For didactic scope only

DIFFERENCES BETWEEN SIX SIGMA APPLICATIONS IN MANUFACTURING AND THE SERVICE INDUSTRY – By Andrea Chiarini – Chiarini & Associates and University of Ferrara – International Journal of Productivity and Quality Management – [email protected]

Abstract

For didactic scope only

The purpose of the research presented in this paper is to investigate the differences between Six Sigma applications in manufacturing and the service industry. A review of the literature produced six research questions related to the effects of the kind of industry (manufacturing and service) on applications of Six Sigma. The questions focus on performance measures, the kind of statistical tools used (advanced and basic), the possibility of using different mapping tools, the Black-Green Belt training path and the organisational climate and human behaviour. In an original and new way, these six research questions were transformed into hypotheses then validated by means of a questionnaire and a Chi-square test. Qualitative suggestions from the respondents were collected at the same time. Findings show that, comparing Six Sigma in manufacturing and service industry, there are many interesting differences. Managerial implications, recommendations and an agenda for future study are presented.

Keywords: Six Sigma, Service Industry, Manufacturing Industry, Performance Indicators, Advanced Statistical Tools, Mapping Tools, Organisational Climate

1. Introduction Six Sigma as a measurement standard in product variation can be traced back to the 1920s when Walter Shewhart showed the correlation between levels of sigma from the Page 1 of 29

mean and the defects produced in a process. When a range around a defined target is fixed it can be statistically demonstrated that the more the number of sigma stays inside the range, the less the probability that the outcome is a failure. Failure means that the outcome is outside the range and consequently the products or services are defective. Many measurement standards entered the scientific and management literature later but the term ‘Six Sigma’ was coined by a Motorola engineer named Bill Smith. In the early and

For didactic scope only

mid-1980s with Chairman Bob Galvin, Motorola engineers decided that the traditional quality levels that measured defects in thousands of opportunities did not provide enough quality results; instead, they wanted to measure the ‘defects per million opportunities’. Motorola developed the new Six Sigma standard, created the methodology and the required cultural change associated with it. Six Sigma helped Motorola realise powerful bottom-line results in the entire organisation; in fact, Motorola documented more than $16 billion in savings because of Six Sigma efforts. Since then, hundreds of companies around the world have adopted Six Sigma as a way of doing business. This is a direct result of many of USA's leaders openly praising the benefits of Six Sigma: leaders such as Larry Bossidy of Allied Signal (now Honeywell) and Jack Welch of General Electric Company (Harry and Schroeder, 2000).

Six Sigma focuses on a precise application pattern called Define-Measure-AnalyseImprove-Control or DMAIC (Adebanjo, 2001; Klefsjo et al., 2001, Starbird, 2002; Desai, 2012). It improves customer satisfaction along with all organisational performance (Przekop, 2003); in particular, companies that apply Six Sigma obtain significant savings in terms of ‘Cost of Poor Quality’ (COPQ).

In an approximate way, Harry and Schroeder (2000) correlated the sigma level of a process (any process, from marketing to customer care) with the number of defects and Page 2 of 29

the COPQ to which the organisation is subjected. Continually obtaining higher levels of sigma, the organisation numerically shows the reduction in the COPQ and obtains a precise saving.

For each DMAIC stage, a team formed by a Black Belt and several Green Belts uses classical tools derived from the quality world. Many authors, especially consultants, list

For didactic scope only

these tools that are by now well established (Pande et al., 2000; George, 2002; Breyfogle, 2003; Snee and Hoerl, 2003; Sadraoui and Kammoun, 2012). Black and Green Belts who follow the DMAIC stages need knowledge of basic tools such as Pareto Analysis, as well as knowledge of advanced statistic tools such as ANOVA and Design Of Experiments (DOE). Black and Green Belts receive a well-coded training and a particular certification. Six Sigma projects, because of their nature, involve a team for a period varying from a few months up to one year, or even more, according to the typology of saving required. The results of Six Sigma projects are measured with performance indicators called Critical Characteristics or Critical Characteristics to Quality. Depending on the projects these indicators can be a physical measure such as temperature, dimension and pressure, as well as non-conformities, such as time, customer satisfaction level and even courtesy of employees and other subjective aspects.

Major worldwide companies use Six Sigma, especially those quoted on Wall Street (Pande et al., 2000; Senapati, 2004; Sadraoui and Kammoun, 2012), as do other companies in all kinds of industries. Since early 2000, Six Sigma has begun to find application in nonmanufacturing processes, such as engineering and product design, marketing, accounting, finance and so on. Within a few years, Six Sigma has been used as a model in service industries in the sectors of finance (banks, insurance), shipping and delivery, education, government offices, public utilities and health care. Page 3 of 29

Even if Six Sigma is widespread in all industries, it is unclear when reviewing the literature what the main differences are between applying Six Sigma in manufacturing and applying Six Sigma in the service industry. Some authors believe there is a slight difference (Etienne, 2009) or no difference at all (Harry and Schroeder, 2000), whereas others have found precise differences (Niyazmetov and Keoy, 2011; Neves and Nakhai, 2011; Chiarini, 2012a). The subject is also important to academics and practitioners so that they can proceed with their researches and applications in a more certain direction. Research into Six Sigma needs to know whether the performance achieved, the tools applied within the DMAIC pattern, people training and involvement are the same.

In light of this, this research investigates whether or not there are differences between the application of Six Sigma in manufacturing and the application of Six Sigma in service industries, and how these differences are managed. Starting from a literature review, the research has set six hypotheses that will be validated by means of a null-hypothesis test and Chi-square. The six hypotheses have been transformed into questions inside a questionnaire given to 572 academics, consultants and managers during the past three years. Each question inside the questionnaire has a scale that indicates agreement or disagreement by the respondents and also a field for some qualitative footnotes. Therefore interesting suggestions and comments from the interviewees have been collected as well. Suggestions and comments are particularly important since they better describe and interpret what the interviewees think about the hypotheses.

2. Literature review There is ample literature in the field of Six Sigma for manufacturing industries. The majority of the authors in this field in the past few years have reached a common point of Page 4 of 29

view concerning the application of the DMAIC pattern (Desai, 2008; Lall and Gupta, 2010; Sadraoui and Ghorbel, 2011). DMAIC is the unquestioned pattern followed for any Six Sigma project in the manufacturing field. Within DMAIC, teams can use tools derived from quality management (Shahabuddin, 2008; Desai, 2008; Gijo and Scaria, 2010; Julien and Holmshaw, 2012). Advanced statistical tools can be used as well, even if some authors have criticised the way of measuring the achieved results (Shahabuddin, 2008; Ray and Das, 2010; Chiarini, 2012a).

For didactic scope only

Many authors investigated Six Sigma in the service industry, describing through case studies (Antony and Fergusson, 2004; Stoiljkovic et al., 2010; Kamble et al., 2011), and in general (Snee and Hoerl, 2004; Antony, 2004; Natarajan and Morse, 2008; Ozcelik, 2010; Erik et al., 2011; Chiarini, 2012a; Shanmugaraja et al., 2012), the possibility of implementing Six Sigma in non-manufacturing organisations.

As a common denominator it seems that there are important differences between managing a physical product and a service (Parasuraman and Grewal, 2000). Even if the service transactions can be as standardised and as repeatable as manufacturing activities they are usually affected by much more variability. Nowadays, electronic devices help people to reduce times and mistakes when they manage transactions, nevertheless, the human factors are definitely more important in service industries than in manufacturing. A nurse can use bar codes and computers in order to administrate the right drug to the patient on time. However, factors such as courtesy and hospitality for the patient, especially for serious diseases, can make a difference. A worker in the manufacturing sector has to use the right machine with the right products and instructions but the worker’s mood probably has less effect on the quality of the outcome.

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George (2002; 2003) investigated the differences between the two sectors that can lead to different considerations about Six Sigma and Lean Six Sigma. According to George, the service industry mainly manages transactions instead of physical products. Transaction is a term derived from Information Technology (IT) but it is impossible to track a specific definition. Processes are made of activities, and a transaction is a logic unit of work carried out inside an activity, like for instance to fill in a screen format. According to George (2003), tools for mapping become very important in the service industry as well as the use of basic tools such as Pareto Analysis and the Fishbone Diagram.

Some authors, especially practitioners and consultants, analysed the tools used for mapping activities inside service processes (Krings et al., 2006) that are based on process flow analysis and can be used along with Value Stream Mapping (VSM). Martin and Osterling (2007) invented Metrics-Based Process Map (MBPM) in order to map, identify wastes and problems and measure service performance such as time and quality of the outputs. The tools can be used during the Measure and Analyse Six Sigma stages. Bicheno (2008) discussed how services need transactional maps. In his book he theorised that in services first of all managers have to draw a high-level map. Then they can use different maps such as ‘Brown Paper’, Material and Information Flow Analysis (MIFA), ‘Interactive Map’, ‘Four Fields Mapping’ and many others to analyse the activities, identify problems and measure service performance. Chiarini (2012b) in his paper described a transactional flow mapping tool called ‘Makigami’. According to the author this particular tool is useful for measuring the lead time of the service process and sorting out problems inside the flow. However the tool is similar to the MBPM previously analysed.

Antony (2004) analysed the differences between Six Sigma implementation in the manufacturing and service industries. As a result of a survey carried out in the UK, he Page 6 of 29

highlighted that in the service industry data follows non-normal distributions, and consequently different statistical tools are needed. Furthermore, improvement of performance such as timeliness, lead time and non-conformities is more important in the service industry than in manufacturing industry. The author also focused on how the human factor can affect Six Sigma implementation in the service industry.

Chakrabarty and Tan (2007) reviewed all the literature concerning Six Sigma in service industries. They found out that in 2007 Six Sigma implementation was limited to a few service industries such as health care, finance and government. The authors presented a list of performance indicators that the service industry tends to pursue. Indicators linked to time and customer satisfaction seem to be used more in service industries than in manufacturing sectors.

Antony et al. (2007) carried out an empirical observation within the UK service industry. The results are similar to those of Antony (2004). Antony et al. presented an interesting analysis concerning how important it is for the service industry to understand the Six Sigma methodology. According to the authors, tools used in the service industry can be rather different from tools applied in the manufacturing industry.

Ozcelik (2010) discussed the general application of Six Sigma within the service sector, especially within major US firms. Ozcelik does not emphasise the differences from the manufacturing sector, rather it highlights the benefits achieved by introducing Six Sigma. It seems that the major US service firms have improved customer satisfaction as well as performance in areas of defectiveness, timeliness and lead time of the processes. Timeliness and time reduction inside the processes are performances particularly pursued inside the service sector. Page 7 of 29

Chiarini (2013) analysed in detail the effects of organisational climate and human behaviour in the health care industry on the results of a Six Sigma project. He also reported that health care professionals are less accustomed to use advanced statistical tools and follow the typical manufacturing training for Green and Black Belt certification. The author also called into discussion this specific certification training path.

A summary of the main differences in service industry applications of Six Sigma from applications in the manufacturing industry collected from a review of the literature is presented in Table 1.

Table 1: The six research questions Id

Research questions

1

The results of Six Sigma projects in service industries are mainly measured by different performance indicators such as timeliness and lead time.

2

Advanced statistical tools are sometimes not as useful as in the manufacturing industry.

3

Basic quality tools such as Pareto analysis and Fishbone diagram are preferred within the Six Sigma projects in the service industries.

4

In order to better understand service process flow, transactional mapping tools different from Value Stream Mapping can be used.

5

The training for achieving Black and Green Belt certification could be different in service industry.

6

Organisational climate and human behaviour can affect Six Sigma results more than in the manufacturing industry.

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In the next section these findings from the literature review will be transformed into hypotheses and then validated by means of a null-hypothesis test.

3. Research methodology The research is based on a survey carried out with three clusters of respondents. The motivations for using a survey rather than other kinds of inquiries are that information and/or data can be collected from a sample portion of a group. No one knows how many Six Sigma experts there are in the world, however, their voice can be captured by means of a sample. Surveys are particularly important to measure opinions about the subject, in this case Six Sigma differences, by making opinions less subjective. An alternative method could be written records derived from qualitative case studies. However, these records are usually less generalisable and more questionable (Bryman, 2004).

According to Bryman (2004), survey design is structured in six steps:

-

research questions;

-

transforming the questions into hypotheses;

-

sampling;

-

measurement;

-

data collection and statistical data analysis;

-

Interpretation and generalisation.

In this study the research questions are derived from the literature review findings in Table 1. For each category derived from the literature review, the question is whether the sector has an influence on the category or not. At this important stage the research questions are actually transformed into hypotheses. A Chi-square test has been used in order to validate Page 9 of 29

the hypotheses. Before proceeding with such a test the null-hypothesis has to be stated. This latter is the assumption that two variables are independent (Plackett, 1983). After the statement of the null hypothesis, the alternative hypothesis must be stated and this will be true if the null hypothesis is rejected. Table 2 shows the null and alternative hypotheses derived from the research questions in Table 1.

Table 2: Null and alternative hypotheses

Id #

Null and alternative hypothesis

1

No association exists between the industry (service or manufacturing) and the kind of performance measurement for Six Sigma projects

The sector and the kind of performance measurement are not independent of one another 2

No association exists between the sector and the use of advanced statistical tools for Six Sigma projects

The sector and the use of advanced statistical tools are not independent of one another 3

No association exists between the sector and the use of quality basic tools for Six Sigma projects

The sector and the use of quality basic tools are not independent of one another 4

No association exists between the sector and the suitability of using mapping tools different from VSM

The sector and the suitability of using mapping tools different from VSM are not independent of one another 5

No association exists between the sector and the training path for Black and Green Belts

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The sector and the training path for Black and Green Belts are not independent of one another 6

No association exists between the sector and the possibility that organisational climate and human behaviour could affect the results

The sector and the possibility that organisational climate and behaviour could affect the results are not independent of one another

The hypotheses have been transformed into questions inside a questionnaire. Among the different kinds of available question styles the Likert scale has been chosen. The Likert scale can be used for validation by means of several statistic tests including Chi square; it ranks people’s levels of agreement or disagreement. Because the scale is cumulative this means that the final score is computed by counting the number of answers. In this research, the respondents can choose one of thefollowing answers to the questions in the questionnaire:

5 – Strongly agree. 4 – Slightly agree. 3 – Neither agree nor disagree. 2 – Slightly disagree. 1 – Strongly disagree.

Figure 1 shows the questionnaire based on the Likert scale that has been administered to a sample of Six Sigma professionals as discussed in the next paragraph. The questions have been formulated twice, once for the manufacturing industry and once for the service industry.

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Figure 1: The questionnaire administered to a sample of Six Sigma professionals

1

Do you believe that performance measurements for Six Sigma projects

1 _ 2 _ 3 _4 _ 5

such as timeliness and lead time are the most important in the manufacturing industry? Do you believe that performance measurements for Six Sigma projects

1 _ 2 _ 3 _4 _ 5

such as timeliness and lead time are the most important in the service industry? (Please add some notes) 2

Do you believe that advanced statistical tools are generally more suitable

1 _ 2 _ 3 _4 _ 5

for the manufacturing sector than other sectors? Do you believe that advanced statistical tools are generally more suitable

1 _ 2 _ 3 _4 _ 5

for the service sector than other sectors? (Please add some notes) 3

Do you believe that basic quality tools are generally more suitable for the

1 _ 2 _ 3 _4 _ 5

manufacturing sector than other sectors? Do you believe that basic quality tools are generally more suitable for the

1 _ 2 _ 3 _4 _ 5

service sector than other sectors? (Please add some notes) 4

Do you believe that mapping tools like VSM are generally more suitable

1 _ 2 _ 3 _4 _ 5

for the manufacturing sector than other sectors? Do you believe that mapping tools like VSM are generally more suitable

1 _ 2 _ 3 _4 _ 5

for the service sector than other sectors? (Please add some notes) 5

Do you believe that GBs and BBs in the manufacturing industry need a

1 _ 2 _ 3 _4 _ 5

different training path? Do you believe that GBs and BBs in the service industry need a different

1 _ 2 _ 3 _4 _ 5

training path? (Please add some notes) 6

Do you believe that in the manufacturing industry organisational climate

1 _ 2 _ 3 _4 _ 5

and human behaviour can strongly affect Six Sigma projects’ results? Do you believe that in the service industry organisational climate and

1 _ 2 _ 3 _4 _ 5

human behaviour can strongly affect Six Sigma projects’ results? (Please add some notes) 5 = Strongly agree; 4 = Slightly agree; 3 = Neither agree nor disagree; 2 = Slightly disagree; 1 = Strongly disagree

Your notes on the questions: 1 2 3 4 5 6

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Multi-stage sampling has been chosen as the probability sampling method. This method aims to decrease the time and cost of sampling and it is particularly suggested when there is no list of people in the population (in this case a list of Six Sigma experts). In multi-stage sampling the researcher first can select a determined number of clusters at random from the population and then can take a random sample within the clusters (Goldstein, 1995). Hence three clusters have been selected:



Consulting firms that have experts in Six Sigma (33%);



Managers who have applied Six Sigma or Lean Six Sigma (40%);



Academics who have been studying Six Sigma and in particular who have got together at a conference or published on the subject (27%).

Three clusters have been used to reduce the possibility of introducing some bias. For instance, a cluster of only academic researchers could have led towards more theoretical results; therefore, academics have been balanced with practitioners. In the same way managers could have introduced bias due to their own experience inside the company they represent. In the past 3 years, 572 people have filled in the questionnaire discussed in the next sections.

4. Data collection and statistical data analysis According to Bryman (2004) there are several ways of delivering questionnaires to respondents. In this study the respondents have been reached by means of email, telephone as well as by directly interviewing people. Each interview, self-administered or interviewer administered, is based on a structured interview. Using this approach each interviewee is presented with the same questions in the same order. In this way the Page 13 of 29

answers can be reliably collected, compared and analysed. Furthermore, the quantitative results have been expanded by suggestions and comments collected from the interviewees through the footnotes in the questionnaire.

The null-hypothesis test has been led using SPSS. The report in Table 3 shows the results for each answer of the questionnaire. The table shows the Chi-square results and in particular the ‘P-value’ or Pearson Chi-square will be taken into account. The Chi-square test gives the researcher the opportunity of determining whether the observed pattern within data could be taken into account as a random pattern. If the pattern is not random an association between the two variables is then concluded. In this way in Table 2 the null hypotheses have been stated. For instance, the first one is: ‘No association exists between the industry (service or manufacturing, first variable) and the kind of performance measurement for Six Sigma projects (second variable).

A ‘P’ value of less than 0.05 (5%) has been set in order to reject the null hypothesis; in this case the alternative hypothesis becomes true (Plackett, 1983). The test assumes 5% value as a cut-off point to reject the null hypothesis. This means that there is a 5% chance of being wrong or that the association is not due to a real association between the two variables but to chance.

Lastly, Table 3 shows the ‘Cramer’s V’ value; the higher it is (from 0 to 1) the stronger the association.

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Table 3: Results of the null-hypothesis tests

Question

Pearson Chi-Square value

P-value

Cramer’s V

Null hypothesis

1

1967.070

0.000

0.927

Rejected

2

2222.561

0.000

0.986

Rejected

3

2201.576

0.000

0.981

Rejected

4

2232.771

0.000

0.988

Rejected

5

2254.023

0.000

0.993

Rejected

6

1946.020

0.000

0.679

Rejected

It can be immediately noticed that all P-values are around zero, in fact SPSS only reports the P-value to the third decimal place. The P-value is much below the 0.05 cut-off, thence it can be claimed that there is very little risk of being wrong. That implies there is an association with the sector, all the null hypotheses stated in Table 2 should be rejected and the alternative hypotheses accepted. Analysing the Cramer’s V values it can be noted that the first five values are very high, close to the maximum. This implies that the association exists and is very strong. Whereas the sixth hypothesis, ‘No association exists between the sector and the possibility that organisational climate and human behaviour could affect the results’, shows a less strong association. In the next section a probable cause will be interpreted and discussed. The next section deals also with the interpretation of the statistical results along with the respondents’ more qualitative comments in the footnote of the questionnaire.

5. Interpretation and discussion of the statistical results The first hypothesis is about the association between the kind of industry (manufacturing or service) and the performance measurement used for Six Sigma projects. The alternative hypothesis has been rejected, consequently the respondents believe that in the

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service industry to measure performance such as timeliness and lead time is more important. However, looking at the notes in the questionnaire, 47 respondents out of 572 underlined that timeliness is more important than lead time even if for the majority of the respondents it is difficult to measure. Furthermore, some respondents suggested that timeliness and lead time can be considered the same performance measure in many service sectors like health care, banking and insurance. However, 28 respondents commented that it is sometimes difficult to register time performance in the service sector. For instance, it would be difficult to measure how much time during a day a nurse dedicates to taking psychological care of patients. Of the 572 respondents, 24 suggested that critical characteristics and success indicators of Six Sigma projects in the service sector are sometimes related to behaviour and people’s skills. However, to measure characteristics such as courtesy, empathy or ability to listen can be more difficult than measuring physical characteristics such as temperature or pressure.

The second alternative hypothesis has been confirmed as well. The respondents claim that the use of advanced statistical tools in the service industry is less than in the manufacturing industry. In addition, 125 respondents out of 572 suggested that in the service sector customers’ needs and requirements differ from client to client, and consequently it is sometimes difficult to gather consistent data. Many statistical tools require a lot of data gathered from similar processes, however, in the service industry customers often have very different care processes, even for the same service. For instance, a request for a loan can be processed in very different ways, depending on the value, the financial conditions of the applicant and several other issues. Of the 572 respondents, 15 also wrote that in sectors such as banking, call centres, hotels and catering, managers do not have a statistical background. Manufacturing sectors usually take on staff that have a more mathematical and statistical backgrounds like engineers. Page 16 of 29

The third confirmed alternative hypothesis is linked to the previous one. Basic tools for brainstorming such as Pareto Analysis and Cause-Effect Analysis are definitely used in both industries: service and manufacturing. However, insofar as service managers are not eager to use advanced statistical tools, they certainly tend to apply less difficult tools.

The fourth alternative hypothesis has been confirmed. Consequently it seems that the respondents consider mapping tools such as VSM more suitable for manufacturing processes. Of the 572 respondents, 47 highlighted that VSM was derived from Lean Production and it is not appropriate for finding problems inside service processes. Respondents suggested that VSM can be appropriate in the Measure and Analyse Stages of the DMAIC pattern, in order to find problems related to change-over time, inventory size and excess information flow. However, they suggested that VSM does not allow entering into a single activity of the process and bringing out problems related to accuracy and completeness of data and information. Moreover, VSM does not show each hand-off among the activities and whether the activity is managed by means of Information Technologies, automation or paper sheets. Twelve respondents named precise tools for mapping service processes such as Process Map Reengineering, Makigami, Activity Based Mapping. Makigami also emerged in the literature review (Chiarini, 2012b). The other named tools are completely unknown in the peer-reviewed literature. Perhaps they are more bound to consultants and practitioners’ ‘fads’ than to academic research. However, the respondents suggested the need for specific tools to map service process flow.

The fifth alternative hypothesis has been confirmed as well. Therefore the respondents claim that in the service industry the training path for Black and Green Belts should be Page 17 of 29

different. But in what way should the training path be different? First of all, 126 respondents wrote in the notes that for both, manufacturing and service industries, Green and Black Belt certification is fundamental. According to the respondents’ opinions, in no sector can a Six Sigma project be led without a Green or Black Belt as a team leader. Nonetheless they believe that the sector can affect the training path and suggested in the footnotes that the path for the service industry has to be personalised. Of the 572 respondents, 65 underlined the importance of receiving part of the training on the peculiarities of the service sectors. In this way there should be a Six Sigma path for banking, for health care, for the software industry and so on. Unfortunately none of the respondents gave examples of the differences related to the sector. Perhaps the differences are regarded as general knowledge of the sector such as organisation, culture, kind of processes, terminology, and so on. Very few respondents, just 6, wrote that the participants training for the service sector should receive fewer notions about advanced statistical tools. Therefore, matching these opinions with the results of the second hypothesis related to advanced statistical tools, it can be assumed that respondents believe that staff in the service industry are less accustomed to using advanced statistical tools; however, at the same time they believe that advanced statistical tools are important for training in whatever sector.

Last, but not least, the sixth alternative hypothesis concerning organisational climate and human behaviour is confirmed: respondents claim that in service sector Six Sigma results can be more biased by organisational climate and human behaviour. In particular, looking at the notes, 75 respondents believe that this is more common in public administrations such as large hospitals, city councils and government in general. They wrote that a Six Sigma project can even fail inside a public administration organisation, especially if European. These opinions reflect the view that managers inside manufacturing Page 18 of 29

companies, particularly worldwide companies, are generally more aligned on values and strategies than public administrators (Parker and Bradley, 2000). If the top management issues precise Six Sigma objectives, and the vision of the company becomes Six Sigma, it is difficult for managers to obstruct such a course. Managers inside worldwide companies can be chosen and even laid off on the basis of their attitudes and skills in order to carry out strategic objectives (Szilagyi and Schweiger, 1984). Organisational climate, rules, roles and human behaviour become something of a modifiable expendable for reaching Six Sigma objectives. Six Sigma projects can be slowed down but sooner or later any kind of obstacle will be removed including managers. By contrast, public administration presents political constraints, strong power of heads of departments, difficulties in changing job descriptions, strict roles and responsibilities as well as participation of the trade union into decision making. However, 53 respondents reckon that public administrations outside Europe, especially in the USA, can be less affected by this phenomenon. This could be also an explanation for the lower Cramer’s V value found for the sixth hypothesis.

Another important issue concerns the variability that human behaviour brings to the service process. Ninety respondents suggested that this is particularly taken for granted when the major part of the service is based on an employee’s aptitude and character. For instance, the following are based on human behaviour: how to answer to a customer at a hotel reception or on a phone at a call centre, or how to psychologically take care of a patient. Respondents underlined how in these situations it is quite impossible to apply a DMAIC pattern and its statistical tools in order to improve the process. Table 4 recaps the results of the null-hypothesis tests and the differences suggested by the respondents.

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Table 4: Results of the null-hypothesis tests and suggested differences

Hypothesis The sector and the kind of performance measurement are not independent of one another The sector and the use of advanced statistical tools are not independent of one another The sector and the use of quality basic tools are not independent of one another The sector and the suitability of using mapping tools are not independent of one another The sector and the training path for Black and Green Belts are not independent of one another The sector and the possibility that organisational climate and behaviour could affect the results are not independent of one another

Null-hypothesis test result True

True

True

True

True

True

Differences suggested by the respondents Timeliness in service industry tends to coincide with the service lead time and it is more important in the service industry than in manufacturing. Some critical characteristics linked to staff’s behaviour and skills are difficult to objectively measure Service processes are not often repetitive. It is difficult to gather consistent samples. People in service industries do not usually have a mathematical and statistical background

Related to the previous point, managers within particular service industries tend to apply basic statistical tools

Value Stream Mapping (VSM) is particularly focused on physical products. Practitioners are used to applying particular transactional mapping tools inside services. These latter are not as standardised as VSM Black and Green Belts need knowledge regarding the service sector in which Six Sigma is to be implemented. Knowing advanced statistical tools is important in whatever sector In some public administrations, factors such as politics, rules and union relationships can interfere with Six Sigma projects. DMAIC pattern and its tools cannot sometimes improve human behaviours and attitudes

6. Conclusions The literature review has highlighted some differences between Six Sigma applications in manufacturing and service industries which are represented by the research questions. For the first time in the literature, these questions have been transformed into six hypotheses and tested by means of a survey. The questionnaire inside the survey has

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operationalised the hypotheses using a Likert scale. In this way a Chi-square has tested the association between the kind of industry (manufacturing and service) and the nullhypotheses in Table 2. All the null hypotheses were rejected and consequently the alternative hypotheses accepted. The novelty of this research lies in the fact that no one has collected the differences between Six Sigma in manufacturing and in the service industry from the literature and then tested them. In this way interesting results have emerged.

First, the respondents believe that performance indicators for Six Sigma projects can be different from the manufacturing ones. Timeliness and lead time seem to be more important in the service industry, and even particular characteristics such as courtesy, empathy and capacity to listen could be measured. The respondents underline that is not easy to measure such characteristics.

The Chi-square results also show that the respondents consider service industry managers to be less accustomed to using advanced statistical tools such as DOE, Anova and many others. According to the respondents it is difficult to create consistent databases because of the non-repeatability of many services. Besides, in some service sectors there is a problem of culture background. In some services there is, for instance, a lack of engineers and other technicians with a mathematical and statistical degree. Related to this issue in some service sectors basic Six Sigma tools such as Pareto and Cause and Effect diagram are preferred.

The results highlight as well that VSM is a tool more suitable for manufacturing flow where a physical product is managed. Practitioners are trying to develop new mapping tools particularly focused on transactional flow. Unfortunately there is no trace of a codified tool Page 21 of 29

such as VSM. Many tools such as Makigami, MBPM, ‘Brown Paper map’, MIFA, Interactive Map and Four Fields Mapping emerged from the literature review. The objective is the same, analysing the activities, identifying the problems and measuring service performances. However, the names, symbols and way of mapping of the above mentioned tools are quite different.

The respondents also accepted the hypothesis that Black and Green Belts in service industries need training that is adapted to their sector. However, looking at the notes written by some respondents, they do not consider that a different approach concerning DMAIC tools is needed. Rather, it seems just a matter of basic knowledge about the kind of service industry. This issue contradicts the previous hypothesis of the suitability of using advanced statistical tools.

The last hypothesis that organisational climate and human behaviour can affect Six Sigma results has been tested. Chi-square demonstrated that this is particularly the case in service industries and the respondents believe that it is typical in public service administrations. Politics, strict roles and rules, and trade union relationships are some of the factors that can influence a Six Sigma project. Respondents also noted that when a service is based on an employee’s aptitude and character then relevant variability is introduced by human behaviour. In this case it is difficult to implement a complete DMAIC pattern for improving performance; the tools should be more psychological and less mathematical.

Limitations of this research are due to the sample of respondents. The sample comprises Six Sigma experts but they could have introduced some bias, especially in the qualitative footnotes of the questionnaire. Indeed a contradiction related to usage of advanced Page 22 of 29

statistical tools training emerged. Furthermore, another contradiction emerged from the sixth hypothesis. The statistical results show there is an association between organisational climate, human behaviour and the service industry, especially if this latter is a European public administration. However, according to some respondents it seems that this is not true outside Europe. More research in these directions is needed.

7. Managerial implications and agenda for future research This research brings to light several implications for practitioners. First of all the differences discussed above can be used to improve the implementation of Six Sigma projects in the service industry. Practitioners should concentrate their attentions on what are the critical characteristics of the Six Sigma project for the particular service sector. Timeliness, lead time and human factors are important performance measures. However, maybe in a particular kind of service industry these performance measures can significantly change. Academics could try to classify performance measures in connection with the kind of industry. In addition, teams should be aware of difficulties such as nonrepetitive data and use of advanced statistical tools. Practitioners and academics should also investigate what kind of tools inside the DMAIC pattern can be used for different types of service sectors. Practitioners could carry out case studies to try to understand the connections among the tools and the different improvement to achieve. Moreover, practitioners could face difficulties in public service administration in managing political relationships with senior managers, as well as unions and other organisational factors. All these particular issues would need a dedicated research study by means of qualitative inquiries such as participant observation and focus groups. For instance, it is important to better analyse how organisational climate and human behaviour can affect Six Sigma projects, especially European public administrations.

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Lastly, it could be also interesting for both practitioners and academics to investigate how to map, analyse and measure transactional flow for services. The literature review and the questionnaires clearly demonstrate a lack of standardised tools such as VSM for the service industries. Practitioners could investigate through multiple case studies and academics could try to generalise the results.

Acknowledgments The author would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the paper.

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