Benchmarking the competitivenessof industrial sectors

Application in textiles

Nikolaos Bilalis, Emmanuel Alvizos and Lukas TsironisDepartment of Production Engineering and Management,Technical University of Crete, Counoupidiana, Greece, and

Luk van WassenhoveTechnology & Operations Management, INSEAD, Fontainebleau, France

Abstract

Purpose – The paper aims to present a clear methodological path for assessing the competitivenessof a specific industrial sector with the use of the Industrial Excellence Award (IEA) model. The paperintroduces the concepts evaluated by the IEA model and addresses the ways with which variedmanagement data may be analyzed in order to provide useful insights for improvement in industrialprocesses such as new product and process development, supply chain management, strategyformulation and deployment.

Design/methodology/approach – Sixty European textile companies provided informationconcerning their business processes over the course of three years in accordance with the IndustrialExcellence Award (IEA) model developed by INSEAD Business Schools. Subsequently, the textileindustry companies were compared with 73 excellence-driven European manufacturers which eitherwon or distinguished themselves in the award competition during the same time period. Themanagement information from both datasets was treated with the proper statistical tools (according totheir nature) in order to attain secure and minimally biased conclusions.

Findings – The benchmarking process revealed the areas in which the textile sector was laggingbehind the excellence-driven manufacturers. Furthermore, it detected their differences in specificmeasures of industrial management and business mentality. On a theoretical level, the analysisverified the general reliability of the IEA model’s scales, aiming at assessing abstract managementconstructs while fine tuning them.

Research limitations/implications – The thorough inspection of the textile companies’performance attributes and characteristics has identified many of the sector’s shortcomings thatmerit further investigation.

Practical implications – The results of the analysis served as valuable feedback to textilemanagers aiming at bettering their industrial processes in many ways, such as benchmarking theirperformance against their sector or other sectors, and observing trends that managers from othersectors are putting effort into in order to improve their performance.

Originality/value – The paper provides a clear-cut methodology for the understanding andstatistical analysis of multifaceted industrial management data included in excellence models.

Keywords Business excellence, Benchmarking, Textile industry, Europe

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1741-0401.htm

The Merit-Tex project was funded by the European Commission’s Leonardo Da Vinciprogramme. The authors would like to extend their gratitude to all the members of the TechnicalUniversity of Crete – CAD Laboratory and INSEAD for their genuine contribution to thesuccessful outcome of the survey. Much appreciation is also due to all the company respondentswho donated their time and energy in order to complete the questionnaire.

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Received September 2006Revised February 2007

Accepted March 2007

International Journal of Productivityand Performance Management

Vol. 56 No. 7, 2007pp. 603-622

q Emerald Group Publishing Limited1741-0401

DOI 10.1108/17410400710823642

IntroductionFrom 1974 to 1994, the textiles and clothing industry fell under the legal orbit of theUnited Nations’ Multifibre Arrangement (MFA) (United Nations, 1981), whichenabled each country to impose quantitative quotas on imports of textile andclothing products. On 1 January 1995 the MFA was replaced by the World TradeOrganization’s (WTO) Agreement on Textiles and Clothing (ATC) (World TradeOrganization, 1994), which established a transitional process for the final removal ofthe quotas policy. On 1 January 2005 the protective umbrella of the ATC was liftedand national governments can now no longer apply quantitative restrictions on theflow of textile products. These agreements have contributed to the creation of a newbusiness landscape, and textile companies throughout Europe and elsewhere can nolonger rely on inter-governmental bilateral agreements in order to ensure thelong-term growth of their operations.

The European textile sector hosts an impressive number of small and medium-sizedenterprises (SMEs), which span the whole chain of operation from spinning to clothingand retailing, with more than two million employees across the continent. Most of theseEuropean textile manufacturers are facing fierce competition due to the tremendousgrowth of imports coming from new industrialized countries (NICs) and especiallyfrom the People’s Republic of China, whose low salaries and social charges harborconsiderable competitive advantages (Organization for Economic Co-operation andDevelopment, 2004). In light of these recent developments, European textile SMEs needto readjust their focus and restructure their goals in order to survive and remaincompetitive. In this paper, we argue that a key instrument that may facilitate thispainstaking process could be found in the industrial excellence (IE hereafter) andmanagement quality frame of thought, as put forward by INSEAD (Loch et al., 2003).

During the last decade INSEAD, among others, has pioneered IE and established aseries of related national awards. The IEA model examines the role of managementquality as a stimulus to excellence at the business unit level. It aims at recognizingexcellent manufacturing processes in companies that operate or have business unitsoperating in Europe. The IEA survey/award is currently in its eleventh and ninthannual editions in France and Germany, respectively, while it has been successfullyintroduced during the last two years in Spain, Portugal, and various other countries inEastern Europe (INSEAD, 2005).

In this article, we report on the application of the IEA model in the European textilesector. Sixty companies, mainly SMEs from the greater European textile industry, tookpart in the survey (Merit-Tex, 2005; Maravelakis et al., 2004; Bilalis et al., 2006) andprovided in-depth information concerning their business performance, processes,strategy, supply chain management, and a variety of other functions over a period ofthree years. The accumulated data were then scrutinized for consistency and furtheranalyzed in order to serve as an internal benchmarking tool for the textile companiesinvolved. Subsequently, we extend the original benchmarking process by introducinginto the analysis data acquired from the finalists and winners of the IEA competitionsduring the same time periods. Thus, the analysis sample was enlarged by the additionof 73 top-tier manufacturing firms of varying sectors, who proactively seek industrialexcellence in their respective fields. Subsequently, the aim of the new benchmarkingendeavor is the comparison of the “traditional” textile firms with the IE-driven

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manufacturers, in order to identify the management areas in which the textile firmsshould improve themselves.

In the beginning, we outline the principles of management quality and overview themore salient concepts of the IEA model. Subsequently, we discuss the IEAquestionnaire indices and measures that were involved in the benchmarking process ofthe textile companies. Then, we report on the specifics of the research methodologiesapplied in the analysis, according to the nature of each proposed measuring index. Theresearch samples, as well as their characteristics, are also presented in the samesection. Finally, we move towards the comparison between the two samples anddiscuss the results. Ultimately, the conclusions, as well as areas and opportunities forfurther research, are presented.

Industrial excellenceIE in a nutshellThe need for industrial excellence arose with globalization and targets overall,integrated, and balanced improvement of company operations. In the words of themodel’s inspirers, the purpose of IE is to identify what it is that an industrial firm needsto do well, in order to perform at an excellent level in a sustainable manner (Loch et al.,2003). IE, however, is not without a temporal factor. The modern business landscape isan ever-changing battleground for free-market economies. Thus, a lasting requirementmay be that the search for best industrial practices is a continuing process. The majorconceptual observation of the IEA model is that the classical industrial process view ofa company’s operations should be overlaid with an additional and distinct dimensioncharacterizing the quality with which the industrial process is managed (Loch et al.,2003), thus leading to the management quality concept.

The last 25 years have seen the emergence and the development of a significantnumber of strategic frameworks directed at improving a company’s performance atevery imaginable level. From total quality management (TQM) to business processreengineering (BPR) (Hammer and Champy, 1993), and from the resource-based view ofthe firm (Wernerfelt, 1984; Rumelt, 1984) to the sustainable competitive advantage anduniqueness doctrine (Porter, 1980), the management literature is richly endowed withbrilliant concepts and numerous benevolent practices that all share the same passionfor improvement. No one can deny the value of all these immensely influential schoolsof thought. However, when an industrial manager strives to form an action plan forimprovement, he is faced with hard to grasp concepts like “corporate culture” and“learning” within the organization (Powell, 1995; Senge, 2006), to name just a few. Mostof the time, it is up to the manager to try and rationalize these concepts into a tangibleand achievable set of goals.

It was only fairly recently that clear and descriptive recommendations of anoperational nature saw the light of day within a strategy framework, such as in the“lean thinking” strategies of Womack and Jones (1996). Based on the same principles ofoperational characterization of abstract concepts, the decade-long work of INSEAD hasled to the inception and the development of the IEA model in an effort to characterizeindustrial excellence and management quality. As a result, the model does purport tointroduce novelties. What it does, however, is make an honest effort to characterizeclearly the operational nature of IE and provide a solid action agenda for improvementefforts.

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Characterizing industrial excellenceDrawing upon the work of Loch et al. (2003), for the purposes of this paper we offeronly a small glimpse of the basic notions surrounding the IEA model in order toestablish the point of view from which we approach our benchmarking efforts. TheIEA model identifies four basic business processes as the fundamental pillars of IE:

(1) supply chain;

(2) product development;

(3) process development; and

(4) operations strategy formulation and deployment (see Figure 1).

Operations strategy is concerned with the formulation and deployment of the strategychosen by the industrial unit in a way that engages the entire unit (and its current andfuture suppliers and clients) and sets clear goals for it. The supply chain processencompasses the physical supply activities of goods and services, from the delivery ofmaterial and information inputs to the industrial unit, to their transformation inside theunit, and their delivery to the external client using existing distribution channelactivities. The product development process consists of activities concerned with theimprovement of the firm’s current portfolio of products and services, as well as withthe development of the industrial unit’s next generation of products and services. Theprocess development process aims at the improvement of the industrial unit’s processesand activities, whether they pertain to the supply chain, product development, or othertasks of the organization, including strategy formulation and deployment.

The basic business processes described above, according to the IEA model, are thencomplemented by the management quality dimension. Management encompassesthree major aspects – direction setting, delegation and integration (see bottom part ofFigure 2) – and affects four personnel enabling factors:

Figure 1.The four basic processesof industrial excellence

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(1) communication;

(2) measurement;

(3) employee participation; and

(4) employee development (see upper part of Figure 2).

The first three aspects of management described are typically considered as formingthe essence of “good management”. IEA places all three at the same hierarchical level.In doing so it accentuates the unity of command among the three towards theachievement of IE. At this point, it should be mentioned that it is beyond the scope ofthis paper to detail the theoretical facets of IEA. For the interested reader we thereforesuggest the detailed and explanatory work of Loch et al. (2003).

Indices and measuresAs mentioned earlier, the IEA model attempts to characterize the abstract notions of atheoretical management construct and transform them into a set of specific operationalitems with which the industrial manager is better acquainted. Repeated observation ofpractices shared by the majority of the best industrial units in France and Germanyhave over the past ten years resulted in the formulation of a rich set of operationalindices and measures that have been reviewed and fine tuned many times over. Theresult of this decade-long research takes the form of the IEA model’s presentsurvey/analysis questionnaire. The ten-page questionnaire, spanning all of thebusiness processes described in the previous section, holds more than 200 fields thatrequire a wealth of information in order to be successfully completed (Loch et al., 2003).

The questions can be generally categorized in two main groups, regardless of theirsubject matter. The first category of questions pertains to indices that incorporate atemporal factor and generally represent the performance level of a company’s attribute.A classical example for this category is the gross profit margin. The questionnaire

Figure 2.Components pf

management quality:integration, delegation and

direction setting alongwith their enabling

processes

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requires the index’s present value (PV), its value two years ago, as well as its goal valuewithin the next two years. Other less classical indices that require the same treatmentmay include such varied items as the percentage of the company’s suppliers usingjust-in-time (JIT) techniques, or the percentage of new product development projectsthat presented schedule overruns. The complete set of indices examined in this studycan be found in the Appendix, labelled “Indices under investigation”.

The second category of questions – indicated here as measures – are without thetemporal factor. They are arranged in the form of Likert scales containing severalrelated items that in total measure a general management construct. These measurestry to grasp the company’s attitude towards such diverse matters as supplier relationsand employee recognition patterns, indirectly targeting the notion of managementquality within the firm. For example, in the case of company-supplier relations, therespective Likert scale (see Appendix: “Joint activities with key suppliers”) requests anassessment of the frequency with which certain activities are performed in cooperationwith the company’s suppliers. These include external (cross-company) meetings, usageof supplier know-how for product design and manufacturing process improvements,joint improvement programs with second-tier suppliers, joint performance evaluationprograms, formal supplier development programs, and machinery and toolspurchasing decisions. The complete set of measures examined in this paper can befound in the Appendix, labelled “Measures (Likert scales) under investigation”.

Finally, there are some questions that do not fit in any of the categories describedabove. Such questions include training expenses per employee (euros per year) as wellas indices representing the number of best practices and established measurements ofperformance improvement. These questions lead to continuous variables that do nothold a temporal attribute; thus an improvement rate may not be calculated. Theexamination of such values will be held on an absolute basis only. These items can befound in the Appendix, labelled “Other measures and indices under investigation”.

Our purpose in this paper is to demonstrate the usefulness of the IEA model in thesearch of the areas in which a company needs to better itself in order to improve itsindustrial competitiveness. Competitiveness can be measured in a variety of wayswithin the IEA model. It can be achieved through the sheer comparison of every aspectof the questionnaire, or it may be done through the targeted examination of specificareas of interest. In this case, we have selected to examine a limited set of attributes,from all four of the business processes identified by the model, which may be indicativeto the usefulness of the model as a benchmarking tool. Both indices as well as measures(as defined earlier) were used in our investigation.

Research samples and methodologyResearch samplesWe tested the IEA methodology in the European textile sector and we collectedinformation from 60 small and medium-sized enterprises from Greece, Italy, and Spainfrom 2001 to 2004. The participants were selected based on their capabilities and theirwillingness to participate. All of these firms were involved in various areas of thetextile supply chain, except retailing. The variety of represented business activitiesspans yarn and thread production to fabric manufacturing, fabric dyeing and finalarticle production. Each textile company conferred with at least one academicrepresentative in order to ensure that there were no misconceptions during the

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completion of the questionnaire. The data collected from this survey formed the basissample of textile SMEs used in this study.

The reference sample for our benchmarking endeavours was provided by INSEADand included the winners as well as the runner-ups of the Industrial Excellence Awardannual competitions held in France and Germany from 2000 to 2004. The samplecontains 73 industrial firms producing such diverse articles as electrical and electronicscomponents, automotive supplies, aeronautics and machine tools, hygiene and make-upproducts as well as biotechnology supplies. It should be noted, however, that all of thesecompanies share a unique drive for excellence and innovation in their respective fields.These excellence-driven companies responded to the same IEA questionnaire, while theirresponses were validated by INSEAD faculty members during plant visits. Tables I andII summarize some of the key characteristics of the two samples.

In Table I, we present the national composition of each sample. The Merit-Texsample contains 20 companies from Greece, Italy and Spain. The IEA sample, on theother hand, is composed of 49 German and 24 French manufacturers. In Table II, wepresent the average sales revenue, GPM, and number of employees reported in eachsample. It is evident that the IEA sample contains much larger industrial units, whilethe Merit-Tex sample focuses on SMEs.

All of the companies included in this study were examined at an individual plantlevel in order to maintain a common denominator throughout the analysis procedure.Concerning the sample sizes used, even though they may appear to be bordering on theedge of acceptable levels for statistical control analyses, they are well within thesample limits used in related management literature. Similar studies have incorporatedsamples varying anywhere from a few dozen to hundreds of companies in the searchfor TQM practice applications and other related management practices (e.g. Saraphet al., 1989; Adam, 1994; Anderson et al., 1995; Powell, 1995; Flynn et al., 1995; Deanand Snell, 1996; Evans, 2004).

Country Merit-Tex IEA

Greece 20 –Italy 20 –Spain 20 –Germany – 49France – 24

Source: Merit-Tex (2005) and INSEAD (2005)

Table I.Merit-Tex and IEAsample composition

by country

Attribute Merit-Tex IEA

Number of companies 60 73Sales revenue (e, millions) 17 385Gross profit margin (e, millions) 3.9 39.8Gross profit margin (per cent) 22.71 10.35Number of employees 118 118

Source: Merit-Tex (2005) and INSEAD (2005)

Table II.Merit-Tex and IEA

sample averagebasic figures

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Research methodologyAt the beginning of the paper, our focus was on the detection of significant differencesbetween the textile SMEs and the excellence-driven manufacturers in aspects ofmanagement practice that are applied in different industry sectors. As mentionedearlier, we have selected an ample number of indices and measures that emphasize theoperational characterization of industrial excellence. The basic principle underlyingour benchmarking efforts is the comparison of independent samples. All statisticalprocedures reported in the following paragraphs were performed with the use of thestatistics software package SPSS 12.0 for Windows (SPSS, 2003).

Each category of attributes (indices and measures) needs to be treated differently.For example, all indices (i.e. improvement rates) may be regarded as individualcontinuous variables, whereas all measures are in essence formulated as Likert scalesof predetermined values for each of their items. Thus, we were driven to adopt a dualstrategy for the statistical analysis of the data.

All measures, regarded as scales, were subjected to reliability tests in order toascertain their internal consistency. The indicator used for reliability and consistencywas Cronbach’s alpha coefficient (Frankfort-Nachmias and Nachmias, 1992). Thisprocedure led to the exclusion of certain items that appeared to hamper each respectivescale’s ability to measure the underlying construct (Oppenheim, 1992; Gable and Wolf,1993). The results of the reliability analyses are presented in Table III. The columnheaded “Cronbach’s alpha (original)” presents the coefficient’s value with all theoriginal items retained. The column headed “Cronbach’s alpha (after item deletion)”presents the coefficient’s value after the exclusion of a specific scale item suggested bySPSS.

From the total of seven measures included in the study, three presented the bestalpha coefficient with the use of all their original items. The remaining four measuresreached their best reliability level with the exclusion of only one item in each scale. Thescale item excluded in each case is reported in the footnote to Table III. The completeitem list of each measure can be found in the Appendix, as indicated earlier. With theconclusion of the reliability tests, the items of each scale were summed to form a totalmeasurement. The total values could from that point on be regarded as continuousvariables and were subsequently treated as such.

MeasureCronbach’sa (original)

Cronbach’sa (after item

deletion)

Formulation of strategy (extent of input from stakeholders) 0.651 0.687a

Implementation of strategy (extent of participation from stakeholders) 0.682 0.727a

Joint activities with key suppliers (frequency of cooperation) 0.849 –Access to customer demand (degree of visibility) 0.792 –Modern HRM practices observed (degree of accordance) 0.767 0.812b

Employee recognition programs and incentives used consistently 0.692 0.732c

Existence of processes for performance improvement initiatives 0.900 –

Notes: aItem “Management of business unit” removed; bitem “People wear highly distinct uniforms”removed; citem “Salary based on qualifications” removed

Table III.Reliability test results formeasures included inthe study

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While keeping in mind that our primary statistical analysis tools would be theparametric independent samples t-test and its non-parametric equivalent, theMann-Whitney U test, we explored our continuous variables in order to assess theircompatibility with each technique. Primarily, all indices were initially subjected tonormality tests, which were evaluated with the use of the Kolmogorov-Smirnovstatistic (Gravetter and Wallnau, 2000; Tabachnick and Fidell, 2006). This initialprobing of the data indicated that almost all the indices involved were, in fact, violatingthe normality assumption (hardly a startling event when dealing with managementdata). The sole “normal” survivors were the “gross profit margin (absolute value)” and“employee productivity” indices for the Merit-Tex sample, as well as the “averageplant total process time (days)” index for the IEA sample.

A scrupulous screening process was then undertaken in an effort to remedy thenormality violation predicament. Several extreme values were detected and removedalong with the more distinct outliers. Again, the data proved to be non-normal indeed,thus leading the parametric statistics techniques to a more supplementary role. Theresults of the normality tests for each index throughout the two samples both beforeand after the screening process may be found in the Appendix of the paper.

Having performed these preliminary tests, we concluded that the best way to proceedwould be the utilization of the Mann-Whitney U test technique in order to detectsignificant differences between the samples. The parametric approach to the comparisonwould then be employed in order to verify the existence of a significant difference whileproviding in parallel an indication concerning the magnitude of that difference. Thisparallel use of both parametric and non-parametric statistics techniques is not objectedby recent studies in various scientific fields (e.g. Tornblom et al., 2005; Xu et al., 2003).Furthermore, according to Vickers (2005) there seems to be no obvious and universalmanner in which non-parametric methods become significantly superior over theirparametric cousins once the data distributions shift from normal. The magnitude, oreffect size, of the difference is assessed through the use of the h 2 statistic (Tabachnickand Fidell, 2006). In any case, the auxiliary use of the independent samples t-test hasbeen escorted by the proper observation of all other related assumptions, for exampleLevene’s test for equality of variances (Stevens, 1996).

ResultsIn accordance to the intentions reported earlier in the paper, we proceeded to thecomparison of the textile SMEs of the Merit-Tex survey with the more consciousindustrial firms of the IEA sample, following the research methodology presented inthe previous section of the paper. Primarily, we will present and analyze thebenchmarking of the continuous variable indices and then we will move towards thecomparison of the Likert scale results.

Indices benchmarkingFrom the total of 37 indices examined, significant differences between the sampleswere detected in 13 attributes. These company traits involve ten indices falling in thecategory of questions that involved a temporal factor (differences detected inimprovement rates) and three indices that pertained information concerning only theabsolute value of the attribute during the time of the survey (differences detected inabsolute values). Table IV contains the statistical significance information of the

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comparison results both for the non-parametric as well as the parametric test. Alsoincluded in Table IV are the values of the h 2 statistic, indicating the magnitude of thedifference between the samples.

The significance levels of both the Mann Whitney U tests as well as the t-testsindicate a significant difference in the samples when the probability value is less than0,05. The h 2 statistic may be interpreted in various ways depending on the researcharea and type of study. The guidelines observed here indicate a small effect forh 2 ¼ 0:01, a medium effect for h 2 ¼ 0:09 and large effects for h 2 ¼ 0:25 (Cohen, 1988).

Having established the significance of the differences, in Table V we present themean index improvement rates of the two samples during a three-year period.

A quick inspection of the mean improvement rates reveals interesting information.Primarily, in order to establish the wellbeing of the excellence driven IEAmanufacturers, we turn our attention to two critical growth indices, namely salesrevenue and annual production capacity. It is evidenced that the IEA companies haveincreased their sales revenue by almost 29 percent within the three-year time frame,while the textile SMEs managed only a mediocre 8.6 percent increase. In terms of

Mann-WhitneyU-test t-test

IndicesAsymp. sig.(two-tailed)

Sig.(two-tailed) h

A. Performance results1. Sales revenue 0.000 0.003 0.073

C. Supply chain management1. Sum percentage of certified suppliers (by company or

by external agents) 0.040 0.017 0.0483. Percentage of defective supplies 0.009 0.000 0.1126. Percentage of suppliers using vendor managed

inventory programs 0.002 0.001 0.1417. Percentage of suppliers with access to compnay’s

planning and control system 0.028 0.024 0.05812. Annual production capacity (in units) (maximum

annual capacity) 0.003 0.008 0.06014. Average (plant) total process time (days) 0.004 0.002 0.15915. Average (plant) throughput time (days) 0.000 0.001 0.13116. Average changeover time (hours) 0.007 0.000 0.146

D. New products and process development4. Percentage of NPPD projects that presented schedule

overruns 0.003 0.000 0.265

E. Organization and human resources management3. Training expenses per employee (euros per year) 0.000 0.000 0.270

F. Continuous improvement (knowledge management)1. Best practices and established measurements of

performance improvement 0.000 0.000 0.3212. Frequency of performance improvement initiatives for

each area 0.000 0.000 0.275

Table IV.Samples parametric andnon-parametriccomparison results

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production capacity, the IEA companies boast a 31 percent increase while theMerit-Tex firms exhibit only an 11 percent improvement.

Moving on to other areas, in the supply chain process, we detect higherimprovement rates in favor of the IEA manufacturers all across the board. Ofparticular interest are the efforts put forward by the IEA companies in order to reducetheir average throughput and changeover times. Within three years they managed toachieve nearly an 18 percent decrease in both figures, while the textile SMEs trailbehind with only 3 percent and 7 percent decreases in each respective index. Athought-provoking figure in Table V may be detected in the decrease of the percentageof defective supplies received by the suppliers. The slower improvement rate of theIEA companies, however, is adequately explained once we review the absolute valuesof the indices at the time of the survey. These figures are presented in Table VI.

As we can see, the defective supplies received by the IEA companies represent onlya 1.7 percent of the total amount, while the same percentage exceeds the 5 percent levelfor the textile SMEs. Furthermore Table VI clearly describes that the IEA companiesare striving towards better supplier quality and integration. A total of 86 percent oftheir suppliers are certified, either by them or by an external agent, and 12.6 percentand 14.3 percent, respectively, of the suppliers have been engaged closely with the IEA

Merit-Tex IEAIndices Mean SD Mean SD

A. Performance results1. Sales revenue 0.086 0.338 0.288 0.380

C. Supply chain management1. Sum percentage of certified suppliers (by company or by

external agents) 0.097 0.224 0.138 0.2093. Percentage of defective supplies 20.020 0.029 20.008 0.0216. Percentage of suppliers using vendor-managed inventory

programs 0.007 0.030 0.056 0.1587. Percentage of suppliers with access to company’s planning and

control system 0.013 0.057 0.077 0.22212. Annual production capacity (in units) (maximum annual

capacity) 0.111 0.312 0.313 0.37614. Average (plant) total process time (delays) 20.045 0.133 20.093 0.16515. Average (plant) throughput time (days) 20.030 0.165 20.181 0.34516. Average changeover tie (hours) 20.068 0.129 20.177 0.311

D. New products and process development4. Percentage of NPPD projects that presented schedule overruns 20.011 0.115 20.041 0.197

E. Organization and human rights management3. Training expenses per employee (euros per year)a – – – –

F. Continuous improvement (knowledge management)1. Best practices and established measurements of performance

improvementa – – – –2. Frequency of performance improvement initiatives for each

areaa – – – –

Notes: All calculations were performed on SPSS. aIndex without a temporal factor

Table V.Merit-Tex and IEA meanindex improvement rates

(three-year period)

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manufacturer through VMI programs or have been granted access to the company’splanning and control system.

Furthermore, IEA companies invest more than twice the funds in employee trainingcompared to the textile firms. The IEA firms incorporate a larger number ofperformance improvement measurements in day-to-day operations, while frequentlytargeting their operations with performance improvement initiatives. All of theseobservations are simply a fraction of the helpful and targeted input that the IEA modelmay provide when used as a benchmarking tool.

Measures benchmarkingA sum of seven Likert scale measures was originally incorporated in our research.Statistically significant differences between the two samples appeared in six of them,as indicated by Table VII.

The only measure in which a significant difference was not detected is the degree ofvisibility that a company enjoys on customer demand. The remaining measuresindicated potent significant differences with elevated h 2 statistics. In Table VIII, wepresent the performances of each sample in the related measures.

Merit-Tex IEAIndices Mean SD Mean SD

A. Performance results1. Sales revenue (millions of euros) 17.07 26.91 384.70 816.46

C. Supply chain management1. Sum percentage of certified suppliers (by company or by

external agents) 44.2 42.7 86.2 21.73. Percentage of defective supplies 5.3 5.8 1.7 2.26. Percentage of suppliers using vendor-managed inventory

programs 6.6 20.6 12.6 27.47. Percentage of suppliers with access to company’s planning and

control system 4.6 17.0 14.3 30.312. Annual production capacity (in units) (maximum annual

capacity)a – – – –14. Average (plant) total process time (days)a – – – –15. Average (plant) throughput time (days)a – – – –16. Average changeover time (hours)a – – – –

D. New products and process development4. Percentage of NPPD projects that presented schedule overruns 18.1 22.1 25.9 25.9

E. Organization and human resources management3. Training expenses per employee (euros per year) 304.51 392.01 808.78 850.13

F. Continuous improvement (knowledge management)1. Best practices and established measurements of performance

improvement 4.97 7.23 13.66 8.762. Frequency of performance improvement initiatives for each

area 2.33 3.50 6.55 3.37

Notes: All calculations performed on SPSS. aSince index performance is associated with eachcompany’s industry-specific characteristics, the absolute value is irrelevant

Table VI.Merit-Tex and IEA meanindex absolute values (atthe time of the survey)

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It is quite apparent from these results that the two sets of manufacturers hold differentattitudes in most of the examined constructs. Strategy formulation and implementationin terms of stakeholders’ input and participation, respectively, appear to be a levelhigher for the IEA firms. Employees in the same companies enjoy far better recognitionprograms, while performance improvement initiatives are carefully nurtured. Theseare both areas in which textile SMEs need to improve their performance.

After recognizing the general problematic areas, one may choose to elaborate evenfurther by investigating the specifics of each measure. For instance, in terms ofemployee recognition programs and incentives, we find that the textile companiesfocus strictly on monetary rewards while the IEA firms also use frequent publicrecognition gestures (not necessarily of monetary nature) and team-based awards (Thecomplete response means for all the measure items included in this study can be foundin the Appendix). The level of the benchmarking analysis and observation to detailrests solely in the eye of the beholder.

Measure

Mann-WhitneyU test asymp.

sig. (two-tailed)t-test sig.

(two-tailed) h 2

Formulation of strategy (extent of input from stakeholders) 0.000 0.000 0.435Implementation of strategy (extent of participation fromstakeholders) 0.000 0.000 0.441Joint activities with key suppliers (frequency of cooperation) 0.000 0.000 0.184Access to customer demand (degree of visibility) 0.209 0.389 –Modern HRM practices observed (degree of accordance) 0.000 0.000 0.120Employee recognition programs and incentives usedconsistently 0.000 0.000 0.702Existence of processes for performance improvementinitiatives 0.000 0.000 0.402

Notes: All calculations performed on SPSS

Table VII.Samples’ parametric and

non-parametriccomparison results

on measures

Merit-Tex IEAMeasure Mean SD Mean SD Max scorea

Formulation of strategy (extent of input fromstakeholders) 15.50 3.60 22.74 4.50 35Implementation of strategy (extent of participationfrom stakeholders) 15.10 4.62 23.72 4.65 35Joint activities with key suppliers (frequency ofcooperation) 17.22 5.83 23.13 6.57 35Modern MRM practices observed (degrees ofaccordance) 20.18 5.52 25.21 8.12 40Employee recognition programs and incentives usedconsistently 0.88 0.72 4.03 1.31 30Existence of processes for performance improvementinitiatives 8.33 4.28 16.75 5.76 25

Note: aTotal sum of Likert scale items reported only for reference reasons

Table VIII.Merit-Tex and IEA

performance in Likertscale measures

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ConclusionsIn this paper, we reported on the application of the Industrial Excellence Award modelin a number of textile-clothing SMEs from the European textile sector. We presentedthe fundamentals of the model and explained the way that the methodology can beused in order to provide operational insights concerning the problematic areas of anailing industrial sector. The nature of the indices and measures collected by the modelwas outlined in detail and a statistical analysis strategy for each category of attributeswas also offered.

Furthermore, an indicative analysis of the benchmarking procedure was conductedin order to illustrate the abilities of the model. The analysis was performed with the useof an additional reference sample of companies containing excellence-driven Europeanmanufacturers who participated in IEA competitions held in Germany and France. Thetextile companies were thus benchmarked against the IEA firms. The comparison ofthe samples through the proposed strategy intentionally overlooked anyindustry-specific attributes, and focused on those key processes that are sharedamong manufacturers of any business sector.

The model’s ability to characterize the operational aspects of industrial excellencewas also demonstrated through the benchmarking process. The statisticallysignificant differences detected between the samples targeted specific proceduresand mechanisms that can be appreciated by any knowledgeable industrial manager. Inaddition, the management quality concept was also addressed through the Likert scalemeasures included in the study. The examination of these measures revealeddifferences in the attitude of each company sample towards such diverse issues asstrategy formulation and deployment and employee motivation.

The IEA model, being adapted and reviewed during the last ten years through therepeated observation and analysis of industrial management practices across Europe,continues to evolve and mature. It is our belief that it is a solid and insightful tool thatcan be used as a versatile diagnostic and development kit by those managers that wishto empower their company in the face of intensifying competition. A new industrialcompetitiveness front is swiftly emerging across Europe, and with rival manufacturersat the gates one should at least know where one stands.

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AppendixThe indices and measures used in the study derive from the six main sections of the IEAquestionnaire, which are:

A. Performance results;

B. Strategy formulation and deployment;

C. Supply chain management;

D. New products and process development (NPPD);

E. Organization and human resources management; and

F. Continuous improvement (knowledge management).

The questionnaire in its entirety may be requested directly from INSEAD, while it can also befound in Loch et al. (2003).

Indices under investigationThe first bracket after each index contains the significance value of the Kolmogorov-Smirnovstatistic for the Merit-Tex and the IEA sample respectively before screening the data for outliers.The second bracket contains the same information after removing extreme values and distinctoutliers from the datasets.

A. Performance results indices

(1) Sales revenue [0.00, 0.00] [0.00, 0.19].

(2) Gross profit margin (absolute figure) [0.17, 0.00] [0.17, 0.35].

(3) Gross profit margin (% relative to sales) [0.00, 0.00] [0.00, 0.00].

(4) On-time delivery rate (% of units delivered on time) [0.00, 0.00] [0.00, 0.00].

(5) Complaint ratio (average number of complaints per million units shipped) [0.00, 0.00][0.00, 0.00].

(6) Product defect rate (average number of defects per million units shipped) [0.00, 0.00][0.00, 0.00].

(7) Warranty cost (annual cost of warranty as a % of total sales revenue) [0.00, 0.00] [0.00,0.00].

(8) Order lead time for standardised/customized products (days) [0.00, 0.02] [0.00, 0.02].

(9) Reaction capability on order increases (% of extra volume absorbed) [0.00, 0.00] [0.00,0.00].

(10) Average time to market from concept to launch (months) [0.00, 0.00] [0.00, 0.00].

(11) Employee productivity (sales revenue/total number of employees) [0.20, 0.00] [0.20, 0.00].

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C. Supply chain management indices

(1) Sum percentage of certified suppliers (by company or by external agents) [0.00, 0.00][0.00, 0.00].

(2) Delayed deliveries from supplier (percentage of total deliveries) [0.00, 0.00] [0.00, 0.00].

(3) Percentage of defective supplies [0.00, 0.00] [0.00, 0.00].

(4) Percentage of rejected deliveries (returned supplies) [0.00, 0.00] [0.00, 0.00].

(5) Percentage of suppliers delivering on a JIT basis [0.00, 0.00] [0.00, 0.00].

(6) Percentage of suppliers using vendor managed inventory (VMI) programs [0.00, 0.00][0.00, 0.00].

(7) Percentage of suppliers with access to company’s planning and control system [0.00,0.00] [0.00, 0.00].

(8) Percentage of suppliers to which there is access to their planning and control system[0.00, 0.00] [0.00, 0.00].

(9) Percentage of transactions with EDI (electronic data interchange currency) [0.00, 0.00][0.00, 0.00].

(10) Percentage of purchases through internet transactions [0.00, 0.00] [0.00, 0.00].

(11) Number of production schedule changes per month [0.00, 0.00] [0.00, 0.00].

(12) Annual production capacity (in units) (maximum annual capacity) [0.00, 0.00] [0.00, 0.00].

(13) Percentage of capacity lost (capacity not used) [0.00, 0.00] [0.00, 0.00].

(14) Average (plant) total process time (days) [0.00, 0.13] [0.00, 0.13].

(15) Average (plant) throughput time (days) [0.00, 0.00] [0.00, 0.00].

(16) Average changeover time (hours) [0.00, 0.00] [0.00, 0.00].

D. New products and process development (NPPD) indices

(1) Annual budget for new product development [0.00, 0.00] [0.00, 0.00].

(2) Annual budget for new process development [0.00, 0.00] [0.97, 0.00].

(3) Percentage of launched projects that were successful in the market [0.00, 0.00] [0.00, 0.00].

(4) Percentage of NPPD Projects that presented schedule overruns [0.00, 0.00] [0.00, 0.04].

E. Organization and human resources management indices

(1) Employee turnover rate (%) [0.00, 0.00] [0.00, 0.02].

(2) Last year’s budget for training (% of total labour costs) [0.00, 0.00] [0.00, 0.00].

(3) Average number of formal training days per employee (days per year) [0.00, 0.00] [0.00, 0.00].

Measures (Likert scales) under investigationThe items marked “†” were found to reduce Cronbach’s alpha scale reliability indicator, and werethus removed. The parentheses after each scale item indicates the mean response reported in theMerit-Tex and IEA samples, respectively.

B. Strategy Formulation & Deployment measuresb1. Formulation of Strategy Input (extent of input: min 1; max 5)

(1) Corporate headquarters (3.17, 4.34).

(2) Functional management (2.73, 4.10).

(3) †Management of business unit.

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(4) Plant management (2.92, 4.05).

(5) Group/team (1.77, 3.19).

(6) All employees (1.23, 2.11).

(7) Customers (2.27, 2.93).

(8) Supplier (1.42, 2.07).

b2. Implementation of strategy participation (extent of participation: min 1; max 5)

(1) Corporate headquarters (2.80, 3.66).

(2) Functional management (2.58, 3.94).

(3) †Management of business unit.

(4) Plant management (3.08, 4.29).

(5) Group/team (2.17, 4.22).

(6) All employees (1.67, 3.27).

(7) Customers (1.73, 1.99).

(8) Supplier (1.52, 2.45).

C. Supply chain management measuresc1. joint activities with key suppliers (frequency: min 1; max 5)

(1) External meetings (cross-company, workshops, etc.) (2.47, 3.72).

(2) Usage of supplier know-how for manufacturing process improvements (3.03, 3.49).

(3) Usage of supplier know-how for product design improvements (3.17, 3.44).

(4) Joint improvement programs with second tier suppliers (2.18, 3.27).

(5) Performance evaluation programs with suppliers (2.23, 3.60).

(6) Formal supplier development programs (1.85, 2.86).

(7) Buying of machinery and tools decisions (2.28, 3.26).

c2. Access to customer demand (degree of visibility: min 1; max 5)

(1) Customer sales forecasts (2.37, 3.36).

(2) Customer’s inventory level (1.90, 2,82).

(3) Customer’s production schedule (2.14, 2,83).

(4) Customer’s order backlog (2.02, 2,84).

E. Organization and Human resources management measurese1. Modern HRM practices (degree of accordance with statements: min 1; max 5)

(1) Senior management makes financial and performance data widely available (2.22, 4.33).

(2) People are encouraged to bypass formal channels to get their job done (2.77, 2.60).

(3) Cross-functional teams are commonly used (2.27, 4.35).

(4) Interplant teams commonly used to address issues regarding multiple plants (2.52, 3.96).

(5) Openly encourage questioning & debate of issues (3.00, 4.43).

(6) Sponsoring and encouragement of social functions (2.40, 3.87).

(7) Sponsoring and encouragement of multi-plant social functions (2.22, 3.17).

(8) †People wear highly distinct uniforms (that indicate position).

(9) Budget and resources fluid. Can be redeployed without much bureaucracy (3.25, 3.42).

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e2. Employee recognition programs and incentives used consistently

(1) Performance based award programs (0.09, 0.77).

(2) Frequent public recognition (non award) (0.12, 0.78).

(3) Team based rewards (monetary/other) (0.21, 0.77).

(4) Monetary rewards for improvement suggestions (0.64, 0.78).

(5) †Salary based on qualifications.

(6) Profit sharing (0.05, 0.67).

(7) Stock options (0.00, 0.36).

F. Continuous improvement (knowledge management) measuresf1. Existence of processes for performance improvement initiatives (extent of existence)

(1) Formal processes for description and distribution of successful PI initiatives (1.72, 3.52).

(2) Formal process for reviewing and learning from unsuccessful PI initiatives (1.75, 2.80).

(3) Formal process for benchmarking performance against other companies (1.52, 3.27).

(4) Documentation of actual performance improvement by past initiatives (1.73, 4.17).

(5) Availability of documented performance improvement by past initiatives (1.62, 3.96).

Other measures and indices under investigationThe first bracket after each index contains the significance value of the Kolmogorov-Smirnovstatistic for the Merit-Tex and the IEA sample respectively before screening the data for outliers.The second bracket contains the same information after removing extreme values and distinctoutliers from the datasets.

E. Organization and human resources management indices

(1) Training expenses per employee (euros per year) [0.00, 0.00] [0.00, 0.00].

F. Continuous improvement (knowledge management) indices

(1) Number of best practices & measurements of performance improvement [0.00, 0.20] [0.00,0.20].

(2) Frequency of performance improvement initiatives in each operational area [0.00, 0.00][0.00, 0.00].

About the authorsNikolaos Bilalis is Associate Professor and the Director of CAD Laboratory in the Department ofProduction Engineering and Management of the Technical University of Crete. He holds a Dipl.Mechanical and Electrical Engineer from the National Technical University of Athens, Greece, aMSc in Mechanical Engineering Design from Aston University of Birmingham, UK, and a PhD inProduction Engineering from Loughborough University of Technology, UK. His current researchand teaching interests are in technologies for product development (CAD, CAM, virtualprototyping and reverse engineering, end of life management) and in manufacturing excellenceapplications in traditional sectors. He has extensive participation and management in Europeanand National Research and Development projects and he has served as evaluator and reviewerfor the EEC, for the Greek General Secretary for Research and Technology on Research andDevelopment Projects and on New and Innovative Products Investments, and he is member ofthe evaluation committee for Patents Grants. Nikolaos Bilalis is the corresponding author andcan be contacted at bilalis@dpem.tuc.gr

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Emmanuel Alvizos is a Research Associate in the Department of Production Engineering andManagement of the Technical University of Crete. He holds a first degree in ProductionEngineering & Management from the Technical University of Crete, Greece, and he is currently apostgraduate student in organisation and management and the Technical University of Crete,Greece.

Loukas Tsironis is a Lecturer in Management Systems at the Technical University of Crete.He holds a first degree in Forestry and the Natural Environment (Aristotelian University ofThessaloniki), and MSc and PhD degrees in Engineering Management Systems from theTechnical University of Crete. He is a Scientific & Project Manager in the Management SystemsLaboratory. His research interests comprise business process management, workflowmanagement, supply chain management, business intelligence systems. His publicationsaddress a number of related topics in several journals and referred conferences.

Luk van Wassenhove is a senior editor of Manufacturing and Service OperationsManagement and an associate editor of Production and Operations Management, Technologyand Operations Review and International Journal of Production Economics. He publishesextensively in Management Science, Operations Research, The International Journal ofProduction Research, The European Journal of Operations Research and in many other academicas well as management journals (such as Harvard Business Review, California ManagementReview and the like). He is the author of many teaching cases and regularly consults for majorinternational corporations. Professor van Wassenhove’s research and teaching are concernedwith operational excellence, supply chain management, quality, continual improvement andlearning. His recent research focus is on closed-loop supply chains (product take-back andend-of-life issues) and on disaster management (humanitarian logistics). Before joining INSEAD,he was on the faculty at Erasmus University and at the Katholieke Universiteit Leuven. AtINSEAD he holds the Henry Ford Chair in Manufacturing.

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