FMEA) can be a useful problem-

identification and ranking technique to develop the project list (Beaver 2004;

Nakhai and Neves 2009). Furthermore, firms may follow the critical to quality

(CTQ) breakdown approach to develop the candidate projects for service operations

(Wyper and Harrison 2000; Sehwail and DeYong 2003; Heckl et al. 2010). Other

commonly used tools and techniques include affinity diagrams, root cause analysis

(RCA), and Pareto analysis (Antony 2004a; Antony et al. 2007). Service firms can

also use these tools for other purposes in the subsequent phases (e.g., the Analyze

phase) of a service Six Sigma project. On the other hand, service firms may employ

empirical techniques (e.g., customer survey) via the designed questionnaire (Wyper

and Harrison 2000; Raisinghani 2005), or the KANO’s model to facilitate the

project selection process (Antony 2004a). Particularly, the KANO’s model helps

understand the intended customer base through categorized customer expectations

(i.e., basic, competitive and delight). Understanding each of these categories is

essential to service Six Sigma project selection since it signifies that firms can better

address customer needs, i.e., create specific value for different customers (Antony

2006). Table 2 summarizes the frequently adopted tools in service Six Sigma

project selection.

A review of the related literature suggests the need for an organized approach

incorporating the appropriate project selection criteria and tools, and extending

them when necessary, for service Six Sigma project selection. Thus, the study

proposes a step-by-step framework that guides the Six Sigma deployment team to

select the right projects. The proposed framework is discussed in the following

section.

Table 2 Summary of service Six Sigma project selection tools

Author(s) Context Six Sigma project selection tools

Wyper and Harrison (2000) Services Process map, customer survey, and CTQ

Krupar (2003) Banking Process map

Sehwail and DeYong (2003) Health care CTQ

Antony (2004a) Services Affinity diagram, RCA, Pareto analysis,

process map, and KANO’s model

Beaver (2004) Services FMEA

Raisinghani (2005) Services Process map and customer survey

Antony et al. (2007) Services Process map and Pareto analysis

Nakhai and Neves (2009) Services Process map and FMEA

Heckl et al. (2010) Financial services Process map and CTQ


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3 Proposed framework

Overall, the proposed framework consists of four phases, namely, initial project

identification, project value assessment, project complexity assessment, and project

prioritization, which are described as follows:

3.1 Phase 1: initial project identification

A firm’s strategy is eventually carried out through projects. Hence, each service Six

Sigma project should support the organization’s initiatives to realize the corporate

vision and is expected to have a clear link to the organization’s business strategy

(Antony et al. 2007). Specifically, initial identification of the projects must involve

the following pivotal activities (Larson and Gray 2011): (1) review the organiza-

tional mission, (2) set long-range goals and objectives, (3) analyze and formulate

strategies to reach objectives, and (4) establish portfolio of project choices.

Nonetheless, the final project list is contingent on further assessment.

3.2 Phase 2: project value assessment

Firms adopting service Six Sigma typically aim to improve business, customer, and

employee value via the project-oriented approach (Antony 2006). As such, firms

enhance their service processes and provide consistent and reliable services to gain

profits and build employee pride, satisfaction, etc. In this regard, firms should

evaluate each candidate project’s value based on three criteria: (1) financial return,

(2) cost, and (3) the impact on employee behavior. First, as aforementioned, service

Six Sigma researchers largely agree on the importance of financial impact,

especially in hard savings and soft savings, when selecting projects (Antony 2004a;

Beaver 2004; Chakrabarty and Tan 2007). Second, researchers likewise stress the

criticality of cost estimation in service Six Sigma project selection (Krupar 2003).

While a number of approaches exist for estimating the financial return and cost

associated with a particular project, the estimation is in itself a complex process

(Larson and Gray 2011). The study thus suggests firms to estimate the financial

return and cost based on the consensus method, a frequently used top-down

approach for estimating project return and cost, for the following reasons (Larson

and Gray 2011). It is ambitious to accurately predict the cost of any project even

with new techniques such as activity-based costing. Furthermore, firms often use the

top-down approach in the ‘‘need’’ phase of a project to get an initial financial

estimate for the project considering that much of the information needed to derive

accurate estimates is not available in the initial phase of the project.

Third, as noted above, there exists a high degree of customer contact in

services. An employee’s attitude and behavior toward a customer may determine

the customer’s wish to continue service with the firm. That is, employee behavior

represents a pivotal consideration in Six Sigma deployment (Eckes 2003; Yang

and Chen 2003; Gels 2005; Chakrabarty and Tan 2007), which leads to positive

morale, satisfaction, etc., thereby creating a cultural change for Six Sigma

(Operation Management Roundtable 2002; Sehwail and DeYong 2003). Hence, it


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is essential to consider a project’s potency to shape employee behavior when

evaluating a service Six Sigma project’s value. Nonetheless, it remains problem-

atic to measure a project’s impact on employee behavior in practice owing to its

intangible nature. This study thus evaluates the impact on employee behavior via

the Analytic Hierarchy Process (AHP), a tool for group decision-making based on

pairwise comparisons (Saaty and Peniwati 2008) that is sometimes adopted in

manufacturing-oriented Six Sigma (Su and Chou 2008). In AHP, every group

member makes judgment on each project’s potential to enhance employee

behavior, and compares these projects in pairs. Specifically, two questions are

asked in each pairwise comparison: (1) Which project is more important with

respect to the criterion (i.e., impact to promote employee behavior)? and (2) How

strongly based on the 1–9 scale (i.e., 1: equally important; 9: extremely much

more important)? AHP converts these evaluations to numerical values that can be

processed and compared over the entire pool of the projects. A numerical weight

or priority is derived for each project, allowing these projects to be compared to

one another in a rational and consistent way. Finally, the study formulates the

project value as follows:

Value ¼ Return

Cost� Employee behavior

3.3 Phase 3: project complexity assessment

In addition to value, there is yet another critical dimension, i.e., complexity, in

assessing the project. The study evaluates the complexity of a service Six Sigma

project according to three criteria: (1) project scope, (2) data availability, and (3)

risk. First, project scope refers to the mission of the project—a service for the

organization’s client/customer, including project objective, deliverables, etc. (Heckl

et al. 2010; Larson and Gray 2011). Defining the project scope sets the stage for

developing an effective service Six Sigma project plan, whereas poorly defined

scope typifies a frequently mentioned barrier to project success (Snee 2002; Larson

and Gray 2011). Nonetheless, leaders of well-managed, large corporations often

neglect the imperative need to define the scope for a service Six Sigma project

(Antony 2004a).

Second, data availability exemplifies another critical consideration when

selecting service Six Sigma projects (Yang and Chen 2003; Hensley and Dobie

2005; Heckl et al. 2010). Particularly, data on the financial impact, cost of poor

quality (COPQ), service level and quality, and service outcomes are crucial to the

success of a project. However, data are mostly subjective and cannot be measured

directly in services. Furthermore, there exists a general problem of data quality in

non-manufacturing Six Sigma projects (Does et al. 2002). Above all, representing

data in the form the project team desires poses a potential challenge for service firms

even if the data are available. Similar to how the impact on employee behavior is

measured above, the study uses AHP to evaluate the relative level of project scope

and data availability for each project. In particular, the project scope is measured

based on the overall assessment of deliverables, whereas data availability is


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evaluated according to the required effort to collect the necessary data in project

implementation.

Finally, the study evaluates the potential risk involved in a project. Researchers

consistently address this selection criterion for a typical or non-Six Sigma project

(Stewart and Mohamed 2002; Enea and Piazza 2004; Daniels and Noordhuis 2005;

Kulak et al. 2005; Lefley 2006; Baird et al. 2008). The consideration of risk,

however, receives insufficient attention in the discussion of Six Sigma projects

(Antony 2004b; Su and Chou 2008). In light of the existence of prevailing

uncontrollable factors in a service setting (Does et al. 2002; Hensley and Dobie

2005; Antony 2006), evaluating the risk prior to project implementation appears to

be crucial. Thus, this study incorporates the risk factor into project complexity

assessment. Specifically, the risk is assessed via FMEA in terms of risk score

according to the following three dimensions, namely, impact, occurrence, and

detection (Adachi and Lodolce 2005; Su and Chou 2008). Each dimension is rated

based on a five-point Likert-type scale anchored by 1 (lowest level of impact, least

likelihood to occur, or highest possibility to detect prior to the occurrence) and 5

(highest level of impact, highest likelihood to occur, or lowest possibility to detect

in advance). The weighting of the risk is then based on the overall risk score. For

example, a risk yielding a minimum impact with a very low likelihood of

occurrence, and an easy detection might score a 1 (1 9 1 9 1 = 1). Conversely, a

high-impact risk with a high probability and impossible to detect in advance would

score 125 (5 9 5 9 5 = 125). This broad range of numerical scores allows for easy

stratification of risk based on overall significance for these projects. Accordingly,

the study defines and formulates the project complexity as follows:

Complexity ¼ Scope

Data availability� Risk

The ‘‘Appendix’’ presents the aforementioned measurement items in detail.

3.4 Phase 4: project prioritization

In the final phase, project prioritization is determined. The priority system can be

managed by the Six Sigma project office or the quality initiatives management

group. The study modifies the matrix originally developed by Matheson and

Matheson (1998) to prioritize the projects (see Fig. 1). The vertical axis represents a

project’s complexity, whereas the horizontal axis corresponds to a project’s

potential commercial and organizational value. The grid has four quadrants, each

with different service Six Sigma project dimensions.

Specifically, any project falling into quadrant I is considered non-viable or

No–Go (NG) as it renders low value while involving high level of complexity.

Hence, it appears inefficient to perform this type of projects. The projects in

quadrant II, though bringing forth relatively low value as well, can be regarded as

just-do-it or low-hanging-fruit projects since they are perceived as less complex.

Exemplary service Six Sigma projects should fall under quadrants III and IV.

Projects in quadrant III are prospective candidates for green belt (GB) projects in

that they yield high value and require less effort. In other words, they are eligible for


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a large-scale implementation in the service organization. In contrast, the projects

assigned to quadrant IV can be problematic in making go/no go decisions. On the

one hand, they are regarded as highly valuable and should be reckoned as black belt

(BB) projects. On the other hand, the extremely complicated nature may impede the

implementation of these projects. Thus, the project team may create a threshold in

project complexity to determine whether these projects should be categorized as BB

or NG projects. Notably, NG projects in quadrant IV, though perceived as

disqualified to be the service Six Sigma projects, bear deliberation on the possibility

of being other types of projects. Indeed, these arduous projects may be potentially

contributive to the organization.

4 Case studies

The study presents two case studies in this section, which focus on two prominent

service industries: the banking industry and the health care industry. Specifically,

the study selected a multinational bank and a leading hospital in Taiwan for the

demonstration of the proposed framework.

4.1 Banking services

4.1.1 The case bank

The case bank is a leading securities investment corporation located in Taipei,

Taiwan (hereafter referred to as ‘‘BNK’’). BNK is a company with more than 10,000

employees worldwide. At the time of this research, BNK had 215 service branches

worldwide; among them, 142 were domestic. BNK had annual revenue of more than

US$ 6 billion. With an outstanding balance of US$ 38 billion in deposits, BNK had

assets of US$ 50 billion, surpassing all other private banks in Taiwan. BNK has

been applying Six Sigma in service quality improvement since 2005 and is regarded

as innovative in its application. Being one of the leading banks in Taiwan, BNK and

its Six Sigma project deployment department anticipate to convert the bank’s DNA

via relentless efforts in performing Six Sigma projects as its benchmark company

I

II

IV

III

Low Project value High

Pro

ject

com

plex

ity

High

Low

No-Go Projects

Just-do-it Projects

Black Belt Projects

Green Belt Projects

Fig. 1 Project prioritization matrix


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GE Capital does. Nonetheless, BNK has not established a standard or paradigm in

business strategy deployment and project breakdown framework even though

hundreds of Six Sigma projects were executed these years. Furthermore, there was

no commonly followed criterion in place to assess the project value and complexity

during the project selection process. As a result, BNK performed the proposed

approach noted above aiming to overcome these problems.

4.1.2 Implementation

4.1.2.1 Phase 1: initial project identification After reviewing BNK’s organiza-

tional mission and vision statements the following strategic goals were identified:

(1) to provide well-rounded products and premium services to clients in the Greater

China market beyond Taiwan and Hong Kong, (2) to establish and put into practice

a corporate philosophy that truly caters to what customers need; uphold BNK’s

leading status in such critical areas as wealth management, syndicated loans, foreign

exchange (i.e., forex), and forex derivatives, and (3) to become Taiwan’s foremost

supplier of payment services and small personal unsecured loans. The above goals

were then broken down into potential Six Sigma candidate projects for selection.

Table 3 presents the deployment of BNK’s strategic goals. For example, the

deployment aligns projects B1–B3 with the first strategic initiative to improve

customer services. Likewise, projects B4 and B5 (B6 and B7) correspond to the

second (third) strategic goal.

4.1.2.2 Phase 2: project value assessment Each project’s value was appraised in

this phase based on financial return, cost, and the impact on employee behavior. As

suggested above, the financial return and cost were estimated based on the

consensus method. The Six Sigma deployment office (including two external

experts) convened the finance department and a master BB (MBB) to assess the

Table 3 BNK’s strategic goals deployment

BNK’s strategic goals Potential projects

Provide well-rounded products and premium services to clients in

the Greater China market beyond Taiwan and Hong Kong

B1: improve customer feedback and

response processes

B2: reduce complaints from new

account openers

B3: enhance external customer

satisfaction

Establish and put into practice a corporate philosophy that truly

caters to what customers need; uphold BNK’s leading status in

such critical areas as wealth management, syndicated loans,

foreign exchange, and forex derivatives

B4: increase associate retention in

key areas

B5: reduce response delays

Become Taiwan’s foremost supplier of payment services and small

personal unsecured loans

B6: eliminate the possibility of

erroneous data entry

B7: reduce electronic financial

transaction costs


123

financial return and cost for each project in a joint meeting. Additionally, each

project’s impact on employee behavior was ranked and rated via pairwise

comparisons by the team. The priority of each project was thus calculated using

the AHP. Specifically, the Expert Choice software package, a multiattribute

decision support software tool based on the AHP methodology, was chosen to

perform the weighting of each project. The software uses the AHP methodology to

model a decision problem and evaluates the relative desirability of alternatives.

Finally, each project’s value was calculated. The resulting estimates for financial

return, cost, employee behavior, and project value are shown in Table 4.

4.1.2.3 Phase 3: project complexity assessment In this phase, each project’s

complexity was evaluated in terms of scope, data availability, and potential risk

involved. The project scope includes deliverables and limits and exclusions of the

project per se. Due to the fact that a bank’s operation must comply with specific

governmental regulations in Taiwan, certain external factors/noises were introduced

to the projects and thus made them less controllable. For example, to avoid the

rampant fraudulent incidents, Taiwanese government started in 2005 to require all

the banks to validate at least two personal IDs before an individual can open a new

account. This constraint created numerous customer complaints for BNK, especially

during the project of reducing complaints from new account openers. Likewise, the

often cross-functional processes involved in a transactional project also extended

the project scope. Regarding data availability, several information systems exist

within BNK that were installed at different times—a typical problem for a bank.

Specifically, the account management system and the customer information system

may contain the same customer’s information in pieces but for different purposes.

BNK noticed that it was ambitious to retrieve the data in the desirable format and

combine the necessary information altogether, e.g., in the project of eliminating the

possibility of erroneous data entry. Apparently, the above concerns drew substantial

attention when the project team ranked the candidate projects via the AHP.

Next, to assess the risk involved in each project, the corresponding overall risk

score for each project was calculated based on its impact, occurrence, and detection.

Table 4 BNK’s project value assessment

Potential projects Financial

return

Cost Employee

behavior

Value

B1: increase associate retention in key areas 5.8 2.4 0.242 0.585

B2: improve customer feedback and response

processes

7.7 0.7 0.062 0.682

B3: reduce response delays 8.2 0.5 0.040 0.656

B4: eliminate the possibility of erroneous data entry 4.9 0.5 0.156 1.529

B5: reduce electronic financial transaction costs 3.5 1.3 0.026 0.070

B6: reduce complaints from new account openers 2.1 1.6 0.099 0.130

B7: enhance external customer satisfaction 9.8 2.1 0.375 1.750

Financial return and cost are measured in million NT dollars


123

Unlike the projects in a manufacturing context where historical data are generally

available for reference in determining the individual scores, BNK encountered

challenges in estimating these scores as expected in a typical service organization.

For example, in the project of reducing response delays, it was arduous to determine

the impact in that the consequence may range from a mere oral complaint to the loss

of the customer. After each score in impact, occurrence, and detection was

estimated, the overall risk score for each project was calculated accordingly using

FMEA. The project team was then able to determine the complexity for each

project. Table 5 demonstrates these estimates for each project.

4.1.2.4 Phase 4: project prioritization In this final phase, all the projects were

prioritized in the project prioritization matrix with the two dimensions of project

value and complexity (see Fig. 2). Each diamond on the matrix corresponds to a

particular project. In this case, the value of 0.5 (100) in project value (complexity)

was adopted to separate the four quadrants. Additionally, a threshold value of

project complexity equal to 150 served as the cut-off line for BB and NG projects.

Table 5 BNK’s project complexity assessment

Potential projects Scope Data

availability

Risk

(impact/

occurrence/

detection)

Complexity

B1: increase associate retention in key areas 0.037 0.410 60 (5/3/4) 5.415

B2: improve customer feedback and response

processes

0.094 0.147 12 (3/2/2) 7.673

B3: reduce response delays 0.156 0.053 10 (5/2/1) 29.434

B4: eliminate the possibility of erroneous

data entry

0.057 0.020 40 (4/2/5) 114.000

B5: reduce electronic financial transaction costs 0.023 0.250 40 (2/5/4) 3.680

B6: reduce complaints from new account

openers

0.390 0.088 24 (2/4/3) 106.364

B7: enhance external customer satisfaction 0.244 0.031 20 (5/2/2) 157.419

Fig. 2 BNK’s project prioritization matrix


123

The cut-off line of 150 is a result of team agreement. The Six Sigma project

deployment team, which comprises two external experts and three internal

executives, reached the consensus mainly based on an overall assessment of what

level of complexity could go beyond the capacity of the implementation team to

finish the project in time. As can be seen in Fig. 2, the seven projects being

evaluated were finally categorized into 1 BB, 3 GB, 1 just-do-it, and 2 NG projects.

4.2 Health care services

4.2.1 The case hospital

The case hospital (hereafter referred to as ‘‘HC’’) is a major medical center located

in Kaohsiung, Taiwan with approximately 340 full-time physicians and over 1,600

patient beds. As an initiative to become a high-quality medical center, HC

established in 2006 the Medical Quality Control Office (MQCO) to oversee and

promote all quality-related activities such as training physicians, monitoring

medical performance, launching campaigns for quality patient care, etc. An

important mission for MQCO was to employ the Six Sigma philosophy in patient

care quality improvement. Fulfillment of this mission, however, required HC to

support its employees with new system of care. Particularly, the pressing need was

to create a systematic approach to select Six Sigma projects for implementation.

Thus, HC adopted the proposed framework in this study to fulfill the above need.

4.2.2 Implementation

4.2.2.1 Phase 1: initial project identification A cross-functional Six Sigma core

team in MQCO led by HC’s Vice President for Six Sigma was established to

identify and supervise the projects. After reviewing HC’s organizational mission

statements the following strategic goals were determined: (1) to avoid providing

ineffective inpatient and outpatient services, (2) to provide appropriate and speedy

support for medical decision-making, and (3) to fulfill social responsibility/

governmental policy compliance. These goals were then broken down into potential

Six Sigma candidate projects for selection. As Table 6 indicates, the deployment of

Table 6 HC’s strategic goals deployment

HC’s strategic goals Potential projects

Avoid providing ineffective inpatient and outpatient

services

H1: reduce start time delays in operating rooms

H2: reduce waiting time for a medical ward

Provide appropriate and speedy support for medical

decision making

H3: improve patient satisfaction

H4: reduce delay of joint consultation in

emergency rooms

Fulfill social responsibility/governmental policy

compliance

H5: increase nurse retention in ICUs

H6: improve inpatient exclusive breastfeeding

rate


123

HC’s strategic goals generated six projects. For example, the deployment aligned

projects H1 and H2 with the first strategic initiative to improve patient services.

Similarly, the second (third) strategic goal lead to projects H3 and H4 (H5 and H6).

4.2.2.2 Phase 2: project value assessment Each project’s value was appraised

subsequently based on financial return, cost, and the impact on employee behavior.

Calculating the financial return and cost was manageable for particular projects such

as the project to increase nurse retention in intensive care units (ICUs), whereas it

was baffling for certain projects (e.g., the project to improve patient satisfaction).

Specifically, it was concluded that the project to improve patient satisfaction should

potentially produce the most profit, even though quite a portion of the financial

return resulted from the soft savings. Conversely, improving inpatient exclusive

breastfeeding rate was considered least profitable. As noted above, each project’s

impact on the employee behavior was ranked via the pairwise comparisons using

AHP. The comparison results suggested that increasing nurse retention in ICUs was

mostly related to the promotion of positive employee behavior. Hence, this project

yielded the highest weight. Accordingly, each project’s value was assessed. Table 7

depicts the detailed estimates for financial return, cost, employee behavior, and

project value.

4.2.2.3 Phase 3: project complexity assessment The implementation of manda-

tory National Health Insurance (NHI) program in Taiwan played a crucial role in the

assessment of project complexity. Launched in 1995, NHI represented the

realization of a primary social and health care policy of the Taiwanese government.

The ultimate goal of the program is the universal coverage for all Taiwan citizens. It

is estimated that NHI currently covers more than 99% of the population. In 2009,

national health care expenditures in Taiwan totaled US$ 13.73 billion, representing

3.51% of Taiwan’s GDP. Recognizing the rapid growth of health insurance

expenditures and a slow increase in premium collection, the NHI Bureau has taken

initiatives to maintain the balance of income and expenditures to reduce deficits and

to be financially self-sustained. These campaigns include, e.g., increasing cost-

sharing from the beneficiaries to prevent the patients from abusing medical services,

Table 7 HC’s project value assessment

Potential projects Financial

returnaCosta Employee

behavior

Value

H1: reduce start time delays in operating rooms 4.2 0.5 0.088 0.739

H2: reduce waiting time for a medical ward 4.4 0.4 0.150 1.650

H3: improve patient satisfaction 7.5 0.7 0.053 0.568

H4: reduce delay of joint consultation

in emergency rooms

2.1 0.2 0.250 2.625

H5: increase nurse retention in ICUs 3.2 0.2 0.429 6.864

H6: improve inpatient exclusive breastfeeding rate 1.2 0.1 0.030 0.360

a Financial return and cost are measured in million NT dollars


123

expanding the case-payment system, and providing patients alternative insurance

plans.

Inevitably, being a participating hospital in the NHI program, HC was impeded

to some extent when performing Six Sigma projects. For example, only a limited

number of HC’s wards were available for NHI subscribers without co-payment.

This resulted in an over usage for those wards while an under usage for other types

of wards that need extra co-payment made by the patients. This external factor

apparently drove patients to wait longer for available wards. After the cross-

functional Six Sigma core team in MQCO examined each project’s scope, data

availability, and potential risk involved, it was concluded that improving patient

satisfaction involved the largest scope. Unexpectedly, the data needed for reducing

the delay of joint consultation in emergency rooms was considered least available as

there was no mechanism in place to effectively monitor the process. Even though

each physician was required to log into the system when he/she arrived at the

emergency room, the time mark could be fraudulently altered. Thus, the data

integrity was dubitable.

Next the project team estimated the scores of impact, occurrence, and detection

for each project to determine the overall perceived risk. It was noteworthy that the

project to reduce start time delays in operating rooms engendered the highest risk.

The project team attributed the high risk to the involvement of several key patient

care participants, namely, the anesthesiologist, nurse, patient, and doctor in the

operating room. In practice, anesthesiologists and nurses need to cooperate

seamlessly in setting up the patient for surgery in advance to avoid the delay.

Table 8 shows the detailed calculation for project complexity for each project.

4.2.2.4 Phase 4: project prioritization In this final phase, each project was

positioned on the project prioritization matrix (see Fig. 3). In this case, the value of

0.5 (100) in project value (complexity) served to separate the four quadrants,

whereas the threshold value of 150 in project complexity was used to differentiate

between BB and NG projects. The six projects being evaluated turned out to be 3

BB, 2 GB, 1 just-do-it, and no NG projects.

Table 8 HC’s project complexity assessment

Potential projects Scope Data

availability

Risk (impact/

occurrence/

detection)

Complexity

H1: reduce start time delays in operating rooms 0.156 0.073 50 (5/5/2) 106.849

H2: reduce waiting time for a medical ward 0.249 0.039 18 (2/3/3) 114.923

H3: improve patient satisfaction 0.409 0.139 45 (5/3/3) 132.410

H4: reduce delay of joint consultation

in emergency rooms

0.092 0.023 18 (2/3/3) 72.000

H5: increase nurse retention in ICUs 0.059 0.260 18 (2/3/3) 4.085

H6: improve inpatient exclusive

breastfeeding rate

0.035 0.466 20 (2/5/2) 1.502


123

5 Conclusions

This study proposes a framework for the selection of service Six Sigma projects and

demonstrates its application with two case studies in banking and health care

services. The project selection process consists of four phases, namely, Phase 1:

initial project identification, Phase 2: project value assessment, Phase 3: project

complexity assessment, and Phase 4: project prioritization. Specifically, the study

highlights projects’ strategic link to business goals in the phase of initial project

identification. The study also develops two pivotal criteria to measure project value

and complexity. These measures are evaluated in Phase 3 and Phase 4, respectively.

In particular, the study evaluates project value based on financial return, cost, and

the impact on employee behavior. Likewise, the study appraises project complexity

according to scope, data availability, and the potential risk involved. In the final

phase, the study establishes a project prioritization matrix to facilitate the

categorization of different types of projects including BB, GB, just-do-it, and NG

projects. Furthermore, the study employs tools such as AHP and FMEA to rank and

rate the potential projects.

Although this study contributes to the extant literature by developing a viable and

systematic approach to facilitate service Six Sigma project selection, several

limitations are of note drawing directions for future research. Particularly,

differences remain across service contexts in implementation. For example, the

measurement of risk, especially on the occurrence and detection, is more difficult in

health care than in banking services due to greater human involvement (Jenkins

2006). Furthermore, data availability varies significantly across service settings,

depending on the level of ‘‘industrialization’’ of the service operations (e.g., banking

versus education). Likewise, financial return is generally limited in non-profit

organizations (e.g., government agencies and hospitals) versus literally unrestricted

in for-profit organizations (e.g., financial services). These issues arising from

context differences apparently affect the determination of threshold values in the

project prioritization matrix. Finally, the enforcement of government laws and

regulations plays a critical role in the cases demonstrated by this study, which merits

further cross-national research on the generalizability of the results.

Fig. 3 HC’s project prioritization matrix


123

Acknowledgment The authors acknowledge and are grateful for the research assistance of Fang-Yi Lin

in data analysis.

Appendix: measurement items: criteria (tools)

Project value assessment

Financial return (consensus method)

How much financial return including hard and soft savings will the project generate

in million NT dollars?

Cost (consensus method)

What is the total incurred cost (e.g., direct cost, direct project overhead cost, general

and administrative overhead cost, etc.) for the project in million NT dollars?

Employee behavior (AHP)

(1) Which project is more influential to promoting employee behavior?

(2) How strongly based on the 1–9 scale (i.e., 1: equally important; 9: extremely

much more important)?

Project complexity assessment

Scope (AHP)

(1) Which project has larger scope (e.g., objective, deliverables, etc.)?

(2) How much larger based on the 1–9 scale (i.e., 1: equally large; 9: extremely

larger)?

Data availability (AHP)

(1) Which project has more data available?

(2) How much more available based on the 1–9 scale (i.e., 1: equally available; 9:

extremely more available)?

Risk (FMEA)

(1) Rate the impact of the risk associated with the project based on the 5-point

Likert-type scale (1: lowest; 5: highest).

(2) Rate the occurrence of the risk based on the 5-point Likert-type scale (1: least

likelihood; 5: highest likelihood).


123

(3) Rate the detection of the risk based on the 5-point Likert-type scale (1: most

likely to detect in advance; 5: least likely to detect in advance).

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