Lean Six Sigma implementation model in manufacturing SMEs in a developing country: a latent variable modelling approach Open Access

Small- and medium-sized enterprises (SMEs), defined as businesses with 10–250 employees, are globally recognised as economy drivers vital to a country’s growth. This is especially true in developing countries, where SMEs significantly contribute to employment and economic stability (Bayraktar and Algan, 2019; Heredia and Sánchez, 2016; Kesk et al., 2017). According to the International Labour Organisation, the proportion of employment attributable to SMEs increases as the country’s income decreases (International Labour Organization, 2019). In Colombia, a developing economy, SMEs form the bedrock of economic growth. They comprise 70.1% of manufacturing sector businesses and contribute 45% to employment and 32% to added value in this sector [Emerging Market Information Service (EMIS), 2020; Ministerio de Comercio Industria y Turismo (MinCIT), 2018a].

Despite their importance, Colombian SMEs face challenges associated with limited resources, which put their management capabilities and sustainability at risk. A troubling trend is the low proportion of Colombian SMEs that survive beyond the start-up phase to become established companies, often due to low productivity and financial difficulties (Consejo Nacional de Política Económica y Social [CONPES], 2016).

To overcome these challenges, SMEs must adopt reliable process management practices and continuous improvement strategies to boost productivity, competitiveness and profitability (Desai, 2008; Kharub et al., 2022; Panayiotou et al., 2022). Lean Six Sigma (LSS) – the integration of Lean Manufacturing and Six Sigma – has been identified as an effective continuous improvement strategy for achieving these goals (George, 2002). LSS focuses on eliminating various types of waste and reducing process variability to achieve statistical control and high quality (Raja Sreedharan and Raju, 2016). Its popularity has grown due to its dual focus on enhancing customer satisfaction and business profitability (Snee, 2010; Wessel and Burcher, 2004).

In Colombia, there were government-led efforts in 2017 and 2018 to promote LSS in SMEs [Ministerio de Comercio Industria y Turismo (MinCIT), 2018b, 2019], but these initiatives lacked continuity. In recent years, few sustained LSS implementations have been documented, and available information suggests limited success and coverage [Ministerio de Comercio Industria y Turismo (MinCIT), 2020, 2021]. This pattern of failed implementations, also observed in other countries, has spurred research into adapting LSS to SMEs’ particular characteristics and identifying the critical success factors (CSFs) necessary for sustainable success (Alexander et al., 2019; Antony et al., 2016; Ben Romdhane et al., 2017; Gaikwad et al., 2020; Sodhi et al., 2020; Timans et al., 2012). Several studies have identified CSFs for implementing LSS in SMEs (Lande et al., 2016; Sreedharan et al., 2018; Stankalla et al., 2018; Timans et al., 2012). However, unlike other sectors where CSFs have been incorporated into implementation models (Gastelum-Acosta et al., 2024; Singh et al., 2007; Swarnakar et al., 2022), little exploration has been done in SMEs on using CSFs in structured models for effective, lasting LSS deployment. Most existing models for LSS in SMEs focus on project phases, tool usage or general frameworks (Alexander et al., 2021) and authors agree that an appropriate, tailored structure for strategic LSS deployment in SMEs is needed (Timans et al., 2016).

In various research fields, CSFs are often identified, prioritised or validated as latent variables – underlying constructs that are not directly observed but inferred through measurable indicators (Ahire et al., 1996; Bagherian et al., 2023a, 2023b; Deshmukh and Lakhe, 2009; Gastelum-Acosta et al., 2022; Nunnaly, 1987; Saraph et al., 1989; Saravanan and Rao, 2006; Véliz Capuñay, 2017). This is typically done using surveys and statistical criteria from psychometrics. Nevertheless, a gap exists in the LSS literature regarding comprehensive studies on CSFs specific to LSS implementation in SMEs using population-wide survey data and latent variable modelling techniques. Previous studies have rarely proposed an implementation structure for LSS in SMEs that is based on a hierarchy of CSFs that have been validated and adjusted to reflect SMEs’ reality.

Purpose of the study: To fill these gaps, this study develops an LSS implementation model tailored to the characteristics of manufacturing SMEs in a developing country (Colombia). A latent variable modelling approach is used to validate the CSFs and propose a hierarchical structure of these CSFs to implement LSS in SMEs. Data were collected from a broad population of Colombian manufacturing SMEs through surveys, and CSFs were identified and prioritised based on both literature and empirical evidence. These factors were validated with statistical tests and CFA, refining the model to ensure it fits the observed data and SME context. The outcome is a structured implementation framework for LSS that reflects the priorities and relationships of CSFs in SMEs. This contributes to both theory and practice by offering a data-driven, context-specific model for LSS deployment in SMEs and by extending the understanding of how established CSFs may vary in importance or applicability in developing-country SME settings. It also offers a new perspective on the application of CSFs in an implementation structure in the specific case of SMEs.

Section 2 provides a detailed literature review. Section 3 describes the methodology. Section 4 presents the results, including survey findings, validation of CSFs and the proposed LSS implementation structure. Section 5 discussion interprets the findings with a triangulation of the results, literature and theory, highlighting managerial and theoretical implications. Finally, Section 6 offers conclusions with a summary of theoretical contributions, managerial insights, limitations and recommendations for future research.

Methodological approach to the literature: The systematic review framework by Tranfield et al. (2003) was followed, as endorsed by several authors (Alexander et al., 2019; Psomas et al., 2022; Sreedharan et al., 2018). In the planning stage, the purpose, scope and criteria of the review were defined, with a focus on LSS implementation in manufacturing SMEs across three key areas:

1. levels and models of LSS implementation;

2. CSFs for LSS; and

3. validation techniques using psychometric methods and factor analysis (Figure 1).

Selection criteria included publication year [post-2000, aligning with the emergence of LSS (George, 2002)], publication type (peer-reviewed research) and language (English). Databases searched included Scopus, Emerald Insight, IEEE Xplore, SpringerLink, Taylor and Francis, Wiley Online Library and Google Scholar. Key terms used were SMEs, Lean, Six Sigma, LSS, implementation models, confirmatory factor analysis (CFA) and CSFs.

In the conducting phase, content was screened and analysed based on relevance, methodology, contributions and context. From an initial pool of 87 sources, 42 high-relevance articles were selected for in-depth review (Figure 1). Information was extracted on identified CSFs, implementation models, research methods and geographic contexts. This review provided benchmarking for previous findings and guided the research design to address identified gaps (e.g. lack of models for SMEs based on validated CSFs). It also offered bibliographic support for interpreting survey results and constructing the implementation framework.

One of the first authors to explore the advantages of integrating Lean and Six Sigma as a single approach was George (2002). Snee (2010) compiled the evolution of this integration. Both concluded that combining these methods leads to more effective improvements by maximising customer and shareholder value. In the specific context of SMEs, Timans et al. (2012) found that about 40% of surveyed firms used the combined LSS approach, underlining that SMEs see value in integrating Lean and Six Sigma rather than treating them separately. Grudowski and Zajkowska (2013) reported that the synergy of Lean and Six Sigma can strengthen overall effectiveness in SMEs. Prasanna and Vinodh (2013) further explored how Lean principles can be anchored to improve LSS adoption in SMEs, and more recently, Bhat et al. (2021) demonstrated strategies to integrate Six Sigma tools into a micro-enterprise already practising Lean, to maximise benefits. In contrast, Ali et al. (2021) evaluated the effect of each methodology separately on the firm’s operational and business performance and found a negative correlation in Pakistani SMEs. This suggests that without integration, benefits may not be achieved in SMEs.

Wessel and Burcher’s (2004) seminal work on Six Sigma in SMEs highlighted two key points: it distinguished Six Sigma from total quality management (TQM) by emphasising financial gains in addition to customer satisfaction, and it was among the first to adapt Six Sigma concepts for smaller businesses. This focus on financial metrics is noteworthy, as SMEs often unknow the emphasis on financial profitability of Six Sigma initiatives.

Several empirical investigation into LSS implementation in SMEs using quantitative methods, such as surveys, have contributed to this field. Antony et al. (2008) and Timans et al. (2012) conducted surveys in the UK and the Netherlands SMEs, respectively, identifying adoption levels and CSFs; Timans et al. (2012) adapted Antony et al.’s (2008) Six Sigma survey for an LSS context. Ali et al. (2021) provided a developing-country perspective, surveying SMEs in a major region of Pakistan (covering ∼65% of the SME population there) and measuring their performance. The present study draws on these methodologies – particularly the survey instrument from Antony et al.’s (2008) as adapted for LSS by Timans et al. (2012) – to enable comparisons with prior results and ensure coverage of relevant CSFs.

The authors emphasise that SMEs have unique traits affecting LSS implementation (Antony et al., 2016; Stankalla and Chromjakova, 2018; Wessel and Burcher, 2004). Consequently, a number of studies have proposed SME-tailored LSS implementation strategies, often involving models or frameworks, to account for limited resources and simpler structures of SMEs. Some of these efforts explicitly start with identifying CSFs for implementation, but do not lead to a deployment based on them. (Ben Romdhane et al., 2017; Felizzola and Luna, 2014; Kumar et al., 2011; Moya et al., 2019; Prasanna and Vinodh, 2013; Wessel and Burcher, 2004). These works suggest that successful LSS adoption in SMEs requires focusing on certain key factors and adapting the deployment method to SME conditions.

The literature review underscored the importance of using a broad sample frame for SMEs when assessing LSS adoption. Whereas some previous studies limited the sample frame (e.g. by region or industry), the present study chose to send the survey to the entire population of manufacturing SMEs in Colombia to gain comprehensive insights into their particular LSS usage. At the same time, it suggests that the relationship of LSS to TQM and profitability influences implementation – a point considered when interpreting results.

CSFs represent managerial or organisational domains crucial for the sustained success of improvement initiatives (Boynton and Zmud, 1984). In the context of LSS in SMEs, identifying and prioritising these factors is crucial for lasting improvement results (Timans et al., 2012).

Researchers have used various methods to identify CSFs for continuous improvement methodologies like LSS. Surveys dominate the literature (56% of studies), followed by literature-based compilations (27%) and interviews (Sreedharan et al., 2018). Table 1 summarises CSFs identified and prioritised across key survey-based studies (Antony et al., 2008; Deshmukh and Lakhe, 2009; Kumar et al., 2009; Timans et al., 2012; Wessel and Burcher, 2004).

Comprehensive literature reviews also contribute to understanding CSFs. Lande et al. (2016) reviewed 143 papers on continuous improvement (2000–2015) and compiled 22 CSFs related to quality and productivity in SMEs using LSS, then selected eight key CSFs. Stankalla et al. (2018) reviewed 18 papers (2002–2016), include surveys and identified 21 distinct CSFs, prioritising 13 of them by frequency/importance across studies. They observed a high level of overlap across multiple sources among the 11 CSFs proposed by Antony et al. (2008). Furthermore, both this review and the one by Sreedharan et al. (2018) concluded – based on frequency analysis – that Jiju Antony is the most active and influential author in this research area. Such reviews provide a consolidated view of what factors are commonly deemed critical.

Based on the literature, this study begins with the 13 CSFs listed by Timans et al. (2012) (which were themselves an LSS adaptation of Antony et al.’s (2008) CSFs for Six Sigma). These 13 CSFs serve as the initial framework (see Table 1). Starting with a well-documented set of CSFs allows comparability and builds on existing knowledge. Moreover, several of these factors are also identified as important for the emerging concept of “LSS 4.0” – the integration of LSS with Industry 4.0 technologies and digital transformation (Khourshed, 2023; Moeuf et al., 2020; Pozzi et al., 2023; Samanta et al., 2024; Sony and Naik, 2020).

Validation of CSFs frequently employs both qualitative and quantitative techniques. Qualitative approaches include expert interviews, case studies, workshops and Delphi studies (Achanga et al., 2006; Sreedharan et al., 2018; Timans et al., 2012) to ensure practical relevance. Quantitative approaches include multi-criteria decision methods like the analytic hierarchy process, frequency-based Pareto analysis of survey data and statistical analysis (Belhadi et al., 2019; Deshmukh and Lakhe, 2009; Jeyaraman and Teo, 2010; Lande et al., 2016; Moya et al., 2019; Sharma and Chetiya, 2012).

A common statistical approach for CSF validation is to apply tests based on response variability and inter-correlations among survey items associated with each factor. Common metrics include tests for unidimensionality (to check if items of a factor measure a single construct), internal reliability (e.g. Cronbach’s alpha), correct item-to-factor assignment and validity tests (of content, construct, convergence and criterion). Studies like Ahire et al. (1996); Deshmukh and Lakhe (2009); Dubey et al. (2016); MacIel-Monteon et al. (2020); Saraph et al. (1989); and Saravanan and Rao (2006) used such validation techniques for quality management CSFs (including Six Sigma), confirming each factor’s reliability and validity. Because these statistical tests help ensure that each CSF is statistically sound and truly represents a distinct aspect of LSS implementation, they were applied in this research.

Researchers have also explored the factor structure of the CSFs in LSS implementation using factor analysis (Bagherian et al., 2023b, 2024; MacIel-Monteon et al., 2020; Singh and Rathi, 2021). Factor analysis originates from the common factor model, which uses latent variables (factors) to explain the variance and covariance among observed variables (Brown and Moore, 2014).

One specific approach is CFA, used to validate theoretical models by comparing collected data with pre-defined factor structures grounded in previous theoretical or empirical research (Brown and Moore, 2014; Hurley et al., 1997). If the initial model fit is inadequate, researchers iteratively refine it using statistical tools like modification indices (MI) while maintaining theoretical consistency (Schmitt, 2011).

Different authors have applied CFA to validate translated or adapted scales across different contexts (Huang et al., 2020; Martinez and Jirsák, 2024; Rosa et al., 2024; Stankovic et al., 2013). This allows replication of prior empirical studies (Jöreskog, 1971), supports accurate comparisons (Stankovic et al., 2013) and significantly enhances factor analysis research contributions (Hurley et al., 1997). For instance, Stankovic et al. (2013) replicated Chow and Cao’s (2008) questionnaire to identify the CSFs of software development projects and refine their findings. Martinez and Jirsák (2024) applied CFA to analyse Lean–Green associations using Shah and Ward’s (2007) lean production survey. Similarly, Huang et al. (2020) improved Loyd et al.’s (2020) Lean assessment tool for Chinese companies, while Tkalac Verčič et al. (2021) adapted a Croatian internal communication scale to English using CFA.

Thus, this study aims to validate the CSFs factor structure for LSS in Colombian SMEs, using a scale initially proposed by Antony et al. (2008), adapted for LSS by Timans et al. (2012) and also used by Desai et al. (2012) and Douglas et al. (2015). To date, the authors are not aware of any CFA-based validation for LSS implementation specifically in manufacturing SMEs, indicating a research gap addressed by this study.

Finally, the literature suggests structuring CSFs hierarchically for strategies such as advanced manufacturing technologies (Singh et al., 2007), Industry 4.0 (Kashyap et al., 2023) and LSS in the environmental and public sectors (Gastelum-Acosta et al., 2024; Mishra, 2022; Rathi et al., 2023; Swarnakar et al., 2020, 2022). Among them, only Gastelum-Acosta et al. (2024) and Mishra (2022) proposed explicit implementation models. However, no previous study was found that proposes a hierarchical LSS implementation model for manufacturing SMEs based on previous empirical validation of CSFs (Ben Romdhane et al., 2017; Felizzola and Luna, 2014; Kumar et al., 2006; Kumar et al., 2011; Timans et al., 2016). Felizzola and Luna (2014) suggested a CSF-based LSS framework for SMEs derived from literature rather than empirical data, highlighting a need for integrated research combining literature review, extensive data collection and latent variable modelling to produce an implementation structure.

In summary, the literature suggests that SMEs require tailored approaches for LSS, certain CSFs are repeatedly identified as critical for LSS success in SMEs and rigorous validation of these CSFs and their structure can strengthen implementation models. This study builds directly on these insights to create an LSS implementation model for manufacturing SMEs in Colombia, contributing new knowledge to both the LSS and SME literature.

It was followed by a triangulation approach (Figure 1), integrating: a comprehensive bibliographic review to identify and select CSFs and establish the survey design, quantitative data collection via two rounds of surveys targeting manufacturing SMEs and statistical validation and modelling of the CSFs through factor analysis. This mixed-method design ensured that the proposed implementation model was grounded in existing theory, empirical data and rigorous analysis. Based on the literature review, the research design was tailored to address identified gaps (such as the need for validated hierarchical models in SMEs). Survey instruments and validation techniques were selected with reference to prior studies to assess the implementation status of Lean, SS or LSS and identify CSFs (Antony et al., 2008; Kumar et al., 2009; Snee, 2010; Timans et al., 2012; Wessel and Burcher, 2004). The subsequent validation of CSFs and factor structure drew on studies that applied psychometric criteria and factor analysis in similar contexts (Ahire et al., 1996; Bagherian et al., 2023a, 2023b; Deshmukh and Lakhe, 2009; Gastelum-Acosta et al., 2022; MacIel-Monteon et al., 2020). The following sections describe these phases in detail.

The data collection consisted of two sequential surveys, S1 and S2, both administered online. The first survey (S1) was a screening survey to identify SMEs familiar with Lean, Six Sigma or LSS, and to gather baseline information on quality improvement approaches in use (Timans et al., 2012; Wessel and Burcher, 2004). The second survey (S2) targeted the identified SMEs to evaluate the importance and implementation of specific LSS CSFs.

The sampling frame was defined as all manufacturing SMEs in Colombia to maximise representativeness, due to low response rates in similar studies (Deshmukh and Lakhe, 2009; Timans et al., 2012; Wessel and Burcher, 2004). SMEs were selected from the 2019 EMIS database based on annual income [Emerging Market Information Service (EMIS), 2020]. A population of 8,884 SMEs was identified, and the S1 digital questionnaire was sent via email of this database (Departamento Administrativo Nacional de Estadística (DANE), 2020; EMIS, 2020).

The original S1 English questionnaire by Timans et al. (2012) was translated into Spanish with support from a native English speaker and included two multiple-selection questions to identify quality management strategies, tools and techniques. Content validity was ensured by four LSS experts – two academics and two consultants – who adapted the language to the local context. As S1 did not measure latent constructs, no further psychometric validations were conducted. Respondents’ IDs and emails were matched with EMIS database records [Emerging Market Information Service (EMIS), 2020] to obtain reliable demographic information.

From equation (1), with n = 8,884, a sample error limit $e$ of 0.05 and a population standard deviation σ of 0.5 (Scheaffer et al., 2011), a sample size (n) of 352 complete questionnaires was obtained, achieving a response rate of 4.0% and a confidence level of 94.4%:

Sample representativeness was verified by assessing average variability relative to the known population characteristics (categories), such as size, industry subsector and city of the firms. Variance V(np), expected value E(np) and confidence intervals were calculated for each category, ensuring sample counts fell within the acceptable range of normal approximation (Cochran, 1977; Sousa et al., 2004). This analysis indicated that the sample accurately represented the population across all categories. For instance, from the population of 8,884 SMEs, 2,338 (26.3%) were medium-sized firms; in the sample of 352 SMEs, 86 (24.4%) were medium-sized, comfortably within the calculated 95% confidence interval (77.13; 108.13). Similar validations showed compliance rates of 91% for the 22 industry subsectors and 93% for the 15 cities studied.

From the S1 results, 80 of 352 respondent SMEs (22.7%) were identified as familiar with Lean, SS or LSS and their tools, which was confirmed through direct communication. This subset was the focus for the next phase.

The main objective of S2 was to assess the importance and implementation of 13 CSFs using Likert scales. Based on the Spanish adaptation of the questionnaire by Antony et al. (2008), adapted for LSS by Timans et al. (2012) and endorsed by CSFs reviews from Sreedharan et al. (2018) and Stankalla et al. (2018), this survey was also validated by the same panel of experts. Psychometric tests – including assessments of unidimensionality, internal consistency and construct validity (convergent and criterion) – were conducted prior to CFA, as summarised in Table 3. The 13 CSFs and their corresponding 51 items, based on Timans et al. (2012), are listed in  Appendix 1.

The sample size calculation of survey S2 considered both logistical limitations and methodological guidance from previous literature. Due to the restricted availability of SMEs with LSS experience resulting from S1, the study was limited to 80 potential participants (García-García et al., 2013). Following recommendations for estimating margin of error from pilot samples or previous research (Fox and Hunn, 2009), studies by Wessel and Burcher (2004) and Timans et al. (2012) were reviewed. Using equation (1), a margin of error e of 0.097 was calculated based on Timans et al. (2012) results with 52 responses from 106 companies, a 95% confidence level and a standard deviation σ of 0.5. Thus, with the same parameters applied to the current study’s population of 80 SMEs, a final sample size of 45 SMEs was determined, consistent with findings by Deshmukh and Lakhe (2009), who reviewed multiple studies. A total of 44 complete surveys and 15 partial responses were collected.

The importance of each of the 13 CSFs was evaluated and ranked in order of priority based on the responses. The rating of each CSF is the ordinary mean of the ratings given by the respondents to the importance of the items or variables that compose each factor on the Likert scale, according to the procedure suggested by some authors (Kumar et al., 2023; Mishra, 2022; Ribeiro de Jesus et al., 2016; Stankalla et al., 2018; Timans et al., 2012). Given the ordinal and non-normal nature of the data, Friedman’s non-parametric test was applied to examine differences in measures of central tendency among the factors, with results indicating significant differences in perceived importance [χ²(12) = 124.33, p < 0.01]. This confirmed that certain CSFs (e.g. “Linking LSS to Customers”) were rated significantly more important than others, statistically justifying the ranked order of these CSFs (Gheni Hussien et al., 2025; Lakshmanan et al., 2018; Naderinejad et al., 2014). The Kendall’s W coefficient of concordance was 0.235 (moderate), suggesting that respondents agreed consistently on the relative ranking of factors. Post hoc comparisons using Wilcoxon signed-rank tests with Bonferroni adjustment were conducted to identify specific differences between CSFs; further details are available upon request.

The questionnaires and data sets used in this research can be consulted in an open-access repository at the reference (Corredor-Rojas et al., 2022).

Validation of the CSFs identified from the S2 survey involved two main stages: initial statistical testing and subsequent CFA for model verification. IBM SPSS® Statistics v26 was used for psychometric testing, and IBM SPSS AMOS® v26 was used for structural modelling.

Six statistical tests were applied (summarised in Table 2) to evaluate the response distributions and inter-item relationships within each CSF, assessing whether observed variables adequately represented their latent constructs. (Ahire et al., 1996; Bagherian et al., 2023a, 2023b; Deshmukh and Lakhe, 2009; Escalante et al., 2002; Gastelum-Acosta et al., 2022; Saraph et al., 1989). Observable variables for the 13 CSFs are detailed in  Appendix 1.

Table 3 summarises test results for reliability (Cronbach’s α), unidimensionality (CFI), convergent validity (NFI) and construct validity (PCA):

• Reliability: All CSFs achieved Cronbach’s α > 0.7, indicating strong internal consistency and supporting their retention (George and Mallery, 2003).

• Unidimensionality: Most CSFs had a comparative fit index (CFI) ≥ 0.9, confirming single-construct measurement (Ahire et al., 1996; Deshmukh and Lakhe, 2009; Saravanan and Rao, 2006). F9, “Linking LSS to Suppliers”, fell below this threshold, suggesting conceptual fragmentation, likely due to limited supplier integration.

• Convergent validity: All CSFs, except F9, met the normed fit index NFI ≥ 0.9 criterion. Following Ahire et al. (1996), F9 was provisionally retained due to its borderline NFI score.

• Construct validity: Principal component analysis (PCA) indicated the first component for each CSF explained over 50% variance (eigenvalue > 1.0), confirming each CSF as a distinct construct reflecting underlying theoretical concepts (Escalante et al., 2002).

• Correct item-factor assignment: Item-to-factor correlations (Nunnally’s method) were examined to confirm item alignment (Deshmukh and Lakhe, 2009; Nunnaly, 1987; Saraph et al., 1989). Items with weak correlations (e.g. P6 from F1, P14 from F2, see Table 4) were removed to reduce ambiguity. Spearman correlation coefficients were applied due to the ordinal data scale, using Colton’s criteria for correlation strength (>0.75) (Escalante et al., 2002).

• Criterion validity: The inter-correlation matrix of CSF importance scores (Table 5) showed positive correlations among all factors, suggesting joint contribution to LSS performance (Ahire et al., 1996; Deshmukh and Lakhe, 2009). High correlations (∼0.83 between F10 “Communication” and F11 “Understanding of LSS”) indicated conceptual overlap, later confirmed by CFA. No other pair exceeded the multicollinearity threshold of 0.85 (MacIel-Monteon et al., 2020). “Communication” also correlated strongly with “Project Management Skills” (∼0.79) and “Linking to Employees” (∼0.76), suggesting potential redundancy. Finally, common method bias (CMB) was assessed using Harman’s single-factor test via exploratory factor analysis (principal axis factoring). Results revealed the main factor explained less than 50% (47.5%) of total variance, suggesting negligible CMB concerns. (Harman, 1976; Kock et al., 2021).

Overall, validation confirmed most CSFs as robust constructs, identified F9 as weaker and highlighted specific items for potential exclusion. With this validated set, the study proceeded to CFA to verify and refine the multi-factor structure.

Following individual CSF validation, CFA was conducted using IBM SPSS AMOS® v26 to evaluate the factor structure. Initially, four problematic items (P6, P14, P31 and P49) identified previously (Table 4) and two factors with insufficient indicators (F3 “Cultural change” and F12 “Project management skills”) were excluded. The authors do not recommend submitting a factor with only two variables to the CFA test since they are insufficient to explain a phenomenon (Brown and Moore, 2014). Therefore, an initial measurement model (Figure 2) included 11 latent variables (F1–F13) and 43 items (P1–P51) ( Appendix 1). Correlations between factors were permitted to reflect theoretical interdependencies.

IBM SPSS AMOS® software estimated the factor loadings and error terms through maximum likelihood estimation, minimising differences between the model-implied and sample covariance matrices (Ahire et al., 1996; Véliz Capuñay, 2017).

Model fit was evaluated using multiple indices (Table 6): Chi-square (χ²), degrees of freedom (with a non-significant χ² or χ²/df < 3 indicating reasonable fit), an absolute fit index like RMSEA (target < 0.08) and incremental fit indices like CFI and NFI (target ≥ 0.90). Some authors recommend reporting at least one absolute and one incremental index in addition to χ² (MacIel-Monteon et al., 2020).

Initial CFA results (Table 7) indicated inadequate fit (CFI and NFI significantly below 0.90; RMSEA above 0.08), suggesting the original 11-factor structure poorly represented observed relationships. Such outcomes are common, necessitating iterative adjustments guided by statistical indicators and theoretical justification.

The initial model (Figure 2) underwent an iterative refinement process based on MI, standardised residuals and factor loadings. MI highlighted areas where adding covariance or adjusting factor loadings could enhance model fit. This systematic approach ensured theoretical consistency while improving statistical fit (Véliz Capuñay, 2017). Four iterations refined the model, with fit improvements evaluated through indices outlined in Table 6:

1. Iteration 1: Error-term covariances among observable variables within factors were introduced, guided by high MI and justified by similar wording or conceptual overlap. For instance, error terms e2 ↔ e4 (within a single factor representing management support) were correlated (Figure 3). This improved the model marginally, reducing Chi-square (χ²) and AIC values.

2. Iteration 2: Factor F9 (“Linking LSS to Suppliers”) and problematic item P27 were removed due to weak validation – low importance ratings, poor unidimensionality (CFI < 0.9) and high error variance in CFA (1.176). This aligned with existing SME-focused studies, which often omit or deemphasise supplier integration as non-critical (Lande et al., 2016; Wessel and Burcher, 2004) and Stankalla et al. (2018) ranked it the lowest priority in their review. Factor F10 (“Communication”) was also excluded due to high residual error (1.077) and low respondent importance ratings (11th out of 13). Statistically, F10 correlated excessively (r ≈ 0.83) with F11 (“Understanding of LSS”), indicating conceptual redundancy (MacIel-Monteon et al., 2020). F10 also showed significant overlaps (>0.75) with factors F8 (“Linking LSS to Employees”) and previously excluded F12 (see Table 8). Literature further supports the embedding of communication within broader factors rather than as an independent CSF in SMEs (Deshmukh and Lakhe, 2009; Stankalla et al., 2018; Wessel and Burcher, 2004). F10 was eliminated, and its conceptual elements were considered to be captured by F11 and F8. Removing F9 and F10 notably improved fit, resulting in a nine-factor model.

3. Iteration 3: Factor F7 (“Linking LSS to Business Strategy”) showed the highest residual error (∼1.18). Typically, such factors might be removed (Cupani, 2012); however, given its moderate importance (ranked 5th) and substantial literature emphasising strategic alignment’s importance (Antony et al., 2008; Deshmukh and Lakhe, 2009; Lande et al., 2016; Stankalla and Chromjakova, 2018; Timans et al., 2012; Wessel and Burcher, 2004), selective item adjustment was pursued. The three items (P28, P29 and P30) of F7 were analysed for redundancy and relative contribution (Table 9). P30 (“Periodic measurement of financial and non-financial indicators associated with LSS improvements”) had the highest importance rating. P28 and P29 had slightly lower importance ratings and strong correlations with items from other factors, suggesting overlap. Conceptually, P30 captured the essence of P28 and P29. Therefore, P30 was retained as a single-item indicator (I1) representing the essence of F7, while P28 and P29 were removed. This decision enhanced model fit (Chi-square and AIC), aligning with SME contexts’ emphasis on measurable financial outcomes (profitability focus). Consequently, the model condensed to eight factors, with F7’s strategic integration essence represented by indicator I1.

4. Iteration 4: Additional variables underwent scrutiny for redundancy or limited contribution. Items P15 (“Implementing a formal Belt system company-wide”) and P10 (“Having dedicated LSS improvement staff”) showed low importance (2.43, 2.77, respectively) and low practice ratings (2.15, 2.35, respectively), suggesting impracticality within SMEs due to resource constraints and operational realities. Literature confirms the infeasibility of traditional Six Sigma belt structures for SMEs (Ben Romdhane et al., 2017; Stankalla et al., 2019). Both items strongly correlated with higher-rated items P16 (“Identifying key roles in LSS initiatives”) (0.682 and 0.727) and P17 (“Training employees in LSS tools and techniques”) (0.675), indicating adequate representation by these alternatives. Thus, P15 and P10 were excluded. This suggested that instead of formal belts or full-time roles, SMEs may prefer to assign LSS responsibilities within existing roles and train their staff accordingly.

Finally, redundancy emerged between management-related items P5 (“Top management discussion of LSS in management meetings”) and P3 (“Top management project review/verification”), with a strong correlation (r = 0.769). Since both reflected executive engagement and P3 held marginally higher ratings, P5 was removed.

After four iterations, the final model demonstrated a significantly improved fit (Chi-square/df = 1.66). The result meets two incremental and one absolute index that give proof of a better fit to the observed phenomenon. It comprises eight latent factors, 28 observable variables and one standalone indicator (I1), formerly part of F7 (see Table 10 and Figure 3). The process led to the removal of two factors (F9 “Suppliers” and F10 “Communication”), six survey items (P5, P10, P15, P27, P28 and P29) and the reclassification of F7. These changes were theoretically justified and consistent with prior research, reinforcing that supplier involvement and communication function differently in SMEs. The next section translates this validated factor model into an implementation framework and explores its implications.

This section presents the results of the data collection and analysis, including the survey findings and the CFA model outcomes. It concludes by outlining the proposed LSS implementation structure for SMEs.

The representativeness of the S1 sample was verified by comparing its demographics – firm size, industry subsector and city – with those of the known population of Colombian manufacturing SMEs. Sample counts were confirmed to fall within the acceptable range based on the normal approximation method (Cochran, 1977; Sousa et al., 2004). These comparisons confirm that the 352 responding firms reflect the broader SME population, supporting the validity of subsequent survey findings on LSS adoption, tool usage and CSF rankings.

Survey S1 provided an overview of quality and improvement methodologies used by Colombian manufacturing SMEs. The most commonly adopted approaches were good manufacturing practices (32.2%) and ISO 9001 quality systems (27.9%), typically driven by regulatory or supply chain requirements. Lean and Kaizen methodologies followed, with a combined adoption rate of approximately 22.5%, indicating growing interest in efficiency-focused practices (Figure 4). By contrast, Six Sigma alone was implemented by only 11.1% of firms, placing it sixth among the nine methods surveyed. This finding aligns with prior studies noting Lean’s greater uptake in SMEs compared to Six Sigma, attributed to Lean’s simplicity and quicker impact, whereas Six Sigma’s statistical approach is perceived as complex and resource-intensive. (Alexander et al., 2019; Antony et al., 2016; Timans et al., 2012).

The limited application of Six Sigma suggests significant scope for broader LSS implementation. Many SMEs already engage with structured improvement frameworks such as ISO or Lean/Kaizen, which could serve as stepping stones for LSS. The results also indicate a tendency among SMEs to prioritise externally required (regulatory compliance and supply chain demands) or conceptually accessible methodologies, while more complex, training-intensive approaches like Six Sigma remain underused. This insight guided the approach in the next survey and is reflected in some of the CSF prioritisation results.

The primary objective of the S1 survey was to identify companies for inclusion in the S2 survey. Of the 352 respondents, 80 firms were selected, representing 22.7% of the total. From this group, a final set of 44 SMEs provided complete responses to the S2 survey. Table 11 details their distribution by size and Lean/Six Sigma usage, highlighting that medium-sized enterprises were more likely to adopt structured methodologies like Six Sigma or combined LSS, while smaller firms tended towards simpler Lean practices. This suggests organisational size influences methodology adoption, aligning with findings from Antony et al. (2016) and Alexander et al. (2019).

Knowledge and utilisation of specific LSS tools were also assessed among these SMEs, revealing gaps in familiarity with straightforward yet beneficial tools such as Force Field Analysis, Affinity Diagrams, SIPOC, Project Charters, FMEA, Kanban and SMED. Advanced statistical tools (e.g. Design of Experiments and ANOVA) were similarly underused, likely due to their complexity. These results indicate an opportunity for targeted training in easily implementable tools, aligning with observations by Alexander et al. (2019) and Timans et al. (2012) that SMEs prefer simpler, Lean-oriented techniques. This finding contrasts with the study by Antony et al. (2008), which focused exclusively on Six Sigma tools; SME preferences shift notably when Lean tools are incorporated.

Researchers agree that tool selection in SMEs should reflect their specific context and capabilities (Alexander et al., 2019; Thomas et al., 2014; Timans et al., 2012). This study corroborates that SMEs favour practical, visually-oriented tools over complex analytical methods, as depicted in Figure 5.

Importance and practice ratings for 13 CSFs were collected, with Figure 6 illustrating their relationship. Two key insights emerged: firstly, a consistent gap exists between perceived importance and actual practice across all CSFs, highlighting implementation challenges. Secondly, the highest-rated CSFs, “Linking LSS to the customers”, “Vision and plan statement” and “Management involvement”, align closely with fundamental quality management principles such as those underpinning ISO 9001 and TQM frameworks. SMEs thus appear naturally inclined to prioritise well-established quality management concepts.

The lowest-rated factors in both importance and practice were F9, “Linking LSS to suppliers” and F4, “Education and training”. The limited emphasis on supplier involvement (mean importance 2.81/5) suggests SMEs prioritise internal resources, consistent with Stankalla et al. (2018), though it also indicates an opportunity for enhanced supply-chain collaboration. The low rating for “Education and training” raises concerns, as training is traditionally deemed critical for successful LSS implementation (Lande et al., 2016). This may reflect SMEs’ perceived resource constraints or misperceptions about training feasibility, highlighting an essential area for further support and investigation, particularly within the Colombian context.

Notably, SMEs’ overall prioritisation of CSFs aligned significantly with previous research. Eight of the 13 factors matched rankings by Timans et al. (2012) in the Netherlands, suggesting a degree of universality in CSF prioritisation across diverse contexts, albeit with local variations, notably in training and supplier engagement.

Figure 7 summarises SMEs’ perceptions of LSS performance outcomes. The highest-rated improvements involved customer-related and quality outcomes, such as reduced warranty claims, quality costs and improved on-time delivery. Conversely, outcomes directly linked to financial metrics – profit growth, inventory reduction and sales increases – received notably lower ratings. This pattern mirrors findings from Timans et al. (2012) and contrasts Antony et al. (2008), who observed higher importance given to profitability in SMEs focused on only Six Sigma. The disparity highlights SMEs’ limited recognition or measurement of financial benefits from LSS, potentially deterring wider adoption. Clearly demonstrating and communicating financial benefits could thus be pivotal in promoting LSS uptake among SMEs.

These survey findings establish the groundwork for subsequent validation and factor analysis, emphasising both SME perceptions of critical factors and areas requiring further development or targeted intervention (Figure 7).

Following four refinement iterations (Section 3.2.3), the final CFA model comprises 8 latent factors and 28 observable variables, along with 1 single-item indicator. As shown in Table 10, model fit was satisfactory: CMIN/df ≈ 1.66 (below the threshold of 2), and both CFI and NFI showed notable improvements, meeting the recommended ranges for incremental and absolute fit indices, indicating substantial improvement from the initial model.

The retained factors are:

• F1: Management involvement and participation;

• F2: Organisational infrastructure;

• F4: Education and training;

• F5: Vision and plan statement;

• F6: Linking LSS to customers;

• F8: Linking LSS to employees;

• F11: Understanding of LSS methodology; and

• F13: Project prioritisation and selection.

Additionally, Indicator I1 (“Periodic measurement of financial and non-financial LSS improvement indicators”) captures the essence of the original F7 (linking LSS to business strategy), reflecting top management’s oversight of LSS outcomes.

Factors F9 (Suppliers), F10 (Communication) and F7 (as a complete construct) were excluded. F9 did not validate, F10 showed redundancy and F7 was represented through I1. The remaining factors were confirmed as well-defined constructs with strong loadings. F3 (cultural change) and F12 (project management skills) were excluded before the CFA due to insufficient indicators (Brown and Moore, 2014), but their underlying concepts were integrated within the operational efforts (training and infrastructure factors). The CSFs and their corresponding observable variables in the final model are listed in  Appendix 2.

This streamlined model (Figure 3) suggests that eight core factors sufficiently capture the critical elements of LSS implementation in SMEs. Excluded factors are not dismissed as irrelevant but may be less central at this stage. For example, communication appears embedded within training and engagement, while supplier integration may gain importance as internal maturity increases (Kumar et al., 2023; Stankalla et al., 2018). The following section translates these findings into a practical deployment framework measurement inherently promotes strategic and financial alignment, guiding SMEs towards recognising and valuing financial impacts from LSS initiatives.

Using the validated factors, a hierarchical structure for LSS implementation in manufacturing SMEs was developed (Figure 8). This structure provides a roadmap demonstrating the relationships and sequence or hierarchy of CSFs necessary for effective LSS deployment, informed by both SMEs’ importance ratings and CFA. The objective is to offer SMEs a clear reference for adopting LSS as a sustainable management strategy, rather than ad hoc projects.

The structure follows a top-down flow, from strategic leadership factors to operational elements. Central to this framework is customer focus, F6, “Linking LSS to the customers”, identified as the highest priority factor (average importance rating of 4.02/5). This aligns with SMEs’ common motivation for quality initiatives and is consistent with customer-oriented performance measures like warranty costs and delivery punctuality. Timans et al. (2016) similarly identified customer expectations as a primary driver for SMEs adopting LSS.

Above the customer focus (F6) in the framework lie strategic and leadership factors. At the top is F5 “Vision and plan statement”, guiding a clear strategic direction, closely supported by F1 “Management involvement and participation”. Both these factors are informed by customer requirements (F6), ensuring that management’s commitment and vision remain customer-oriented. Additionally, F11 “Understanding of LSS methodology” acts as a critical bridge, essential for both management leadership and operational implementation, reinforcing the centrality of comprehensive LSS education.

Top management responsibilities also include the single indicator I1 (“Periodic measurement of financial and non-financial LSS indicators”) and F13 “Project prioritisation and selection”, which underline LSS’s distinctive emphasis on financial measurement and strategic project selection. Despite financial metrics currently being undervalued by SMEs, explicitly incorporating them early in the framework encourages SMEs to adopt this critical practice, differentiating LSS from other quality initiatives.

Below customer focus (F6), operational-level factors focus on workforce engagement and everyday practices. F8 “Linking LSS to employees” is the primary operational goal, connected directly back to customer outcomes, creating a feedback loop reflecting SMEs’ strengths in customer–employee interactions, as opposed to large corporations. This reciprocal relationship aligns with findings by McAdam (2000) and Wessel and Burcher (2004), highlighting the importance of employee-driven customer satisfaction. The reciprocal relationship is explicitly illustrated: employees contribute to customer satisfaction and customer focus demands empowered employees. This is shown by an arrow from F8 to F6 in Figure 8, leveraging the SME trait of high customer contact by staff.

Supporting employee engagement (F8) are four foundational factors: F2 “Organisational infrastructure”, F3 “Cultural change”, F4 “Education and training” and elements of the previously identified F12 “Project management skills” (now integrated into training and infrastructure). They are conceptually crucial and are included as integrated components in the operational activities. Thus, SMEs are encouraged to strengthen infrastructure (e.g. cross-functional teams and resource allocation), foster a receptive culture and enhance employee skills through targeted training. Successful implementation of these supporting elements will empower employees (F8), driving customer-centric improvements.

The proposed structure corresponds to the results of the model adjusted with the CFA. Two factors, F9 (linking LSS to suppliers) and F10 (communication), were intentionally excluded. Supplier involvement (F9), the lowest-ranked factor, was not statistically validated, reflecting SMEs’ current lower priority. SMEs may defer supplier engagement until internal processes mature, which the structure implies. Similarly, communication (F10) was omitted because CFA indicated its embedded nature within other factors like employee engagement (F8) and understanding of LSS (F11), thereby implicitly included within those actions. The strategic alignment aspect, initially represented by F7, is captured through I1. The analysis of its variables concluded that it would be better expressed by its variable P30, now the I1 indicator, emphasising that performance measurement inherently promotes strategic and financial alignment, guiding SMEs towards recognising and valuing financial impacts from LSS initiatives.

In summary, the proposed structure is grounded in practical applicability, guiding SMEs to build on existing strengths, namely, customer focus and management commitment – as starting points for LSS implementation. It then supports the gradual development of key capabilities such as methodological knowledge, infrastructure and training to foster employee engagement. The framework’s top-down orientation ensures leadership-driven deployment, reinforced by operational feedback loops integral to continuous improvement. Overall, this sequential and relational model provides SMEs with a tailored roadmap for sustainable LSS adoption, addressing a key gap in the literature by offering a context-specific and actionable deployment strategy.

Expected practical benefits include enhanced relevance and engagement by addressing SMEs’ own prioritised factors, contextual suitability due to representative Colombian SME data and a robust evidence base combining existing research, empirical survey data and statistical validation.

This study aimed to develop and validate a model for LSS implementation in manufacturing SMEs, resulting in an empirically grounded framework featuring eight critical factors. The following discussion integrates these findings with existing literature and theory to assess their implications for both research and practice.

A key result was the prominence of customer-centricity (CSF F6), suggesting SMEs primarily regard continuous improvement as a route to enhance customer satisfaction (Timans et al., 2012). This priority aligns with TQM principles, where customer satisfaction is fundamental (Wessel and Burcher, 2004). Empirical results reinforce this alignment, with SMEs reporting the greatest LSS-related improvements in customer-facing outcomes such as on-time delivery and reduced quality costs. This suggests that, despite differing motivations – such as regulatory compliance or business continuity – SMEs pursue similar quality principles as larger firms.

The significance of top management involvement (CSF F1) and vision and plan statement (CSF F5) was reaffirmed, supporting the centrality of leadership commitment across quality frameworks from Deming to contemporary LSS literature (Laureani and Antony, 2017; Stankalla and Chromjakova, 2018). The findings highlight that SMEs, despite their smaller scale, equally require strong management leadership, potentially even more crucial given their flatter structures and direct employee interactions (McAdam, 2000). Practically, this means SME leaders should embed LSS within their regular management activities, regularly tracking project outcomes and aligning LSS objectives with strategic planning, thus positioning LSS as integral rather than supplementary.

Importantly, the model also reveals deviations from commonly accepted CSFs. “Linking LSS to suppliers” (F9), for example, was excluded from the final model, highlighting contextual limitations. In large organisations, supplier integration is essential to Lean (Habidin and Yusof, 2013), but in SMEs – particularly in developing economies – supplier-related initiatives appear less critical, likely due to limited influence or internal focus. This finding is consistent with those of Stankalla et al. (2018) and Timans et al. (2012), suggesting that the applicability of CSFs is moderated by contextual factors such as firm size and supply chain maturity. Theoretically, this challenges the universal applicability of LSS models and calls for contingent approaches that account for organisational scale and environment.

The role of “Communication” further illustrates this contextual nuance. While widely recognised as a CSF, communication in SMEs appeared inseparable from training, engagement and comprehension (Lande et al., 2016). Unlike large firms, which may use newsletters or dashboards (Tkalac Verčič et al., 2021), SMEs tend to embed communication within informal channels such as meetings and training sessions. As such, communication may not operate as a discrete factor but rather as a supporting element of broader engagement strategies. This suggests that in SMEs, CSFs are often interrelated, reinforcing the need for more integrated models of organisational change.

Methodologically, the study demonstrates how CFA can be used not only to test but also to refine theoretical constructs (Huang et al., 2020; Hurley et al., 1997; Martinez and Jirsák, 2024; Stankovic et al., 2013; Tkalac Verčič et al., 2021). By analysing correlations and error terms, conceptual overlaps were identified – such as those between communication and understanding or training and the belt system – enabling a more parsimonious and context-sensitive model. For SMEs, rather than replicating the belt hierarchy typical of Six Sigma in large firms, a focus on “key improvement roles” may be more appropriate (Ben Romdhane et al., 2017; Stankalla et al., 2019). Similarly, “ensuring understanding” may adequately encompass communication functions. This methodological approach illustrates how theoretical constructs can be streamlined to reflect organisational realities.

A further theoretical insight concerns how SMEs interpret LSS outcomes. Survey responses showed that SMEs associate LSS with quality and customer satisfaction rather than financial gains (Wessel and Burcher, 2004). This is supported by low ratings for profit or sales improvements and by the limited emphasis on “Periodic financial/non-financial measurement” (Indicator I1). This suggests that SMEs often approach LSS as an extension of quality management, rather than a profitability strategy. While large firms typically link Six Sigma to ROI, SMEs may prioritise operational benefits or compliance. Over time, as LSS maturity increases, a shift towards financial metrics may occur. The framework includes this dimension (I1), even if it is not yet widely adopted, and may serve to encourage this transition. As noted by Wessel and Burcher (2004), Six Sigma introduced a financial perspective absent from TQM; SMEs may still operate largely within a TQM paradigm, and a key contribution of this study is to suggest how they might evolve towards a Six Sigma mindset.

The findings of this study provide practical guidance for SME managers and LSS implementers:

1. Focus on customers and leadership: SMEs should centre their LSS initiatives around customer requirements, with visible and active leadership commitment. Managers should create a clear LSS vision linked explicitly to customer satisfaction metrics, leveraging employees’ direct customer interaction to facilitate rapid and effective feedback loops (McAdam, 2000; Timans et al., 2016). By emphasising customer-centric goals such as faster delivery and reduced defects, managers can motivate staff with clear, tangible outcomes.

2. Develop understanding and skills: the prominence of “Understanding LSS methodology” in the validated model underscores education’s critical role. SME leaders should invest in targeted training for themselves and key staff. Even without formal belt structures, identifying and training internal LSS champions can effectively disseminate knowledge. Regular integration of LSS principles within standard communication channels – meetings and training sessions – is advised to reinforce understanding continuously.

3. Tailor infrastructure to SME realities: given SMEs’ limited resources, managers should integrate LSS responsibilities into existing roles rather than adopting complex belt systems common in larger firms. Assigning LSS tasks to current employees (e.g. operations managers as LSS coordinators) is practical and effective (Ben Romdhane et al., 2017; Stankalla et al., 2019). Emphasising key roles, such as improvement champions or cross-functional committees, enables SMEs to drive improvement without extensive additional infrastructure.

4. Strategic project selection and outcome measurement: strategically selecting projects aligned with business priorities and systematically tracking outcomes are crucial. SMEs should prioritise initiatives based on potential customer impact, cost savings and feasibility. Managers should establish straightforward metrics (e.g. quality costs and delivery times) before and after interventions, even without sophisticated analyses. Initiating basic financial tracking can gradually shift SMEs towards recognising LSS’s value beyond quality improvements, fostering a broader appreciation for its business benefits.

5. Enhance employee engagement and cultural shift: sustaining LSS requires engaging employees through supportive infrastructure, cultural change, skill-building and training. Managers should allocate regular time for continuous improvement, reward problem-solving efforts and offer accessible skill-development opportunities. Addressing the low current practice in “Education and training” is critical – implementing affordable methods such as on-the-job training, mentoring and small-group workshops focused on practical LSS tools (e.g. FMEA and SIPOC) can progressively overcome resource constraints and resistance, embedding continuous improvement into organisational culture.

6. Supplier integration as a future opportunity: Although linking LSS to suppliers was excluded from the primary model, managers should consider supplier engagement as a future step. SMEs typically focus initially on internal processes but may benefit significantly from collaborating with suppliers experiencing quality issues. Managers should regard supplier involvement as a subsequent implementation phase, particularly relevant as SMEs grow or face external pressures from larger customers.

This structured roadmap allows SMEs to deploy LSS more effectively, prioritising leadership, customer alignment, strategic measurement and employee engagement, thus improving implementation success rates.

This study contributes to the academic literature by providing one novel LSS implementation model, derived from CSFs validated, specifically designed for manufacturing SMEs in a developing country. The research combined a comprehensive survey of an entire SME population with advanced validation techniques, which is relatively rare in SME studies. The novelty lies in both the approach and the outcome:

• A latent variable modelling approach (CFA) was applied to refine the concept of CSFs in the SME context. This methodological contribution demonstrates how statistical modelling can complement traditional factor lists to yield a model that is both statistically sound and contextually relevant. This represents an innovative way to build implementation frameworks – marrying practitioner insights with rigorous data analysis.

• The implementation structure proposed is novel in concept: while prior studies have suggested frameworks, none, to the authors’ knowledge, has offered a hierarchical deployment model for LSS in SMEs that is directly derived from validated CSFs with a clear top-down flow. This structure offers a new vision by prioritising CSFs (as opposed to merely identifying them, as most studies do) in a deployment sequence. The idea of prioritising and sequencing CSFs for implementation is a fresh contribution, expected to have practical functionality in guiding SMEs step-by-step.

• From a geographical standpoint, the study enriches Latin American LSS research, particularly within the Colombian context, an area scarcely covered in existing literature. It identifies how cultural and economic factors, such as limited resources and strong customer relationships, influence LSS deployment, enhancing global understanding of context-specific implementation requirements.

• From a theory perspective, the findings suggest certain theoretical refinements, for example, not all widely accepted CSFs are universally applicable – context matters. The concept of “critical success factors” may need to be viewed as fluid, with some factors becoming critical only in certain contexts.

• Furthermore, the study links traditional LSS practices with contemporary operational excellence trends, including Industry 4.0 and the Circular Economy. The successful adoption of LSS in SMEs could serve as a foundation for embracing technological innovations, bridging classical continuous improvement methods and future-focused operational strategies. Thus, the proposed model positions SMEs advantageously for transitioning towards LSS 4.0, integrating quality management with technological advancements.

The validated model emphasises a top-down deployment sequence, starting from management-level factors such as customer focus, strategic vision and management involvement, flowing down to operational-level factors. SMEs are advised to leverage existing quality management experiences, such as ISO practices, to streamline LSS adoption. The importance placed on customer-related factors reflects SMEs’ inherent strengths and operational realities, highlighting their close customer interactions.

Additionally, the study underscores the importance of integrating LSS roles within existing employee responsibilities, recognising that extensive belt structures (such as company-wide Black or Green Belts) are typically unfeasible in SMEs due to resource constraints. Instead, SMEs should train versatile employees capable of managing improvement projects alongside regular duties, promoting cross-functional teamwork and reducing reliance on formal hierarchical roles.

A notable finding is the significant gap between the perceived importance and practical implementation of CSFs in SMEs. To address this gap, the study suggests focusing efforts on enhancing education, training and overcoming internal resistance and resource limitations. Moreover, SMEs are encouraged to expand their perception of LSS beyond quality improvements, recognising and tracking financial outcomes to bolster organisational support.

Despite the valuable insights provided, this study has several limitations which open avenues for future research. Firstly, its entirely quantitative approach based on surveys and statistical analysis would benefit from complementary qualitative methods. Future studies should include case studies or interviews to provide deeper insights into practical implementation challenges and successes.

Secondly, reliance on self-reported Likert-scale surveys poses risks of CMB and respondent subjectivity. Although bias was not significantly detected, future research could use multiple data collection techniques, such as observations or document analysis and diverse question formats or timing strategies, to improve data validity.

Thirdly, the CFA conducted began with predefined CSFs drawn from existing literature. Future research might use exploratory factor analysis to discover additional or alternative factors relevant specifically to SMEs that the current method may have overlooked.

Additionally, the geographical scope limited to Colombia may restrict generalisability. Future comparative studies across different regions – such as Asia or Africa – could help identify universally applicable aspects versus context-specific factors, clarifying the international applicability of the proposed model.

Finally, practical testing of the implementation model is necessary. Future action research could deploy this framework within SMEs, tracking outcomes to validate its effectiveness further and refining the model based on empirical evidence.

In conclusion, this research offers a statistically validated, context-sensitive LSS implementation framework tailored for SMEs, highlighting critical context considerations. It aims to inspire further scholarly research and practical application to advance the efficacy and sustainability of LSS in small and medium enterprises.

Initial critical success factors for Lean Six Sigma implementation in manufacturing SMEs (Antony et al., 2008; Timans et al., 2012)

Initial latent (F) and observable (P) variables

P1. Understanding of Lean Six Sigma methodology by top management

P2. Participation of top management in Lean Six Sigma projects

P3. Project review/verification by top management

P4. Provision of appropriate budget and resources for the project by top management

P5. Top management discussion on Lean Six Sigma-related issues in the management meetings

P6. Top management focuses on the production process and service quality rather than on the yield

P7. Top management is encouraging employee participation in Lean Six Sigma implementation

P8. A credible and effective leadership in deploying Lean Six Sigma

P9. Creation of cross-functional teams within the organisation

P10. To have employees entirely dedicated to Lean Six Sigma deployment

P11. Facilitative leadership behaviour

P12. Showing the difference between Lean Six Sigma and other quality improvement initiatives

P13. Demonstrating the need for Lean Six Sigma in terms of benefits to the employees

P14. Few status distinctions between managers and workers to create an open, highly empowered work environment

P15. Application of the belt system throughout the company

P16. To identify the key roles of the people directly involved in

applying Lean Six Sigma

P17. Training employees on how to use tools and techniques within Lean Six Sigma

P18. Management attitude and action are fully committed to educating and training people before implementing Lean Six Sigma

P19. The organisation has a clear long-term vision statement

P20. Statement communicated throughout the company and supported by employees

P21. Manufacturing operations are effectively aligned to the central business mission

P22. Written statement of strategic plans covering all manufacturing operations, clearly articulated and agreed upon by senior management

P23. Employees at different levels are involved in planning and policymaking

P24. Identification of customer (internal/external) needs

P25. To implement projects with a high impact on customer satisfaction

P26. Understanding your market and evaluating it periodically

P27. To have an effective process in place to resolve external customer complaints

P28. Financial appraisal of Lean Six Sigma projects

P29. Target Lean Six Sigma projects on improvements that have a direct impact on the financial and operational goals of the company

P30. Regular measurement of key financial and non-financial indicators of improvement in Lean Six Sigma

P31. Employees are empowered to act whenever they encounter a problem likely to impact cost, quality, delivery or/and input

P32. To make Lean Six Sigma training mandatory for promotion consideration

P33. To award monetary bonuses to employees based on the successful implementation of Lean Six Sigma projects

P34. To make every employee responsible for the detection of potential and actual problems

P35. To involve suppliers in Lean Six Sigma projects

P36. To have suppliers who have implemented Lean Six Sigma

P37. To establish effective two-way communication with the supplier

P38. To have detailed information about supplier performance

P39. Early and effective communication on the why and how of Lean Six Sigma

P40. Major achievements stemming from Lean Six Sigma implementation were formally communicated and celebrated

P41. Information passing processes, such as team meetings and “state-of-the-business”, are a regular part of work

P42. To establish honest, open, two-way communication between management and employees for proper functioning

P43. To understand fully ALL steps of the define, measure, analyse, improve and control (DMAIC) methodology

P44. To adapt the Lean Six Sigma methodology to your organisation

P45. To use simple tools and techniques during Lean Six Sigma implementation

P46. To develop project management skills

P47. To establish a project scorecard

P48. Project selection based on financial returns

P49. Project prioritisation based on customer requirements

P50. Project selection focused on poorly performing areas of the company

P51. Project selection based on a brainstorming sessions involving cross-functional teams, suppliers and customers

Final critical success factors for Lean Six Sigma implementation in manufacturing SMEs.

Final latent (F) and observable (P) variables

P25. To implement projects with a high impact on customer satisfaction

P26. Understanding your market and evaluating it periodically

P19. The organisation has a clear long-term vision statement P20. Statement communicated throughout the company and supported by employees

P21. Manufacturing operations are effectively aligned to the central business mission

P22. Written statement of strategic plans covering all manufacturing operations, clearly articulated and agreed upon by senior management

P23. Employees at different levels are involved in planning and policymaking

P1.Understanding of Lean Six Sigma methodology by top management

P2.Participation of top management in Lean Six Sigma projects

P3.Project review/verification by top management

P4.Provision of appropriate budget and resources for the project by top management

P7.Top management is encouraging employee participation in Lean Six Sigma implementation

P8.A credible and effective leadership in deploying Lean Six Sigma

P43. To understand fully ALL steps of the define, measure, analyse, improve and control (DMAIC) methodology

P44. To adapt the Lean Six Sigma methodology to your organisation

P45. To use simple tools and techniques during Lean Six Sigma implementation

P48. Project selection based on financial returns

P50. Project selection focused on poorly performing areas of the company

P51. Project selection based on a brainstorming sessions involving cross-functional teams, suppliers and customers

P32. To make Lean Six Sigma training mandatory for promotion consideration

P33. To award monetary bonuses to employees based on the successful implementation of Lean Six Sigma projects

P34. To make every employee responsible for the detection of potential and actual problems

P9. Creation of cross-functional teams within the organisation

P11. Facilitative leadership behaviour

P12. Showing the difference between Lean Six Sigma and other quality improvement initiatives

P13. Demonstrating the need for Lean Six Sigma in terms of benefits to the employees

P46. To develop project management skills P47. To establish a project scorecard

P16. To identify the key roles of the people directly involved in applying Lean Six Sigma

P17. Training employees on how to use tools and techniques within Lean Six Sigma

P18. Management attitude and action are fully committed to educating and training people before implementing Lean Six Sigma

Source(s): Author’s own work