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Introduction
Global companies face intense pressures due to several factors, like the high rivalry for market share, higher quality, and more strict legislation for the protection of the environment. Companies must respond by increasing their competitiveness with better quality, higher productivity, and innovative technologies. Among the strategies deployed for these purposes are the applications of Six Sigma (SS) and Lean Manufacturing (LM) projects.
Six Sigma is a methodology based on a set of quality improvement techniques and statistical methods applied by highly trained work teams focused on finding the causes of variation in a process and applying the corrective measures needed for its reduction. This methodology is applied in five stages: Define, Measure, Analysis, Improve, and Control, these stages include a high diversity of contents and applications (Erdogan and Canatan, 2015). Lean Manufacturing is a methodology and a set of techniques based on Just in Time and Total Quality Management for the systematic identification and elimination of waste activities, organizing people on a continuous search for improvement (Phan et al., 2019), it is a tool to improve the operational performance of industrial processes (Zhang et al., 2020).
In manufacturing environments SS and LM projects improve product technologies, production equipment, and processes; enhance innovation and technology capabilities, leading to the creation and development of competitiveness (Swarnakar, Singh, and Tiwari, 2019) and deployment of the company’s strategy (Osorio et al., 2014). Commonly, their success is measured by the benefits, times, costs, quality, or productivity. In Ciudad Juarez 400 plus maquiladora industrial plants SS/LM projects are a standard for the improvement of quality and productivity, nonetheless, also are reports of lesser than expected benefits. That is why it is important to determine the factors influencing the success of those projects.
The remaining part of this study is structured in five sections, as follows: the second section is devoted to the review of the literature about the factors of Six Sigma and Lean Manufacturing projects. The third section, methodology, presents the research methods, followed by the statistical analysis tools. The fourth section presents results and the model, followed by conclusions, discussions, and limitations, including the recommendations for future research.
Literature Review
Despite that SS/LM projects provide competitive advantage and possess the utmost importance, their effectiveness is a high concern issue, reports abound about the late delivery, over budgets and lesser than expected performance. Because the success of the project depends on the understanding of the factors influencing it, management must focus the attention on their control and potential effects (Baccarini, 2009; Sánchez and Terlizzi, 2017). In the search of factors, the literature is extensive and inconclusive because the lists of factors vary depending on the type of industry, the projects, and the theory of Project Management. There are two types of factors to consider, the factors of the project performance, and those that influence the project management.
Regarding the former, the quantity of factors influencing the performance of a project is wide and they are not discriminated by their relative contribution to performance, although the theory and practice of SS and LM are quite standard. Besides, Marzagão and Carvahlo (2016) comment that not all the factors mentioned in the literature have significant contribution to project performance. Some reports pinpoint the relative contributions, while others only identify the critical success factors, with some differences regarding the quantity and the factors. Fortune and White (2006) report the lack of agreement regarding the quantity of the factors and consensus in the listings, their review of sixty-three publications gave a list of 27 factors; while Tabish and Jha (2011) found 36 factors; Gudienė et al. (2013) list 71, Van Loenhout (2013) reports 19 factors; Alias et al. (2014) report 25; García et al. (2017) report 27; Radujković and Sjekavica (2017) report 21 related to the management of the project; Tsiga et al. (2017) identify 58 success factors, and Yadav et al., (2021) report 18 factors. Table 1 presents a list of factors.
Table 1 Factors of Projects Success and Effective Management
| Factor | Author |
|---|---|
| Leadership and Team; Policy and Strategy; Stakeholder management; Resources; Contracting; Project management; Scheduling; Budget; Organisation; Quality; Information; Risks | Westerveld (2003) |
| Support from senior management; objectives; Strong plan; Good communication/ feedback; Client involvement; Skilled staff/team and project manager; Resources; Use of Technology; Realistic schedule; Risks managed; Effective monitoring/control; Adequate budget; Organizational culture/structure; Training provision; Political stability; Project management methodology/tools; Environmental influences; Experience; Project size, Level of complexity, People involved. | Fortune and White (2006); |
| Top management’s support; Owner's need; Monitoring and feedback; Scope of work; Adequate staff for planning and execution; Timely and valuable decision; Skills of project manager and staff; Availability of resources; Timely finalization; Regular design and construction control meetings; Schedule and budget updates; quality control and assurance activities; Adequate communication. | Tabish and Jha (2011); |
| Project efficiency: Meeting schedule and budget goal; Skill development. Customer: functional performance; technical specifications; Customer satisfaction. Business success: Commercial success; Creating a large market share. Preparing for the future: Creating a new product line and a new market; Developing a new technology. | Serrador and Turner (2015) |
| Communication, Coordination, Balances of member contribution; Mutual support; Effort; Cohesion; Team performance; Team members' success. | Lindsjørn et al. (2016) |
| Support from senior management; Objectives; Plan; Communication; Involvement; Team; Competent project manager; Business Case; Resources; Leadership; Schedule; Proven Technology; Risk Management; Monitoring; Senior Responsible Owner; Budget; Organization; Suppliers; Planned Close; Training; Project Management Methodology; Environment; Politics; Learning. | Frijns et al. (2017) |
| Technical competence; Behavioral competence; Contextual competence; Project team’s competence; Coordination; Organization; Organizational structure; Organizational culture. | Radujković and Sjekavica (2017) |
| Project success; Mission; Top management; Project schedule; Client consultation; Personnel; Technical task; Client Acceptance; Monitoring feedback; Communication; Troubleshooting | Iram et al. (2017) |
| Experience, organization size, emphasis on cots quality and time, ability to brief, decision making, roles and contribution, expectations and commitment, influence. Support given to project head, support to critical activities, understanding of project difficulty and stakeholder influence. Project type, size, nature, complexity, design, resources allocation. Coordinating and motivating skills, communication and feedback, conflict resolution skills and organizing skills. Planning and control effort, team structure and integration, safety and quality program, schedule and work definition, budgeting and control of subcontractors. Contract type, tendering and procurement process. | Tsiga et al. (2017) |
| Funds/Resource Availability; High-Volume and Low-Variety Set-up; Project Management Skills; Use of Technology to manage the project; Mechanism of Feedback-to-Processes, Corrective-Action from Data-Analysis; Skilled-Employees; Automation; Use of Data-Analysis and Prediction System; Prior Quality-Management-System; Use of Line-Balancing and Production-Levelling Practices; Change Management Culture; Timely and Accurate Data Availability. | Yadav et al., (2021) |
Source: Self-made
About the management of SS/LM projects because they are complex, unique, and with different theoretical contents and technologies, and work teams vary from project to project under diverse application contexts, management and deployment are not repetitive tasks. A common practice is to have internal development because there is no generally accepted industrial practice to manage the SS/LM application. In this sense, Collins and Baccarini (2004) confirms a positive relationship between project management and project success, its efficient deployment requires effective management (Zhang, 2020) and Alias et al. (2014) recommend the use of critical success factors given they predict success.
Therefore, this work follows the concept of Critical Success Factors, (CSF’s). In the context of Project Management, when properly managed CSF led to the project’s success (Milosevic and Patanakul, 2005; Iram et al., 2016; Iram et al., 2017), specifically, those few variables or factors that leaders and teams must manage carefully to assure the effectiveness of the project (Amade et al., 2015), certain inputs of the project leading directly or indirectly to success (Alias et al., 2014). Although Prabhakar (2017), comments that neither theory nor practice coincides regarding what is a successful project, it is an elusive idea, the success of SS/LM projects commonly is measured with the accomplishments of the objectives, and the successfulness of management by the accomplishments of on-time delivery, budget, quality, and its effective management (Radujković and Sjekavica, 2017; Hajiali et al., 2020). A predictor type relationship is assumed between factors and output variables, in this case, affecting the project performance (Haleem, 2012), with variables such as “planned accomplishments of goals” to measure the success of the project (Srimathi, 2017). Four success criteria were identified: 1) Time, 2) Quality, 3) Budget and 4) Successful Project Management, which contain the factors reported in the reviewed literature.
The factors related to the management of projects reported in the literature have a wide variation, there are authors who report up to 45 factors (Westerveld, 2003) while others who report 13 factors (Cruz-Villazón et al., 2020) or even 11 factors (Iram et al., 2017). In general, the factors found in the literature are highly coincidental and critical for the effective project management, such as organizational knowledge (Spalek, 2015), Competent Team (Jaturanonda and Nanthavanij, 2011), Lindsjørn et al. (2016) Focused Team -on goals and customer satisfaction-, Serrador and Turner (2015); Management Support (Kostalova et al., 2015); Laureani and Antony (2018) point out that the most important factors are project management, leadership, selection of top talented people and financial accountability. While others are evidently, only present in certain industries, such as Local Capabilities (Andersen and Bøllingtoft, 2011) in International Development Projects.
Because of the multifactorial variation, it has been difficult to establish the most important factors influencing the project's effectiveness and therefore, the management of the project. The theory is in full development for the identification of factors, relationships, and associations among intangible variables and the construction of models capable to predict the successful management and deployment of a project. In this search and to enhance the explanation capability, more empirical evidence is required to obtain a better and more general explanation of successful SS/LM projects.
Methodology
Research begins with a literature review of the factors influencing the project performance of SS/LM to obtain a list of factors, followed by a Meta-Analysis. The CSF SS/LM literature is a review of 52 publications from 2015 to this date, from EBSCO, Elsevier, Emerald, Springer, and Taylor and Francis. The publications gave a list of 29 factors and by Meta-Analysis the list was reduced to 21 factors, as Garcia et al. (2017) report. The operationalization of constructs is done with the list of factors and key variables for their measurement, applying several tests for the validation of the questionnaire.
The questionnaire measures the differences between theory and practice of SS and LM and identifies the factors influencing a successful deployment of a project. A Likert five scale is used, where 1 is the lowest level (or not important) to 5 as the highest. The constructs are Time, Quality, and Budget, commonly referred as evaluation criteria in the literature (Alvarenga et al., 2020; Cooke-Davies, 2002). The CSF are contained in 16 items identified by Garcia et al. (2017). An additional construct, Successful Project Management (SPM) has 7 items, using a total of 23 indicators for the measurement.
The results of reliability by Cronbach’s Alpha are 0.891, 0.918, and 0.899 for the three constructs (Time, Quality, and Budget), being reliable according to decision rule (0.70) proposed by Hair et al. (1999). The construct SPM gave 0.564, although by elimination of the item S-OT, Alpha increases to 0.749, therefore, this indicator is not included in the model’s specification.
Data Recollection. 225 questionnaires were distributed among professionals working in areas deploying SS/LM projects in a list of 82 high-tech industrial plants owned by multinational companies. From the 120 returned, 8 were discarded due to lost data, the Little MCAR Independence test of the random trend of lost data gave a p-value higher at 0.05 and the lost data is given by the expected maximization imputation method (Stavseth, Clausen, and Røislien, 2019). 54% of the questionnaires came from general manufacturing operations, followed by 17.8% from automotive parts, 10.7% from information and environmental businesses, and 17.5% from other high-tech companies. 61.6% of the respondents are engineers or middle managers (operations or quality), 20.7% Project Management professionals, 17.7% other middle management positions involved in improvement projects.
The Statistical Analysis follows two stages. First is an exploratory factorial analysis -EFA-, with oblique rotation (Hair et al., 2019), which is the base for the specification of a structural equations model (Byrne, 2010), following Tlapa’s (2016) five stages process, Specification, Identification, Parameter Estimation, Fit Evaluation, and the Modification. The specification bases on the EFA with the method of Principal Axes with Promax oblique rotation adequate for high multicollinearity data, using the Maximum Likelihood method for the parameter’s estimation (Hair et al., 2017).
The empirical evaluation of the model is tested with the statistical significance X2 (Chi Square) and the correspondent p-value, given that it tends to reject models because of the sample size, is used the Byrne (2010) normalized index, (X2/Degrees of Freedom), accepting a value lower than 2. Jak (2015) uses the Root Mean Square Error Approximation to correct the X2 trend to reject large models, with too many variables. A value lower than 0.05 indicates a close, good adjustment and up to 0.08 are satisfactory. Also is used the Comparative Fit Index, (CFI), indicates a relative lack of fit, [0,1] values close to 1 indicate good fit, and it is insensitive to the model size. The Tucker-Lewis (TLI) compares the fit by degrees of freedom between the model and a model without relationships among its variables, values of 0.9 and higher indicate good fit (Hair et al., 2005). The Expected Cross Validation Index (ECVI), recommended by Byrne (2010) for the evaluation of how good the model as a predictor is, selecting the best from a set with the lowest ECVI. Also, with the value and significance of the standardized regression coefficients are evaluated the quality of the model’s parameters, indicating the force with which the observable variables are measuring the latent variables.
Results
This Section presents the specification, identification, fit of the model, and parameters estimation. Regarding the specification and identification, beginning with the adequacy of the sample, the Kaiser-Meyer Olkin Measure of Sampling Adequacy gave 0.79; the Bartlett’s Test of Sphericity of 4.45 and Approx. Chi-Square, 4.45; with 1326 DF and a significance of 0.000. With these results is concluded that the variances - covariance’s matrix is not an identity one, having relationships among the indicator variables.
In relation with the final pattern matrix, table 2 shows the results of the factorial analysis using the method of extraction of principal axis with a PROMAX rotation with Kaiser normalization Rotation converged in 6 iterations, having four constructs corresponding to the latent variables of Time, Quality, Budget, and SPM, identifying several crossed items, and eliminating the ones with factorial loads lower than 0.5, and the explained variance given by this solution is 50.4%.
Table 2 Final Pattern Matrix.
| Pattern Matrix | Component | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| B. Budget | .882 | |||
| B. Plan | .840 | |||
| B. Control | .744 | |||
| B. Objectives | .726 | |||
| B. Manager | .716 | |||
| B. Risk | .593 | |||
| T. Knowledge | .843 | |||
| T. Supply | .746 | |||
| T. Management | .707 | |||
| T. Support | .635 | |||
| Q. Costumer | .752 | |||
| BQ. Personnel | .700 | |||
| Q. Support | .660 | |||
| Q. Communication | .589 | |||
| Q. Technology | .586 | |||
| Q. Culture | .563 | |||
| S-CRm | .752 | |||
| S-CRw | .745 | |||
| S-CSp | .656 | |||
| S-MgR | .578 | |||
| S-ExP | .552 | |||
| S-ShC | .549 | |||
Source: Self-made
Figure 1 shows the model’s final version, composed by a second-order variable with three dimensions, which relate to the criteria and classification of the CSF identified in the literature. The second-order latent variable predicts the variable success. Table 3 gives the Model’s fit indexes, complying with the decision criteria and their values.
Table 3 Model’s Adjustment Indexes.
| Model | CMIN/DF <2 | TLI >0.90 | CFI>0.90 | RMSEA <0.08 | ECVI |
|---|---|---|---|---|---|
| Default model | 1.417 | 0.906 | 0.920 | 0.061 | 3.217 |
| Saturated model | - | - | 1.000 | - | 4.162 |
| Independence model | 5.455 | 0.000 | 0.000 | .200 | 10.699 |
Source: self-made
Table 4 gives the factorial loads of the latent variables and the corresponding indicator variables. Also presents the path values. The less important factors are related to Time (0.595) having Budget and Quality a higher contribution to the project success with estimation values of 0.92 and 0.888, respectively; although the prediction relationship is moderate, with a significance value of p=0.028.
Table 4 Standardized Regression Weights of structural paths.
| Structural Paths | Criteria | Estimation |
|---|---|---|
| TIME | Criteria Project Management | 0.595*** |
| BUDGET | Criteria Project Management | 0.920*** |
| QUALITY | Criteria Project Management | 0.888*** |
| Successful Project Management | Criteria Project Management | 0.254* |
*** p<0.001, **p<0.01, *p<0.05
Source: self-made
Table 5 presents the factorial loads for each factor by criteria. The factors for criteria “on-Time delivery of the project” are four. The factors with higher value of estimation of this criteria are support (0.765) and supply (0.718); for “Budget” criteria the success factors are six, in this case the factors with higher estimation value are objectives and plan whit estimation values of 0.802 and 0.732, respectively; while Quality criteria has five factors, its factors with the highest estimation value are communication (0.797) and culture (0.700); finally, the factors for successful project management criteria are six and the factors with major importance are: CRw: Renewal of the company contract and CRm: Company recommendation, whose values are .966 y.909, respectively. In manufacturing industry of Ciudad Juárez the factor with lowest contribution is Efficiency in the administration of project resources and Execution of the project program; In figure 2 we show graphically contribution of each factor.
Table 5 Standardized Regression Weights of factorial loads.
| Factorial Loads | Criteria | Estimation |
|---|---|---|
| T. Support | <--- TIME | 0.765*** |
| T. Supply | <--- TIME | 0.718*** |
| T. Knowledge | <--- TIME | 0.567*** |
| T. Management | <--- TIME | 0.765*** |
| B. Budget | <--- BUDGET | 0.585*** |
| B. Plan | <--- BUDGET | 0.732*** |
| B. Control | <--- BUDGET | 0.723*** |
| B. Objectives | <--- BUDGET | 0.802*** |
| B. Manager | <--- BUDGET | 0.708*** |
| B. Risk | <--- BUDGET | 0.696*** |
| Q. Customer | <--- QUALITY | 0.667*** |
| Q. Personnel | <--- QUALITY | 0.631*** |
| Q. Communication | <--- QUALITY | 0.797*** |
| Q. Technology | <--- QUALITY | 0.572*** |
| Q. Culture | <--- QUALITY | 0.700*** |
| CSp | <--- Successful_Project Management | 0.441* |
| MgR | <--- Successful_Project Management | 0.291* |
| ExP | <--- Successful_Project Management | 0.262* |
| CRw | <--- Successful_Project Management | 0.966*** |
| CRm | <--- Successful_Project Management | 0.909*** |
| ShC | <--- Successful_Project Management | 0.313* |
Note: CSp: Compliance with project specifications; MgR: Efficiency in the administration of project resources. ExP: Execution of the project program; CRw: Renewal of the company contract. CRm: Company recommendation; ShC: Compliance with the programmed budget.
*** p<0.001, **p<0.01, *p<0.05
Source: self-made
The model predicts a successful project when is managed mostly depending on the accomplishment of the goals, the managerial efficiency of the resources, the execution of activities, and the budget.
Discussion
The effective management of the factors influencing the successful deployment of SS/LM has three essential variables confirmed by the empirical significance of the sample. The CSF related to the on-Time variable have lower predictive relevance than the factors related to Budget. This is understandable given that external, environmental conditions, influence on-Time delivery, which are factors with low control by management; while for Budget and Quality variations, management exerts more control. This explains because SS/LM are projects quite standardized, deployed on the floor, projects have highly technical contents, deployed in the floor, with experience in the improvement of products, equipment, processes, and operations.
Regarding the predictive relevance of the CSF in CRw (More Contracts) and CRm, (Competitive Position) they are top management tasks and strategic topics, although not directly related to the deployment of SS/LM projects, their influence is high. While, with lower relative weights, the CSF related to CSp, MgR, Exp and ShC, explains their moderate importance, because management personnel of SS/LM projects are project champions and leaders that just keep execution control and supervision to rather well-trained teams. Although, the indicators of operational performance still depend on external conditions, distinct to the time, budget, and quality factors.
Conclusions and Recommendations
The main purpose is the determination of the factors influencing project success, developing a list of factors, and discriminated by relative contributions. Because the empirical evidence indicates that the effective management of the factors influencing the on-time delivery, budget, customer’ perceived quality, possess the utmost importance for the accomplishment of operational performance and competitiveness goals, objectives are accomplished. Although more research is needed to establish objectively, under a general accepted model, the factors with higher influence to project effectiveness, the factors of effective management and, also about adequate definitions of what is project success and the indicators for its measurement, we have determined the listing mentioned and the usefulness of SEM for these purposes. No less important is the development of management models to deploy projects considering these factors.
We consider with a high importance, the replication of these types of projects in diverse manufacturing environments, to gather more empirical evidence and look for general model with more explanation power.
Final Remarks
The results obtained are important to understand the feasibility of applying Six Sigma and Lean Manufacturing projects, as well as showing the factors that can be a barrier. This importance lies in the fact that the economic development of the study region is based on the manufacturing industry, which is a field of application of these methodologies, since they offer an improvement in the processes.
Although the main limitation of the study is the size of the sample, several aspects indicate that the study is still valid. These include:
Kaiser-Meyer Olkin Measure of Sampling Adequacy gave 0.79.
Bartlett’s Test of Sphericity of 4.45.
Approx. Chi-Square 4.45.
1326 DF, and
significance of 0.000.
This work constitutes evidence that SEM is a powerful tool to determine the influence factor to apply projects of Six Sigma and Lean Manufacturing.
An invitation to researchers, consultants and professionals in academia and industrial plants to test the proposed listings while managing SS/LM projects.
Future Research Line
As mentioned, and according to the results, the replication of these types of projects in diverse manufacturing environments, to gather more empirical evidence with the purpose to look for a general model with more explanatory power. Therefore, necessary future line research is to apply the measuring instrument designed in this work to obtain decisive results.
Special thanks to Consejo Nacional de Ciencia y Tecnología - CONACYT for the support given to Eduardo Rafael Poblano Ojinaga through the Estancias Posdoctorales por México 2022 Program, for his participation in this paper.
