Total quality management implementation: the "Core" strategy.

Ryan, Chuck ; Moss, Steven E.

ABSTRACT

This research presents an empirical investigation of total quality management (TQM) implementation in small- to medium-sized manufacturing firms. The study introduces a new TQM implementation strategy: the "Core" approach and tests the efficacy of a five-element quality management model. Factor analysis, cluster analysis, and ANOVA are used to test relationships among implementation, resulting practices, and performance. Results suggest TQM implementation transcends industry type and is most successful when viewed as a holistic process rather than either selective or contingent.

INTRODUCTION

Most American and European businesses have deployed some type of quality initiative in their operations (Silvestro, 2001). Yet, many firms have seen little to no benefit from their quality management efforts. Research has attributed many of these disappointments to improper quality management program implementation (Belohav, 1993; Cole, Bacdayan, & White, 1993; Smith, Tranfield, Foster, & Whittle, 1994; Hackman & Wageman, 1995; Douglas & Judge, 2001; Yusof & Aspinwall, 2002). Indeed, recent work suggests that the high failure rate of quality management initiatives results from a mismatch between these processes and critical problems in their respective environments; in short, that quality management should be seen and properly executed as a contingent process (Melcher, Khouja, & Booth, 2002; Das, Handfield, Clalantone, & Ghosh, 2000; Claycomb, Droge, & Germain, 2002; Wang, 2004).

While there is a growing body of literature studying the linkage between quality management practice and performance, most research is not empirically-based and centers on large manufacturing companies (Rahman, 2001). Furthermore, Ingle (2000) noted that little discussion has focused on total quality management (TQM) implementation methodologies and that further work in the area is called for. It is these gaps that this research will address by investigating the relationships among implementation practices and performance in small-to-medium manufacturing businesses. This research will show that, for these firms, quality management implementation transcends industry type and is most successful when viewed as a holistic process, as opposed to either a step-wise or contingent process.

The next section of the paper features a review of the literature relevant to the current study. We follow with the operational definition of TQM upon which our research is based. Research methodology is then presented, followed by an analysis of the demographics of firms included in the study. Empirical results are then shown. A final discussion of results and implications is presented in the conclusion section.

EXECUTION, CONTINGENCY THEORY, AND IMPLEMENTATION

Powell (1995) hypothesized that TQM firms outperform those without quality management programs in a survey of CEOs and quality executives in the Northeastern U.S. Powell utilized financial performance as a dependent variable and evaluated it on the basis of profits, sales growth, and overall financial performance, reported subjectively by the senior manager responding to the survey. He found that certain behavioral aspects of TQM result in improved performance, and concluded that firms with a formal quality management program outperform those without a TQM program.

Ahire (1996) studied the impact of TQM programs centering on the following question: Is TQM a long campaign, one taking several years before desired results are seen? He surveyed a total of 499 U.S. and Canadian plant managers and found that successful firms see measurable benefits of the quality management efforts in 2-3 years. In addition, he found that higher levels of top management commitment, customer focus, supplier relations, design quality, training, use of quality management tools, and employee involvement were associated with better operational results. Ahire (1996) suggested that execution level would continue to be associated with performance in the future.

Ellington, Jones, and Deane (1996) studied 500 manufacturing firms and identified eight components of quality management adoption. The dimensions they identified were: 1) customer focus, 2) breadth of quality definition, 3) managerial role, 5) employee involvement, 5) process capability, 6) vendor and manufacturing conformance, 7) priority and structure for continuous improvement, and 8) use of quantitative measurement systems. Ellington, et al. used cluster analysis to group firms based on level of execution in these key eight areas. ANOVA tests, similar to the methodology used in this research, showed significant relationships between cluster membership and firm performance. Higher levels of quality management implementation intensity were found to be associated with higher firm performance.

Douglas and Judge (2001) surveyed 229 senior hospital administrators and noted that adoption level was positively related to performance. A total of seven quality management components were used in the study: 1) top management involvement, 2) breadth of quality philosophy, 3) quality-oriented training, 4) customer focus, 5) process improvement, 6) management by fact, and 7) use of TQM methods. An aggregate average of the seven was computed for each firm and this average was used as the TQM practices variable in a subsequent regression analysis.

The essence of contingency theory is that an organization's processes must fit the environment, and that not all environments are the same. A classic work in the field is that of Burns and Stalker (1961). They proposed two basic organizational structures in their work with U.K. manufacturing firms. The first, a mechanistic structure, features centralized and formal decision making, with strict rules and top-down communication. Decisions are made at the top and employees have a very narrow set of responsibilities. The second, the organic structure, features flatter, informal communication lines and flexible roles. Decision making is decentralized and responsibility and authority are pushed as low as possible.

Lawrence and Lorsch (1967) studied firms in plastics, food processing, and can manufacturing. Firms in these industries were selected owing to differing levels of environmental uncertainty in each. They found that no one set of practices fit all three industries; that complex and unstable environments better fit an organic structure, while a mechanistic structure should be deployed in a stable environment. Note that the mechanistic environment maps to a quality management implementation that relies heavily on tool deployment, whereas the organic structure links to a more team-based implementation.

Terziovski and Samson (1999) surveyed 1,341 manufacturing firms in New Zealand and Australia. Participating firms were mixed in size and industry classification. The authors suggested that quality management is best implemented when applied as a strategic initiative, linked to activities on the "shop floor" (p. 228). They tested this relationship by factor analyzing 40 quality management variables (a procedure incorporated in this research), followed by analysis of variance routines. Terziovski and Samson found that quality management practice and organizational performance were significantly related, and that industry sector and firm size have an affect on quality management program effectiveness. As a result, they advocated that no one set of quality management practices will be effective across different industries, noting that manufacturing firms in wood processing industry had lower levels of implementation intensity that than firms in the metals industry.

Yusof and Aspinwall (2000) observed that few small- to medium-sized company quality management frameworks have been presented in the literature. Their review showed that existing work promotes some type of step-wise implementation. In addition, they reported that small-business managers might be confused as to where to begin, given the proliferation of implementation strategies in the quality management literature taken as a whole.

Ingle (2000) proposed four quality implementation approaches in her work with automotive component manufacturers in Ireland. The strategic approach is based on the idea that departments within organizations can provide competitive advantage when these functions are linked to both business strategy and long-term success. This type of implementation requires greater planning and commitment to be successful. Plans must be shared at all levels of the organization and changes allowed at the functional level that would best support the aims of the organization in total.

The philosophical approach emphasizes more human resource involvement and flatter organizational structure. The focus is on giving employees not only responsibility but also the authority to achieve common goals within an overall quality management culture.

Firms that take a continuous improvement approach are characterized as learning organizations that experiment and use continuous improvement tools. The idea is that the tools are deployed to analyze what happened in the past and how the business can shape future initiatives and processes. This deployment means that the driver of continuous improvement is organizational learning, not simply the tools themselves.

A selective adoption approach is identified by firms initially picking and choosing initiatives with a view towards eventually moving to full adoption, as long as the selected initiatives work. Ingle notes that the selective adoption approach has not been examined in the literature heretofore, a gap we seek to close in the current research.

WHAT IS TOTAL QUALITY MANAGEMENT (TQM)

While scholars continue to write their own and varied definitions of total quality management (Ingle, 2000), we believe that TQM is best operationalized by Hackman and Wageman (1995). They championed that quality management is an all or nothing process consisting of five core features: 1) Customer focus, 2) supplier relations, 3) cross-functional teams, 4) scientific thinking and statistics, and 5) process management heuristics. The process is binomial (0,1) since one either deploys all five or one doesn't practice TQM. Therefore, those firms that say they are customer focused, yet ignore statistical tools such as SPC, are not practicing TQM. Under this definition, firms using step-wise adoption methods would not be practicing TQM until their implementation efforts were complete. We believe that Hackman and Wageman's definition is appropriate as the five core features map to the teaching of the guru's, ISO requirements, the Baldrige Criteria, and work of recent scholars, tying all of them together in one concise package. The efficacy of Hackman and Wageman's definition has not been previously tested; another gap the present research seeks to close.

METHODOLOGY

This research attempts to answer three questions: 1) Is implementation, in practice, related to performance in small- to medium-sized firms, 2) Does Hackman and Wageman's definition hold up under empirical testing, and 3) Does industry sector have an impact on the outcome of quality management initiatives. The answers to these three questions will serve as a basis to discuss if TQM is best deployed as a contingent process.

Data used to answer the research questions were collected from a random sample of 210 small- to medium-sized manufacturing firms (SMMs) located in the Southeastern United States. We elected to investigate these firms since they are key contributors to the economy, providing most of the opportunity for employment (Gunasekaran, Forker, & Kobu, 2000). In fact, data from the latest available U.S. Census report show that firms with = 999 employees hire fully 80% of all those working in the manufacturing sector. In addition, SMMs account for 73.8% of total manufacturing payroll (U.S. Bureau of the Census, 2001).

There are many notions as to what constitutes a small business (Yusof & Aspinwall, 2000). For example, Gunasekaran, et al. (2000) studied firms in the U.K. with 500 or less employees. Tseng, Tansuhaj, and Rose (2004) sampled firms with as many as 1,500 workers, noting that this approach was consistent with certain maximums of the US Small Business Administration. For the purposes of this study, we take the midpoint and define SMMs as those with less than 1,000 employees on site, consistent with Moini (1991).

A total of eight quality management elements were evaluated in the study. The internal consistency of the elements was checked using reliability analysis, which shows how the items are related to each other. The Cronbach's Alphas for those elements range from .74 to .87, a result satisfactory for this type of analysis (Nunnally, 1978). The quality management elements are presented in Table 1.

The elements and underlying survey variables center on fundamental concepts identified in the previous empirical work. For example, both Ellington et al. (1996) and Douglas & Judge (2001) included measures of customer focus, breadth of quality definition, continuous improvement, managerial role, and process capability/quantitative measurement systems. In addition, the training variables used in this research are linked to Ahire (1996), while the conformance measures are those used by Ellington et al. (1996). Finally, the eight elements map directly to quality management precepts embodied in both the Malcolm Baldrige Award (National Institute of Standards and Technology, 2004) and the five core features of TQM proposed by Hackman and Wageman (1995).

DEMOGRAPHICS

A key goal of the research is to test for interaction between industry type and the outcome of TQM programs. Thus, a heterogeneous sample is needed. Table 2 presents a summary of industries represented in the survey.

The respondent percentages by industry feature a broad cross-section of manufacturing industries. In addition, the plastics, metals, food products, and wood industries discussed in the contingency literature are included in the sample. This broad mix of firms augurs well for generalizability of the results to the population of small-medium sized manufacturers, and for our

ability to test whether TQM is a process contingent on industry type, as suggested in the literature review.

RESEARCH APPROACH

The first step in the analysis was to factor analyze the survey variables that formed the eight quality elements in an effort to identify underlying quality management constructs. Firms were then clustered into groups on the basis of those factors. The resulting groups formed a hierarchy of quality management implementation or execution. Hierarchy membership (independent variable) and performance (dependent variable) were tested using ANOVA routines, and minimum significant difference tests were conducted to determine differences in group performance. Chi-Square analysis was then used to determine if the mean group performance varied by industry. Finally, cluster profiling was deployed to determine the practices of higher performing groups, and if these practices could be mapped to Hackman and Wageman's definition of TQM (1995).

UNDERLYING QUALITY MANAGEMENT CONSTRUCTS

To address research question 1, principal components analysis was conducted separately on each of the eight quality management elements using the latent root criterion (mineigen = 1) to determine significant factors (Hair, Anderson, Tatham, & Black, 1995). These analyses resulted in a total of 29 factors. Each item making up the respective orthogonal factor had a loading of 0.38 or greater, which supports construct validity (Terziovski & Samson, 1999). Table 3 summarizes the factor analysis and describes each of the quality management dimensions.

The table shows that each of the factors features a logical theme and maps to one of the eight quality management elements. The total variance accounted for by the factor solutions ranged from a low of 53.63% to a high of 76.28%, a result Hair et al. suggest is satisfactory for this type of study (1995).

QUALITY MANAGEMENT EXECUTION

Factor scores were computed for each of the 29 factors, and these scores were standardized to remove scaling differences. Using these standardized factor scores, the 210 firms in the study were clustered into groups. Consistent with Ellington, et al. (1996), a four-group solution was found. Table 4 details the results of the analysis.

Group 4 scores are generally very high across all 29 quality management execution factors. Group 3 scores are somewhat lower than group 4, but higher than group 2. Finally, group 1 scores are generally very low on all factors.

Thus, we describe group 4 firms as holistic quality management implementers. Group 3 firms show a relatively high level of quality management implementation, albeit at a lower level than the holistic adopters. Group 2 firms appear to be unfocused in their quality management efforts, seeming to pick and choose their initiatives. Therefore, group 3 and group 2 members deploy a selective adoption implementation approach. Finally, those in group 1 ignore the quality management model altogether.

EXECUTION LEVEL VS. PERFORMANCE

The first research question centers on whether group membership within the quality implementation hierarchy is statistically related to firm performance. To answer this question, the following measures were used to capture firm performance: 1) return on sales, 2) return on assets, 3) return on investment, 4) overall profit, 5) delivery dependability, 6) delivery speed, 7) customer service, 8) customer service, 9) product quality, 10) technical support, 11) market share, and 12) pricing. The 12 measures were factor-analyzed to reduce dimensionality. Two underlying factors of firm performance were identified: financial performance and operational performance. Financial performance consists of traditional measures such as return on sales, return on assets, return on investment, and overall profit. The operational performance dimension is a combination of delivery dependability, delivery speed, level of customer service, product quality, and level of technical support. These two performance factors were used as dependent variables in subsequent ANOVA tests.

The relationship between the dependent variable firm performance (both operational and financial), and the independent variable, level of quality execution (the four previously-discussed clusters), was tested using two analysis of variance models (ANOVA). The ANOVAs show that both financial performance (200 d.f., F = 6.11, Sig. = .0001) and operational performance (200 d.f., F = 4.87, Sig. = .0005) are related to position in the hierarchy, indicating significant differences in performance across groups.

The analysis of variance tests only tell us that at least one of the groups is statistically different than the others, but not the direction of the relationship. In order to identify specific differences among the groups, Scheffe's minimum difference tests were conducted on each of the dependent variables. The results of the minimum difference tests are shown in Table 5.

Holistic implementers (group 4) were consistently in the highest performance group, followed by high implementers (group 3), marginal or unfocused implementers (group 2), and nonadopters (group 1), respectively. These results provide empirical support for the contention that higher levels of quality management implementation are associated with both higher financial and operational performance.

CLUSTER PROFILE

Figure 1 shows the relative emphasis that the holistic implementers place on each of the underlying quality management factors, in practice. Higher levels of deployment are shown as taller cylinders.

[FIGURE 1 OMITTED]

Notice that very heavy emphasis is placed on training and linking customer requirements to the production process. In addition, relatively high emphasis is given to facilitating customer feedback, working to improve supplier quality, deploying process tools on the manufacturing floor to improve conformance, involving support functions in the problem-solving process, and using a team approach in continuous improvement efforts. But, do these highly deployed initiatives map to Hackman and Wageman (1995)?

Yes, they do. Notice that every highly deployed initiative fits well into their model of TQM. These firms not only focus on customer needs, but also their processes are designed so that those needs are met. Suppliers are made part of the overall "team" and are part of quality improvement initiatives. Cross-functional teams are deployed throughout the organization and feature members from support departments. Finally, everyone is trained in scientific thinking and process management heuristics.

These results and those of the previous section provide strong support for the contention that Hackman and Wageman's core features of TQM are important and hold up under empirical testing. We agree with Ingle (2000) that the definition of TQM should be clear to practitioners, and that academicians create confusion (havoc?) with various and sundry definitions of total quality management. Therefore, we advocate the consistent use of the five core features to define TQM, thereby ending any potential misunderstanding as to exactly what total quality management consists of, in practice. The final question of this study is whether these results are contingent on industry sector membership, which is the subject of the next section.

INDUSTRY SECTOR EFFECTS

We tested for sector effects using Chi-Square analysis. This goodness of fit test compares observed and expected sets of frequencies. If there is no difference, the two frequencies should be approximately equal (Lind, Marchal, & Mason, 2002). We tested for differences in industry sectors by comparing the makeup of the four quality management clusters (holistic through non-adopters, respectively). The p-value of the test was .138 (51 d.f.), suggesting no difference in industry classification by cluster, an outcome that diverges from Terziovski and Samson (1999) and classic contingency theory. We interpret this significant finding to mean that TQM implementation for SMMs is not a contingent process. These manufacturers appear to be best served by a holistic implementation of TQM.

In addition, notice that the use of cross-functional teams and supplier relations maps to an organic structure, whereas statistics and scientific thinking relate to a mechanistic structure. The two structures are said to be distinct in the contingency theory literature. That holistic firms deploy both structures concurrently is further evidence that suggests TQM is not a contingent process.

CONCLUSION

The purpose of this study was to investigate relationships among quality management implementation and performance. This research was able to discern significant relationships between level of implementation and firm performance. Irrespective of industry classification, higher levels of TQM execution were shown to be associated with higher levels of both financial and operational performance. Simply put, it appears that implementation is not a contingent process and the more holistic the execution or implementation of total quality management, the more successful the firm, relative to its peers.

The results suggest that while taking a Pareto (Price & Chen, 1993), step-wise (Huxtable, 1995; Ho and Fung, 1994), or selective adoption approach (Ingle, 2000) is not fatal, SMMs that are able to deploy quality management on a wholesale basis, or those that eventually reach holistic levels, should be more successful than those taking a more piece-meal quality implementation strategy. Therefore, we add one more implementation strategy to that of Ingle's work (2000). We term the holistic implementation methodology the "Core" strategy.

The results of this study also provide empirical support for the use of Hackman and Wageman's five essential features as the consistent definition of TQM in practice, and the notion that total quality management implementation strategies of small- to medium-sized manufacturers should not be viewed as a contingent process based on industry type.

While our conclusions are supported by empirical testing, one should be cautioned that there is always a small chance of Type I error. It is a fact that data were self-reported and suffers from the standard limitations of such approaches. Second, our data are cross-sectional and, as such, represent only one period of time. Temporal affects could result in different conclusions. Finally, our sample is limited to SMMs conducting business in the Southeastern United States, and outcomes might not hold for either large manufacturing firms or those located in other parts of the globe.

Further research into TQM implementation strategy is necessary. Are there significant cross-cultural differences in implementation results? What happens when a firm revises its TQM approach over time? Does the "Core" implementation strategy hold for service firms? These are interesting questions that beg investigation.

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Chuck Ryan, Georgia College and State University

Steven E. Moss, Georgia Southern University

Table 1: Quality Management Elements

Feature Measures Description

1. Customer focus 15 Assessing and meeting
 customer needs.
2. Breadth of quality 7 Centers on design quality of
 definition both the product and support
 processes.
3. Analysis and results 12 Quality analysis and process
 capability in line and staff
 functions.
4. Quality of conformance, 7 Supplier capability and
 Suppliers performance.
5. Quality of conformance, 9 Manufacturing process
 Manufacturing management.
6. Continuous improvement 29 Employee involvement,
 improvement priority, and
 improvement structure.
7. Role of the first line 7 Managerial functions.
 manager
8. Training: Managerial, 6 Leadership and technical
 Supervisory & Employee training.

Table 2: Distribution of Survey Respondent by Industry

Industry Classification Frequency Percent

Textile Mill Products 34 16.1%
Paper and Allied Products 28 13.3
Fabricated Metal Products 23 11.0
Food Products 19 9.2
Machinery 19 9.2
Apparel and Finished Products 18 8.7
Lumber and Wood Products 15 7.3
Rubber and Plastic Products 13 6.4
Chemical and Allied Products 9 4.1
Clay, Concrete, Glass, and Stone 6 2.8
Primary Metals 2 .9
Miscellaneous Manufacturing 24 11.0
Totals 210 100%

Table 3: Underlying Quality Management Factors

Element Factor Name Description

1. Customer Focus F1-TRAD Customer interaction by
 non-traditional groups.
 F2-CUSTREQ Emphasis on meeting
 customer requirements.
 F3-CUSTFEED Customer feedback practices.
 F4-TRADIT Customer interaction by
 traditional groups.

2. Quality Def. Breadth F5-AFTRSALE After sale service emphasis.
 F6-DELVPERF Delivery performance
 emphasis.

3. Analysis & Results F7-QUANTSUP Use of quantitative
 measurement in support
 areas.
 F8-QUANTPRD Use of quantitative
 measurement in production
 areas.
 F9-CUSTLINK Customer requirement-
 production process linkage.

4. Vendor Conf. F10-VENDQUAL Vendor emphasis on quality.
 F11-VENDSERV Vendor emphasis on service.

5. Mfg. Conf. F12-PROSTOOL Use of process tools.
 F13-PREVTOOL Use of prevention tools.

6. Continuous Impr. F14-SUPTPROB Support department
 involvement.
 F15-PRODPROB Production team involvement.
 F16-SUPLPROB Supplier team involvement.
 F17-COMPQUAL Link between compensation
 and quality.
 F18-XTRFOCUS Externally-focused
 performance meas.
 F19-NTRFOCUS Internally-focused
 performance meas.
 F20-INDIVSUG Individual suggestion
 approach.
 F21-TEAMAPCH Team approach.
7. Mgr Role F22-FACILTATE Emphasis firm places on
 facilitative activities.
 F23-TRDITION Emphasis on traditional
 supervisory roles.

8. QM Training F24-MGTQM Hours managers trained in
 leadership, etc.
 F25-MGTTOOLS Hours managers trained in
 use of QM tools.
 F26-SUPQM Hours supervisors trained in
 leadership, etc.
 F27-SUPTOOLS Hours supervisors trained in
 QM tools.
 F28-EMPQM Hours employees trained in
 leadership, etc.
 F29-EMPTOOLS Hours employees trained in
 use of QM tools.

Table 4: Standardized Factor Scores by Group

Measure Cluster 1 Cluster 2 Cluster 3 Cluster 4

NONTRAD -.83986 -.39560 .52826 .34511
CUSTREQ -2.17874 -.10477 .09092 .50726
CUSTFEED -1.09314 -.38549 .30724 .76255
TRADIT -1.11268 .17234 -.16590 -.02168
AFTRSALE -1.27950 -.33604 .37054 .61196
DELVPERF -1.23314 -.06963 .14922 .22538
QUANTSUP -.69148 -.38379 .30597 .63403
QUANTPRDD -2.01219 -.21949 .41799 .28808
CUSTLINK -1.37937 -.47974 .36884 1.01465
VENDQUAL -1.63969 -.50895 .52552 .80039
VENDSERV -.80398 .01885 -.02812 .16467
PROSTOOL -1.20734 -.43392 .49251 .76546
PREVTOOL -1.91036 -.00479 .13865 .25615
SUPTPROB -1.12349 -.22534 .10226 .69688
PRODPROB -.50526 -.08945 -.05205 .48058
SUPLPROB -.35199 -.41837 .39477 .51117
COMPQUAL -.68251 -.42453 .34931 .87670
XTRFOCUS -.50565 -.30837 .40401 .34947
NTRFOCUS -1.64594 -.12690 .22621 .38767
INDIVSUG -.47105 -.11768 .00615 .43881
TEAMAPCH -1.60124 -.28933 .29985 .66392
FACILTATE -1.1941 -.08438 .12635 .29116
TRDITION -.42644 -.16179 .04847 .47660
MGTQM -.53863 -.50320 .00074 1.56101
MGTTOOLS -.65172 -.46746 -.02498 1.58371
SUPQM -.63510 -.30901 -.19330 1.52737
SUPTOOLS -.64206 -.39450 -.07813 1.54363
EMPQM -.58274 -.46158 .04938 1.47956
EMPTOOLS -.60382 -.41852 .06874 1.41074
Firms/Cluster 8 101 65 36

Table 5: Scheffe's Minimum Difference Tests Firm Performance and Group
Membership

Financial
Performance Operational Performance

Grouping Mean Cluster Grouping Mean

A .5243 Holistic A .5940
B A .0447 High BA .2748
B A -.1768 Unfocused B -.2507
B -.7134 NonAdopter C -1.2539

* Significant differences among groups are denoted by different letter
groupings. Groups with the same letter(s) are not significantly
different.

Table 6: TQM Core vs. Holistic Group Implementation Profile

Hackman & Wageman Core Factor Factor Description

Customer Focus Custreq Meeting customer requirements.
 Custfeed Customer feedback practices.
 Aftrsale Service after the sale.
 Custlink Customer requirement-production
 process linkage.

Supplier Relations Vendqual Initiatives to improve supplier
 quality.
 Suplprob Supplier team involvement in
 continuous improvement.
Cross Functional Teams Suptprob Support dept. involvement in
 continuous improvement
 Teamapch Team approach to continuous
 improvement.
 Mgtqm Management training in
 leadership, communications,
 customer service, TQM, and
 team-building.
 Supqm Supervisor training in same
 areas above.
 Empqm Employee training in same areas
 above.

Statistics and Scientific Quantsup Use of quantitative measurement
Thinking in support areas.
 Mgttools Management training in data
 collection & analysis, problem
 solving, SPC, and facilitation.
 Suptools Supervisor training in same
 areas above.
 Emptools Employee training in same areas
 above.

Process Management Prostool Use of process management
Heuristics tools.
 Mgttools Management training in data
 collection & analysis, problem
 solving, SPC, and facilitation.
 Suptools Supervisor training in same
 areas above.
 Emptools Employee training in same areas
 above.