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How to Write a Research Paper

Writing a research paper is a bit more difficult that a standard high school essay. You need to site sources, use academic data and show scientific examples. Before beginning, you’ll need guidelines for how to write a research paper.

Start the Research Process

Before you begin writing the research paper, you must do your research. It is important that you understand the subject matter, formulate the ideas of your paper, create your thesis statement and learn how to speak about your given topic in an authoritative manner. You’ll be looking through online databases, encyclopedias, almanacs, periodicals, books, newspapers, government publications, reports, guides and scholarly resources. Take notes as you discover new information about your given topic. Also keep track of the references you use so you can build your bibliography later and cite your resources.

Develop Your Thesis Statement

When organizing your research paper, the thesis statement is where you explain to your readers what they can expect, present your claims, answer any questions that you were asked or explain your interpretation of the subject matter you’re researching. Therefore, the thesis statement must be strong and easy to understand. Your thesis statement must also be precise. It should answer the question you were assigned, and there should be an opportunity for your position to be opposed or disputed. The body of your manuscript should support your thesis, and it should be more than a generic fact.

Create an Outline

Many professors require outlines during the research paper writing process. You’ll find that they want outlines set up with a title page, abstract, introduction, research paper body and reference section. The title page is typically made up of the student’s name, the name of the college, the name of the class and the date of the paper. The abstract is a summary of the paper. An introduction typically consists of one or two pages and comments on the subject matter of the research paper. In the body of the research paper, you’ll be breaking it down into materials and methods, results and discussions. Your references are in your bibliography. Use a research paper example to help you with your outline if necessary.

Organize Your Notes

When writing your first draft, you’re going to have to work on organizing your notes first. During this process, you’ll be deciding which references you’ll be putting in your bibliography and which will work best as in-text citations. You’ll be working on this more as you develop your working drafts and look at more white paper examples to help guide you through the process.

Write Your Final Draft

After you’ve written a first and second draft and received corrections from your professor, it’s time to write your final copy. By now, you should have seen an example of a research paper layout and know how to put your paper together. You’ll have your title page, abstract, introduction, thesis statement, in-text citations, footnotes and bibliography complete. Be sure to check with your professor to ensure if you’re writing in APA style, or if you’re using another style guide.


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Using Lean Six Sigma techniques to improve efficiency in outpatient ophthalmology clinics

BMC Health Services Research volume  21 , Article number:  38 ( 2021 ) Cite this article

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Increasing patient numbers, complexity of patient management, and healthcare resource limitations have resulted in prolonged patient wait times, decreased quality of service, and decreased patient satisfaction in many outpatient services worldwide. This study investigated the impact of Lean Six Sigma, a service improvement methodology originally from manufacturing, in reducing patient wait times and increasing service capacity in a publicly-funded, tertiary referral outpatient ophthalmology clinic.

This quality improvement study compared results from two five-months audits of operational data pre- and post-implementation of Lean Six Sigma. A baseline audit was conducted to determine duration and variability of patient in-clinic time and number of patients seen per clinic session. Staff interviews and a time-in-motion study were conducted to identify issues reducing clinic service efficiency. Solutions were developed to address these root causes including: clinic schedule amendments, creation of dedicated postoperative clinics, and clear documentation templates. A post-implementation audit was conducted, and the results compared with baseline audit data. Significant differences in patient in-clinic time pre- and post-solution implementation were assessed using Mann-Whitney test. Differences in variability of patient in-clinic times were assessed using Brown-Forsythe test. Differences in numbers of patients seen per clinic session were assessed using Student’s t-test.

During the baseline audit period, 19.4 patients were seen per 240-minute clinic session. Median patient in-clinic time was 131 minutes with an interquartile range of 133 minutes (84–217 minutes, quartile 1- quartile 3). Targeted low/negligible cost solutions were implemented to reduce in-clinic times. During the post-implementation audit period, the number of patients seen per session increased 9% to 21.1 ( p  = 0.016). There was significant reduction in duration ( p  < 0.001) and variability ( p  < 0.001) of patient in-clinic time (median 107 minutes, interquartile range 91 minutes [71–162 minutes]).


Lean Six Sigma techniques may be used to reduce duration and variability of patient in-clinic time and increase service capacity in outpatient ophthalmology clinics without additional resource input.

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Medical services worldwide face an aging population and with it, an increasing burden of disease [ 1 ]. Continuous improvement in diagnosis and management is resulting in better patient outcomes, but also increasing demands on healthcare resources. Together, increasing patient numbers, increasing complexity of patient assessment and management, and limitations on healthcare resources have resulted in prolonged patient wait times, decreased quality of service, and decreased patient satisfaction in many outpatient services across many medical specialities in both developed and developing nations [ 2 , 3 , 4 ]. With a focus on improving workflows, process efficiency, and reducing variability in production/service delivery, Lean and Six Sigma are two well-known management methodologies from manufacturing that may be used to help address these growing issues in outpatient healthcare settings [ 5 , 6 ].

Lean, derived from the Toyota Production System, is a process improvement methodology focused on reducing ‘waste’ (steps that do not add value to the final service/product) to improve efficiency. ‘Waste’ is typically considered in 7 categories being: waiting, unnecessary transport, unnecessary human motion, inventory, over-processing, rework, and overproduction [ 5 ]. Examples of ‘waste’ in outpatient clinics include patients waiting (inventory), inappropriate testing (overproduction), or idle staff (waiting). As the patient journey through an outpatient clinic is similar to a production process, with creation of relative value units through multiple steps e.g. patient check-in, initial nursing/allied health evaluation, ophthalmologist examination, and check-out, Lean techniques may be adapted to optimise patient flow and reduce ‘waste’ [ 5 ].

Six Sigma, originally developed by Motorola in 1986, is a structured methodology to identify and eliminate defects, and reduce variation in production processes. The methodology consists of five steps [ 5 ]. Define, where issues in a process are defined from business and customer perspectives; Measure, where the process is broken down and explored; Analyse, where data is analysed to identify underlying root causes of issues; Improve, where solutions are developed, piloted and implemented to address root causes; and Control, where solutions are sustained through process control plans and ongoing monitoring. Outpatient clinics often have a high degree of variability contributing to clinic inefficiency e.g. different pathologies, differing clinician preferences etc. Six Sigma focusses on minimising variability where possible to streamline processes.

Due to their overlap, Lean and Six Sigma are often combined in a “Lean Six Sigma” approach. In recent times, Lean Six Sigma has been increasingly applied in healthcare [ 7 ]. There are few studies, however, examining its efficacy in improving publicly-funded, outpatient ophthalmology services [ 5 , 8 ]. This project studied the effect of applying Lean Six Sigma in a publicly-funded tertiary referral outpatient ophthalmology service to reduce duration and variability of patient in-clinic times and improve service efficiency.

Practice setting

Royal North Shore Hospital Eye Clinic is a publicly-funded multi-subspecialty outpatient ophthalmology service in Sydney, Australia. Over 8,000 appointments are seen every year across 6 subspecialties, with referrals received from primary care and specialist doctors, optometrists and general ophthalmologists. The clinic also provides ‘on-call’ ophthalmic care to inpatients of Royal North Shore Hospital (> 600 beds) and patients presenting to emergency departments across the Northern Sydney Local Health District (> 185,000 presentations/year).

The clinic runs nine half-day sessions (240 minutes) every week. It is staffed by a roster of eight consultant subspecialist ophthalmologists (one on the floor for each subspecialist session and one always ‘on-call’), three ophthalmology registrars (two for all sessions, one of which is ‘on-call’ for emergency and inpatient consults), six nurses (two for all sessions) and one orthoptist (for all sessions). In any session, patients are evaluated in a multi-step process including check-in, screening (nursing/orthoptic staff assessment), investigations, ophthalmologist review and check-out. Between each step, if patients are not passed directly onto the next staff member immediately, they are returned to the waiting area or sat outside the next applicable room in the patient journey (e.g. outside the investigation room or the ophthalmologist’s room).

Within the clinic there are three rooms for screening, three rooms for ophthalmologist review, two rooms for investigations and two rooms for procedures. When a session is in progress, all rooms are dedicated to that session alone. In general, patients are booked into planned appointment slots within a session. When emergency or inpatient consults are requested however, they may be fit in on an ad hoc basis depending on clinical urgency.

Key measures (“Define” phase)

This study’s outcome measures were: duration (median) and variability (interquartile range) of patient in-clinic time, and number of patients seen per session pre- and post-implementation. Patient in-clinic time was defined as the number of minutes from whichever was later of the appointment time, or the patient check-in time, until patient check-out. This was done to reduce the effect that patients arriving early (in which case appointment time was used) or late (in which case check-in time was used) to their appointments had on variability of in-clinic time.

Data collection

Cerner Scheduling Appointment Book (Cerner, North Kansas City, USA), was used to schedule patient appointments. This program allowed creation of a timetable with specific appointment times and types (e.g. new, follow-up, emergency etc.) for patients to be booked into. When patients attended appointments, it recorded the time patients were checked-in and checked-out by administrative staff. Waiting time before check-in or after check-out (e.g. waiting for transport) was not captured.

Two five-month data audits of all attended appointments were conducted to determine the efficacy of the Lean Six Sigma process. A baseline audit (“Measure” and “Analyse” phases) was retrospectively conducted from February 1st to June 30th 2018. A post-implementation audit (“Control” phase) was conducted from February 1st to June 30th 2019.

Data analysis

Patient age, gender, appointment time, appointment type, check-in time and check-out time were captured. Appointments with incomplete time data or coding errors (i.e. visits with no end time or total duration of 0 or greater than 480 minutes) were included in the count of patients seen but excluded from analysis of duration and variability of patient in-clinic time.

Difference in duration of patient in-clinic times pre- and post-implementation was assessed using Mann-Whitney-U test on SPSS (v24, IBM Corporation, Armonk, USA). Difference in variability of patient in-clinic times was assessed using Brown-Forsythe test on Excel (Microsoft, Redmond, USA) [ 9 ]. Difference in number of patients seen per session was assessed using Student’s test (SPSS). Differences in the proportions of patient appointment types seen were assessed using chi-squared tests, with Z-tests (with Bonferonni correction) used to compare pairwise differences between pre- and post-implementation proportions of appointment type (Excel). Difference in mean ages of patients with valid versus invalid in-clinic time data was assessed using Student’s t-test while differences in proportions of gender were assessed using chi-squared test (SPSS).

Process flow maps and time-motion analysis

Two patient process flows fit most patient journeys through the clinic; one where investigations were performed, and one without investigations. Process flow maps outlining steps in these journeys were created (Fig.  1 ).

figure 1

Patient flow through the Eye Clinic and the associated proportion of time spent. In both pathway one and two, over 70% of patient in-clinic time was spent waiting. Note: numbers do not sum to 100% due to rounding

A two-week time-in-motion study was conducted from June 11th to June 24th 2018 to determine proportions of total in-clinic time spent in each step along the patient journey. In this time-in-motion study, staff members noted the times they commenced and ended their roles in the patient journey on a dedicated audit document. Time between each staff member’s contact time was treated as waiting time.

The time-in-motion study data was analysed in Excel. Visits with coding errors (i.e. no time entered, times with inconsistent patient flow) were excluded. Proportions of total in-clinic time were determined and superimposed on patient process flow maps to identify bottlenecks in the patient journey (Fig.  1 ).

Root cause analysis

Staff interviews, workshops, and review of patient complaint data were used to identify issues causing prolonged duration and increased variability of patient in-clinic time and clinic inefficiency. Following this, root cause analysis of issues was undertaken using the “Five Whys Technique” [ 10 ]. Resulting root causes were grouped and the most common root causes targeted for solution development.

Baseline audit (“Measure” and “Analyse” phases)

During the baseline audit period there were 3624 visits over 187 240-minute sessions (average 19.3 patients/session). Of these visits, 2241 had valid time data for analysis. Median patient in-clinic time was 131 minutes and the interquartile range 133 minutes (84–217, quartile 1- quartile 3). Of visits with invalid data, 13 had invalid in-clinic times (due to patients arriving, being seen and discharged before their appointment time), while the remaining 1370 had invalid check-out times (checked-out the following day). Comparing invalid to valid data cohorts, there were no significant differences in age (invalid: 58.4 ± 23.2 years; valid 58.0 ± 23.4 years, p  = 0.568) or gender (invalid: female 49.2%; valid: female 48.8%, p  = 0.743), and only minimal differences in proportions of appointment types (Table  1 ).

There were 329 visits during the two-week time-in-motion study. Of these, 195 had valid data for analysis. Two bottlenecks within the clinic were identified. The first, between patient check-in and screening, accounted for 33–39% of total in-clinic time depending on the care pathway. The second, before seeing the ophthalmologist, accounted for 35% of total in-clinic time. Overall, over 70% of patient in-clinic time was spent waiting in both care pathways (Fig.  1 ).

Through ten patient interviews, ten staff interviews, two staff workshops (including all staff working in the clinic), and an audit of patient complaint data, 100 unique issues causing prolonged patient in-clinic time and clinic inefficiencies were identified. Ten common root causes emerged from root cause analysis, with four contributing to 77% of issues encountered (Fig.  2 ).

figure 2

Root causes of issues in the Eye Clinic. Of issues encountered in the clinic, 77% were due to 4 root causes: scheduling, staffing, patient communication and inefficient clinic processes. These root causes were targeted in solution development and implementation

Scheduling was the most commonly occurring root cause identified in root cause analysis (32% of identified issues). Therefore, further exploration of scheduling data was undertaken. As seen in Fig.  3 a, most patients were scheduled to arrive in the middle of clinics. This was due to the clinic schedule design, and ad hoc addition of inpatient and emergency patients into already fully-booked sessions through the clinic’s ‘on-call’ service. Patient influxes at these times were the primary contributor to the bottleneck at the start of the care pathway between check-in and screening.

figure 3

Appointment times February-June 2018/2019.Note: morning clinic sessions ran from 8am to 12 pm, afternoon clinic sessions ran from 12:30 pm to 4:30 pm. Prior to solution implementation (Fig.  3 a ), most patients were scheduled to arrive in the middle of clinics (between 9:00am-10:30am for the morning clinic and 2:00 pm-3:00 pm for the afternoon clinic). After solution implementation (Fig.  3 b ), patient arrival times were smoothed throughout the day

Process improvements (“Improve” phase)

Four main root causes: scheduling, staffing, patient communication, and clinic processes, were responsible for 77% of issues encountered (Fig.  2 ). Although funding was not available to address staffing, several other targeted negligible cost interventions were implemented to address the remaining three main root causes.

To address poor patient scheduling, the clinic schedule was revised to control patients’ arrival times. This involved: moving the start time of screening staff and patient appointments to 7:30am so patients could be screened and ready to see the ophthalmologist at 8am; revising appointment slot time lengths to better align with the needs of each appointment type; creating dedicated ‘on call’ emergency and inpatient appointment placeholders to reduce ad hoc scheduling of these patients; and providing the ‘on-call’ registrar with a ‘live’ scheduling app to allow easier identification of available appointment slots for ad hoc bookings. Furthermore, a dedicated postoperative clinic was introduced for 1-week and 4-week postoperative follow up visits as these had low variation care pathways amenable to optimisation through grouping into a dedicated clinic. The impact of these solutions is shown in Fig.  3 b.

To address inefficient clinical processes, further staff feedback was sought on potential solutions and the following three solutions developed:


Initially, many frequently used medications (e.g. valacyclovir, timolol, brinzolamide, preservative free lubricants) were often not readily available in-clinic. This disrupted patient flow, requiring clinicians to call the hospital pharmacy to request the medications and patients to wait for them to be delivered. To address this, imprest medication lists were reviewed and updated to include these medications. Daily checks were implemented to ensure that adequate supplies of medications were available in-clinic.

Initially, there was no standard order to see patients in after check-in, with different staff using different approaches. There was no prioritisation system for patients with higher clinical need, e.g. inpatients, unwell persons, and no clear instruction for paper files of newly checked-in patients to be put in appointment order in the clinic’s ‘patients to be seen’ box. As clinicians generally picked up patient files from the top of the box, patients were therefore seen out of chronological order, disrupting patient flow and increasing variability in in-clinic time. To address these issues, defined escalation criteria were made for patients with clinical or other special requirements. Clear instructions were made to put paper files of newly checked-in patients in appointment order in the ‘patients to be seen’ box. Clinicians were instructed to see all patients in order of appointment time, unless there was an urgent clinical need.


Initially, there was no process to clearly document investigations needed for follow up patients at their next appointment. This resulted in inefficiency as some patients occasionally needed to return to the investigation room after seeing the ophthalmologist for further tests, whilst others underwent unnecessary non-invasive investigations. To address this, a standard clinic documentation template was introduced for investigations required at the next follow up visit. This was done with the aim of prompting clinicians to consider and order appropriate investigations in advance (Online supplement: Documentation Template).

Based on the root cause analysis finding that poor patient communication accounted for 16% of issues in the clinic, all written patient communications were reviewed. Referral acknowledgement letters were updated to provide more accurate information regarding wait times for an initial appointment. Clinic information sheets and posters were developed to inform patients what to expect during their clinic visit. Fact sheets for common ophthalmological conditions and surgical procedures were introduced to improve and standardise patient education, while also potentially reducing the clinician face time needed to provide this education. Consumer representatives were used to review and provide feedback on all revised patient communications.

Follow up analysis (“Control” phase)

During the post-implementation period there were 3853 clinic visits over 183 240-minute sessions (average 21.1 patients per session), a 9% increase in patients per session compared to the baseline period ( p  < 0.016). Of these visits, 3490 had valid data for analysis. Median patient in-clinic time was 107 minutes and the interquartile range 91 minutes (71–162, quartile 1- quartile 3). This was a significant reduction in duration and variability of patient in-clinic time compared to baseline (both p  < 0.001). (Fig.  4 ). Of visits with invalid data, 11 cases had no check-out time, 71 cases had invalid waiting times (patients arriving, being seen and discharged before their appointment time), and 281 had invalid check-out times (checked-out the following day). Comparing the invalid to valid data cohort, there were no significant differences in age (invalid: 56.5 ± 23.3 years; valid 58.1 ± 22.8 years, p  = 0.190), gender (invalid: female 46.5%; valid: female 43.8%, p  = 0.321) or proportion of appointment types (Table  1 ).

figure 4

Distribution of patient in-clinic time pre- and post-implementation. Comparing pre- (Fig.  4 a ) to post-implementation (Fig.  4 b ), patient in-clinic time significantly decreased as shown through a left-shift in the distribution of in-clinic time post-implementation. IQR: interquartile range

In this study, application of Lean Six Sigma techniques in a publicly-funded tertiary outpatient ophthalmology clinic led to development of solutions that significantly reduced duration and variability of patient in-clinic time. Median patient in-clinic time was reduced by 18% and the interquartile range by 32%. These results were achieved while patients seen per session increased 9%. Solutions used to achieve these results were: clinic schedule amendments to prevent sudden influxes of patients, a dedicated weekly postoperative patient clinic for one week and four week postoperative visits, checks to ensure frequently used medications were always available in the clinic, defining a standard order to see patients in, clear follow-up patient investigation planning documentation templates, and patient information pamphlets for common ophthalmic conditions/surgeries. Of note, these solutions were implemented without additional capital requirements (e.g. purchasing new devices) or ongoing staffing costs.

This study adds to the growing body of literature demonstrating that techniques from business and industry, such as Lean Six Sigma, can be used in healthcare settings to improve system efficiency. Specific to ophthalmology, one North American group who applied Lean Six Sigma techniques to a subspecialist retina clinic (subsequently hiring an extra technician, creating a dedicated intravitreal injection patient pathway, and improving clinic scheduling), reduced mean patient visit times by 18% ( p  < 0.05) and variation in visit time by 5% [ 5 ]. A second North American group who applied Lean thinking (decentralising their optical coherence tomography machines from a central photography suite into technicians’ screening rooms), reduced patient wait times by 74% ( p  < 0.0001) and in-clinic time by 36% ( p  < 0.0001) [ 11 ].

Outside of ophthalmology, Lean Six Sigma has been shown to be effective in a range of healthcare contexts. The Cleveland Clinic Cardiac Catheterisation Laboratory, as an example, applied Lean Six Sigma techniques subsequently improving patient turnover times, the number of on-time patient and physician arrival times and reducing physician down times [ 12 ]. A further example was seen in Indiana pertaining to orthopaedic inpatient care at the Richard L. Roudebush Veterans Affairs Medical Centre in Indianapolis. Their group used Lean Six Sigma techniques to reduce length of stay of joint replacement patients by 36% from 5.3 days to 3.4 days ( p  < 0.001) [ 13 ]. Finally on a hospital-wide basis the University Hospital “Federico II” of Naples, used Lean Six Sigma techniques to reduce healthcare-associated infections in inpatients across multiple medical specialties including general medicine, pulmonology, oncology, nephrology, cardiology, neurology, gastroenterology, endocrinology and rheumatology [ 14 ].

Process improvement methodologies such as Lean Six Sigma, present a significant opportunity to deliver better value in healthcare through improved efficiency and reduced ‘waste’. More broadly, as demands on healthcare services continue to grow across most medical specialties, a focus on service improvement will be needed to best utilise the limited resources available. This is particularly true within publicly-funded healthcare systems where long waiting times for non-emergency services are an increasingly common feature [ 15 ].

Service improvement, particularly in organisations utilising Lean Six Sigma methodology must incorporate the feedback of all their people including patients and the multidisciplinary healthcare team. Input from the entire team not only allows for better issue identification and solution generation, but also has the potential to increase team cohesiveness and motivation to actively participate in service improvement [ 16 ]. In this study, broad staff engagement through interviews and workshops allowed a comprehensive diagnosis of issues facing the Eye Clinic, identification of suitable, low/negligible cost solutions, and motivated all staff, from check-in desk to ophthalmologists to contribute to the service improvement effort. Going forward, we believe it has helped facilitate the development of a continuous improvement culture not only in the Eye Clinic, but also more broadly in our organisation, with the lessons learnt in this study now being applied to other outpatient clinics at our hospital.

This study has several limitations. Firstly, only qualitative data (i.e. staff interviews) was used to determine inefficient clinic processes. A quantitative investigation defining exact contributions of these issues to pre- and post-implementation in-clinic times would have better clarified the efficacy of each solution. Secondly, this study did not formally measure the effect of our solutions on patient and staff satisfaction. Staff interviews suggest however, staff satisfaction and engagement in improving clinic efficiency has improved. Other studies in outpatient clinics have demonstrated that reduced patient wait times improve patient satisfaction [ 3 ]. Thirdly, as the baseline audit was performed retrospectively, many patient visits had invalid data and were excluded from in-clinic time analysis (1383 of 3624 visits). This was noted in the improvement process and the check-out process was subsequently standardised, resulting in less invalid data in the post-implementation audit (363 of 3853 visits). Overall, most invalid data was due to administration staff oversight in checking-out patients at the end of their appointment (these patients were checked-out the following day). As such, it is likely the invalid data is missing completely at random, as opposed to being missing due to patient or in-clinic time related factors. This is supported by there being no differences in age or sex between invalid and valid data cohorts, and only minimal differences in the proportion of appointment types between the total and valid data cohorts.

There are, however, many strengths to this study. Firstly, this study was conducted in a large publicly-funded tertiary referral outpatient ophthalmology service with both inpatient and emergency services, a setting at high risk of facing resource constraints. The fact that the improvements seen in this study were delivered without significant additional capital or ongoing staffing costs increases its applicability to other services with similar characteristics. Secondly, this study had a large sample size, including all patients seen, across a range of subspecialties over the audited periods. This further increases applicability of this study’s results to other large, multi-subspecialty ophthalmology services. Thirdly, as many of the solutions implemented are not specific to ophthalmology, they could potentially be applicable to other outpatient specialties. Finally, by auditing patient wait times over two corresponding five-month periods in the year (February to June), the potential for holiday periods and seasonality confounding the results was reduced.

In summary, this study demonstrates that applying Lean Six Sigma to publicly-funded outpatient ophthalmology clinics can reduce duration and variability of patient in-clinic time and increase service capacity, without significant upfront capital expenditure or ongoing resource requirements. It outlines an approach to applying Lean Six Sigma that may be used in other healthcare contexts and some potential solutions that may be applicable to all outpatient clinics, ophthalmology or otherwise. As demands on healthcare resources continue to increase in the future, Lean Six Sigma techniques may play an increasingly important role in improving the delivery of healthcare services.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


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The authors would like to thank all members of the NSW Health Agency for Clinical Innovation and the Department of Ophthalmology, the Ambulatory Care Centre and the Division of Surgery at Royal North Shore for their support of the project.

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Andrew W. Kam, Tae Park, Michael Mihail, Noni L. Lewis, Daniel Polya, Samantha Fraser-Bell, Timothy V. Roberts & James E.H. Smith

Northern Sydney Local Health District, St Leonards, New South Wales, Australia

Andrew W. Kam, Scott Collins, Tae Park, Michael Mihail, Noni L. Lewis, Daniel Polya, Samantha Fraser-Bell, Timothy V. Roberts & James E.H. Smith

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Andrew W. Kam, Noni L. Lewis, Samantha Fraser-Bell, Timothy V. Roberts & James E.H. Smith

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Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia

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AK, SC, TP, MM, FS, SFB and JS made substantial contributions to the conception and design of the work; AK, SC, TP, MM, NL, DP, SFB, TR and JS acquired the data and performed the analyses; all authors made substantial contributions to interpretation of the data for the work; all authors participated in the drafting and revision of the manuscript and gave final approval on the version to be published.

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Kam, A.W., Collins, S., Park, T. et al. Using Lean Six Sigma techniques to improve efficiency in outpatient ophthalmology clinics. BMC Health Serv Res 21 , 38 (2021).

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Six Sigma in Health Literature, What Matters?

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Six Sigma has been widely used in the health field for process or quality improvement, constituting a quite profusely investigated topic. This paper aims at exploring why some studies have more academic and societal impact, attracting more attention from academics and health professionals. Academic and societal impact was addressed using traditional academic metrics and alternative metrics, often known as altmetrics. We conducted a systematic search following the PRISMA statement through three well-known databases, and identified 212 papers published during 1998–2019. We conducted zero-inflated negative binomial regressions to explore the influence of bibliometric and content determinants on traditional academic and alternative metrics. We observe that the factors influencing alternative metrics are more varied and difficult to apprehend than those explaining traditional impact metrics. We also conclude that, independently of how the impact is measured, the paper’s content, rather than bibliometric characteristics, better explains its impact. In the specific case of research on Six Sigma applied to health, the papers with more impact address process improvement focusing on time and waste reduction. This study sheds light on the aspects that better explain publications’ impact in the field of Six Sigma application in health, either from an academic or a societal point of view.

1. Introduction

Six Sigma seeks quality, understood as less variability in a process result [ 1 ]. Despite it coming from the manufacturing industry, as Motorola created it, it has been applied to a diverse set of non-manufacturing-related issues, giving excellent results [ 2 , 3 ]. It is based on the principle of measuring, monitoring, and controlling processes through the DMAIC steps (define, measure, analyze, improve, and control) [ 4 ]. The goal is to reach 99.9997% accuracy, with only 3.4 defects per million opportunities.

This management philosophy is widely used in the health sector to reduce error, cost, and time [ 5 , 6 , 7 , 8 ]. In today’s complex environment, with financial constraints on the healthcare system, increased efficiency could help health institutions to maintain or improve outcomes [ 9 ]. In this sense, the use of Six Sigma for addressing health process improvements is gaining scholars’ and professionals’ interest [ 10 ]. Most of this research is carried out through case studies showing Six Sigma implementations in different areas of a medical organization [ 5 ].

As the field attracts more attention, the impact of its publications also grows. In this regard, citations have been the traditional way to assess research impact [ 11 , 12 ], as well as journals’ impact factor [ 13 ], or other indexes, such as the FWCI (Field-Weighted Citation Impact). FWCI is an indicator that compares the actual number of citations received by a document with the expected number of citations for documents of the same type (article, review, book, or conference proceeding), publication year, and subject area [ 14 ]. The relevance of these traditional metrics is based on their use in performing evaluations of individual academics, research groups, and universities [ 15 ]. However, the criticism received for these classic metrics approaches is growing because they do not analyze the reasons for citations or the impact that research exerts beyond academia [ 16 ]. Besides, in the specific case of the health field, the time normally required to accumulate citations may overlook important societal and clinical impacts and new scholarly channels are increasingly used to disseminate scientific results [ 16 ].

This critical movement has led to the rise of alternative metrics, commonly known as altmetrics, representing another way of assessing research impact based on relationships and sharing academic publications in online environments [ 17 ]. They capture the relevance of research based on metrics such as article views, downloads, and mentions on social media or news media [ 16 ], considering channels such as Twitter, Mendeley, CitedULike or blogging [ 18 , 19 ], among others. Given the growth and relevance of social media for sharing scientific knowledge [ 20 ], these novel metrics have become necessary and represent another way to assess research impact.

Altmetrics complement citation analysis and other traditional metrics; they overcome their limitations and provide new insights into research impact study [ 16 ]. At the same time, altmetrics can provide better signaling of significant publications according to different audiences, which do not necessarily need to be academic, being more suitable for capturing the societal impact of research [ 21 ].

Previous research on traditional and altmetric scores of publications has mostly been interested in analyzing the relationship between both kinds of metrics [ 22 ]. The conclusions of these studies are not unanimous. While some of them find some degree of correlation between altmetrics and citations [ 22 ], others claim that altmetrics cannot predict future citations [ 23 ] and that some altmetrics are only weakly correlated with traditional citation metrics [ 24 ]. These conflicting results suggest that these two approaches are related; they complement each other, but they do not provide identical information on the visibility and impact of academic research. The conflicting results also point out the necessity of conducting more studies providing additional empirical evidence to support the notion of the determinants that explain what is important in order to increase the citations and the altmetric scores.

In the health field, and more specifically among the extensive literature that considers Six Sigma for addressing health process improvements, we are unaware of any study that has analyzed the determinants that have more impact in the citations and altmetric scores obtained by these publications.

The paper aims to determine which factors are the determinants of the impact of the publications, considering traditional and alternative impact indicators. Specifically, we aim to do this in health publications applying Six Sigma.

These different ways of addressing impact represent important opportunities for academics that concern the objectives of their research. Academics want their contributions to reach the greatest audience. Therefore, they will value finding out the main determinants of their research impact.

After explaining the methodology in Section 2 , results are presented in Section 3 , and finally, discussion and conclusions are summarized in Section 4 and Section 5 , respectively.

2.1. Search Strategy

A systematic search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was carried out [ 25 ]. We used three databases for guaranteeing greater coverage [ 26 ]: WoS (Web of Science) core collection, Scopus, and Medline, for its relevance at the medical level. The analysis included research articles [ 27 ] published until 2019, written in English and gathered through the next search engine:

Figure 1 shows the process carried out for reaching the final sample analyzed, 212 articles.

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2.2. Variables Examined

2.2.1. dependent variable.

The impact of the publications can be measured with different indicators, typically separated into traditional or alternatives. Previous research claims they have a different nature, related to academic or social parameters [ 12 ]. While traditional metrics assess the impact of a publication based on citations, altmetrics are based on social networks.

Among the traditional metrics, we choose the citations (citations per year to diminish the influence of tenure) and FWCI in the study, as they are commonly accepted in academia [ 14 ].

Mendeley and the databases consulted provide much data used as alternative metrics for assessing impact to capture the research’s societal impact. In this sense, we had information on paper usage regarding abstract views, full-text views, link-outs, or times downloaded, besides mentions on Twitter or readers on Mendeley [ 28 ].

In the analysis, we included Mendeley readers and the abstract views. Most of the papers had information related to those variables. On the other hand, only 46 papers of the sample showed interaction on Twitter, causing us to choose another alternative social evaluation tool such as Mendeley. Moreover, previous studies claim that an estimated one-third of the scientific community comprises readers [ 16 ]. Therefore, the selected variables can gather the scope that research can obtain.

In Table 1 , descriptive data of those dependent variables are detailed. As can be seen, except for abstract views, the information is available for all the sample papers. Abstract views also have the highest mean and greater dispersion.

Descriptive data and correlation matrix of the numeric variables.

Correlation significant at the level of * 0.05, ** 0.01, and *** 0.001 (bilateral).

2.2.2. Independent Variables

The independent variables were the determinants that may influence the research impact. These determinants are varied. Research has pointed out that bibliometric information can make the paper more remarkable, easy to find, or more visible for an interested audience [ 29 ]. Besides, the content addressed by the research, including their objectives, themes, or the methodologies applied, can also serve as an attractor for gaining readers [ 30 ].

Among the bibliometric predictors, previous literature identifies the authors and their bibliometric characteristics as variables influencing the publication impact [ 12 , 31 ], e.g., recognized authors or top authors with many papers and citations typically gather more attention [ 32 ], as well as authors from leading universities [ 33 ].

We considered several items regarding authorship, such as the number of authors of the paper (N_authors), the type of authorship (academic/professional/both), and the first author’s information, as a proxy of authors’ impact [ 34 ]. In this sense, we included the URC (university of affiliation research score) of the first author according to the Times Higher Education ranking [ 35 ], first author citations the year before the publication of the paper for capturing its notoriety, and the total number of documents he/she authored (first author citations, first author N_papers).

We also included bibliometric variables regarding the source of the publications [ 36 ]. It is expected that well-known journals positively contribute to the impact of the research. This means considering the impact factor, the number of fields or categories indexed, or its quartile for addressing journal influence on a paper’s impact. The quartile is used to evaluate the relative importance of a journal within the total number of journals in its category. Therefore, if we divide a list of journals ordered from highest to lowest impact index into four equal parts, each of these parts will be a quartile. We chose Scopus metrics as most of the papers were indexed in this database (SJR, Q, and N_fields). Previous research found a concordance between JCR and SJR metrics in percentiles and ranks [ 37 ]; therefore, any could be considered.

Regarding information extracted from the paper itself [ 31 ], we added, on the one hand, bibliometric variables such as tenure, references, and keywords (paper tenure, N_references, and N_keywords) [ 12 , 30 ]. Previous research found that bibliometric characteristics affect the impact of research in terms of citations; its effect on social impact should be analyzed as well.

Among the content determinants of the research impact, we included the paper’s objectives, its main themes, and the unit of analysis contemplated in the research. This means, in our specific case, the health department where Six Sigma was applied. In this sense, based on a recent review [ 5 ], the main objectives of using Six Sigma in healthcare are reducing cost, time, waste, and errors (OBJ). Besides, the main themes of the publications were determined through their keywords. To do so, the 10 most recurrent keywords were identified (K_). Finally, Six Sigma has been applied to a great range of hospital departments, including cardiology, laboratory, management, medication & pharmacy, nursing, obstetric, pediatric, radiology, rehab, surgery and anesthesiology, traumatology, and UCI and Emergency.

Variables included in the model are summarized in Figure 2 .

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Graphic model of variables analyzed.

Statistical data analysis was conducted using RStudio (version 1.4.1103) (RStudio, Inc., Boston, MA, USA) [ 38 ]. Firstly, we developed descriptive statistics on the numeric variables and the correlation matrix. The results are shown in Table 1 . It shows that the dependent variables were sometimes significantly correlated, but we considered separated regression models for each one. Between the independent variables of the models, the significant correlations only reached moderate levels, confirming that there were no multicollinearity problems.

The results of the Shapiro–Wilk normality test revealed that dependent variables did not follow a normal distribution. Moreover, there was a preponderance of zeros and highly dispersed data, with variances much higher than the means of the dependent variables, as Table 1 shows. Therefore, we used zero-inflated negative binomial regressions for the model estimation.

Table 2 shows the estimation models for the traditional impact metrics (Models 1 refers to citations per year as the dependent variable, and Models 2 has FWCI as the dependent variable). Table 3 shows the estimation models for altmetrics (Model 3 refers to Mendeley readers and Model 4 to abstract views as dependent variables, respectively). Each of the four models was split in two, labeled as submodel a (including the bibliometric determinants) and b (the paper’s content variables).

Zero-inflated negative binomial regression model for traditional metrics of research impact.

Significant at the level of + 0.10, * 0.05, ** 0.01, and *** 0.001 (bilateral). Note: EST: estimate; SE: standard error. 1st author: first author information.

Zero-inflated negative binomial regression model for alternative metrics of research impact.

Overall, all models had excellent goodness of fit ( p < 0.001) considering the likelihood-ratio tests and Wald tests. It is noteworthy that the AIC (Akaike information criterion) is always lower for estimation models based on the publications’ content than for the models based on the bibliometric indicators. This means that the content models exhibit a better fit, and therefore, the content indicators better explain the impact of the publications, with independence for how this impact is measured.

3.1. Traditional Metrics Models

Focusing on the bibliometric models’ results (models 1a, 2a), they show a significant and negative influence of the lower quartiles (Q3 and Q4) on all impact metrics, especially on traditional metrics. On the other hand, we found that publishing in Q2 is not significantly worse than publishing in Q1.

Particularly for citations, even if they are corrected using the citations per year, tenure appears significant and positive (β = 0.038, p < 0.05), as well as the number of papers authored by the first author (β = 0.030, p < 0.001). Old articles written by a productive principal author achieve greater success in terms of citations. Besides, the number of references seems to affect both traditional metrics (β = 0.014, p < 0.001 (citations), β = 0.007, p < 0.1 (FCWI)), which means that the use of more references impacts positively on citations.

Moreover, when we consider the impact of content indicators on traditional metrics, it is noteworthy that some keywords positively affect citations and FWCI. We can conclude from the content models that papers using healthcare, process improvement, and quality improvement as keywords have a significantly higher impact in terms of citations and FWCI. In particular, articles addressing process improvement are highly cited (β = 1.384, p < 0.01 (citations), β = 1.827, p < 0.001 (FCWI)). Additionally, some objectives have more interest for academics considering the traditional metrics. Papers pursuing time and waste reduction are more significantly cited and have higher FWCI according to our results (β = 0.917, p < 0.001 and β = 1.478, p < 0.001 (citations), β = 0.743, p < 0.05 and β = 1.312, p < 0.05 (FCWI)).

We could not find any significance regarding the influence of the health departments on FWCI. However, the results show a slightly significant and negative relationship between nursing (β = −1.146, p < 0.1) and pediatrics (β = −2.902, p < 0.05) compared to cardiology, which was the default department category in the models, concerning the citations.

3.2. Altmetrics Models

In the estimation models for the altmetric scores, we found some conflicting results compared with traditional metrics, and less consistency between the effects of the variables analyzed in both dependent variables (Mendeley readers and abstract views). Despite the explanatory capacity of the models, there is much information included in the intercept. This did not occur in the traditional models, giving us insights into the variety of variables that could affect altmetrics.

Models 3a and 4a on bibliometric indicators show the same significant and negative influence of the lower quartiles (Q3 and Q4) also found in traditional metrics. Moreover, tenure plays a negative role in Mendeley readers and abstract views (β = −0.074, p < 0.001 and β = −0.072, p < 0.05, respectively). Contrary to what occurred with citations and FWCI, new papers achieve more social impact. This result is explained as the most recent articles are usually more promoted on social networks or appear in the researchers’ alerts when new works related to their topic are published.

In those models also, the number of keywords was a significant and positive variable. Using more keywords positively affects both metrics (β = 0.079, p < 0.01 (Mendeley readers), β = 0.139, p < 0.01 (abstract views)). On the contrary, papers included in more categories, i.e., less focused on a specific research area, experienced a negative relationship with altmetric scores (β = −0.17, p < 0.01 (Mendeley Readers), β = −0.265, p < 0.05 (abstract views)). Finally, there are two variables with significant impact on abstract views: the number of authors, with a positive influence (β = 0.130, p < 0.01), and the SJR that appears to have a negative impact (β = −0.879, p < 0.01). This last finding is quite unexpected and is evidence that the scholars find and consider abstracts interesting for themselves, even if they do not belong to high-ranked journals. However, these results are only proven on a specific altmetric score, so more empirical evidence would be necessary to obtain robust conclusions.

The content models, model 3b and 4b results, showed that the papers addressing time reduction are the most relevant for causing researchers to view the papers’ abstract (β = 1.336, p < 0.01), while for Mendeley readers, only error reduction is an objective without impact; Mendeley readers are significantly interested in the rest of the identified objectives of this research.

There are several keywords with a significant and positive influence on content scores. The most influential are process improvement (β = 1.405, p < 0.01 for readers and β = 3.349, p < 0.001 for abstract views), quality improvement (β = 0.853, p < 0.001 for readers and β = 1.684, p < 0.001 for abstract views), and quality management (β = 2.990, p < 0.001 for abstract views). These findings agree with those obtained for traditional metrics where process and quality improvement were also the most influential keywords. Additionally, keywords such as Lean Six Sigma or healthcare also exerted a significant and positive influence on bibliometrics, while Six Sigma or just Lean yielded a negative impact on some of the altmetrics considered.

Finally, our results point out that the department investigated in the publications does not seem to explain their impacts in terms of readers. On the contrary, it is more important to explain the abstract views, especially in publications on rehab. Some others were also significant but negative in comparison with cardiology, which is the default category department. This is the case for obstetric, trauma, or management departments.

4. Discussion

Research impact can be addressed using different metrics and can also be affected by various determinants. Although some previous research found a positive correlation between traditional metrics and altmetrics [ 20 , 39 ], others claim they are weakly correlated [ 24 ]. Our study has also constated that the items influencing both metrics are different [ 29 ]. However, in both cases, the content better explains the impact of the publications rather than bibliometrics.

We have concluded that the determinants affecting traditional metrics are clearer and more specific, so researchers know better what is important to consider when they address improving the impact of their publications in terms of traditional scores based on citations. In this sense, papers published in lower-ranked journals (journals in the Q3 and Q4 of their database) show lower values in traditional metrics. On the contrary, including more references positively affects traditional metrics. Citing other works is a way of gaining visibility. Authors usually receive alerts when they are mentioned, and they may be tempted to read or share the study where it is cited.

The number of previous papers of the first authors seems to be only important when we look at the citations. This finding has a logical explanation. First of all, citations do not exclude self-citations, so authors with previous papers can increase their citations, including their own works. Moreover, if an author is working in the field or has some research experience, his/her work can be better known, compared to novel authors. This fact does not occur in the other traditional metric considered. FWCI compares the actual number of citations received by a document with the expected number of citations based on some aspects. So, the power or influence of an experienced first author may have less weight.

Altmetrics, on the contrary, more based on the societal impact of research, can be influenced by varied determinants beyond bibliometric indicators, especially indicators related to the content of the publications. A paper may have more readers or views because of other reasons, that countenance moving beyond the traditional standards of academia [ 40 ]. Baek et al. [ 41 ] analyzed the top-cited articles versus top altmetric articles in a particular field, finding no overlaps between the two samples. This confirms that traditional and academic metrics do not always go in the same direction [ 40 ].

Previous research agreed that journals’ impact is one of the most relevant determinants of citations and altmetrics [ 29 ]. In this study, we emphasize the importance of the quartile where the journal is positioned more than its impact factor. As a matter of fact, our results point out that abstract views can be higher in the case of journals not well ranked, perhaps because these journals make more effort to attract a bigger audience, knowing that visualization is the first step to attract future readers and higher impact. Moreover, other article characteristics such as authorship or the number of references increase the article’s impact, especially in traditional metrics [ 29 , 31 ]. Considering the bibliometric determinants of impact, it is also noteworthy to highlight the different effects of tenure. We agree with previous research that altmetrics can provide more real-time information [ 13 ], as the effect of tenure works contrarily than for traditional metrics. Similar to Araújo et al. [ 40 ], our results also confirm that recent publications receive more attention with altmetrics, and older, seminal works benefit from using more conventional metrics for measuring research impact.

As aforementioned, we found that the content models better explain the data. This means that papers analyzing Six Sigma in the health field are more cited or have higher social visibility for what they investigate (their main objectives, themes, and the units of analysis) rather than for their bibliometric characteristics. Those results align with other previous research that pointed out the research topic as the key to success [ 42 ]. According to our results, the objective of the article is essential for achieving academic and societal impact. In this regard, time and waste reduction are the main goals of highly cited papers in the studied field. Moreover, the more relevant themes were common for traditional and altmetric scores, which were related to process and quality improvement and quality management. The relevance of the objective analyzed and the theme addressed by the publication was higher than the impact of the unit of analysis or the department considered in the research.

Table 4 summarizes the main effects found, highlighting the importance of the content of the paper in all metrics. In red are the determinants that negatively affect the impact of the publication; in green, the positive.

Most important research impact determinants.

Beta higher than 1 in bold. Green: positive impact. Red negative impact.

5. Conclusions

This research provides additional evidence on the determinants that explain what matters in order to increase the scientific and societal impact of research on Six Sigma applied to health. There is an open debate on the scope and determinants that influence the different metrics that capture the impact of publications. Moreover, besides providing more evidence to support the inconsistent findings of previous research on the topic, we also pointed out the relevance of this kind of study in the health field, because in this case, the time required for traditional metrics of impact may overlook important scientific advances with societal and clinical impacts, which justifies the relevance of alternative metrics in this field and the need to understand their determinants better [ 18 ].

As a conclusion, our results confirm that it is more complicated to apprehend the varied factors that explain the societal impact of academic research. Contrary to traditional metrics, which are more stable, influenced by time, and solidly based on bibliometric and content determinants, altmetrics can also be affected by other factors. They are not as well known, given the novelty of social media and academic networks to divulge research, and also given the varied methods and tools that journals and scholars themselves can use to promote and obtain higher visibility.

Despite these difficulties, there are some determinants in the bottom-line of impact, independently of how it is measured, which allow us to reach conclusions on the relevance of the paper’s content rather than their bibliometric characteristics.

In the specific case of research on Six Sigma applied to health, process, and quality improvement, the themes and objectives that assure the highest impact, for both traditional and alternative metrics, are addressed mainly by time and waste reduction, independently of the department or unit of analysis used.

However, even if this kind of study provides additional insights into what matters to enhance the academic and societal impact of research, it is necessary to mention the limitations in obtaining these alternative metrics. Data quality problems are usual and constitute a relevant issue in the field, inviting us to consider the findings with caution and suggesting the need to make additional efforts in the future to overcome this limitation.

Although it seems complicated that citations and the journal impact factor stop being crucial, currently the use of social networks and support software, like Mendeley, are enhancing the relevance and weight of altmetrics in academic research.

Despite altmetrics not representing an alternative to the traditional methods to measure research output impact, some organizations such as DORA (Declaration on Research Assessment) [ 43 ] are pressing to improve how scientific research is evaluated by funding agencies, academic institutions, and other parties, trying to go beyond traditional indicators such as the impact factor of the journal.

In this sense, Scopus has a tool known as PlumX Metrics [ 44 ] that gathers people’s footprints when interacting with research and categorizes them into five categories—Usage, Captures, Mentions, Social Media, and Citations. It would be interesting for all parties involved in research to establish an overall indicator giving different weights to those categories to find a representative parameter agglutinating all the impact indicators. Thus, both metrics, academic and societal, could contribute to assessing the impact of a publication.

Author Contributions

All the authors designed the research. A.N. and M.-V.S.-R. collected the data. A.N. and A.-B.H.-L. performed the analysis of data. Finally, the paper was written by A.N., A.-B.H.-L. and M.-V.S.-R. All the authors have read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Production & Manufacturing Research

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The implementation of a Lean Six Sigma framework to enhance operational performance in an MRO facility


Lean six sigma – a literature review, methodology, case study – amro ltd, implementation of the lssf, conclusions, disclosure statement.

Lean Six Sigma (LSS) has rapidly established itself as the key business process improvement strategy of choice for many companies. The LSS approach provides significant benefits to companies through its dual focus on reducing waste and increasing value whilst resolving Critical to Quality (CTQ) issues that affect consistency and repeatability in a product and process. The implementation of LSS is finding wider application in many different environments. Through a case study approach, this paper describes the novel implementation of an integrated LSS framework and outlines how it was used to identify the factors that affect supply chain performance in an aerospace Maintenance Repair and Overhaul (MRO) facility. The study outlines the application and measures the effectiveness of the integrated LSS framework through its ability to achieve new and enhanced performance through simultaneously reducing late material calls and reducing and stabilizing Order To Receipt (OTR) times.

Both Lean and Six Sigma have gained acceptance as industry recognised business improvement methods and their popularity has grown significantly (Nonthaleerak & Hendry, Citation 2006 ; Schroeder et al.,   Citation 2008 ). The Six Sigma approach is aimed at achieving sustained customer satisfaction through its continual focus on customer needs (Seth & Rastogi, Citation 2004 ). By placing emphasis on customer requirements and, on the issues that affect customer satisfaction, Six Sigma eliminates potential performance issues before they occur by focusing on process variables that are Critical to Quality (CTQ), (Snee, Citation 2004 ). The Define, Measure, Analyse, Improve and Control (DMAIC) cycle inherent within Six Sigma describes the basic logic of a data-centric process improvement approach (Gijo, Citation 2011 ; Harry & Schroeder, Citation 2006 ; Pande, Neuman, & Cavanagh, Citation 2014 ). In theory, completion of each DMAIC cycle will realise project goals, improve performance, and sustain quality (Gijo, Scaria, & Antony, Citation 2010 ).

Lean on the other hand is a value focused, waste reduction strategy, which aims to eliminate non-value-added activities and other forms of waste from a process (Bhasin & Burcher, Citation 2006 ; El-Kateb, Citation 2015 ). With a specific focus on manufacturing, Lean can be defined as ‘A systematic approach to identify and eliminate waste through continuous product improvement to achieve customer needs’ (Bhuiyan & Baghel, Citation 2005 ).

Womack & Jones ( Citation 1996 ), outline that the critical starting point for Lean process implementation is understanding and specifying Value as specified by the customer. Early attempts to reconcile the both strategies in to a unified Lean Six Sigma model was proposed by George ( Citation 2002 ) in which was highlighted an operational framework for implementing both approaches in order to achieve parallel benefits from both.

Lean Six Sigma (LSS) aims to drive business process improvements through adopting the key features of both Lean and Six Sigma and combining these features in to a single approach towards business performance enhancement (Corbett, Citation 2011 ; Thomas, Francis, Fisher, & Byard, Citation 2015b ). In so doing, companies focus on adding value and then systematically reducing and removing waste (the lean element of the approach) whilst employing Six Sigma to focus on and eradicate the Critical to Quality (CTQ) issues that affect an organization (Drohomeretski, da Costa, de Lima, & de Rosa Garbuio, Citation 2014 ). In applying this combined approach, LSS aims to achieve fast flexible flow of goods and services whilst systematically eradicating any issues that could adversely affect the quality of that product or service. LSS employs the traditional Six Sigma DMAIC cycle where, Lean tools can be integrated in to the phases to produce a range of benefits for the customer. Utilising the correct tools for the specific area of need is critical to yielding the improvements desired (Thomas, Barton, & John, Citation 2008 ). A further detailed discussion on Lean Six Sigma is provided in the next section.

LSS is gaining wider acceptance as an improvement strategy of choice in a range of industries and sectors. Traditional models and applications of LSS have focussed upon its implementation in manufacturing and production improvement environments. However, LSS is being increasingly applied with great success in healthcare (Laureani, Brady, & Antony, Citation 2013 ), construction (Van den Bos, Kemper, & de Waal, Citation 2014 ) and, education (Thomas et al., Citation 2015b ). Focussing upon LSS and its application in manufacturing and production environments, most LSS implementation projects have focussed on the systematic and rigorous application of the Six Sigma oriented DMAIC approach to characterise processes and specify solutions which are delivered through the effective use of a number of lean and Six Sigma tools such as DOE, VSM, SIPOC and 5S (Albliwi, Antony, & Lim, Citation 2015 ; Chakravorty & Shah, Citation 2012 ; Chen & Lyu, Citation 2009 & Gnanaraj, Devadasan, Murugesh, & Sreenivasa, Citation 2012 , Vinodh, Gautham, & Ramiya, Citation 2011 and Vinodh, Kumar, & Vimal, Citation 2012 ) (See Table 1 ).

Published online:

Table 1. systematic review of lean six sigma applications literature..

Whilst the application of LSS in manufacturing and production environments is popular, its specific application in areas such as Maintenance Overhaul and Repair (MRO) functions and supply chain operations is less well advanced. A strong body of academic knowledge exists on the application of specific Lean implementations in MRO functions (De Jong & Beelearts van Blokland, Citation 2016 , Mathaisel, Citation 2005 ; Kumar, Sharma, & Agarwal, Citation 2015 and, Ayeni, Ball, & Baines, Citation 2016 ). Likewise, the application of Six Sigma in MRO facilities has also gained significant attention especially through Jack Welch’s work in driving forward Six Sigma as the key business improvement strategy whist at General Electric (Deshmukh & Chavan, Citation 2012 ).

The authors undertook an extensive review of academic literature focussing specifically upon the implementation and application of Lean Six Sigma in companies. The search of academic databases returned over 200 journal articles. These articles were subsequently reviewed and a number of key texts specifically relating to Lean Six Sigma implementation were identified for further analysis. Table 1 provides a literature analysis of key academic case studies focussing upon applications of LSS in a wide range of industries. The analysis identifies a number of key issues. Firstly, the number of specific applications of LSS applied to MRO operations cited in academic journals in very low. Thomas, Mason-Jones, Davies, and John ( Citation 2015a ) applies Monte Carlo analysis of the failure of aircraft Display Units (DU) through the adoption of a standard Six Sigma methodology and provides a predictive cost model for DU replacements and suggests through the model how different maintenance strategies may be employed at different points in the life of the DUs. Hwang ( Citation 2006 ) focussed upon the implementation of six sigma projects in aerospace companies and identified amongst other issues that human error and inadequate data were major causes of six sigma project failures in aerospace applications. Mostafa, Lee, Dumrak, Chileshe, and Soltan ( Citation 2015 ) develop a theoretical framework around integrating Lean thinking in to maintenance systems whilst Price ( Citation 2010 ) implemented a combined Lean/TQM methodology in to aircraft operations in order to manage human errors which affect quality and safety leading to improvement in business performance. Karunakaran ( Citation 2016 ) is one of few researchers that applies the standard LSS approach towards reducing aircraft maintenance cycle times through simulating the new ‘future state’ and, provides improvements in performance through the implementation of the LSS approach.

Secondly, the LSS methodology is primarily driven through the application of the five stage six sigma DMAIC approach. Table 1 highlights that the primary focus of the literature being analysed shows a quality improvement perspective with few articles focussing on the simultaneous application of Lean. The resulting focus of most LSS projects therefore being quality orientated with most LSS tools and techniques employed being Six Sigma oriented. Thomas et al. ( Citation 2015b ) identified that this natural pull to employing primarily a six sigma focus limits LSS teams towards quality based projects at the detriment of driving simultaneously Lean or a combination of Lean and Quality oriented projects. It seems therefore that MRO companies still remain fixated on the application of single paradigm approaches of Lean or Six Sigma.

How effective is the application of an Integrated Lean Six Sigma Framework in an Aerospace MRO facility and, to what extent has the company’s operational performance improved as a result of its implementation?

Introducing the integrated Lean Six Sigma framework (LSSF)

In the development of an integrated LSS framework, the focus is on ensuring the simultaneous development of both the Lean and Six Sigma phases with the aim that the company simultaneously tackles both the ‘waste reduction’ element and, the Critical to Quality element of the business. George ( Citation 2002 ) suggests that the connectivity between Six Sigma and Lean is linear in its approach with Six Sigma being applied first with Lean being applied once the process variation has been reduced and secures through the Six Sigma phase. However, the work of Shah, Chandrasekaran, and Linderman ( Citation 2008 ) offers a different perspective and suggests that the success of Six Sigma projects are greatly increased if Lean principles and tools are included in the implementation of Six Sigma. Hines, Holweg, and Rich ( Citation 2004 ) clearly identify the role of Six Sigma as one of feeding in to and supporting the higher level strategic Lean implementation process. This would therefore suggest that Six Sigma forms part of a sub-set of operational strategies that fit in to the higher order lean thinking process.

The construction of an initial conceptual LSS Framework was developed from a systematic approach to the review of existing LSS Frameworks and implementation methods employed from a wide range of academic journals. Table 1 shows the research resources used to establish an operational framework for implementation. Since Lean and Six Sigma theoretical models have existed for some considerable time, the journal sources for this paper were case study papers where the authors were also able to study the implementation mechanisms of the various frameworks described in the texts as well as the respective structure and design of the LSS programmes identified.

A conceptual LSS framework was initially developed following which the author team then undertook a series of iterative developments in an attempt to improve its effectiveness and suitability to MRO implementation. Table 2 shows the final matrix including the various elements for each stage in the LSS programme. Adjustments to the framework included redesigning the framework to change the points at which the various tools are used as well as adding a Stage (0) in which preparing the company for undertaking LSS implementation was undertaken. Figure 1 shows the generic form of the LSS Framework (LSSF) that was adopted in this study. It shows how each of the Six Sigma DMAIC phases are applied systematically to each of the Lean stages. With reference to the Matrix shown in Table 2 , it can be seen that the output of the Define phase of Lean Stage 1 for instance (specify value) feeds in to the input of the Measure stage within Lean Stage 1. This continues with the output of the measure phase feeding in to the Analyse phase and so on thus forming a series of linked activities within Lean Stage 1. Finally, at the end of that stage, the output of the Control stage then feeds in to the Define phase of the next Lean stage. For example, the activities of the Control stage on Lean Stage 1 (LSS team develop action plan to resolve CTQs and meet customer value requirements) is then used as the feed in to Lean Stage 2 where the CTQ plan is enacted. In this instance, the output of the Control stage provided the team with a plan to identify and tackle the ‘missed inspections’ within the company. Likewise, the input in to the Create perfection stage is in the defining of new levels of performance (i.e. reducing the OTR to less than 59 days). The output of this Lean stage is that through continuous improvement of processes and systems that the 59 day target is achieved and systems and processes are then locked to the new standard practices. Therefore, the DMAIC phases flow systematically through the Lean stages. The final output from the Control phase in Lean Stage 5 feeds back to the start of the project for process to cycle through again where a new CTQ issue is defined.

Table 2. Completed LSSF Matrix indicating the key tools and techniques.

Figure 1. The generic Lean Six Sigma Framework.

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The LSSF attempts to provide a more balanced approach to the simultaneous application of both Lean and Six Sigma in that the DMAIC cycle is implemented at each stage in the Lean thinking cycle. The LSS Framework is shown in matrix form in Table 2 and, identifies the key stages of the LSS Framework paying particular attention to the location and application of specific tools at each stage. The authors suggest that the application of these tools are likely to be different for each project type as well as their location within the matrix is likely to change due to the uniqueness of each project. Further development and application of the LSSF is shown late in the paper.

The next section highlights the work undertaken to develop the conceptual LSSF in to an operational framework and introduces the reader to the company and the improvement operations conducted in this project.

The LSSF and its pre-implementation phase

Stage (0) of the LSSF was the starting point of the implementation stage and consisted of a series of awareness raising sessions in which the implementation process was outlined and where all staff were given the opportunity to contribute to the implementation process and, to jointly discuss the direction of travel and, most significantly, to prepare themselves for LSS implementation (Kumar, Antony, & Tiwari, Citation 2011 ; Mostafa, Dumrak, & Soltan, Citation 2013 ; Spina et al., Citation 1996 ). The core values of people, education and defining a vision for improvement were identified as key variables in developing high performing LSS projects (Shokri, Waring, & Nabhani, Citation 2016 ).

Further and more focussed training sessions were introduced for staff in order to develop expertise in LSS implementation. Also, the project team delivered practitioner level training to academic staff who would need to carry out much of the developmental tasks. Most importantly, company management were given awareness sessions and an end of Stage (0) meeting clarified the roles and responsibilities of the staff and the outlined the timescales and project plans for the implementation of the LSSF. Early stage work in identifying the typical tools and techniques to be employed in the project was also undertaken at this point. The project team therefore mapped the tools and methods required for each stage of the LSS cycle. The key issue here was to minimise the over-use of tools and to focus upon a core set of key toolkit for implementation. The next section of this paper briefly introduces the company before continuing to detail the remaining stages of the LSSF

The company in which this case study was developed is an aerospace engine Maintenance Repair and Overhaul (MRO) facility. Its identity has been protected on request of the company and offered a pseudonym AMRO Ltd. The facility has a global supply chain network that consists of over sixty separate vendors. Within one specific engine specification, seventeen repair facilities and twelve different supplier accounts are initiated in order to support engine rebuild requirements. The MRO facility has seen considerable disruption to its operational build cycle due poor internal operations leading to late requests for components from the supply chain to meet current engine build requirements. Furthermore, there is a high proportion of non-value-added activity carried out at the facility to rectify late component requests caught within the MRO cycle. Any process errors that disrupt the operational stability of both AMRO’s shop floor and the supply chain network has a negative effect on its Order to Receipt (OTR) times. Footnote 1 The exact cause of late component requests has traditionally been put down to the problems associated with the company’s product mix where one engine type is slowly being phased out of operation (JET A) and hence shows a reducing service volume whilst another engine type is rapidly increasing by way of service volume demand (JET B). As a result, the AMRO workforce are undergoing a rapid reconfiguration of its supply chain and operational systems due to the rapid decline of the JET A engine service volume and, the rapid increase of JET B engine service volume. Forecasted shop volume figures to 2017 are shown in Figure 2 . Apart from reorganizing the supply chain, vendors and internal operations, the company has needed to embark on a programme to cross-train their engineering workforce to ensure staff are able to service JET B engines whilst also ensuring the JET A legacy engines are also catered for. This has created a shared labour pool where JET A maintenance technicians are now considered to be able to work on both JET A and JET B engine types competently.

Figure 2. JET A and JET B service volumes (2014–2017).

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With a focus upon the expanding JET B servicing volume, the company saw that the strategic challenge for them was to try and streamline the JET B service facility whilst ensuring that the quality of their operations were maintained at the highest standards.

The starting point for the study in to the JET B service facility was to undertake a detailed analysis of the operations in this facility. Figure 3 shows the value stream analysis undertaken for the JET B process. Each process in the facility was mapped and timed in order to characterise the overall process and their connectivity to each other. Figure 3 shows the current and potential future states relating to the Non-Value-Added (NVA) activities that were identified in this analysis.

Figure 3. Non-value-added analysis of Jet B facility – current and future states.

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The analysis showed that the area in which the greatest level of NVA was in the post-inspection phases of the work where the components were required for rebuild. Large delays were seen in the areas identified by the red circle. Further investigation of this area suggested that all NVA could be eliminated as a result of improved operational improvement. Further investigation of this area highlighted that the facility suffered from components arriving late at the various sub-assembly points in the engine build system. The company’s focus was on how to increase Jet B engine throughput (given the increased volume requirements and importance of the engine type to future company operations). Systematically reducing the NVA at the points identified in Figure 3 was seen as critical to meeting tis target.

Identifying the problem

Each engine within the JET B cell is repaired (and or) overhauled by being pulled through a single flow operational ‘Four Stage’ system. The stage system consists of four different operational build stages that each have different requirements and targets that must be satisfied before progressing to the next. The stages are shown in Table 3 .

Table 3. Operational build stage system.

The target time to process a JET B engine through all stages is 62-days. As part of the strategic materials and planning initiative, a ‘Phase Review’ (PR) process is employed across the JET B engine cell. The impact of ensuring all operations are completed by their stage closure point is critical. The critical stage for ensuring OTR is Stage 1. If components and parts are requested after Stage 1 closure, they are classed as ‘late calls’ and, depending upon how quickly the supply chain can respond to this late call for components, it will place a risk on achieving the target OTR. Late calls impact hugely on the external supply chain and internal engineering operations and this ultimately leads to engine build delays. It is therefore critical that all components within the engine are accurately inspected and tested and that all requests for new components from the supply chain are requested by the end of Stage 1.

The analysis of the NVA undertaken and shown in Figure 3 suggests that the highest areas of NVA are seen just before rebuilding of the modules and engine. This matches closely to the Stage 1 closure point (and Stage 2 start) further indicating an issue which could be associated with poor control at the Stage 1 closure point.

Table 2 shows the specific detail of each of the stages in the LSSF as applied to the company and the tools and activities used at each stage. Due to extensive nature of the application of this LSS Framework, only the key stages of the framework are covered in this paper. Therefore, the reader is directed to Table 2 to see the full sequence of operations undertaken through this LSSF.

LSS Phase 1 – specify value by defining the CTQ issue

A workshop was held whereby managers, engineers as well as a number of key customer facing personnel from within the company focused upon the identification of the Critical to Quality (CTQ) issues that the company were experiencing. Systematic evaluation of each CTQ as well as determining the severity of each enabled the company to identify that the 62 day OTR was not being met and this was down primarily to variations being seen around Stage 1 material ordering. In order to minimise (with the longer aim to eliminate) late component requests arising after Stage 1, the project team needed to ensure that no late calls were to be received after 10 days from (i.e. Stage 1 closure point – Table 3 ). This depended also on ensuring that the work undertaken on Stage 0 was correctly carried out and all documentation was correctly processed and inputted during that stage which had a maximum time envelope of 3 days.

Stage 1 activities involve the inspection of all engine components with a view to identify whether the components have: (a) residual service life left and hence can be put back in to the engine following cleaning and minor maintenance or, (b) the component has no residual life left rendering it scrap or, (c) has no residual like left but can be repaired or serviced for return to the engine in the future. It is here that the engine technician must decide which category each engine part falls in to and this then allows the supply chain team to procure components and/or services to support engine build. In order to start to identify key areas for further investigation by the LSS team, a Cause and Effect session was held by a multi-disciplinary team of engineers, supply chain and operations managers. Figure 4 shows the C + E diagram.

Figure 4. Cause & effect diagram.

Category 1 – Part is inspected and considered serviceable when indeed it is not serviceable (known as a missed inspection). Build technicians find the problem in Stages 2 and 3 of build process. This directly impacts on the OTR measure as the component requests needed at Stage 1 are now being requested at Stage 2 or 3.

Category 2 – Part is inspected and considered unserviceable when indeed it is was serviceable. This does directly impact on OTR but can lead to good parts being scrapped or unnecessary servicing and repairs being undertaken.

Category 3 – Part is inspected and considered serviceable when it is was actually serviceable. No issue

Category 4 – Part is inspected and considered unserviceable when it is was actually unserviceable. No issue

Action plans were developed to focus in upon this problem statement and the LSS team was expanded to include a range of facilitators and workers to effect change later in the process.

LSS Phase 2 – align the internal operations through measuring the extent of the problem

The LSS team focussed on aligning the internal Value Stream with an attempt to identify the full impact of Stage 1 late calls. In this situation the LSS team employed a series of observational activities and VSM exercises aimed to provide a detailed understanding of the different factors involved in the MRO production cycle. Tables 4 and 5 (alongside Figure 3 ) current and future Value Stream Analysis with its associated VA and NVA activities and an OTR time of 81 days. The future state analysis shows the potential to reduce OTR to under 62 days by focussing upon eliminating NVA and focussing upon eliminating the variability of activity at Stage 1 (i.e. variability of late calls at Stage 1).

Table 4. Current state value stream analysis.

Table 5. future state value stream analysis..

The LSS team also collected primary data over the previous 12 months MRO activity at the facility and this represented 86 engines. Data analysis identified that out of a total of 3733 component reservations made, 867 (23%) of components were considered ‘late calls’. Further investigation of the 867 late call components identified that 83% of these could be attributed to just five specific engine modules. Focusing specifically on the engine modules, a Pareto study was undertaken to identify whether any specific engine module was causing the late call issue or, whether the problems lay across all five modules fairly evenly. The Pareto Analysis shown in Figure 5 identifies that the M20 module provides the greatest cause for concern accounting for 492 late calls from a total of 867 late calls.

Figure 5. Pareto diagram for late calls (defective items).

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As a result of the VSM study and the Pareto exercise undertaken in this stage of the LSS cycle, the LSS team were able to focus in quickly on the main components causing the issue around late calls and hence built up an evidence trail to go forward to resolving the CTQ issue further in the programme. The data gathering carried out during the Measure phase has offered direction for the project as it progresses into the Analyse stage.

LSS Phase 3 – create flow by identifying constraints in the system

The LSS team focussed upon the issue of creating flow of parts through the engine MRO cycle. The team focussed upon the systematic elimination of the inefficiencies that prevented swift and even flow of parts through the facility. Referring back to the C + E exercise and armed with the data obtained from LSS Phases 1 and 2, a more focussed analysis as to the reasons why the Stage 1 build stage developed so many ‘late call’ triggers from so few module components was undertaken. However, the data within the ‘Measure’ stage did not explain why the late calls occurred. A series of meetings were held with stage team members working in the five critical modules in order to explore the reasons why the late calls existed. Tracking of the data around missed inspections and measuring this against the experience levels of the staff within these inspection areas provided key information as to the root cause of the problems. Table 6 shows the raw data relating to the number of contact hours per month an employee was receiving on JET B and the occurrences of missed inspections.

Table 6. Missed inspection data.

The data shows that there were a total of 867 late calls that were directly caused by missed inspections in Stage 1. Of this, 77% of the missed inspections were driven by members of operational staff with less than 12 months engine type experience. Figure 4 alongside Table 7 show that there is a clear and strong correlation between the amount of engine type experience and number of missed inspections.

Table 7. Correlation matrix between missed inspections and contact hours.

The Correlation coefficients of correlation matrix shown in Table 7 identifies that a strong inverse relationship exists between missed inspections and, the number of contact hours a technician has with the engine thus suggesting that the higher the number of contact hours a technician has with the engine, the fewer missed inspection occurrences there are missed inspection data is which is due to human experience ( Figure 6 ).

Figure 6. Missed inspection data versus no of contact hours.

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The JET B facility has seen a steady increase in operational staff as JET A trained technicians were transferred to the JET B facility in order to fill the capacity issues due to the increased service volumes from JET B. Following a period of engine specific training, the technicians were then fully employed at the JET B facility. Further analysis showed that there were a total of 85 operational staff who have regular contact with the JET B engine. The data shown in Table 6 identified that there were a total of 27 new staff that were introduced to the JET B facility with fewer than 12 months experience. Both JET B and JET A facilities are separate and have their own individual process and, whilst there is an element of similarity between both engine types, new operational staff that had been transferred to the JET B facility needed time to develop their experience of working with this engine. It is likely that the number of inexperienced JET B employees working on the engines are a root cause behind some of the late call activity. This analysis enabled the LSS team to initiate changes to the existing training and supervisory plans. Changes by way of team composition and supervisory arrangements were considered so that the essential CTQ causes based around human error and basic quality issues were eradicated through an effective and fit for purpose training and mentoring programme.

LSS Phase 4 – create flow through process improvement

a series of training and technical updating of the technician workforce,

a change in the composition of the maintenance teams in order to develop a more cohesive and supportive learning environment and,

a change in the supervisory arrangements especially where new and inexperienced staff are present within the maintenance teams.

Changes to the maintenance team composition was undertaken where each individual maintenance team would now have to include 4 experienced staff (more than 24 months engine experience) to every ‘junior’ team member (less than 12 months engine experience). Each team would also include an ‘allocator’ who was an operational staff section team member given the responsibility of overseeing team activities, assisting the junior team members with on-hand training and reporting daily outputs and findings to the operations management team. The allocator was introduced to provide a team member from each section that would provide the link with management, identifying any areas of concern.

Changes undertaken to supervisory activities included refresher training for supervisors in being to coach and mentor their teams as well as providing the standard leadership training. Furthermore, the aim of the changes to the supervisory team was to reinstate the supervisor as the team lead and for the supervisor to be considered as the team expert and not as an extension to the workforce as they were currently being seen. Supervisors adopting the new leadership style were then seen by the team members as their local point of contact where any concerns or issues could be dealt with immediately within the team. This quicker and more direct approach towards local decision-making eliminated the need for team members to make individual decisions on specific component issues.

Alongside the training and supervisory arrangements, the LSS team returned to the Value Stream Analysis (VSA) activity initially developed in Stage 1 with a view of identifying the constraints in the system that needed to be removed in order for pull systems to be employed. Through the detailed and systematic analysis of the VSA, the team were able to identify the constraints that prevented full operational performance to be achieved and employed a series of small improvement projects to systematically remove the constraints at each stage. Secondly, a control board system was set up within the supervisory area in order to map and control the flow of engine parts through the maintenance cells. Daily meetings centered around the control board ( Figure 7 ) and provided a forum at the beginning of each day to identify actions required, issues noted, and hard points such as Stage 1 closure dates to be relayed to the team. Each team member was given the opportunity to freely contribute to the meeting providing ideas as to how any problematic issues could be addressed to prevented from occurring. Furthermore, a colour coding system was utilised to provide quick visual representation of targets met versus not met etc. where; Black meant that the Target had been met, OTR and ‘late call’ targets met in full: Orange: Target date not met but actions taken meant that OTR and ‘late call’ values were improving continuously and, Red: Targets not met processes require significant improvement as matter of priority. Figure 7 shows the control board after four months of operation after LSS implementation. All Red marked parts had been eliminated by this stage.

Figure 7. JET B production control board.

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A further change incorporated in to the Improvement stage was introducing a member of the planning team member into the production meetings. This ensured that a communication link was established between the technicians and the supply chain team thus attempting to provide supply chain support to the maintenance teams and therefore more timely and rapid procurement of parts prior to Stage 1 closure.

Furthermore, the planning team members undertook an analysis of all engine modules and components held in stores and provided a daily stock level feed in to the control plan meeting. This analysis focussed mainly on C-class components (<£3000) and was used to identify the number and type of components held in the buffer stock location. The analysis not only identified 48 different C-class components were used on a weekly basis. These components were then cross referenced with the late call data (identified in the LSS Phase 2) and if required the immediate release of these components to the shop floor was undertaken. This in itself reduced Stage 1 late calls by 20%.

LSS Phase 5 – continuous improvement and control of future processes

The initial stage of this LSS project identified the target for improvement as zero late component calls after Stage 1 closure. The operations management team now conduct weekly feedback sessions in order to maintain focus on operating procedures and quality best practice to ensure that late calls are eliminated from the process. The procurement materials team made the commitment to analyse stockroom materials usage patterns on a monthly basis to address any new spikes in demand relating to late call activity. The procurement, engineering, and operations teams agreed to meet on a bi-weekly basis to discuss any logged late call activity and address potential root causes. The control process is still seen as work in progress and continues to be refined and improved going forward. Standard Operating Procedures combined with new supervisory and QA arrangements are enacted alongside the control board and as a result, late calls at Stage 1 are reducing significantly on a monthly basis.

What became apparent at this stage was the need for strong and effective leadership to be initiated and adopted by the planning and maintenance management teams. Closer collaboration between departments was needed and, the control of the system processes needed to be enacted and maintained by close monitoring and action by the management teams. Therefore, the control board was an essential management tool for driving and subsequently managing change. Any out of control conditions were acted upon on a daily basis through LSS blitz teams who undertook rapid analysis and action to resolve any issues that arose from the daily meetings. These teams were targeted with resolving any out of control conditions within the working day so that system flow could be maintained with minimum disruption.

Actions to eliminate the CTQ issues are now in place and the company is rapidly moving towards its 62 Day OTR. Figure 8 shows the performance of the company against its OTR target of 62 days and, the number of ‘late calls’ following Stage 1 closure for the six months after the implementation of the ‘Improve’ phase. The figure shows the impact of the measures introduced in to the system and how through continuous incremental improvement, the targets will be met.

Figure 8. OTR and late call improvement trends.

A drop of 53% in ‘late calls’ was seen within the first month following LSS implementation with late calls dropping from 29 per month to just over 13 per month on average. More recent results have seen more modest improvements as the system settles but, six months in to the improvement cycle has seen a drop of over 73% in late calls at Stage 1 (equating to 7 late calls per month on average). Variability around ‘late calls’ has improved with a reduction in the Std Dev from 2.64 in month one to just over 0.5 in month six which suggests that that a simultaneous improvement in both measurements as a result of LSS implementation.

Similarly, improvements were seen in the ‘Order to Receipt’ targets where the OTR continuously improved from its initial value of 81 days to a value of 65 days. However, whilst the OTR has continuously improved, the variability of the OTR has increased from a Std Dev of 0.96 in month one to 1.71 in month six. Further focus on reducing the variability around this OTR value is being undertaken with the team working on characterising the nature of this variability in order to determine whether action needs to be undertaken to improve variability or, whether variability will drop as the system adjusts to its new OTR value of 65 days.

As a result of this improvement, costs are expected to reduce in the form of direct labour required as well as time and effort spent undergoing non-value-added tasks such as ERP reversal transactions, repeated data loading of engine data, transportation costs within the company in the form of reallocation of modules and hardware, and the total supply chain cost in expediting material driven by late calls that is required immediately (Snee & Hoerl, Citation 2007 ). Through streamlining the MRO supply chain cycle to minimise total late calls, the business is now able to reduce its total labour costs (Bhasin & Burcher, Citation 2006 ). This will come in the form of reducing headcount against the facility and reassigning the labour to a required bottleneck area, such as material incoming inspections. Through the application of a streamlined, value-focused workforce, the business is able to focus on applying value where it is required and minimising waste. It will provide the managerial team with the relevant downtime to innovate existing processes and future ideas. From an operational perspective, sectional areas such as sub-assembly and final assembly will benefit from smoother process flow without interruption (George, Citation 2002 ; Womack & Jones, Citation 1996 ).

This reduction in defect variability will drive a better opportunity for the JET B facility to hit its 62 day OTR requirement for each engine serviced and therefore will improve its ability to deliver the engine to the customer on-time without incurring any contractual penalties.

This paper has described through a case study the application of a novel integrated Lean Six Sigma Framework for MRO operations improvement. The authors believe that this work extends and enhances the limited contribution to the application of LSS in MRO facilities. Through its simultaneous focus on tackling the CTQ issues around missed inspections resulting in ‘late calls’ and missed OTR, the company was able to move towards its Lean target of ensuring a 62 Day OTR was consistently met by focussing also on the variability of each target measure. By integrating the Lean and Six Sigma processes, the company were able to focus quickly on Stage 1 inspection as being the major area for late component calls. Late inspection calls were considered the major cause of the company missing its 62 day OTR. This focus quickly led to the LSS team identifying the human factors that adversely affected the performance of their internal operations. Whilst the company has not achieved its final OTR and Late Call target, the trajectory shows very promising signals that both targets are likely to be met soon provided that consistency of purpose is applied to the project is maintained. LSS Black Belts are employed to ensure project momentum is achieved going forward.

The adoption of the Lean Six Sigma framework in a supply chain based MRO context is first of its kind within the company. The project has been able to increase the knowledge base of supply chain and operations managers (Pande, Neuman, & Cavanagh, Citation 2000 ) whilst also impacting positively on the operations effectiveness of the company. Further work is underway in quantifying the bottom line benefits as well as rolling out the LSSF to other areas within the company. The initial customer analysis stage involved the identification of the key variables considered important by the procurement team, maintenance technicians and, the end user. The use of standard tools such as Pareto Analysis and C + E diagramming were still seen as effective approaches to enable the LSS team to identify the variables affecting performance. The design and development of the LSSF was then key to creating a working environment around which the business improvement work could be enacted.

As with any change management project, the LSS team hit a number of problems with staff who were closed to the idea of change. Barriers encountered included a lack of commitment from operational staff and a misunderstanding of the desired outcome. These barriers were overcome by collaborating with departmental management. By ensuring a shared need, justifying the rationale behind the desired change and mobilizing commitments, teams and sections were able to embrace the necessary changes while being guided through regular staff contact meetings. In answering the Research Question ‘How effective is the application of an Integrated Lean Six Sigma Framework in an Aerospace MRO facility and, to what extent has the company’s internal and supply chain performance improved as a result of its implementation?’ then the following conclusions can be made.

The LSSF and the application of key improvement tools shows that LSS can be effectively delivered in to MRO operations. Although it can be argued that the LSSF is lengthy by way of application requiring the LSS teams to go through more stages than a traditional LSS programme of work, it was considered by the LSS implementation team as being more effective in simultaneously introducing the Lean and Six Sigma approaches in one coherent format to tackle the specific CTQ. The LSS team members had highlighted the frameworks as being particularly effective without being hugely burdensome. The LSSF was seen as the main change agent for the project. Feedback from the management team showed that the improvements adopted by the procurement and supply chain team would not have happened unless the LSSF system had been adopted. Furthermore, staff motivation was seen as having improved as a result of having a greater input in to the development of the LSS project.

Table 2 shows the completed LSSF with the tools and techniques specific to this project. The balanced approach towards multiple stakeholder analysis was seen as being particularly effective and that the C + E diagramming, Pareto analysis and, the subsequent focus on the human resources element of the project was very useful in developing a sound improvement platform and action plan. Whilst it is too early in the improvement process to total the sum of the savings and improvements, the company’s management team found the exercise to be key in initiating and driving change in to the procurement and supply chain areas (where previously Six Sigma had only been applied specifically to MRO operations). Roll out of the LSSF programme is being considered for further areas within the company.

Strong leadership was a key issue in driving this project forward and, in overcoming the tensions that existed around human resources. Knowledgeable, motivated and experienced staff were in high demand for inclusion in both the LSS improvement teams and, for their production related work. Splitting these resources across both areas called for effective communication between the production teams and the LSS teams and, in some instances, the LSS teams were required to relinquish staff in order to ensure that production targets were maintained. This did not seriously affect the LSS project but had potentially delayed the completion of this particular programme by a number of weeks.

During the initial stages of LSS project, it proved difficult to gain the necessary traction to drive the improvement programme forward. Defining a project that had sufficient depth in order to provide a significant impact on business operations required a number of ‘define’ stage iterations. The initial project definition stage identified a number of small, ‘surface level’ type projects that would have consumed resources and returned little by way of impact. Again, effective leadership was needed to lead the team in to undertaking a larger scale project where the risks of failure were higher but, the benefits to the company were greater.

The need for continuous training, development of staff and, effective communication throughout the full duration of the project was essential to project success. The LSS team noticed early in the programme that without constant updating of staff and ensuring that the team were fully informed of progress on the project that project momentum quickly dropped off. The introduction of daily meetings and frequent training and development sessions introduced early in to the project helped maintain project momentum.

Although a significant proportion of time (approximately six months) was spent in training and preparing the workforce through Stage 0 of the LSSF, it was felt that this stage could have been extended even further before the company progressed to the Stage 1 of the LSSF. The LSS team were still finding fundamental misunderstandings amongst the staff even after the Stage 0 completion which slowed down progress of the project. Further and more detailed training and assessment is being brought in for future projects.

No potential conflict of interest was reported by the authors.

research papers on six sigma

1. OTR – Order to Receipt – the time taken for the product to reach the customer from the time the order was first placed.

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Research on using Six Sigma management to improve bank customer satisfaction

International Journal of Quality Innovation volume  5 , Article number:  3 ( 2019 ) Cite this article

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In the banking industry, which aims to serve customers, management level and service level are one of the criteria for measuring the core competitiveness of banks. An important indicator of management and service levels is to ensure customer satisfaction with the bank used. Six Sigma management is customer-centric, based on data and facts, adopting improvement measures for the process, focusing on preventive control, emphasizing borderless cooperation, continuous improvement, and the pursuit of quality and efficiency management mechanisms. In this paper, we empirically analyze the reasons why banks affect customer satisfaction and design the bank’s Six Sigma service process based on empirical analysis. Finally, in the “Conclusion and discussion” section, the research suggestions for improving bank customer satisfaction are given.


Customer satisfaction and loyalty are vital differences between better performing and underperforming businesses in most markets [ 1 ]. Customer satisfaction refers to how customers feel about their happiness, depending on the comparison and differences between the customer’s expectations and the products/services they receive. This difference is also referred to as the difference between “cognitive quality” and “perceived quality” when the perceived quality is equal to or greater than the cognitive quality, customer satisfaction, or loyalty achieved, and the customer is not satisfied [ 2 , 3 ]. Previous research has made service quality, expectation, uncertainty, performance, desire, influence, and fairness an essential cause of customer satisfaction [ 4 , 5 , 6 , 7 , 8 ]. In the banking industry aiming at serving customers, the core and relationship dimension of service quality and customer satisfaction is relevant [ 9 ]. Therefore, management level and service level have become one of the criteria for measuring the core competitiveness of banks. An important indicator of management and service levels is to ensure customer satisfaction with the bank used.

The customer-oriented service concept has become the company’s purpose. Therefore, the demand for banking services by users is getting higher and higher. At present, due to the large population of China, the drawbacks caused by poor banking services are becoming more and more apparent, especially for the long-term waiting time for consumers. According to statistics, from the queuing or taking the number to the counter to handle business, the average waiting time for bank outlets exceeding 30 min is more than 30%. The opening of the window of banks is unreasonable. The working day is only about 60% open rate and only a 50% open rate on non-working days. This work is inefficient, resulting in generally low customer satisfaction.

We used to do business at Bank of X and found that the bank only opened two processing windows. The customer was noisy because of the long waiting time and asked to open a few windows. We consider whether we can find a scientific method to optimize the management of banking business processes, which constitutes the research topic of this paper. Among all the business of the bank, the most important thing is the counter service. The quality of the counter service directly reflects the strength of the bank’s overall service level and affects customer satisfaction with banking services. So we mainly strengthen banking business process management through Six Sigma management, to shorten customer waiting time and improve bank customer satisfaction.

Six Sigma management is a new process of a process change that reduces customer operating costs and cycles while improving customer satisfaction [ 10 ]. Six Sigma is a management model that enhances the profitability of an organization by improving the quality of its operations. Six Sigma is an effective management strategy for companies to gain competitiveness and sustainable development in a new economic environment [ 10 , 11 ]. Relevant scholars have studied customer satisfaction, but these studies involve not many banks, and the use of Six Sigma for research is even rarer [ 12 , 13 , 14 , 15 , 16 ]. Based on the field survey, we designed the Six Sigma process for commercial bank customer satisfaction, and applied the method to retail bank customer management, enriched the customer satisfaction theory, and had a specific theoretical value for the development of customer satisfaction theory.

The research structure of this paper is as follows:

Introduction: This section mainly introduces the problems in customer satisfaction and bank customer satisfaction and the main issues to solve in this paper.

Literature review: This section mainly introduces the research status of customer satisfaction and the application of Six Sigma management in the financial industry.

Empirical research: This section uses empirical analysis to illustrate the issues affecting customer satisfaction.

Six Sigma process design: This section uses the Six Sigma principle to design a bank to improve customer satisfaction.

Research conclusion: Based on empirical analysis and the Six Sigma process design, we propose research recommendations.

Literature review

The situation of customer satisfaction is closely related to the core competitiveness of the bank. Therefore, many experts and scholars have conducted a series of studies on this. Rust and Zahorik constructed a mathematical model for urban retail banks to assess the value of customer satisfaction [ 17 ]. Hallowell studied the relationship between customer satisfaction and customer loyalty and customer loyalty to profitability through the use of multiple measures of achievement, reliability, and profitability [ 11 ]. Moutinho and Smith used a linear structure approach to construct a model that plays a crucial role in assessing bank customers’ attitudes toward manual tellers and electronic banking and can adjust banking factors/perceived satisfaction vinculum of convenience [ 18 ]. Rod et al. studied the nature of the relationship between customer perceptions of first-line employee services, satisfaction, and selected behavioral intentions by using the customers of Russian commercial retail banks as their background [ 19 ]. Ndikubwimana and Berndt believes that the physical environment and facilities of the bank are conducive to providing excellent services, that customers are satisfied with the tangible aspects of the service, and that they are prepared to reflect this satisfaction in their actions [ 20 ]. Jalali et al. found that the consequences of greater competition and financial crises mean that banks are increasingly focusing on improving service quality and achieving higher levels of customer loyalty [ 21 ]. The research proposes and tests a comprehensive model based on cognitive mapping and multi-criteria decision analysis (MCDA), which combines metacognition and psychometric decision-making methods to create a framework for assessing bank customer loyalty [ 21 ].

Although these experts and scholars studied the bank’s customer satisfaction and built related models, however, these studies do not design processes using Six Sigma principles. We mainly use Six Sigma management to optimize customer satisfaction.

The core idea of Six Sigma was born in 1970 because the senior leader Art Sundry criticized Motorola for its poor production quality [ 22 ]. Geoff uses Six Sigma quality management to control long-term defect levels to 3.4 in the short term DPMO within the above, and based on this, the "Six Sigma flow chart" was established [ 23 ]. Cmglee according to calculations in the process performance study, if people achieve six standard deviations between the process average and the most recent specification limits, there will be no defective products (Fig. 1 ) [ 24 , 25 ].

figure 1

Six Sigma Normal distribution [ 24 ]

As can be seen from the flow chart, the normal distribution graph is the root of the Six Sigma model. The Greek letter σ (Sigma) on the horizontal axis marks the distance between the arithmetic means, μ , and curve inflection point, and the greater the distance, the greater the difference encountered. In the green curve above, μ  = 0 and σ  = 1. The above and below parameters define (USL and LSL) the distance from the mean within 6σ. Stamatis summarizes several ways to influence quality improvement over the past few decades, including quality control, total quality management(TQM), and zero defect method, forming a Six Sigma quality management theory [ 26 ]. Gygi & Williams, and El-Haik perfected the Six Sigma management theory and studied the long-term Sigma level DPMO value (Table 1 ) and control chart, DanielPenfield draws DPMO control chart of Six Sigma based on their research (Fig. 2 ) [ 27 , 28 , 29 ].

figure 2

X-bar chart for a paired X-bar and s Chart [ 29 ]

In the field of banking services, Fornell et al. proposed a customer satisfaction index model. As a new measure of performance, the customer satisfaction index model is the match between customer expectations and customer experience [ 12 ]. Riley et al. demonstrated through case studies that financial institutions use Six Sigma (DFSS) to develop policies and procedures to eliminate compliance gaps and improve the lending process for banks and customers [ 13 ]. Antony studied the role of Six Sigma in a bank customer call center, where the metric-based environment complements Six Sigma’s application of process improvement [ 14 ]. Sunder emphasizes the importance of LSS in the banking industry through real-time process improvement research and provides a theoretical contribution to the Bank’s detailed introduction to customer-oriented metrics and the processing of key performance indicators (KPIs) that require Lean Six Sigma [ 15 ]. Bazrkar et al. designed the overall quality model of the “accounting process” of Ghavamin Bank [ 16 ].

The true essence of lean is to leverage the enthusiasm and knowledge of frontline employees and enable them to focus on ensuring that as much activity as possible in the end-to-end process supports delivering value to customers [ 30 ]. Therefore, we believe that customer identification is a valuable intangible asset of commercial banks, and customer satisfaction surveys are closely related to the development of commercial banks. Thus, the introduction of the Six Sigma method for the design of bank management processes is of strategic importance.

Empirical research

Although the overall service level of the Chinese banking industry has significantly improved in recent years, there is still a big gap with the leading international banks in terms of customer satisfaction, overall banking service level, and management level.

We randomly investigated the counter business of a branch of X Commercial Bank and found that there were a series of problems such as long waiting time for customers to handle business, unreasonable window opening, and inefficient bank staff. That is also a common problem for many banks.

According to the survey data, we randomly selected data sample from four time periods (9:00–11:00, 11:00–12:00, 12:00–14:00, 14:00–17:00) (Table  2 ).

Referring to the research of related scholars [ 31 , 32 , 33 ], we use the method of hypothesis testing in probability theory and mathematical statistics to analyze data. Before a data analysis, we first analyze the basic principles of the relevant methods.

Under the condition of \( {\sigma}_a^2=0 \) , F obey normal distribution of the degree of freedom df 1 =  k  − 1 and df 2 =  k ( n  − 1). Then

If the calculated F value is greater than F 0.05 ( df 1 ,  df 2 ), the F value is significant at the level of σ  = 0.05. We conclude that the overall variance of MS t is greater than the total variance of MSe with 95% reliability (i.e., 5% risk). That is, the method σ a 2  ≠ 0, which uses the magnitude of the probability of occurrence of the F value to infer whether the population variance is greater than the other population variance, is called the F -test.

The analysis of variance for a single-factor completely randomized design test data:

Invalid hypothesis H 0 : μ 1  =  μ 2  =  ⋯ μ k .

Alternative hypothesis H A : each μ i is not equal, then,

\( F=\frac{MS_t}{MS_e} \) , that is, to determine whether the mean square between treatments is significantly larger than the intra-process (error) mean square.

Based on the randomly selected sample data, to verify the results of the random survey, we propose the following three research hypotheses:

H1: Assume that the period for handling business has a significantly affected customer waiting time.

According to the survey data, the customer’s waiting time is randomly selected from four time periods (9:00–11:00, 11:00–12:00, 12:00–14:00, 14:00–17:00). Assume that the waiting time of the customer in each period obeys the normal distribution, assuming that the test H0: μ 1  =  μ 2  =  μ 3  =  μ 4 , obtained by the analysis of variance (Table  3 ):

From 3.33 > 3.24, H0 is rejected at the level of α  = 0.05 significance, that is, the waiting time of customers at different periods is significantly different at the 0.05 level.

H2: Assume that business content has a significantly affected on processing time.

According to the survey data, four different business contents (receipt and deposit, account opening/banking, loss reporting, transfer) and the teller’s processing time are randomly selected, assuming that each business processing time is subject to normal distribution, hypothesis testing H0: μ 1  =  μ 2  =  μ 3  =  μ 4 , obtained by the analysis of variance (Table  4 ):

From 4.07 > 3.24, H0 is rejected at the level of α  = 0.05 significance, and the business content has a significant difference in the processing time at the 0.05 level.

H3: Assume that different window numbers have a significantly affected on processing time.

According to the survey data, the waiting time of three windows is randomly selected (in the case that the business is for deposit and withdrawal and transfer), assuming that the processing time of each window follows a normal distribution, assuming H0: μ 1  =  μ 2  =  μ 3 using the analysis of variance (Table  5 ):

From 3.56 < 3.68, H0 cannot be rejected at the level of α  = 0.05 significance, that is, different window numbers have no significant effect on the processing time.

Through hypothesis verification, it can found that X commercial banks have different differences in different services at different times. Different windows have not been significantly affected on the processing time. That shows problems in the management of banks, and there is room for improvement. How to improve management level and service level, and growing customer satisfaction has become the management problems that banks need to solve urgently.

Process design of Six Sigma in improving bank customer satisfaction

Six Sigma management is customer-centric, based on data and facts, adopting improvement measures for the process, focusing on preventive control, emphasizing borderless cooperation, continuous improvement and the pursuit of quality and efficiency management mechanisms. It always revolves around customer satisfaction and loyalty. Based on the empirical analysis, we designed the bank’s service flow using the Six Sigma theory and plotted the SIPOC diagram.

Define stage (D)

The main content of the definition phase is to determine the flow chart of the banking service and the needs of the customer. We have developed a project plan based on the characteristics of the bank’s business processes: the project plan includes the setting of goals, the definition of scope, the division of labor, and the collaboration of team members. At the same time, according to the characteristics of customers’ needs, the leading indicators affecting customer satisfaction are determined.

Drawing a SIPOC diagram (Fig.  2 ): the elements of a SIPOC diagram are the supplier (S), input (I), process (P), output (O), and customer (C).

The main task of this phase is to determine the bank’s customer satisfaction improvement project. The goal of the project is to eliminate various factors that are not conducive to process performance and improve customer satisfaction. According to the results of the empirical analysis, in this step, the following questions should be clear: What are the customer’s needs? What is the critical quality factors (the essential elements of quality refer to the core standards required by the customer for the product or service)? What is the definition of a project’s defect (a defect is “anything that cannot meet the criteria required by a critical quality element”)?

Measurement phase (M)

This stage further describes the whole process based on the SIPOC diagram. Develop data collection and sample collection plans and measure process capabilities by identifying key quality characteristics that affect process performance. The measurement content mainly includes two aspects: the service efficiency of the banking outlets and the customer service of the banking outlets. There are four main measurement methods, including manual field measurement, counting machine statistics, viewing monitoring video and background data extraction, and measurements mainly taken by random sampling.

At this stage, after making the conditions of the project clear, the following things need to be done according to the customer’s requirements:

Select evaluation indicators: According to the critical quality factors of the customer and the essential quality factors of the project, the impact points and specific requirements on the quality of the business process are derived, that is, the particular needs of the customer for the products and services are translated into the standards to be achieved by the bank process.

Identify the measurement objects and develop a data collection plan: Conduct an assessment of an existing process to understand the process capability or level of a current method; at the same time, develop a data collection plan that plans a data collection plan based on the selected measurement object. The data collected during the measurement phase laid the groundwork for the analysis phase.

Verify the measurement system: With the data collection scheme, data collection activities cannot implement immediately. Before the measurement, it is necessary to verify whether the measurement system is available because the measurement data is the primary input in the analysis stage. If the data quality is not high, it will affect all subsequent activities.

Analysis phase (A)

The main task of this phase is to identify key influencing factors and analytical work on the data. The raw data were obtained by designing customer satisfaction questionnaires and field research, and the causal relationship was established and verified through data analysis. According to the results of the empirical study, identify the critical defects and causes that affect performance indicators.

The analysis phase is the most critical part of the process improvement process, designed to identify and validate the root cause of the original problem. At this stage, the project team needs to analyze and improve the most critical objectives of the various objects (variables) that cause defects. It should note that experience and intuition cannot replace the work of the analysis phase. Because the root cause of the problem buried deep in the file heap and the old program is not intuitive and empirical, therefore, the analysis stage is to use a variety of useful tools and methods to analyze existing data and processes and identify solutions to project improvements.

The analysis phase is a process of continuously cycling the root cause, which can be represented by Fig.  3 .

Perform data or process analysis: Its purpose is to detect the data collected during the measurement phase to help the team find relevant clues about the cause of the problem to be improved.

Establish assumptions or models of the cause of the incident: That is, based on the analysis results, all possible hypotheses that may lead to the problem are raised as much as possible, and a model for the cause of the problem is established. Brainstorming methods are often used at this stage [ 34 ].

Perform data and process analysis again: This phase of work is similar to the first phase, but it is not a simple repetition. After listing the possible causes through the brainstorming method, the project team will use the data collected during the measurement phase and the new data collected during the analysis phase (Fig.  4 ) to re-analyze the development trend of the problem and other related factors, proposing new hypotheses or models.

Revise the hypothesis or model: After another data and process analysis, the goal of this phase is to reduce or eliminate a large number of causes in brainstorming to a more manageable amount. If the result of the reduction does not achieve satisfactory results, it is necessary to start the first phase and re-make the hypothesis until the goal of confirming the root cause can be made.

Identify and select several key reasons: That is, analyze the root cause of the problem.

figure 3

Service operation SIPOC diagram [ 14 ]

figure 4

Analysis phase cycle diagram [ 26 ]

Improvement stage (I)

Suggestions for improvement are proposed based on facts and data, and improvement plans are determined. A partial test run can be performed to verify the improvement. The improvement scheme can be given in the form of an improved strategy table. After the improvement plan is formed and the improvement plan specification is written, the improvement plan implementation process is entered.

That is the core process of the Six Sigma project. The work during the definition, measurement, and analysis phases are all prepared for the improvement phase. Therefore, the main task of the improvement phase is to find the optimal solution that will enable the bank to improve customer satisfaction. The steps in the improvement phase are:

Seek creative customer satisfaction improvement programs: Similar to the analysis phase, in this step, brainstorming can help the group gain more opinions on how to solve the problem.

Identify the solution and develop an implementation plan: In this step, all the ideas and suggestions put forward by the brainstorming activities are discussed and classified, and the repeated and excluded are not feasible, and the most likely to form a solution is selected and organized merely. The team then revisits and evaluates the selected ideas and decides the most promising and practical solutions based on cost and possible benefits. After that, develop a detailed implementation plan.

Full implementation of the solution: If not implemented, the best solution is just a piece of paper. Therefore, the team’s next job is to overcome the obstacles and achieve improvement activities throughout the process.

Control phase (C)

Incorporate the improvement phase measures into daily management, and carry out lean and traditional control of banking business processes by establishing work performance appraisal standards and improving incentive measures.

Control activities enable the organization to continue to maintain the initial improvement activities of the project team and ensure that continuous improvement is achieved after the unit is disbanded. The long-term impact on people’s working methods and the sustainability of their needs, not only the measurement and monitoring results, but also the constant persuasion and marketing of ideas, are both necessary. Therefore, in the control phase, the work of the project team includes explicitly:

Confirm performance improvement and compare the results with improvement goals.

Establish a rapid response mechanism to adjust strategies, products, and services promptly based on changes in vital information.

Build a Six Sigma management culture and establish an organization that will continue to promote Six Sigma management.

The final success of the Six Sigma project lies in those who work well in the areas of interest to the project. Only when these people see the value of generating a new solution through the DMAIC process and begin to understand and believe the potential that the Six Sigma system can provide can the goal of continuous improvement be genuinely achieved.

Conclusion and discussion

Six Sigma is a new management strategy that has achieved great success in many areas of the world. Six Sigma is a management model that continuously improves and breaks through and pursues excellence. It creates a “customer satisfaction” Six Sigma quality culture through Six Sigma management, continually improves process design, reduces process defects, achieves excellent customer satisfaction, and achieves higher customer requirements. We believe that in the specific practice, attention should be paid to the following aspects:

Establish and adhere to a quantitative analysis culture: Six Sigma emphasizes the concept of data, pays attention to data and quantitative analysis, and strictly divides management activities based on statistical analysis of data collection. It uses objective data and quantitative indicators to objectively reflect the current situation of the bank and analyze the crux of the problem. Therefore, strong data support and a complete measurement system are the basis for the successful implementation of Six Sigma.

Build an efficient Six Sigma infrastructure and establish a Six Sigma work management and incentive mechanism: Six Sigma-specific organization implementation is usually delivered and implemented by executive leaders, advocates, black belt masters, black belts, green belts, and project teams. Commercial banks should establish a Six Sigma organizational structure, clarify important responsibilities and authorities, select an efficient group with a good business foundation, be familiar with business processes, have a strong sense of change and teamwork, and start to eliminate the reasons for customers getting defective products or dissatisfied services, prioritize actions, and solve problems.

Master the critical links of the DMAIC process: Six Sigma management is a flexible and comprehensive system and business improvement method system. All operations and activities are usually carried out according to the process. The first is the definition phase. It is defined to identify and identify goals that need improvement. The second is the measurement phase. It requires employees to be trained in basic statistics and probability theory and can use data as a benchmark to measure the gap between current conditions and customer needs. The third is the analysis phase. It is applying many statistical tools to explore the critical causes of the difference between the status quo and the demand and identifying the potential variables that affect the outcome. The fourth is the improvement phase. Statistical tools are used to analyze the entire system and determine the gaps between existing systems and process performance and established goals and solutions, requiring the use of project management tools to find useful improvements. The fifth is the control phase. The focus is on how to monitor new system processes, correct and standardize the effectiveness of the entire process, make the improvement measures long term at a new level, and continue to improve the results.

Adhere to continuous improvement: continuous improvement is also a management and cultural foundation for Six Sigma management. The DMAIC process of the Six Sigma project itself is a cyclical process of discovering problems, solving problems, rediscovering problems, and resolving issues. That is an endless process of perfection and continuous improvement. It is necessary to avoid the Six Sigma as a “one gust of wind” quality movement, to establish the so-called “no best, only better” continuous improvement concept, and ultimately to form a corporate culture.

The customer-oriented service concept has become one of the most fundamental codes of conduct in all walks of life. Banks that are strictly related to the production and growth of the general public are increasingly aware of the importance of “customer-centric” and realize the value of the company in the process of pursuing customer satisfaction: improve service quality, improve service management, optimize the investment environment, actively develop financial derivatives, and connect with leading international banks. That is also the direction that all joint-stock banks including X commercial banks are working hard. Based on this, we propose the following suggestions and improvement strategies.

Implement a flexible working system and some windows to meet the peak period of customers in different periods fully: The degree of leisure and leisure in the banking hall is different, and there will be several peak periods. The number of windows at peak times does not meet the needs of customers. In this regard, the problem can be solved by the flexible working system and the number of windows to meet the needs of customers.

Defining functional areas, conducting customer diversion management, and vigorously developing electronic channels: Banks can identify various functional areas, which effectively divert customers through the establishment of consulting service areas, automated service areas, customer lounge areas, wealth management service areas, and customer manager offices. Banks should actively guide customers to use electronic channels to handle business and ease the pressure on the business hall window.

Improve service efficiency and continuously optimize services: Increasing the ability of the staff can shorten the waiting time for customers. The bank shortens service time by identifying the best work routes and steps, unifying service standards and processes.

Establish a feedback mechanism for customer satisfaction and timely adjust the management plan of the work system: Banks can establish customer feedback mechanisms. Through feedback from customers, the bank improves service processes and enhances customer satisfaction.

In the process of applying Six Sigma management, the following points should also be noted: The value of customer satisfaction in different periods is drawn into a control chart to obtain dynamic information on customer satisfaction. Every improvement should find the most critical factors affecting customer satisfaction, each time improving for one element.


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