1 INTRODUÇÃO

Companies continuously try to improve their performance and stay competitive (Laser, 2020; Martins et al., 2019). According to Gamme and Aschehoug (2014), the increase of global competition and production costs prompted a new attitude and critical analysis of concepts associated with the productive system.

In this sense, lean manufacturing has proved to be a successful alternative since it aims to identify and improve activities that add value to the customers while it seeks to eliminate all activities and operations that do not add value (Nascimento et al., 2019; Nunes et al., 2019; I. S. Rampasso et al., 2017; Shahin et al., 2020; Yang et al., 2020). However, as Shi et al. (2019) emphasise, companies need to completely implement lean production to observe more significant benefits from it. These authors also show the moderating role of research and development for lean manufacturing to enhance companies productivity. In addition to the notable contributions of lean manufacturing to operational performance, it was also verified its contribution to companies innovation processes (Möldner et al., 2020; Solaimani et al., 2019) and can contribute to waste management (Vasconcelos et al., 2019).

Despite benefits, Maskell et al. (2007) emphasise that transforming a traditional company into a lean one is not easy. To Jadhav et al. (2014) and Laureani and Antony (2012), it is possible to observe many barriers to lean manufacturing implementation: lack of resources, the resistance of workers, and lack of senior management involvement. Focusing on senior management involvement, it is possible to say that it is crucial and the basis for successful lean implementation (de Oliveira et al., 2019). Nogueira et al. (2018) and Tortorella and Fogliatto (2017) corroborate this argument and emphasise that leadership performs an essential role in lean implementation.

Considering the importance of senior management support for lean success and the automotive industry relevance, this article aims to assess the perception of shop floor employees regarding senior management support in lean projects and its relation with initiatives success.

2 THEORETICAL BACKGROUND

Lean manufacturing is based on several fundamental principles: elimination of waste, minimisation of process variability, continuous process improvement, employee involvement, transfer of activities such as quality inspections and periodic maintenance to operators and maintenance of synchronised production flow through visual signals (Angelis et al., 2011). The mix of hard and soft practices in lean improves companies cost-saving, products quality and delivery reliability (Signoretti, 2020).

According to Lewis (2000) and Bhasin (2012a), each lean journey is unique for each company. Many aspects need to be considered on a lean journey. Through a literature review process, 18 aspects of a lean journey were identified. They are presented in Table 1. Table 1 also shows the codes used to identify each aspect in data analyses.

Table 1

Lean journey aspects identified in the literature (see references in Table)

A lean journey is long and hard. For Bhasin (2012a), despite the prevalence of lean philosophy for more than three decades, some aspects do not receive enough attention, resulting in a low number of successful lean implementations.

Lean manufacturing, based on the Toyota model, involves a more profound cultural transformation (Bhasin, 2012b). Many barriers to the implementation of the lean philosophy can be noted. Zhang et al. (Zhang et al., 2017) identified, through a literature review, 44 barriers to the implementation of the referred philosophy, which were classified in 10 areas, being the same ones related to knowledge (which includes, among others, lack of training, insufficient understanding of potential benefits, lack of implementation knowledge, lack of management skills to implement the philosophy, lack of methodology, and lack of willingness to learn and see), conflicts, resources, management, technology, employees, financial situation, culture, clients and experiences.

To minimise these barriers, the authors suggest developing a lean culture and in this situation, it is essential the senior management support in all lean projects implementations. (Wong et al., 2009) emphasise de importance of a supportive culture of employees engagement.

For Binti Aminuddin et al. (2016) and Scherrer-Rathje et al. (2009), lack of commitment of senior management is the most critical barrier to lean manufacturing success.

3 METHODOLOGICAL PROCEDURES

This section presents the steps carried out in this study. The first step was the literature review, performed to establish the study’s theoretical background. As mentioned, the 18 aspects of a lean journey as identified and presented in Table 1. In the sequence, a panel of specialists composed of five doctors in production engineering and five lean companies managers was performed to divide the 18 aspects into constructs. Table 2 presents the 18 aspects divided into four constructs.

Table 2

Aspects of the lean journey divided into constructs.

The structure presented was used on questionnaire development. As recommended by Hair et al. (2011), a pre-test was performed on the questionnaire. Researchers on Industrial Engineering analysed the first version of the questionnaire in this pre-test process and proposed improvements regarding corrections and understanding.

Before survey conduction, the study was approved by an ethical committee. The survey was conducted with 198 professionals. Respondents indicated for each aspect, through a scale from 1 to 10, the intensity (1 = aspect unnoticed or minimally perceived observation; 10 = high observation of the aspect)

Data was analysed through Structural Equation Modeling (SEM), using the Partial Least Squares method (PLS), known by its acronym (PLS-SEM). PLS-SEM is a valuable tool to analyse data in exploratory research (Imam & Chambel, 2020). In order to facilitate the analysis, nine steps were defined based on several authors (Henseler et al., 2009; Júnior et al., 2012; Ringle et al., 2014).

The first step was the definition of the model to be tested. The second step consisted of the calculation of the minimum sample required using G*Power software. We considered test power of 80%; error probability of 5%; and median effect size of 15% (Hair et al., 2014).

The third step aimed to start the validation of the proposed model. For this analysis, the PLS algorithm is run using parameters recommended by Ringle et al. (2014).

The fourth step is characterised by convergent validity evaluation through analysis of Average Variances Extracted (AVE). According to Henseler et al. (2009) and Ringle et al. (2014), AVE values should be higher than 0.5 for convergent validity. Ringle et al. (2014) highlight that if AVE values are lower than 0.5, variables with the lowest factorial loads from the construct need to be removed. Since it is a reflective model, this remotion does not change construct meaning (Jarvis et al., 2003).

The fifth step consisted in verifying the internal consistencies through Cronbach’s alpha and Composite Reliability. These two parameters are used to confirm if the sample is bias-free and if its data are reliable (Rampasso, Anholon, da Silva, Ordóñez, & Quelhas, 2019; Silva et al., 2018). Values of references are Cronbach’s alpha above 0.60 and Composite Reliability above 0.70 (Hair et al., 2014; Ringle et al., 2014). Ringle et al. (2014) argue that Composite Reliability is adequate for the technique applied.

In the sixth step, it was analysed the discriminant validity. Chin criterion was used to this (Ringle et al., 2014). Under this criterion, the factorial loads of each variable should be higher in their constructs than in the others.

In the seventh step, the analysis of the structural model was performed through the Pearson determination coefficients (R²). According to Ringle et al. (2014), R² = 2% is classified as small effect, R² = 13% as medium effect and R² = 26% as large effect.

In the eighth step, the ‘Bootstrapping’ function was used to verify if it is possible to use linear regression and correlation in the PLS-SEM. Values above 1.96 correspond to p-values ≤ 0.05, making it clear that in less than 5% of cases’ correlations and linear regressions cannot be used (Silva et al., 2018).

In the ninth step, other indicators of adjustment quality for the proposed model were analysed: Redundancy (Q²) and Cohen’s indicator (f²). Redundancy should have values bigger than zero, and Hair et al. (2014) considers f² = 0.02, 0.15 and 0.35 as small, medium and large, respectively. Hair et al. (2017, p. 201) emphasise that “effect size values of less than 0.02 indicate that there is no effect”.

It is essential to highlight that authors such as Wetzels et al. (2009) argue that the model quality can be measured through an indicator called Goodness of Fit (GoF). However, Hair et al. (2017) argue that this indicator is not always adequate and that the previously mentioned steps are more reliable to validate the model.

4 RESULTS AND DISCUSSIONS

Through literature review followed by a panel of specialists, it was possible to define the model to be tested, presented in Figure 1. In this theoretical model, Senior Management Support influence other constructs.

Figure 1
The model used on the SEM

In the sequence (Step 2), using the G*Power software, it was calculated the minimum sample required (55 respondents). The sample used was composed of 198 respondents, higher than that recommended.

In the first validation round, some AVE values did not reach the minimum value (0.5), indicating that it is necessary to eliminate the variables with the lowest factor loadings values (constructs that presented AVE values lower than 0.5). Figure 2 shows the new version of the model after excluding the variables with lower factor load (P1, P6, P10 and P16).

Figure 2
Validated model from PLS-Algorithm

For the model presented, AVE values are ensured for all constructs (step 4). Composite Reliability values were above 0.70, and for Cronbach’s alpha, only one construct (Operational Management) did not reach the minimum value of 0.60. Considering that Ringle et al. (2014) argue that Composite Reliability is an adequate to applied technique and the value obtained was close to the reference value, the authors of this study decided to keep this construct. These values are presented in Table 3.

Table 3

Analysed values of the quality of the validated model

The discriminant validity was analysed (Step 6). The analysis was performed according to Chin criteria, in which the factor load of each variable should be higher in its construct than in the others. All variables presented higher loads in their constructs, showing that their allocation was correct.

Step seven analysed the Pearson determination coefficient (R²), and all values were above 0.4. According to Cohen (1988), values of this intensity indicate a significant effect. Values of AVE, Composite Reliability and R² together indicate that the proposed model is adjusted and presents quality to be interpreted (Júnior et al., 2012), considering only essential variables for the analysis.

The evaluation of the correlations and linear regressions (Step 8) was made by Bootstrapping re-sampling, using parameters recommended by Ringle et al. (2014). Figure 3 show Bootstrapping results with all values above 1.96; then, correlations and linear regressions logics are acceptable.

Figure 3
Bootstrapping results

In the sequence (Step 9), other indicators of adjustment quality for the proposed model were analysed: Q² and f². The results are present in Table 4.

Table 4

Stone-Geisser (Q²) and Cohen (f²) values

From Table 4 analysis, it is possible to note that the proposed model presents accuracy and that the constructs are essential for the general adjustment of the model studied. Analysing path coefficients, via Figure 2, the minimum value is 0.636 between construct Senior Management Support - Lean Projects Management (see other values in red in Figure 2).

Through the results presented, it is concluded that the proposed model was validated. Considering the high values for path coefficients, it is possible to confirm the argument that when shop floor employees of auto parts companies perceive the support of senior management, they notice the success of the lean philosophy in their companies. Thus, it is interesting that senior management involvement occurs in all stages of projects. The results described here have practical implications mainly for managers of companies interested in lean projects implementations; they must be aware of the importance of top management support for the success of these projects beforehand. As mencioned previously, lack of commitment of senior management is the most critical barrier to lean manufacturing success (Binti Aminuddin et al., 2016; Scherrer-Rathje et al., 2009).

The success of global manufacturing strategies, such as lean, is not only based on the application of appropriate tools and techniques but also on the development of strategies to overcome implementation barriers. Thus, senior management can play a significant role in developing these strategies (T. & K.P., 2021).

5 CONCLUSION

This research aimed to analyse the perception of shop floor employees regarding senior management support in lean projects and its relation with initiatives success. Considering the results presented, it is possible to affirm that the main objective was achieved.

From the presented findings, it can be concluded that lean implementation success does not depend only on the use of techniques and tools; the support and commitment of senior management are essential; when shop floor employees note this support, the perception of success in initiatives is better.

This research presents some limitations that should be mentioned, such as the sample size and the fact that the respondents act only in two auto part companies. However, we believe that the exploratory character of this research justifies the mentioned limitations. The results presented here can be used for senior management in order to improve its participation in lean activities and academics in futures studies.

As future research, we suggest the development of studies with employees from other sectors, different from the sector analysed here (automotive) and performing comparisons to evidence similarity and differences among results.

REFERENCES

Alagaraja, M., & Egan, T. (2013). The Strategic Value of HRD in Lean Strategy Implementation. Human Resource Development Quarterly, 24(1), 1-27. https://doi.org/10.1002/hrdq.21155

Angelis, J., Conti, R., Cooper, C., & Gill, C. (2011). Building a high commitment lean culture. Journal of Manufacturing Technology Management, 22(5), 569-586. https://doi.org/10.1108/17410381111134446

Azambuja, R. (2011). Influence of the knowledge of workers in a lean manufacturing system on the production indicators of an automobile company (Influência do conhecimento dos trabalhadores em sistema de manufatura enxuta sobre os indicadores de produção de uma empresa automo. University of Campinas.

Ballé, M., & Régnier, A. (2007). Lean as a learning system in a hospital ward. Leadership in Health Services, 20(1), 33-41. https://doi.org/10.1108/17511870710721471

Bevilacqua, M., Ciarapica, F. E., & De Sanctis, I. (2017). Relationships between Italian companies’ operational characteristics and business growth in high and low lean performers. Journal of Manufacturing Technology Management , 28(2), 250-274. https://doi.org/10.1108/JMTM-02-2016-0024

Bhasin, S. (2012a). Prominent obstacles to lean. International Journal of Productivity and Performance Management, 61(4), 403-425. https://doi.org/10.1108/17410401211212661

Bhasin, S. (2012b). An appropriate change strategy for lean success. Management Decision, 50(3), 439-458. https://doi.org/10.1108/00251741211216223

Binti Aminuddin, N. A., Garza-Reyes, J. A., Kumar, V., Antony, J., & Rocha-Lona, L. (2016). An analysis of managerial factors affecting the implementation and use of overall equipment effectiveness. International Journal of Production Research, 54(15), 4430-4447. https://doi.org/10.1080/00207543.2015.1055849

Birkie, S. E., Trucco, P., & Kaulio, M. (2017). Sustaining performance under operational turbulence. International Journal of Lean Six Sigma, 8(4), 457-481. https://doi.org/10.1108/IJLSS-12-2016-0077

Botti, L., Mora, C., & Regattieri, A. (2017). Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers & Industrial Engineering, 111, 481-491. https://doi.org/10.1016/j.cie.2017.05.011

Braglia, M., Gabbrielli, R., & Marrazzini, L. (2019). Overall Task Effectiveness: a new Lean performance indicator in engineer-to-order environment. International Journal of Productivity and Performance Management , 68(2), 407-422. https://doi.org/10.1108/IJPPM-05-2018-0192

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Psychology Press.

de Oliveira, R. I., Sousa, S. O., & de Campos, F. C. (2019). Lean manufacturing implementation: bibliometric analysis 2007-2018. The International Journal of Advanced Manufacturing Technology, 101(1-4), 979-988. https://doi.org/10.1007/s00170-018-2965-y

Dombrowski, U., & Mielke, T. (2014). Lean Leadership - 15 Rules for a Sustainable Lean Implementation. Procedia CIRP, 17, 565-570. https://doi.org/10.1016/j.procir.2014.01.146

Emiliani, M. L. (1998). Lean behaviors. Management Decision , 36(9), 615-631. https://doi.org/10.1108/00251749810239504

Gamme, I., & H. Aschehoug, S. (2014). Assessing lean’s impact on operational integration. International Journal of Quality and Service Sciences, 6(2/3), 112-123. https://doi.org/10.1108/IJQSS-02-2014-0013

Guzzo, C., Maccari, E. A., & Piscopo, M. R. (2012). Sistematização de um modelo de lições aprendidas em projetos como contribuição à aprendizagem organizacional. Revista Gestão e Planejamento, 12(3), 578-593.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications, Inc.

Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2011). Essentials of Business Research Methods (2nd ed.). Routledge.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In Advances in International Marketing 20 (pp. 277-319). https://doi.org/10.1108/S1474-7979(2009)0000020014

Imam, H., & Chambel, M. J. (2020). Productivity or illusion? Assessing employees’ behavior in an employability paradox. Employee Relations: The International Journal, 42(6), 1271-1289. https://doi.org/10.1108/ER-11-2019-0446

Jadhav, J. R., Mantha, S. S., & Rane, S. B. (2014). Exploring barriers in lean implementation. International Journal of Lean Six Sigma , 5(2), 122-148. https://doi.org/10.1108/IJLSS-12-2012-0014

Jagoda, K., Lonseth, R., & Lonseth, A. (2013). A bottom-up approach for productivity measurement and improvement. International Journal of Productivity and Performance Management , 62(4), 387-406. https://doi.org/10.1108/17410401311329625

Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research. Journal of Consumer Research, 30(2), 199-218. https://doi.org/10.1086/376806

Júnior, S. B., Silva, D. Da, Moretti, S. L. do A., & Lopes, E. L. (2012). UMA ANÁLISE DA CONSCIÊNCIA ECOLÓGICA PARA O CONSUMO “VERDE” NO VAREJO SUPERMERCADISTA. Revista de Gestão Social e Ambiental, 6(2). https://doi.org/10.5773/rgsa.v6i2.533

Karim, A., & Arif-Uz-Zaman, K. (2013). A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations. Business Process Management Journal, 19(1), 169-196. https://doi.org/10.1108/14637151311294912

Kim, B.-C. (2015). Dynamic Control Thresholds for Consistent Earned Value Analysis and Reliable Early Warning. Journal of Management in Engineering, 31(5), 04014077. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000309

Lantz, A., Hansen, N., & Antoni, C. (2015). Participative work design in lean production. Journal of Workplace Learning, 27(1), 19-33. https://doi.org/10.1108/JWL-03-2014-0026

Laser, J. (2020). The best equilibrium in organizational flexibility-stability continuums. International Journal of Organizational Analysis. https://doi.org/10.1108/IJOA-09-2019-1875

Laureani, A., & Antony, J. (2012). Critical success factors for the effective implementation of Lean Sigma. International Journal of Lean Six Sigma , 3(4), 274-283. https://doi.org/10.1108/20401461211284743

Lewis, M. A. (2000). Lean production and sustainable competitive advantage. International Journal of Operations & Production Management, 20(8), 959-978. https://doi.org/10.1108/01443570010332971

Mainga, W. (2017). Examining project learning, project management competencies, and project efficiency in project-based firms (PBFs). International Journal of Managing Projects in Business, 10(3), 454-504. https://doi.org/10.1108/IJMPB-04-2016-0035

Martins, V. W. B., Rampasso, I. S., Anholon, R., Quelhas, O. L. G., & Leal Filho, W. (2019). Knowledge management in the context of sustainability: Literature review and opportunities for future research. Journal of Cleaner Production, 229, 489-500. https://doi.org/10.1016/j.jclepro.2019.04.354

Milton, N. (2010). The Lessons Learned Handbook: Practical Approaches to Learning from Experience. Chandos Publishing.

Möldner, A. K., Garza-Reyes, J. A., & Kumar, V. (2020). Exploring lean manufacturing practices’ influence on process innovation performance. Journal of Business Research, 106(September 2018), 233-249. https://doi.org/10.1016/j.jbusres.2018.09.002

Nascimento, D. L. de M., Goncalvez Quelhas, O. L., Gusmão Caiado, R. G., Tortorella, G. L., Garza-Reyes, J. A., & Rocha-Lona, L. (2019). A lean six sigma framework for continuous and incremental improvement in the oil and gas sector. International Journal of Lean Six Sigma . https://doi.org/10.1108/IJLSS-02-2019-0011

Nogueira, D. M. da C., Sousa, P. S. A., & Moreira, M. R. A. (2018). The relationship between leadership style and the success of Lean management implementation. Leadership & Organization Development Journal, 39(6), 807-824. https://doi.org/10.1108/LODJ-05-2018-0192

Nunes, R. D. S., Jacobsen, A. D. L., & Cardoso, R. D. S. (2019). Manufatura enxuta em um fabricante de produtos hospitalares: implantação e avaliação na percepção dos gestores. Revista de Administração Da UFSM, 12(1), 88-106. https://doi.org/10.5902/1983465917638

Rahbek Gjerdrum Pedersen, E., & Huniche, M. (2011). Determinants of lean success and failure in the Danish public sector. International Journal of Public Sector Management, 24(5), 403-420. https://doi.org/10.1108/09513551111147141

Rampasso, I. S., Anholon, R., Gonçalves Quelhas, O. L., & Filho, W. L. (2017). Primary problems associated with the health and welfare of employees observed when implementing lean manufacturing projects. Work, 58(3), 263-275. https://doi.org/10.3233/WOR-172632

Rampasso, Izabela S., Anholon, R., da Silva, D., Ordóñez, R. E. C., & Quelhas, O. L. G. (2019). Maturity analysis of manufacturing cells. Production Planning & Control, 30(15), 1250-1264. https://doi.org/10.1080/09537287.2019.1612108

Randhawa, J. S., & Ahuja, I. S. (2017). 5S - a quality improvement tool for sustainable performance: literature review and directions. International Journal of Quality & Reliability Management, 34(3), 334-361. https://doi.org/10.1108/IJQRM-03-2015-0045

Rich, N., & Bateman, N. (2003). Companies’ perceptions of inhibitors and enablers for process improvement activities. International Journal of Operations & Production Management , 23(2), 185-199. https://doi.org/10.1108/01443570310458447

Ringle, C. M., Da Silva, D., & Bido, D. D. S. (2014). Structural Equation Modeling with the Smartpls. Revista Brasileira de Marketing, 13(2), 56-73. https://doi.org/10.5585/remark.v13i2.2717

Scherrer-Rathje, M., Boyle, T. A., & Deflorin, P. (2009). Lean, take two! Reflections from the second attempt at lean implementation. Business Horizons, 52(1), 79-88. https://doi.org/10.1016/j.bushor.2008.08.004

Shahin, M., Chen, F. F., Bouzary, H., & Krishnaiyer, K. (2020). Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. The International Journal of Advanced Manufacturing Technology . https://doi.org/10.1007/s00170-020-05124-0

Shi, Y., Wang, X., & Zhu, X. (2019). Lean manufacturing and productivity changes: the moderating role of R&D. International Journal of Productivity and Performance Management , 69(1), 169-191. https://doi.org/10.1108/IJPPM-03-2018-0117

Signoretti, A. (2020). Overcoming the barriers to the implementation of more efficient productive strategies in small enterprises. Employee Relations: The International Journal , 42(1), 149-165. https://doi.org/10.1108/ER-11-2018-0298

Silva, M. C., Anholon, R., Rampasso, I. S., Quelhas, O. L. G., Filho, W. L., & Silva, D. Da. (2018). Analysis of the Brazilian entrepreneurial ecosystem in the perception of business incubator professionals. International Journal of Business Innovation and Research, 16(4), 507. https://doi.org/10.1504/IJBIR.2018.093524

Sisson, J., & Elshennawy, A. (2015). Achieving success with Lean. International Journal of Lean Six Sigma , 6(3), 263-280. https://doi.org/10.1108/IJLSS-07-2014-0024

Slack, N., Brandon-Jones, A., & Johnston, R. (2013). Operations Management. Pearson.

Solaimani, S., Haghighi Talab, A., & van der Rhee, B. (2019). An integrative view on Lean innovation management. Journal of Business Research , 105(July), 109-120. https://doi.org/10.1016/j.jbusres.2019.07.042

T., R., & K.P., S. (2021). Identification and modeling of process barriers. International Journal of Lean Six Sigma , 12(1), 61-77. https://doi.org/10.1108/IJLSS-09-2016-0044

Tortorella, G., & Fogliatto, F. (2017). Implementation of lean manufacturing and situational leadership styles: An empirical study. Leadership and Organization Development Journal, 38(7), 946-968. https://doi.org/10.1108/LODJ-07-2016-0165

Vasconcelos, D. C., Viana, F. E., & Neto, J. D. P. B. (2019). Lean and green: the contribution of lean production and environmental management to the waste reduction. Revista de Administração Da UFSM , 12(2), 365-383. https://doi.org/10.5902/1983465921750

Wetzels, M., Odekerken-Schoroder, G., & Oppen, C. V. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195. http://www.jstor.org/stable/20650284

Wong, Y. C., Wong, K. Y., & Ali, A. (2009). A study on lean manufacturing implementation in the Malaysian electrical and electronics industry. European Journal of Scientific Research, 38(4), 521-535.

Worley, J. M., & Doolen, T. L. (2015). Organizational structure, employee problem solving, and lean implementation. International Journal of Lean Six Sigma , 6(1), 39-58. https://doi.org/10.1108/IJLSS-12-2013-0058

Wu, D., & Passerini, K. (2013). Uncovering knowledge-based time management practices. International Journal of Managing Projects in Business, 6(2), 332-348. https://doi.org/10.1108/17538371311319052

Yang, T., Wen, Y. F., Hsieh, Z. R., & Zhang, J. (2020). A lean production system design for semiconductor crystal-ingot pulling manufacturing using hybrid Taguchi method and simulation optimization. Assembly Automation, 40(3), 433-445. https://doi.org/10.1108/AA-11-2018-0193

Zhang, L., Narkhede, B. E., & Chaple, A. P. (2017). Evaluating lean manufacturing barriers: an interpretive process. Journal of Manufacturing Technology Management , 28(8), 1086-1114. https://doi.org/10.1108/JMTM-04-2017-0071

Notes

Author notes

Conflict of interest declaration