Best manufacturing practices What do the best-performing companies do?

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Best manufacturing practices

What do the best-performing companies do?

Bjørge Timenes Laugen

Stavanger University College, Department of Business Administration,

Stavanger, Norway

Nuran Acur and Harry Boer

Aalborg University, Centre for Industrial Production, Aalborg, Denmark, and

Jan Frick

Stavanger University College, Department of Business Administration,

Stavanger, Norway

Abstract

Purpose– Research on best practices suffers from some fundamental problems. The problem addressed in the article is that authors tend to postulate, rather than show, the practices they address to be best – whether these practices do indeed produce best performance is often not investigated.

Design/methodology/approach– This article assumes that the best performing companies must be the ones deploying the best practices. In order to find out what are those practices, the highest performing companies in the 2002 International Manufacturing Strategy Survey database were identified, and the role 14 practices play in these companies was investigated.

Findings– Process focus, pull production, equipment productivity and environmental compatibility appear to qualify as best practices. Quality management and ICT may have been best practice previously, but lost that status. E-business, new product development (NPD), supplier strategy and outsourcing are relatively new, cannot yet be qualified as, but may develop into, best practice. Four other practices do not produce any significant performance effects.

Research limitations/implications– There are four limitations to the research: Incompleteness of the set of practices tested: lack of insight into the effects of interaction between practices and the way in and extent to which they were implemented; good explanatory but poor predictive power; and lack of contextuality.

Originality/value– Taking the position that best practice must be what best performing companies do, the paper is useful for managers using benchmarking to review the design and performance of their manufacturing system, and for scholars engaged or interested in best practice studies.

KeywordsOperations and production management, Performance management, Working practices, Strategic manufacturing

Paper typeResearch paper

Introduction

Today’s market and competitive pressures require companies to develop and maintain a high level of coherence between their strategy (objectives), action programmes (implementation), practices (instantiation) and performance (realisation). A lot of effort has been put into identifying “best” practices to support companies achieve superior performance. However, most research has failed to investigate the effect of these practices on performance, whilst perhaps even less is known about the extent to which they are indeed generic. Therefore, this paper will take a different perspective. Focusing on manufacturing practices and performance and defining best practices as the practices used by, and having significant effect on the performance of, the best

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International Journal of Operations & Production Management Vol. 25 No. 2, 2005 pp. 131-150

qEmerald Group Publishing Limited 0144-3577 DOI 10.1108/01443570510577001

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performing companies, the research question is: “Which manufacturing practices are used by the best performing organisations?”.

The article is structured as follows. First, we present the background of the research. Next we describe the research design (question, operationalisation, methodology). Subsequently we present and then discuss the results of an analysis of data from the Third International Manufacturing Strategy Survey. Finally, we present the main contributions of the research, identify the main weaknesses and propose directions for further research.

Background

In the early 1970s the “MRP crusade” was an attempt to spread MRP to as many companies as possible. Although the word “best practice” had not been invented yet, the underpinning assumption was that MRP is good, i.e. good in terms of practice and performance effects, for all companies. Today we know better.

In the late 1970s and early 1980s, the “best practice” approach to manufacturing strategy seriously entered the industrial and academic agenda with the recognition of the extraordinary process and product improvement success of Japan Inc. Western industries and academics alike began to look at Japanese companies’ achievements in order to understand the principles behind that. “Best practice” achievement has since become a driving force amongst industry. The best practice approach to manufacturing strategy encapsulates the world class manufacturing (WCM) philosophy and benchmarking, and is based on the assumption that “The continuous improvement of best practice in all areas of the organisation will lead to superior performance capability leading to increased competitiveness” (Voss, 1995a).

The following analysis of the WCM and best practice literature, summarised in Table I, uncovers what we think are the key weaknesses of the field.

Hayes and Wheelwright (1984) introduced the term WCM, and described this as a set of practices, including quality management, continuous improvement, training and investment in technology. The implementation of these “best practices” would lead to

superior performance (Flynnet al., 1999, p. 250).

Schonberger (1986) argued that many lessons could be learned from the Japanese manufacturing industry. He regarded improving the material flow in the production as one of the most important issues, and the flow could be improved through implementing just-in-time (JIT), total quality control and total preventive maintenance (TPM). In addition, still according to Schonberger, WCM means continual and rapid improvement in all areas of the company, and training is the catalyst (Schonberger, 1986, p. 207).

The basic principle of the best practice thinking is that operations philosophies, concepts and techniques should be driven by competitive benchmarks and business excellence models to improve an organisation’s competitiveness through the

development of people, processes and technology (Greswellet al., 1998; Voss, 1995b).

In these studies, e.g. JIT, total quality management (TQM) and the European Foundation of Quality Management (EFQM) model are defined as best practices and assumed to imply improved performance.

Useful as they may be, the WCM and best practice studies suffer from three weaknesses.

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Researchers

Key concept to best practice

Results regarding practice-performance relationships

Swamidass and Newell (1987)

Cross-functional co-operation, design for manufacturability

Corporate performance is positively related to the role of manufacturing managers in strategic decisions

Voss (1995a) World-class

manufacturing, bench-marking, business process re-engineering, TQM, learning from the Japanese, continuous improvement (CI)

Implementation of best (world class) practices leads to superior performance and capability

Ahmedet al.(1996) TQM, JIT, FMS, CE,

benchmarking

When practices (operations strategies) are examined individually, companies using any of seven practices (FMS, CE, benchmarking, TQM, JIT, manufacturing cells and computer networking) have higher performance than those not using them

Boldenet al.(1997) WCM, employee

development

The classification of manufacturing practices taxonomies developed provides insight into the role of individual practices, implementation and outcomes

Flynnet al.(1997) WCM, TQM, JIT The best users of unique TQM practices, combined

with common infrastructure practices, are capable of solving problems to improve production processes

Harrison (1998) WCM, CI WCM appears to facilitate operator commitment to

continuous improvement, but leaders become more frustrated because they expected to achieve more. Cellular manufacturing in a UK-based company acted as a powerful change agent, which has led to more in terms of manufacturing improvement than previous initiatives, such as MRP II

Flynnet al.(1999) WCM, CI, JIT, TQM The use of WCM, alone and together with other new

practices, leads to improved competitive performance

Kathuria and Partovi (1999)

Cross-functional co-operation

Better performing manufacturing managers strongly demonstrate relationship-oriented practices, such as team building and support, participative leadership and delegation, especially when the emphasis on flexibility is high

Rondeauet al.

(2000)

Work system practices, time-based competition

Time-based manufacturing practices tend to lead to standardisation, formalisation as well as integration Davies and Kochhar (2002) Best practices, performance, manufacturing planning and control

A structured approach used to identify direct qualitative relationship between practice and performance

Garver (2003) Benchmarking, CI Integrating customer performance measures with

internal performance measures (internal quality, productivity etc.) to identify improvement opportunities is found to be critical Ketokivi and

Schroeder (2004)

TQM, JIT, WCM, contingency

There are only few best practices contributing to competitive manufacturing performance in multiple dimensions

Table I.

Main theoretical contributions to links between best practices and performance

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First, the field is rather scattered with many articles focusing on one or a limited set of new practices, while the reasons why these practices are considered best are often not

accounted for. Flynn et al.(1997), for example, investigated the influence of quality

management practices on quality performance and the interrelationship between JIT

and quality management. Hanson et al. (1994), Hanson and Voss (1995) and Voss

(1995a) focused on TQM, concurrent engineering (CE) and lean production. Ketokivi and Schroeder (2004) chose to study computer-aided design (CAD), computer-aided manufacturing (CAM) and statistical quality control (SQC). Why these practices, not others, and whether the authors regard the set as comprehensive remains unclear. Related to this, WCM and best practice studies do not take into consideration that other practices, or configurations of practices, might be even more important for the overall performance of the companies than the predefined best practices. There may be companies that do not reach world-class status, due to the definition of best practices in these studies, which are really world class in terms of performance, but have implemented another set of practices to reach that level of performance.

Second, best practice studies only rarely link the practices investigated to company performance. At best, a positive effect of the practices on performance is assumed

(Voss, 1995a), or (implicitly) considered self-evident (Vosset al., 1997, p. 284). Only a

few studies confirm that the use of best practices leads to improved performance (e.g.

Hanson and Voss, 1995; Vosset al., 1997). However, if an explicit link is made, this is

done only for a limited set of performance criteria. An example is Davies and Kochhar (2002), who showed that the implementation of quality programmes leads to increased quality performance. The few (Davies and Kochhar, 2002) more holistic publications linking practices to performance are usually based on one or a few single case studies (e.g. Peters and Waterman, 1982), which offer limited possibilities for generalisation. Finally, too little effort is put into analysing the relationship between the different practices and the relative effect both individual practices and their interaction have on performance.

Third, best practices are considered generic, that is, best for all companies, always. The potential influence of factors like type of industry, company size, processes and products is not considered, nor is the fact that practices, even the best ones, may become obsolete in the course of time.

In other words, the practices studied are often not accounted for and postulated as, rather than shown to be, best, always and for all. Consequently, there may be best practices that have never been studied and also the relative contribution of individual practices to performance and as well as the interaction between practices is insufficiently studied. Accordingly, Davies and Kochhar (2002) put forward three main points for future research on best practices:

(1) Best practices are those that lead to improvement in performance. That is, they help a low performing company become a medium performer, a medium performer become a high performing company, and a high performer stay successful.

(2) Best practices must be investigated within the specific context in which they operate.

(3) The investigation of best practices should be approached holistically.

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Research question

In fact, Davies and Kochhar (2002) suggest that the reasoning behind best practice studies should be turned around. Focusing on these authors’ first suggestion (i.e. the second problem identified above), this article is based on the assumption that the best performing companies are the ones that (must) have the best practices. The question we address in this article therefore is: “Which practices are used by the best performing organisations?”

The answer to this question will tell us what are “best practices”. The data we had available (see below) did not allow us to pay serious attention to the other two suggestions in the analysis presented here. The work presented here should therefore be regarded as a first step towards developing a best performance based theory of best manufacturing practices.

Research design

Sample

This article is based on the 2002 International Manufacturing Strategy Survey (IMSS-III) database, which contains data from 474 manufacturing companies in 14 countries. IMSS is a co-operative research network of business schools, which aims at developing, maintaining and analysing using a variety of perspectives and research questions, a global database for the study of manufacturing strategies, practices and performances. The respondents are representing five industrial sectors: ISIC 381 (metal manufacturing), 382 (machinery), 383 (electrical equipment), 384 (automotive) and 385 (professional measurement equipment).

Data analysis

We analysed the data in three steps. First, based on 17 manufacturing performance criteria investigated, we introduced four variables to measure the improvement in the categories quality, flexibility, speed and cost (see Table II for details). We divided the respondents into two groups, high performers and low performers, for each of these four performance categories and for each of the eleven combinations of these categories (see Table II). Next, we performed an ANOVA to determine the differences in the adoption of action programmes between the two groups for each of the (in total) 15 categories (see Table III). Finally, we developed a regression model in order to find which action programmes have most influence on manufacturing performance (see Table IV).

Operationalisation of variables and descriptive results

This section explains the data concerning performance improvement and action

programmes indicated by the sample companies.

Performance improvement. Manufacturing companies must continuously adapt to

new performance requirements in terms of quality, flexibility, speed and cost. The IMSS survey asked the respondents to rank their company’s performance improvements within the last three years on the basis of 17 indicators (see Table II). The questions were

measured on a five-point Likert scale (1¼performance has strongly deteriorated over

the last three years, 3¼no change, 5¼performance has strongly improved). We

defined high performers as companies reporting an average score 4 or higher on all the performance indicators taken into the analysis. Low performers are respondents

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indicating an average score 3 or lower on all the performance criteria, meaning either deterioration in performance or at best maintaining status quo.

Table II shows that a relatively large percentage of the IMSS companies (187) have strongly improved their quality performance during the last three years, and can be categorised as high performers in quality. For 70 respondents, quality performance has stayed the same or even deteriorated. While 135 of the respondents have strongly improved their flexibility performance over the last three years, 76 respondents have at best maintained status quo. A total of 145 companies have strongly improved their speed performance, while 102 respondents have maintained status quo or not even that. For cost performance the picture is that 72 of the respondents claim to have strongly improved on cost performance within the last three years, while 110 respondents failed to achieve that.

Improvement in manufacturing performance

during the last three years Average SD

Number of high performers Number of low performers Quality (Q) 3.7 0.5 187 70 Manufacturing conformance 3.7 0.6

Product quality and reliability 3.8 0.7

Customer service and support 3.7 0.7

Delivery reliability 3.7 0.8

Environmental performance 3.5 0.8

Flexibility (F) 3.6 0.5 135 76

Product customisation ability 3.6 0.8

Volume flexibility 3.8 0.8

Mix flexibility 3.6 0.7

Time to market 3.5 0.7

Speed (S) 3.6 0.5 145 102

Delivery speed 3.7 0.8

Manufacturing lead time 3.6 0.7

Procurement lead time 3.3 0.7

Cost (C) 3.4 0.5 72 110 Procurement cost 3.3 0.8 Labour productivity 3.6 0.7 Inventory turnover 3.4 0.7 Capacity utilisation 3.5 0.8 Overhead cost 3.2 0.8 Combination of:

Quality and flexibility (Q-F) 81 19

Quality and speed (Q-S) 88 41

Quality and cost (Q-C) 48 33

Flexibility and speed (F-S) 56 25

Flexibility and cost (F-C) 27 23

Speed and cost (S-C) 46 41

Quality, flexibility and speed (Q-F-S) 43 12

Quality, flexibility and cost (Q-F-C) 22 12

Quality, speed and cost (Q-S-C) 34 22

Flexibility, speed and cost (F-S-C) 22 16

Quality, flexibility, speed and cost (Q-F-S-C) 18 10

Table II.

Average values for the manufacturing

performance criteria and number of respondents for the groups of high and low performing

companies

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Impr ovement in manu facturing performance during last Ac tion pro grammes last three years a three years A B C D E F G H I J K L M N Qualit y Mean high 3.50 3.47 2.76 3.33 2.11 2.97 2.80 3.33 3.08 3.48 3.02 3.25 2.96 3.60 Mean low 2.86 3.12 2.17 2.89 1.73 2.45 2.25 2.37 2.16 2.56 2.21 2.53 2.68 2.88 Flexibi lity Mean high 3.48 3.38 2.64 3. 18 1.99 3.03 2.67 3.33 3.08 3.28 2.97 3.13 3.14 3.53 Mean low 2.99 2.93 2.28 3. 00 1.75 2.64 2.45 2.33 2.02 2.88 2.23 2.62 2.29 3.06 Speed Mean high 3.42 3.33 2.79 3.36 2.10 3.11 2.85 3.43 3.18 3.51 2.95 3.28 3.05 3.60 Mean low 3.06 3.30 2.33 2.95 1.78 2.41 2.30 2.67 2.32 2.78 2.31 2.55 2.62 3.04 Cost Mean high 3.57 3.46 3.19 3.67 2.34 3.23 3.19 3.41 3.31 3.52 3.19 3.32 3.26 3.71 Mean low 3.16 3.35 2.46 3.14 1.97 2.63 2.35 2.86 2.52 2.91 2.39 2.81 2.57 3.11 Q-F Mean high 3.58 3.49 2.77 3. 29 2.08 3.19 2.88 3.48 3.06 3.53 3.19 3.39 3.22 3.75 Mean low 2.24 2.24 1.82 2. 71 1.59 2.35 2.19 1.47 1.47 2.41 1.65 2.29 2.24 2.59 Q-S Mean high 3.54 3.51 2.85 3. 42 2.23 3.22 2.99 3.51 3.22 3.60 3.17 3.36 3.04 3.69 Mean low 2.69 3.03 2.08 3. 00 1.73 2.17 2.36 2.40 2.06 2.51 2.14 2.59 2.72 2.87 Q-C Mean high 3.61 3.55 3.07 3.59 2.33 3.29 3.24 3.49 3.28 3.62 3.26 3.45 3.29 3.93 Mean low 2.67 2.94 2.23 2.45 1.58 2.39 2.13 2.32 1.97 2.48 2.16 2.48 2.48 3.03 F-S Mean high 3.45 3.35 2.6 0 3.32 2.07 3.29 2.82 3.51 3.20 3.31 3.08 3.1 1 3.23 3.59 Mean low 2.50 2.67 2.0 5 2.62 1.81 2.10 2.25 1.81 1.48 2.38 1.86 2.3 8 2.33 2.76 F-C Mean high 3. 57 3.22 2.9 1 3 .4 2 2.33 3.30 2.95 3.32 3.26 3.40 3.09 2.9 1 3.52 3.71 Mean low 2. 95 2.80 2.2 0 2 .8 0 1.80 2.35 2.00 1.75 1.60 2.55 1.80 2.5 0 2.35 2.80 S-C Mean high 3.58 3.56 3.21 3.62 2.32 3.26 2.90 3.44 3.40 3.58 3.19 3.31 3.20 3.77 Mean low 2.84 3.11 2.18 2.65 1.60 2.40 2.24 2.54 2.18 2.68 2.20 2.51 2.54 2.89 Q-F-S Mean high 3.54 3.39 2.6 9 3 .4 6 2.11 3.42 3.06 3.70 3.27 3.49 3.31 3.3 5 3.28 3.73 Mean low 1.80 2.10 2.0 0 2 .5 0 1.50 1.90 2.33 1.40 1.30 2.40 1.50 2.40 2.50 2.50 Q-F-C Mean high 3.56 3.28 2.9 4 3.47 2.33 3.18 3.06 3.29 3.17 3.55 3.24 3.1 1 3.44 3.84 Mean low 2.20 2.10 2.0 0 2.20 1.70 2.00 2.30 1.30 1.20 2.20 1.40 2.4 0 2.40 2.70 Q-S -C Mean high 3.59 3.50 3.00 3.52 2.32 3.20 2.93 3.50 3.19 3.59 3.23 3.4 5 3.16 3.84 Mean low 2.32 2.65 2.10 2.55 1.45 2.05 2.25 2.25 1.95 2.45 2.10 2.6 0 2.50 2.80 F-S-C Mean high 3.60 3.35 2.9 5 3 .4 4 2.33 3.18 2.83 3.28 3.32 3.29 3.16 2.8 9 3.42 3.75 Mean low 2.38 2.58 2.2 5 2 .3 3 1.83 2.17 2.00 1.75 1.67 2.50 1.75 2.5 0 2.42 2.75 Q-F-S -C Mean high 3.56 3.38 3.0 0 3 .5 4 2.38 3.07 3.00 3.29 3.20 3.47 3.33 3.1 3 3.33 3.81 Mean low 1.75 2.00 2.0 0 2 .2 5 1.63 1.75 2.25 1.38 1.25 2.38 1.50 2.5 0 2.50 2.50 Notes : a A ¼ process equip ment; B ¼ manufacturing capac ity; C ¼ process auto mation; D ¼ ICT; E ¼ business; F ¼ supplier strat egy; G ¼ outsourcing; H ¼ process focus; I ¼ pull production; J ¼ quality management; K ¼ equipment productivity; L ¼ workplace development; M ¼ NPD; N ¼ environmental compatibility. Figures in bold are significant at p # 0 : 01, figures in italics are significant at p # 0 : 05 Table III. Differences in mean values between high and low performing companies (ANOVA) for the investigated action

programmes (1¼no use, 5¼high use)

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Action programmes last three years Action programmes last three years a AB C D E F G H I J K L M N Quality Beta 0.046 0.047 0.111 0.080 0.172 0.064 2 0.135 0.216 Sign 0.388 0.380 0.079 0.197 0.003 0.297 0.019 0.000 Flexibility Beta 0.074 0.070 2 0.086 2 0.065 2 0.118 0.202 0.127 2 0.070 0.143 0.120 Sign 0.192 0.190 0.157 0.242 0.029 0.001 0.041 0.239 0.034 0.034 Speed Beta 2 0.040 2 0.020 0.068 0.127 0.083 0.101 0.084 0.149 Sign 0.455 0.739 0.240 0.052 0.194 0.091 0.180 0.08 Cost Beta 2 0.053 0.075 0.094 0.148 0.069 Sign 0.365 0.188 0.115 0.019 0.247 Q-F Beta 0.057 0.033 2 0.080 2 0.039 2 0.12 0.213 0.129 0.059 0.152 0.150 Sign 0.311 0.534 0.178 0.481 0.826 0.000 0.036 0.312 0.022 0.007 Q-S Beta 0.035 0.124 0.089 0.073 2 0.108 0.246 Sign 0.484 0.037 0.127 0.211 0.044 0.000 Q-C Beta 0.054 0.073 0.102 0.167 2 0.004 0.138 Sign 0.325 0.183 0.079 0.007 0.952 0.018 F-S Beta 0.046 2 0.063 2 0.052 0.093 0.236 0.143 0.175 Sign 0.384 0.284 0.330 0.094 0.000 0.021 0.004 F-C Beta 0.038 0.058 2 0.062 2 0.101 0.059 0.134 0.119 2 0.050 0.189 0.107 0.043 Sign 0.528 0.301 0.323 0.080 0.316 0.039 0.069 0.425 0.008 0.073 0.472 S-C Beta 0.075 0.068 0.107 0.118 0.133 Sign 0.155 0.262 0.082 0.044 0.015 Q-F-S Beta 2 0.036 2 0.041 0.037 0.188 0.111 0.073 0.061 0.049 0.195 Sign 0.513 0.419 0.486 0.002 0.062 0.208 0.342 0.413 0.000 Q-F-C Beta 0.000 2 0.049 0.153 0.118 0.067 0.108 0.171 Sign 0.994 0.339 0.010 0.051 0.244 0.095 0.002 Q-S-C Beta 0.074 0.040 0.067 0.059 0.183 0.082 2 0.118 0.201 Sign 0.166 0.471 0.287 0.343 0.002 0.191 0.038 0.000 F-S-C Beta 2 0.072 0.153 0.129 0.145 0.085 0.105 Sign 0.172 0.011 0.035 0.014 0.121 0.055 Q-F-S-C Beta 2 0.047 0.148 0.122 0.063 0.086 0.057 0.172 Sign 0.361 0.014 0.042 0.275 0.163 0.338 0.001 Notes : aA ¼ process equip ment; B ¼ manufacturing capac ity; C ¼ process auto mation; D ¼ ICT; E ¼ business; F ¼ supplier strat egy; G ¼ outsourcing; H ¼ process focus; I ¼ pull production; J ¼ quality management; K ¼ equipment productivity; L ¼ workplace development; M ¼ NPD; N ¼ environmental compatibility. Coefficients in bold are statistically significant at p # 0 : 1 Table IV. Regression analysis: standardised coefficients

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This distribution indicates that quality performance is (still) important for the engineering industry. Cost reduction is the least important goal. This finding is

consistent with recent research by Caglianoet al. (n.d.), which showed that price is

losing ground as a competitive priority. Another explanation may be that cost reduction programmes have been used in manufacturing companies for so long now, that possibilities for further reductions are small, leading companies to focus on improving in other areas.

Action programmes. In the survey questionnaire, the term “action programmes” is

used instead of “(manufacturing) practices”, for two reasons. First, the term programme reflects the implementation of bundles of practices. Pull production, for example, is an action programme; Kanban and single-minute exchange of dies (SMED) are practices underpinning pull production. Second, following Davies and Kochhar’s (2002) recommendations for best practice studies, we are interested in performance improvements and, thus, changes in practices (i.e. action programmes), rather than (bundles of) practices in place.

In the survey we investigated 14 action programmes (i.e. implementation of new practices), which are listed and defined in Table V. The degree of use during the last

three years, measured on a 1-5 Likert scale (1¼no usage, 5¼high usage), represents

the independent variable we used in our analysis.

Results

The adoption of action programmes

The ANOVA (Table III) shows that there are many significant differences in the degree of implementation of action programmes between high and low performing companies. This indicates that most of the single action programmes have been implemented differently among the high and low performing companies.

E-business, process automation and outsourcing are the least implemented programmes among the respondents in all categories of performance. The difference between the high and low performers is not significant in any of the performance categories.

High performers in all categories implement programmes directed towards updating process equipment, process focus, pull production and equipment

productivity to a significantly (p#0:01) higher degree than the low performers.

The exception to this is the difference in implementation of process equipment for flexibility-cost (F-C), which is non-significant. These four action programmes are also used more by the high performers in the single performance categories, although the

significance is lower (p#0:05).

Finally, programmes directed towards supplier strategy, quality, workplace development, new product development and environmental compatibility are in general also significantly more adopted by the high performers.

The differences between high and low performers suggest that high performers implement more and gain more from the action programmes they adopt. Another possible explanation is that high performers continue implementing the programme until it is finished, instead of stopping the implementation after a short period, for example if the results are not as expected. So, the difference between high and low performers seems to be related to implementation “width” and “depth”.

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The performance effects of the action programmes

The ANOVA does not reveal the performance impact of the different action programmes. In order to investigate that, we performed a regression analysis. The results are shown in Table IV and discussed next.

Quality performance. Implementing action programmes that aim at improving

manufacturing capacity, improving quality management and environmental compatibility, and obtaining process focus are positively related to quality performance improvement.

The effect of quality management and environmental compatibility programmes on quality performance and its underpinning indicators, especially product quality and reliability, manufacturing conformance and environmental performance, is not surprising. Essentially, this finding confirms that these programmes pay off as intended.

Action programme Operationalisation

Process equipment Updating the company’s process equipment to industry standard

or better

Manufacturing capacity Expanding manufacturing capacity (e.g. buying new machines,

hiring new people, building new facilities)

Process automation Engaging in process automation programmes

ICT Implementing information and communication technologies

and/or enterprise resource planning software

E-business Reorganising the company towards e-commerce and/or

e-business configurations

Supplier strategy Rethinking and restructuring the company’s supply strategy,

and the organisation and management of the company’s suppliers portfolio

Outsourcing Concentrating on the company’s core activities and outsourcing

support processes and activities (e.g. IS management, maintenance, material handling)

Process focus Restructuring the company’s manufacturing processes and

layout to obtain process focus and streamlining (e.g. re-organize to plant-within-a-plant, cellular layout)

Pull production Undertaking actions to implement pull production (e.g. reducing

batches, set-up time, using kanban systems, etc.),

Quality management Undertaking programmes for quality improvement and control

(e.g. TQM programmes, 6sprojects, quality circles)

Equipment productivity Undertaking programmes for the improvement of the company’s

equipment productivity (e.g. total productive maintenance)

Workplace development Implementing actions to increase the level of delegation and

knowledge of the company’s workforce (e.g. empowerment, training, improvement or autonomous teams)

NPD Implementing actions to improve or speed-up the company’s

process of new product development through, e.g. platform design, product modularisation, component standardisation, concurrent engineering (CE), quality function deployment (QFD)

Environmental compatibility Putting efforts into and commitment to improving the company’s

environmental compatibility and workplace safety and healthy

Table V. The 14 action programmes investigated

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The observation that increased process focus has positive effects on quality is less trivial. One explanation might be that process focus often is implemented to improve delivery reliability, one of the quality indicators. Additionally, process-based production is more transparent than function-based production. Thus, quality problems are visible and, thus, solved sooner. Finally, process-based manufacturing requires high and predictable quality in order to become a success. This may indicate that it is not the action programme alone, but a combination with other programmes, that explains the impact of increased process focus on quality performance.

Somewhat surprisingly, actions aimed at improving NPD have a significant negative effect on quality. The most obvious explanation is that some of the practices underpinning this action programme, in particular platform design and modularisation, are relatively new and actually quite complex and difficult to implement. Involving a review of the company’s whole product portfolio, but also a range of organisational and managerial changes throughout the company’s value chain, the implementation of platform design and modularisation may have an, initially, negative effect on product quality.

Flexibility performance. Implementing process focus, pull production and

programmes to improve equipment productivity and NPD are all positively related to improvement in flexibility performance.

Process focus especially has a positive influence on volume and mix flexibility, as the development and manufacturing of different products are managed separately and take place on different production lines (plant-within-a-plant). Pull production (including reduced batch sizes and set-up times) increases the speed and reduces the cost of changing the mix of existing, and launching new products, thus allowing a company to produce to order rather than to forecast. This helps the company improve its mix and volume flexibility as well as its customisation capability. Reduction of set-up times is also an important element of TPM, which is one programme to improve equipment productivity.

While negatively associated with quality performance, the findings indicate that improving NPD is positively associated with improvement in flexibility performance. The most obvious explanation is that some of the practices underpinning NPD improvement support customisation (modularisation) and time to market reduction (CE).

Speed performance. Process focus, quality and environmental programmes all

significantly contribute to better speed performance. Increased process focus helps reduce manufacturing lead time and delivery speed. In addition, quality programmes reduce scrap, losses and rework, and through that improve the speed performance.

A little surprising is that improving and speeding up NPD does not have a significant influence on speed performance. The reason for this is probably that the measurements for speed performance are related to procurement and manufacturing, not to the whole process from product development to customer delivery.

Cost performance. For cost performance, programmes that are implemented to

improve equipment productivity appear to have a positive influence, as should be expected. After all, one of the main purposes with equipment productivity programmes, e.g. TPM, is to increase the Overall Equipment Efficiency (OEE), which is closely related to cost reduction and improvement of capacity utilisation.

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There is a positive relationship between pull production and cost performance. Although, the relationship is not significant, the level of significance is fairly good. Reduced inventory levels and higher inventory turnover are important goals with pull production and explain the effect found.

The other action programmes do not affect cost performance. The most obvious explanation is that actually relatively few respondents go for cost reduction, which

confirms findings reported elsewhere (Cagliano et al., n.d.) that cost seems to have

become a less important performance indicator for ISIC 38 companies.

Combination of quality and flexibility performance. The regression analysis shows

positive relationships between the implementation of process focus, pull production, equipment productivity and environmental compatibility, and improvement of quality-flexibility (Q-F) performance. While this is hardly surprising, the finding that improved quality management practices do not lead to improved Q-F performance is. The only explanation we can think of is that the product quality/reliability and manufacturing conformance aspects of quality have long been solved, while the delivery reliability and environmental aspects have not. The four programmes just mentioned address these aspects.

Combination of quality and speed performance. Programmes aiming to implement

process focus and increased environmental compatibility are positively related to improved quality-speed performance, which is not surprising given the influence of these programmes on quality and speed considered individually. Improving and speeding up the NPD processes is negatively related to quality-speed improvement. We could not find a reasonable explanation for that other perhaps than that the negative impact on quality (see above) is larger than the positive impact on speed.

Combination of quality and cost performance. Pull production, equipment

productivity and environmental compatibility have a positive effect on quality-cost performance. The most likely quality aspects affected are delivery reliability and environmental performance. Through reduction of inventory, batch sizes and set-up times, pull production leads to cost reduction. Apparently, equipment productivity programmes such as TPM also do what they are supposed to do: contribute to quality improvement and cost reduction.

Combination of flexibility and speed performance. In line with the improvement

programmes being positively related to flexibility performance, implementing process focus, pull production and equipment productivity are also positively related to improving flexibility-speed performance. While implementing a clear supplier strategy does not have a significant effect on flexibility and speed considered individually, it does in companies pursuing improvement on the combination of these performance areas, and that effect is positive. In other words, one of the cornerstones of supply chain management does indeed have positive effects on the logistical aspects of manufacturing performance.

Combination of flexibility and cost performance. The pattern of relationships

between action programmes and flexibility-cost performance does not produce any surprises, except for the negative effect of implementing e-business. The most likely explanation is not flexibility but cost/benefit related: e-business is a relatively recent phenomenon and as in many other previous innovations, the initial costs are higher than expected, while the financial benefits are lower. It will be interesting to see if the cost effects of e-business are still negative in a couple of years time.

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Combination of speed and cost performance. Pull production, equipment productivity and environmental compatibility are positively associated with improving speed-cost performance. Interestingly, the relationship between pull production and speed-cost performance is significant, while not significant for speed and cost performance analysed separately. This may indicate that pull production provides a good basis for achieving a good speed performance and, through that, also has significant impact on cost.

Combination of quality, flexibility and speed. Companies with a high performance

improvement in quality-flexibility-speed appear to be able to achieve that by implementing process focus, pull production and environmental compatibility. This confirms the picture starting to emerge, namely that these three action programmes taken together produce a range of different performance effects. The fourth apparently rather influential action programme, equipment productivity, does not have significant influence on Q-F-S improvement, and the question is why. We do not have an answer.

Combination of quality, flexibility and cost performance. Programmes directed

towards process focus, pull production, equipment productivity and environmental compatibility are positively related to improving quality-flexibility-cost performance. The essence of the explanation has been given above.

Combination of quality, speed and cost performance. Improving quality

management practices and environmental compatibility affect quality-speed-cost performance positively. Attempts to improve NPD, however, have a negative effect on this performance combination. The most obvious explanation is that introducing standardisation and especially modularisation, though possibly beneficial in the longer term, involves a major and grossly underestimated change process, in spite of all the glossy stories currently being told. Some negative effects, especially on conformance quality, delivery reliability, manufacturing lead time, labour productivity and capacity utilisation, should therefore be expected, at least initially so.

Combination of flexibility, speed and cost performance. Companies that have

achieved a high degree of performance improvement in flexibility-speed-cost have implemented process focus, pull production, equipment productivity and environmental compatibility. Again, these programmes do exactly as they are supposed to do.

Combination of quality, flexibility, speed and cost performance. Apparently a high

degree of improvement on all four categories of performance is associated with the implementation of programmes aimed at process focus, pull production and environmental compatibility. Exactly why equipment productivity is missing in this list is not clear to us.

Discussion

Action programmes directed towards improving environmental compatibility have a significant positive effect on all combinations involving quality performance. This is not surprising considering that environmental performance is one, relatively recent yet key, aspect of quality performance (see Table II). Similarly, all performance improvement combinations including quality, except the combination of quality, speed and cost (Q-S-C), are positively affected by the implementation of either pull production, increased process focus, or both. Again, this is hardly surprising as both pull production and process focus will have a positive effect on delivery reliability, one

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of the other aspects of quality (see Table II). We do not have a good explanation for why these two programmes do not appear to have a significant effect on the Q-S-C combination.

More surprising is the finding that action programmes aimed at improving quality management practices are only related to quality, speed and the combination of quality, speed and cost (Q-S-C), but not to any of the other combinations including quality. A possible explanation is that most companies today have solved “the quality problem”, especially as regards manufacturing conformance and also the mostly design-determined product quality and reliability. Putting more effort into improving these aspects of quality is likely to produce only marginal effects.

Flexibility, whether stand-alone or in combination with any other (set of) performance area(s), is affected positively by the implementation of process focus and pull production. Attempts to improve equipment productivity have the same effect, with the exception of the Q-S-F and Q-S-F-C combinations. A possible explanation is that this action programme is too narrow. That is, especially in order to achieve quality plus speed in addition to flexibility effects, this programme has to be combined with other actions. Programmes focused on the improvement of quality management practices seem to be the most obvious candidate.

All combinations in which cost performance is included, except Q-S-C and Q-S-F-C, are affected positively by actions aimed at improving equipment productivity. This corresponds well with the main targets of equipment productivity programmes, namely reducing waste and increasing OEE. In many of these combinations, pull production and/or process focus also have a positive effect.

Which action programmes represent best practices?

Process focus, pull production, equipment productivity and environmental compatibility.

The combination of process focus, pull production, equipment productivity and environmental compatibility has a significant positive effect on three of the fifteen (combinations of) performance areas. Any combination of three of the four programmes leads to significant improvement in another seven (combinations of) performance areas. A combination of two of the four programmes positively affects three combinations of performance areas. Only in the case of cost, and of cost combined with speed and quality (Q-S-C), a combination of these programmes does not show any synergetic effect. This finding suggests that these four programmes should be qualified as best practice, that is, they support companies achieve significant improvements in most performance areas and combinations thereof. Furthermore, these programmes seem to reinforce each other.

E-business, supplier strategy and outsourcing. E-business is the least adopted action programme among the 14 studied here. The action programme is negatively related to all combinations of flexibility performance. That pattern indicates that companies have problems gaining benefit of programmes directed towards e-business. This may be due to the fact that the concept is rather new, especially in the engineering industry. Furthermore, it is not likely anyway that e-business will have a great impact on the performance of manufacturing operations; the opposite effect, that manufacturing is one of the enablers of successful e-business performance, is much more likely. We conclude that, from a manufacturing performance perspective, e-business is not currently a best practice.

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The impact of the supply chain management related action programmes supplier strategy and outsourcing on manufacturing performance is rather limited. To be sure, especially outsourcing does have positive effects, mainly on cost, but they are not significant. The effects of supplier strategy on operations performance are much weaker. As with e-business, the main reason could be that the effects of these action plans are mostly outside the operations function. Anyway, the conclusion is that especially supplier strategy, but also outsourcing, do not appear best practices from an operations performance perspective.

NPD improvement. Attempts to improve the NPD function have mixed effects on

manufacturing performance. The effects on flexibility and the combination of flexibility and cost are positive; the effects on quality, quality-speed, and quality-speed-cost are negative. NPD improvement does not have any significant effect on any of the other performance areas. The mixed role of NPD is probably due to the way we operationalised this action plan. Concurrent engineering, for example, and also standardisation of components are likely to have positive effects. Platform thinking and modularisation, in contrast, may, certainly initially, produce negative results, due to the fact that these programmes require companies to review their whole product portfolio, quite likely change at least part of the products, and also implement a range of organisational and managerial changes in order to make these practices a success. “Learning” about new products and practices is not for free. We conclude that, currently, NPD improvement does not qualify as a best practice.

Quality management. With new quality aspects, especially delivery reliability and

environmental performance, playing an ever-more important role, and traditional quality problems such as conformance quality and product reliability “solved” in many companies, the role of TQM as a best practice in the sense of contributing to performance improvement is over – these aspects of quality have become qualifying criteria, and their realisation a routine.

Other action programmes. Here the conclusion is straightforward: none of the other

action programmes investigated in the IMSS III survey appears to produce any significant effect on performance improvement. These programmes are: implementing new process equipment, increasing production capacity, process automation, implementing ICT, and work place development.

How valid are our conclusions?

Table VI summarises our conclusions. The question is, how valid and complete they are.

The analysis does make unambiguously clear that process focus, pull production, equipment productivity and environmental compatibility have a variety of performance effects and reinforce each other. Thus, these four action programmes investigated appear to represent best practice.

ICT and quality management, whichmayhave been best practices in the past, have

lost that status. Both are quite common in industry, and they do not distinguish anymore between high and low performers. It is “routine” to be and stay up-to-date in both areas.

The status of NPD, e-business, supplier strategy and outsourcing is less straightforward. NPD produces mixed results. CE and standardisation seem to have positive effects, while platform design and modularisation have a negative impact on

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performance. All practices underpinning NPD improvement programmes are actually quite complex, involving considerable changes in organisation, management and, quite often, products and possibly also in processes and technology. Furthermore, various practices are relatively new. So, our analysis suggests NPD improvement as operationalised in this article is not a best practice. However, the programme may actually well develop into a best practice. E-business does not produce significant manufacturing performance effects. Similar to NPD, though, this action programme may develop into best practice, however with manufacturing as an enabler. The direct effects on manufacturing performance will be limited while, conversely, a company’s e-business success will greatly depend on its manufacturing performance. Two supply chain management practices, a well-developed supplier strategy and outsourcing, will affect the functioning of operations and may have impact on manufacturing performance, but such effects are not visible, yet. We conclude that NPD, e-business,

Action programme Best practice Remarks

Process equipment No No manufacturing performance effects

Manufacturing capacity No No manufacturing performance effects

Process automation No No manufacturing performance effects

ICT No longer

No manufacturing performance effects. Probably so widely adopted that the programme does not distinguish between high and low performers

E-business Possibly

Hardly any manufacturing performance effects. E-business success likely to depend on operations rather than the other way around. May develop into sales best practice

Supplier strategy Possibly Hardly any manufacturing performance effects

Outsourcing Possibly Hardly any manufacturing performance effects

Process focus Yes

Strong manufacturing performance effects, often together with pull production, equipment productivity and/or environmental compatibility

Pull production Yes

Strong manufacturing performance effects, often together with process focus, equipment productivity and/or environmental compatibility

Quality management No longer

Hardly any manufacturing performance effects. Probable cause: the quality aspects covered are qualifiers. The underpinning practices have become routines

Equipment productivity Yes

Strong manufacturing performance effects, often together with process focus, pull production and/or environmental compatibility

Workplace development No No manufacturing performance effects

NPD Possibly

Mixed manufacturing performance effects: CE and standardisation probably positive, platform design and modularisation probably negative. Potential best practice, though, but rather complex and difficult to implement successfully

Environmental compatibility Yes

Strong manufacturing performance effects, often together with process focus, pull production and/or equipment productivity

Table VI.

Practices (14) and best practices (four)

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supplier strategy and outsourcing are not currently, but may develop into, best practices.

The other four practices, aimed at process equipment and manufacturing capacity improvement, process automation and workplace development, respectively, do not have any significant manufacturing performance effects and should therefore not be considered as best practice.

As to the influence of context on the choice of practices and their influence on manufacturing performance, the study has focused on a limited set of industries (ISIC 38), representing a variety of companies in terms of size and process type. An analysis not presented in this article suggests that the influence of industry type is very weak, while size and product type do not play any role whatsoever. This means that the findings are valid for all ISIC 38 companies, but whether they also hold for other types of industry is not clear.

Finally, we believe this article presents an improvement in best practice research in terms of its starting point: the best performing companies are the ones that (must) have the best practices. Our findings seem valid for the fourteen action programmes investigated, in ISIC 38 companies, irrespective of process and size, at present. However, our analysis suffers from four weaknesses, each reducing the validity of the findings. First, the study is based on, and only allows an evaluation of, pre-listed practices. We cannot exclude the possibility that the best performers’ manufacturing performance is based on additional practices, unknown to us. Second, the data suggest that the four best practices identified reinforce each other. They do appear together, in pairs, trios or even as a quartet having a significant positive effect on manufacturing performance. We are not sure though whether this is coincidence or not. A third weakness of the study is that it does not allow for an estimation of the potential of emerging practices. Fourth, it is not clear whether or to what extent the findings also hold for non-ISIC 38 companies.

Conclusion

The purpose of this article was to investigate what are the differences, in terms of the adoption of a range of action programmes, between the high and low performers in a sample of 474 manufacturing companies from the IMSS III database.

High and low performers differ in terms of implementation “width” and “depth” of action programmes. Not only do the high performers implement more of the concepts compared to the low performers, they also seem to be more committed to continue implementing the programmes even if the results are not improved on the short term. An apparently very strong configuration is process focus and pull production, combined, in many cases, with actions aimed at increasing equipment productivity and/or environmental compatibility. These action programmes currently represent best practice. Reinforcing each others’ effects, they contribute to improvement in all the four manufacturing performance areas addressed in this article.

NPD, e-business, supplier strategy and outsourcing are emerging practices, do not currently represent best practices, but may develop into that direction.

Former best practices, in particular quality management and ICT, have lost that position. They should now be regarded as a routine practice, supporting companies to qualify for the market place and, thus no longer distinguishing between the best and the rest.

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Surprisingly many action programmes, notably process equipment and manufacturing capacity improvement, process automation and workplace development, do not have a significant influence on manufacturing performance, either negatively or positively.

Weaknesses and further research

The analysis presented in this article suffers from four weaknesses, which are related to:

(1) Completeness– we tested a list of pre-defined practices but cannot exclude the

possibility that there are additional practices explaining the best performers’ manufacturing performance. We propose the use of expert panels and open interviews with manufacturing directors of highly successful companies to identify such practices, and a survey study to test their role in and impact on manufacturing performance.

(2) Interaction and implementation – although our findings suggests that

implementation “width” and “depth” makes the difference, further research is needed to unravel how companies make various best practices reinforce each other. We propose in-depth longitudinal case studies of implementation processes to find out more about this question.

(3) Predictive power – new practices are emerging all the time, some of which will

develop into best practices, others will not. Probably the only way to tackle this problem is identification of, followed by in-depth case studies at, innovators/early adopters.

(4) Contextuality – we checked the influence of industry type, company size and

process type, did not find these factors to exercise major influence, but cannot exclude the possibility that a wider set of industry sectors and also other contingencies not included in the present analysis will produce a different picture. Further survey-based research involving a broader set of industries and inquiring a wider set of contingencies will contribute to addressing this problem.

References

Ahmed, N., Montagno, R. and Firenze, R.J. (1996), “Operations strategy and organisational performance: an empirical study”, International Journal of Operations & Production

Management, Vol. 16 No. 5, pp. 41-53.

Bolden, R., Waterson, P., Warr, P., Clegg, C. and Wall, T. (1997), “A new taxonomy of modern manufacturing practices”,International Journal of Operations & Production Management, Vol. 17 No. 11, pp. 112-1130.

Cagliano, R., Acur, N. and Boer, H. (n.d.), “Strategic flexibility: patterns of change in manufacturing strategy configurations”, paper submitted to International Journal of

Operations & Production Management.

Davies, A.J. and Kochhar, A.K. (2002), “Manufacturing best practice and performance studies: a critique”,International Journal of Operations & Production Management, Vol. 22 No. 3, pp. 289-305.

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Flynn, B.B., Schroeder, R.G. and Flynn, E.J. (1999), “World class manufacturing: an investigation of Hayes and Wheelwright’s foundation”,Journal of Operations Management, Vol. 17, pp. 249-69.

Flynn, B.B., Schroeder, R.G., Flynn, E.J., Sakakibara, S. and Bates, K.A. (1997), “World class manufacturing project: overview and selected results”,International Journal of Operations

& Production Management, Vol. 17 No. 7, pp. 671-85.

Garver, M.S. (2003), “Best practices in identifying customer-driven improvement opportunities”,

Industrial Marketing Management, Vol. 32, pp. 455-66.

Greswell, T., Childe, S. and Mull, R. (1998), “Three manufacturing strategy archetypes – a framework for the Aerospace industry”, in Bititci, U. and Carrie, A. (Eds),Strategic

Management of the Manufacturing Value Chain, Kluwer Academic Publishers, Dordrecht,

pp. 53-61.

Hayes, R.H. and Wheelwright, S.C. (1984),Restoring our Competitive Edge: Competing through

Manufacturing, John Wiley, New York, NY.

Hanson, P., Voss, C.A., Blackmon, K. and Oak, B. (1994),Made in Europe: A Four-nations Study, IBM, London.

Hanson, P. and Voss, C.A. (1995), “Benchmarking best practice in European manufacturing sites”,Business Process Re-engineering and Management Journal, Vol. 1 No. 1, pp. 60-74. Harrison, A. (1998), “Manufacturing strategy and the concept of world class manufacturing”,

International Journal of Operations & Production Management, Vol. 18 No. 4, pp. 397-408.

Kathuria, R. and Partovi, F.Y. (1999), “Workforce management practices for manufacturing flexibility”,Journal of Operations Management, Vol. 18, pp. 21-39.

Ketokivi, M. and Schroeder, R. (2004), “Manufacturing practices, strategic fit and performance: a routine-based view”, International Journal of Operations & Production Management, Vol. 24 No. 2, pp. 171-91.

Peters, T.J. and Waterman, R.H. Jr (1982), In Search of Excellence: Lessons from America’s

Best-run Companies, Harper & Row, New York, NY.

Rondeau, P.J., Vonderembse, M.A. and Nathan-Ragu, T.S. (2000), “Exploring work system practices for time-based manufacturers: their impact on competitive capabilities”,Journal

of Operations Management, Vol. 18, pp. 509-29.

Schonberger, R.J. (1986),World Class Manufacturing: The Lessons of Simplicity Applied, The Free Press, New York, NY.

Swamidass, P.M. and Newell, W.T. (1987), “Manufacturing strategy, environmental uncertainty and performance: a path analytical model”,Management Science, Vol. 11 No. 8, pp. 509-24. Voss, C.A. (1995a), “Alternative paradigms for manufacturing strategy”,International Journal of

Operations & Production Management, Vol. 15 No. 4, pp. 5-16.

Voss, C.A. (1995b),Manufacturing Strategy: Process and Content, Chapman & Hall, London. Voss, C.A., A˚ hlstro¨m, P. and Blackmon, K. (1997), “Benchmarking and operational performance:

some empirical results”,Benchmarking for Quality Management & Technology, Vol. 4 No. 4, pp. 173-285.

Further reading

Collins, R.S., Cordon, C., Cornaz, J.L., Eugster, H.R., Gemoets, O.G., Jakob, R., Julien, D. and Stu¨cheli, G. (1996),Made in Switzerland: A Benchmarking Study of Manufacturing Practice

and Performance in Swiss Industry, IBM, Zu¨rich.

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Dixon, J.R., Nanni, A.J. and Vollman, T.E. (1990),The New Performance Challenge: Measuring

Operations for World Class Competition, Dow Jones-Irwin, Homewood, IL.

Miles, R.E. and Snow, C.C. (1978),Organisational Strategy, Structure, and Process, McGraw-Hill, New York, NY.

Rathuria, R. and Partovi, F.Y. (1999), “Work force management practices for manufacturing flexibility”,Journal of Operations Management, Vol. 18, pp. 21-39.

(Bjørge Timenes Laugen is a PhD student at the Department of Business Administration at Stavanger University College, Norway. He received his MSc in engineering from Aalborg University in 2000. His main research interest is the link between new product development, production, organisational development and continuous innovation.

Dr Nuran Acur is a graduate of Yildiz Technical University, Turkey, where she gained a BSc in Statistics. This was followed by a Masters degree in Statistics from Istanbul University. The following year she came to the University of Strathclyde (UK), where she gained a PhD in Strategic Management. After graduation, she joined Worldmark as an Engineering Consultant. During this period she developed a detailed understanding of Strategic Management and Quality Management. In 2002 she joined Aalborg University. Her current research focuses on benchmarking, operations management and strategy.

Dr Harry Boer is Professor of Organisational Design and Change at the Center for Industrial Production at Aalborg University, where he teaches various courses in organisation design and change, operations and service management and innovation management. He also teaches at the MBA programmes at TSM Business School, The Netherlands, and Politecnico di Milano, Italy, the EurOMA Doctoral Consortium and the CINet Doctoral Seminar on Research in Continuous Innovation. His main research interest is in resourcing, organising and managing the link between day-to-day operations, continuous improvement/learning and radical innovation, so as to improve both the short-term and the long-term performance of industrial companies. Harry Boer has written numerous articles and (co-)authored four books in the fields of organisation theory, operations management and strategy, innovation management, and continuous improvement.

Dr Jan Frick is Associate Professor at School of Hotel and Business Management, Stavanger University College. He holds an MSc in Operations Management and ICT from the Norwegian Institute of Technology in Trondheim, Norway, and a PhD in Industrial Production from Aalborg University, Denmark. Jan Frick has previously worked at the research institutes SINTEF and Rogaland Research Institute and at the industrial collaboration institutions Jærtek at Bryne, and TESA at Sandnes, all in Norway. He has managed several Norwegian and international research projects, and published at several international conferences, journals and book chapters. Jan Frick is member of the IFIP workgroup 5.7 “Integration in Production Management”.)

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Table II shows that a relatively large percentage of the IMSS companies (187) have strongly improved their quality performance during the last three years, and can be categorised as high performers in quality

Table II

shows that a relatively large percentage of the IMSS companies (187) have strongly improved their quality performance during the last three years, and can be categorised as high performers in quality p.6
Table III.

Table III.

p.7
Table VI.

Table VI.

p.16

References

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