To stay afloat in this globalized context, companies must adopt and implement tools, techniques, and methods that have helped other companies to succeed. One of the most popular of these methodologies is Six Sigma (SS), which is part of Total Quality Management (TQM). SS has its origins in the 1980s, in the Motorola industry and, due to its success, years later, other companies such as Allied Signal, Bombardier, Siebe, Sony, Polaroid, Toshiba and Texas Instruments implemented it. However, its recognition as an efficient methodology for the control of variability was obtained in General Electric [1
The application of SS evolved rapidly to other sectors, and, already in the year 2000, its applications were associated with plant distribution [2
] and education [3
], and it was formally established as a methodology for control of quality [4
]. According to their use, SS can be defined as a methodology for the continuous improvement of customer satisfaction thanks to a reduction of defects in products/services to increase economic benefits [6
Although SS can be defined in many ways, from a statistical viewpoint, it is a controlled production process methodology with a near-perfect rate of 3.3 defects per million opportunities. This is perhaps the most important definition of SS as a methodology, since it implies that processes must be appropriately standardized [7
]. However, as a procedure, SS is also treated as an integrated methodology consisting of two sub-methodologies: defining, measuring, analyzing, improving, and controlling (DMAIC), used when products or processes are in existence; and defining, measuring, analyzing, designing and verifying (DMADV), used when products or processes are not in existence and the company needs to develop them [8
]. In this research, SS is considered as a methodology integrating other techniques, but this research is only focused on DMAIC, due to the geographical context.
Products/services defects are areas of opportunity that can be addressed with SS. Examples of companies having successfully implemented this methodology in the 1990s include Motorola, General Electrics, and Allied Signals [9
]. Investments in education and training in these companies showed remarkable cost–benefit relationships. Unfortunately, since not every organization implementing SS obtains satisfying results, it is important to identify the critical success factors (CSF) of this methodology to prevent companies from throwing SS projects away.
It is common for managers to think that SS is not a suitable methodology to be implemented because its benefits are obtained only in specific contexts. Fortunately, current studies have demonstrated that SS is not only appropriate for manufacturing systems, but also for the services sector [10
Research has provided an overview of the critical success factors of SS for countries such as India [11
], Brazil [12
], Malaysia [14
], Italy [15
], and Mexico [7
]. Some factors seem to be consistent across countries, whereas others greatly differ, especially in industrial contexts. For instance, authors such as Chakraborty and Chuan Tan [16
] reported the 19 critical success factors of SS for the services industry in Singapore, while Jayaraman et al. [17
] found 25 of these factors for Malaysian industries. As regards Australian industries, a study conducted by Kumar et al. [18
] discussed 14 critical success factors for SS implementation. Finally, a study led by Lande et al. [19
] managed to compare the key success factors from different countries thanks to a collection of 63 SS-related scientific articles. Thus, there are many reports from industries that have successfully implemented SS, and for a full report, please consult Kwak and Anbari [20
Organizational learning, a culture of innovation and change, leadership, consistency, Communication
, integration, understanding of the SS principles, and managerial commitment are some of the most reported critical success factors of SS [19
]. However, being SS a methodology, we believe that the first implementation stages have an impact on subsequent ones. Therefore, it is important to analyze, from a quantitative perspective, how and to what extent SS implementation stages are interrelated.
The literature also reports education and training as key factors to explain the success of SS. In this sense, some companies measure the cost–benefit relationship of investments in education and training as an efficiency indicator [21
]. In any case, an educated and trained workforce, which is also expert in the necessary tools for SS deployment, helps companies improve Communication
and solve problems more efficiently. Consequently, the learning process and feedback are facilitated thanks to shared experiences [22
]. Obviously, companies invest in a complete education and training process to obtain the best Operational benefits
, which would reflect on product quality and process cycle times. However, we should bear in mind that, to obtain such benefits and a strong competitive advantage, teamwork is also crucial [23
Although many studies have identified the critical success factors of SS across contexts and industrial sectors, few of them have managed to find how these factors are interrelated and to what extent, especially in Mexican maquiladora sector. Moreover, there seems to be no consensus regarding the level of importance of these critical success factors in the SS implementation process in the manufacturing industry. To address these gaps, our research quantifies the impact of education and training (access to information, Didactic material, and understanding of statistical tools) on Communication and operational performance using a structural equation model with ten hypotheses. The model is tested statistically with information from Mexican maquiladora and focused on DMAIC only, because there is a product or process in existence and the problem to solve is the quality in production lines.
The main motivation for this research is that, in Ciudad Juárez (Mexico), there are 326 maquiladora companies, which are subsidiaries that belong to foreign companies established in other countries, but that perform activities of assembly of products in other countries. Those maquiladoras are characterized by having an extensive import of raw materials, and export of finished products, since the production and assembly process is carried out in Mexico, and SS is considered an efficient methodology to ensure quality. Nevertheless, there is an special emphasis on the role of access to information, training in Statistical techniques and teaching material, as well as the administration and Communication of knowledge to achieve Operational benefits.
The remainder of this paper is structured as follows: Section 2
proposes and justifies the research hypotheses; Section 3
describes the methods used to test such hypotheses; Section 4
presents our findings; and Section 5
discusses the research conclusions and industrial implications of results.
To reach our objective and validate the ten hypotheses depicted in Figure 1
, we adopted the following methodology.
3.1. Defining Variables and Creating the Survey
In this research, we discuss three basic concepts related to SS: Education and Training (Information, Didactic material, and Statistical techniques), Communication, and Operational benefits. To find the observed variables of each of these concepts, we conducted a literature review on databases such as Sciencedirect, Ingenta, and Ebscohost, among others.
Once we identified the observed variables, we constructed a survey. The literature review thus represented the rational validation of this survey [55
], which was composed of three sections. The first section was aimed at collecting sociodemographic data of the participants, and the second section assessed the critical success factors of SS. As regards the third section, it analyzed the different types of SS benefits; however, for the purpose of this study, only took into account items collecting data on Operational benefits
were taken into account.
The first version of the survey was submitted to a content validation among a group of subject-matter experts. The purpose of the experts validation was to make sure that our instrument had been appropriately adapted to the context of the research [57
], since data had been collected from previous studies conducted worldwide, not just in Mexico.
Finally, the survey had to be answered using a five-point Likert scale, where a one value indicated that an SS activity was not important or an SS benefit was never obtained. On the other hand, the highest value indicated that an SS activity was highly important or an SS benefit was always obtained. Values of two are used for activities frequently executed or benefits gained, while three for regular, and four for usually.
3.2. Survey Administration
The model and its hypotheses were validated with data collected from the Mexican maquiladora industry during May to July 2016. More specifically, we administered the survey to manufacturing company managers, GBs, BBs, Champions, and group leaders having at least two years of experience in SS or who had participated in SS initiatives at least three times. A list of maquiladoras, manager names and contact information was provided by AMAC (Maquiladoras Association AC) and then, survey administration meetings were scheduled in advance to managers having experience in SS implementation, and the questionnaire was to be answered individually. After a first interview with managers, the snowball method was used to identify other possible responders (managers, green belts, black belts and champions).
However, for participants who cancelled the meeting three times, we stopped insisting due to time restrictions.
3.3. Data Capture and Validation
We constructed an electronic database with gathered data using SPSS software. Then, we performed a screening process to identify missing values and outliers, which were replaced by the median value of items. However, cases or surveys showing more than 10% of missing values were removed.
Latent variables were validated through the following indices:
Cronbach’s alpha and composite reliability index: Used to measure internal validity. Acceptable values must be higher than 0.7 [57
) and Adjusted R-Squared: Used to measure the predictive validity of dependent latent variables from a parametric perspective. Acceptable values must be higher than 0.2 [60
): Used to measure predictive validity from a non-parametric perspective. Acceptable values of Q2
must be similar to their corresponding R2
and adjusted R2
Average Variance Extracted (AVE): Used to assess convergent validity, setting 0.5 as the threshold [61
Variance Inflation Factors (VIFs): Used as a measure of collinearity. Acceptable values must be below 3.3 [62
Note that sometimes it is possible to increase the Cronbach’s alpha value in a latent variable after removing items that seem to compromise its validity. For this reason, we ran several iterations to validate each latent variable.
3.4. Descriptive Analysis of the Sample
At this stage, we created contingency tables to analyze trends in the sample characteristics. As previously mentioned, we analyzed the genre of participants, number of SS projects that they had taken on, company size, and industrial subsector, among a few.
3.5. Descriptive Analysis of Items
Since we worked with ordinal data, we used the median as a measure of central tendency and the interquartile range (IQR) as a measure of data dispersion. Both measures helped us identify which SS activities and benefits are the most important to manufacturing companies, from the sample’s viewpoint. High median values indicated that an SS activity was important to the sample or an SS benefit was always obtained, whereas low median values indicated that an SS activity was not important to the sample or an SS benefit was not obtained. As regards the IQR, high values revealed low consensus among respondents regarding the median value of an item. Low IQR values revealed high consensus among respondents with respect to the median value of an item.
3.6. Hypotheses Validation
To validate the research hypotheses, we created a structural equation model (SEM) using WarpPLS v.5. This software relies on partial least squares (PLS) and is regularly recommended for studies working with Likert scales, small samples, and non-normal data [39
]. Likewise, WarpPLS v.5 has been a useful tool for validating theories in SS research. For instance, it was reportedly employed to know the impact of knowledge created in SS on organizational performance [45
Before interpreting the model, we analyzed its efficiency by computing the model fit and quality indices in Table 1
Once the model proved to be statistically stable, we proceeded to interpret it. For this interpretation, we analyzed three types of effects in every relationship:
Direct effects: They validate hypotheses presented in Figure 1
. Every direct effect corresponds to a hypothesized relationship between latent variables.
Indirect effects: These occur between two latent variables through a mediating variable.
Indirect effects are always interpreted using two or more model paths.
Total effects: They are the sum of direct and indirect effects in a relationship.
All effects were associated with a beta (β) value—expressed in standard deviations—and a p
-value for the statistical significance of effects at a 95% confidence level, thus setting 0.05 as the cutoff and testing the null hypothesis: β = 0, against the alternative hypothesis: β ≠ 0. Finally, every effect also included an effect size (ES) to represent the amount of R2
or explained variance contained in dependent latent variables [64
5. Conclusions and Industrial Implications
In this study, we assessed 20 activities related to educational processes (Information, Statistical techniques, and Didactic material) and Communication as critical success factors of SS. We associated these activities with seven Operational benefits of SS. In the multivariate analysis performed on 301 surveys, all SS activities showed a median value higher than 3 but lower than 4, implying that they are regularly performed in the Mexican manufacturing sector. On the other hand, two of the seven Operational benefits reported a median value higher than 4, thereby implying that they are always obtained.
Although Mexican manufacturing companies seem to rely on effective rules to guarantee confidentiality of Information, SS team members report that access to such data as resource material to plan and start SS projects is not easily granted. Organizations should further analyze this issue, since it may affect employee engagement in SS initiatives. In other words, it is good to motivate employees to improve the production process, but it is equally important to grant them access to the necessary data and information, especially during the first implementation stages of an SS project. If companies do not work on this, SS projects are likely to be incorrectly planned, because of a lack of information related to the production process status. However, this can be a hard activity, because that production process can generate much information for different departments and data analysis requires big data techniques.
As regards Statistical techniques
, graphs and figures seem to be the main statistical tool to support SS, whereas the DMAIC approach proved to have a less significant place among Mexican manufacturing companies. Such results suggest that organizations approach SS as a statistical technique rather than as a problem-solving methodology. It is important for companies to find a balance between these two conceptions [20
], otherwise SS may become a significant obstacle for those who are not experts in statistics [65
]. In this sense, other studies, having detected an imbalance between the different ways SS can be approached, also report that, in such cases, SS projects are more often abandoned [66
] and that is why this is an opportunity for techniques such as big data or novel techniques to help analyze information obtained from production process, because it allows finding trends, as it is applied in education [68
], and SS is a philosophy based in education and training.
In addition, our study reports that GBs and BBs usually provide clear instructions on how to work with Didactic material
. However, we also found that this material is not always useful for future projects, and team members must thus be trained every time they initiate a new project with SS, which increases final costs. Concerning Communication
, GBs and BBs in Mexican manufacturing companies seem to provide appropriate and sufficient assistance to team members; nevertheless, when it comes to supervising and monitoring SS projects, their performance appears to be less regular. Sadly, employees may lose their motivation when they perceive a lack of consistency when it comes to monitoring projects [69
Finally, product quality and process cycle time reduction seem to be the main Operational benefits
of SS, whereas teamwork and multifunctional skills are less common. In this case, it is important to add human attributes to SS, since human resources know the administrative procedures, the production processes, and the company’s opportunities for improvement [21
After analyzing the relationships between the latent variables, the following conclusions regarding direct effects were reached:
Information available to solve problems defines which Statistical techniques employees will be trained and how Didactic material must be designed, so they can be clear to all team members and reusable in future projects. However, big data can be implemented as a Statistical technique for yellow belts, green belts and champions, because it can help to find hidden patterns into Information.
Having available Information does not automatically guarantee Economic Benefits, because direct effect is statistically not significant. First, managers must be focused on teaching and training employees in the use of the necessary Statistical techniques through clear and meaningful Didactic material. The fact that Information does not have a direct impact on Economic benefits implies that it is not appropriately analyzed or it cannot directly become a benefit. Communication and education are therefore required, and managers should be part of appropriate Communication channels and training sessions. However, managers must also encourage that workers integrate in SS projects and share the knowledge gained among them as a way to disseminate their experiences solving problems.
Information has a direct effect on Statistical techniques, which denotes the importance of this variable at the first implementation stages of an SS project, where employees identify the problem and define it. In addition, Information has the largest direct effects on all subsequent latent variables, meaning that, if Information is not reliable or easily accessible, companies must have problems implementing SS and probably, they abandon this philosophy, resulting in a lack of quality in their production process.
Another important relationship involves Statistical techniques and Didactic material. This relationship again reflects the importance of education and training for an appropriate SS implementation. In fact, the three largest effects, considering the β values, involve the three latent variables that make up the education process.
Managers and SS members must strive to implement an appropriate education and training scheme in which Information is reliable but also easily accessible. Likewise, education and training must focus on the use of basic Statistical techniques through clear Didactic material, since these three variables affect Communication, both vertical and horizontal. In other words, without a suitable education and training process, Information cannot properly flow; consequently, all involved variables may diminish the indirect effects that Information has on Economic benefits through Communication as the mediating variable.