People-Centered Lean Manufacturing: Drivers of Operational Performance in Saudi Arabian Industries

Abstract

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives.

1. Introduction

Manufacturing sectors in emerging economies face unique challenges when adopting operational improvement frameworks developed in different cultural and economic contexts. Saudi Arabia’s Vision 2030 initiative has placed renewed emphasis on industrial diversification and operational excellence, creating both urgency and opportunity for examining how established frameworks like Lean Manufacturing (LM) can be effectively implemented in the Kingdom’s distinctive business environment. This study addresses the question: What role do people-centered practices play in the successful implementation of LM within Saudi Arabian industries, and how do these practices interact with other LM constructs to influence operational performance?

The study makes three primary contributions to the literature. First, it provides empirical evidence on LM implementation in a Gulf region context, addressing the scarcity of research from this economically significant region. Second, it develops and tests a structural model that positions people integration as a foundational element rather than a peripheral concern, offering a theoretically grounded “people-centered” perspective on lean implementation. Third, it offers practical insights aligned with Saudi Arabia’s economic transformation goals, providing actionable guidance for firms and policymakers.

Regulatory bodies at both international and national levels have been urging manufacturing corporations to prioritize sustainability, driven by mounting concerns related to climate change and waste reduction [1,2,3]. Lean Manufacturing is an analytical structure or methodology that is used to achieve organizational sustainability, competitiveness, and overall performance [4,5,6]. The primary objectives of LM focus on optimizing value, eradicating wasteful practices, and augmenting productivity. Originally implemented within manufacturing companies, LM has progressively extended its application to service sectors like education, healthcare, hotels, and transportation [7,8,9].

The principles of LM conform to management strategies through a sequence of steps and methodologies, including the concept of lean bundles. Several obstacles hindering the successful implementation of LM primarily stem from human factors, such as managerial inattentiveness and reduced motivation among workers [5,10,11]. This observation underscores the theoretical rationale for adopting a people-centered approach: if human factors represent primary barriers to LM success, then systematic attention to workforce engagement should serve as a foundational enabler rather than an afterthought. Various highly industrialized countries have implemented LM to enhance efficiency and productivity of operations and product development [12,13]. Conversely, developing countries are endeavoring to practice LM principles to reduce costs associated with manufacturing and uphold the competitiveness of their products or services [11,14,15]. Nevertheless, manufacturing companies in several developing nations continue to face challenges, necessitating enhancements in their Operational Performance (OP) and efforts toward waste reduction [16]. Figure 1 illustrates the five core principles of LM [14,17,18].

The five principles shown in Figure 1 represent the foundational logic of lean manufacturing: (1) defining value from the customer’s perspective, (2) mapping the value stream to identify waste, (3) creating flow by eliminating interruptions, (4) establishing pull-based production driven by customer demand, and (5) pursuing perfection through continuous improvement. These principles provide the conceptual foundation upon which the Toyota Production System and subsequent lean frameworks have been built.

Value Stream Mapping (VSM) stands out as an effective tool in pinpointing areas of waste within a process framework. VSM provides a comprehensive visual representation of all processes and activities involved in producing a particular item or providing a service [19,20]. LM applications can be broken down into six areas: design improvement, planning and control, supplier interactions, process and equipment efficiency, human resource management, and enhancing customer satisfaction [21,22,23]. Saudi Arabia announced Vision 2030 in 2016 as a long-term strategy aimed at extending the economy, raising efficiency, and improving the economic viability of its production and industrial sectors.

Despite this initiative, limited efforts have been made to implement LM in both public and private enterprises. While numerous global works have explored the effects of LM on improving OP, there is a notable scarcity of research focused on applying these principles within the industry of Saudi Arabia. Research from the Gulf Cooperation Council (GCC) region more broadly remains sparse, with existing studies primarily focused on individual case studies rather than systematic empirical investigation [24,25]. Accordingly, this work aims to assess the impact of LM on OP by proposing a Structural Model for evaluation and analysis within selected organizations in the Saudi context.

2. Literature Review

2.1. Lean Manufacturing in Emerging Economies and the Gulf Region

Research on LM implementation in emerging economies has grown substantially over the past two decades, yet studies from the Gulf Cooperation Council (GCC) region remain relatively scarce. Karim et al. [25] conducted one of the first empirical studies on LM adoption in Saudi manufacturing, finding that larger organizations demonstrated higher adoption rates but that overall implementation levels lagged developed country benchmarks. Abualfaraa et al. [24] examined the integration of lean and green manufacturing practices in Saudi SMEs, highlighting the potential for synergistic benefits but noting significant implementation barriers related to workforce skills and management commitment.

Regional studies from neighboring economies provide additional context. Al-Najem et al. [16] investigated lean readiness in Kuwaiti manufacturing industries, identifying cultural factors and workforce characteristics as significant determinants of implementation success. Hani [26] examined the moderating role of lean operations in Saudi supply chain performance, finding that operational practices significantly influenced the relationship between supply chain integration and performance outcomes. Ur Rehman et al. [27] presented case study evidence from a Saudi factory demonstrating productivity improvements through lean implementation, though the single-case design limited generalizability.

These regional studies collectively suggest that while LM principles are applicable in Gulf contexts, successful implementation requires attention to contextual factors including workforce characteristics, management practices, and alignment with national development objectives. The current study builds on this foundation by developing and testing a comprehensive structural model that positions people integration as a central enabling factor.

2.2. Historical Development and Theoretical Foundations

Toyota Motor Company, Toyota City, Japan, pioneered the principles of LM. Prior investigations have extensively examined the correlation between LM and financial efficiency, recognizing it as a pivotal factor in promoting sustainability and efficient waste management procedures [28,29,30]. Initially, to contrast the Toyota Production System (TPS) with the dominant large-scale manufacturing systems employed in Western nations, the term LM was developed. Following World War II, Toyota encountered significant challenges such as labor strikes and teetered on the brink of bankruptcy. During this period, Taiichi Ohno developed the concept of the TPS, focusing on waste elimination [31]. TPS has evolved into a widely referenced model across various industries worldwide [32,33,34]. Figure 2 presents the TPS house model, which serves as the underlying framework for lean practices.

The TPS house model depicted in Figure 2 provides the conceptual framework for this study. The structure comprises several interconnected elements: the foundation of stability and standardization, the two pillars of Just-in-Time (JIT) and Jidoka (autonomation), and the central element of people integration. The roof represents the ultimate goals of shortest lead time, lowest cost, and highest quality. This visual metaphor emphasizes that lean manufacturing is not merely a collection of tools but an integrated system where each element supports and enables the others. Critically, people integration occupies the center of the house, reflecting the theoretical position that workforce engagement is essential for the effective functioning of all other elements. Jidoka—sometimes translated as “autonomation” or “automation with a human touch”—is the TPS principle of building quality into the process itself by giving both machines and workers the authority and means to stop production the moment an abnormality is detected [31,35]. Rather than letting defects flow downstream, Jidoka requires immediate problem-solving at the source, thereby preventing waste and reinforcing quality discipline throughout the production system.

Subsequently, lean practices have been advocated and implemented across various sectors, encompassing pharmaceuticals, electronics, ceramics, aerospace, and automotive industries [36,37,38]. Many researchers shifted towards evaluating the positive outcomes and improvements in operative performance resulting from the employment of LM tools [39,40,41,42].

Studies examined the impacts of variables such as plant dimensions, age, and unionization status. Moreover, four cohesive sets of practices—Just-in-Time (JIT), Total Quality Management (TQM), Total Preventive Maintenance (TPM), and Human Resource Management (HRM)—were formulated and studied for their interrelated effects [43,44]. Prior research has demonstrated that the adoption of lean techniques significantly contributes to a company’s operating performance. For example, studies found that lean practices directly impact organizational performance, including reducing defects and production costs [45,46,47]. Belekoukias et al. [48] explored the impact of specific lean methods such as JIT and autonomation on OP, with JIT and autonomation showing the strongest significance.

2.3. The Role of Human Factors in Lean Implementation

A growing body of literature emphasizes that human factors are not merely supportive of LM implementation but rather foundational to its success. Shah and Ward [49] identified HRM practices as integral components of lean bundles, noting that workforce flexibility and involvement significantly predicted operational performance outcomes. Dal Pont et al. [50] found that human resource practices demonstrated stronger effects on performance than technical practices alone, suggesting that the “soft” side of lean deserves greater theoretical and practical attention.

This perspective aligns with the sociotechnical systems view, which holds that optimal performance emerges from the joint optimization of social and technical subsystems [51]. From this viewpoint, people integration serves not as an auxiliary practice but as an enabling condition that allows technical practices like JIT and Jidoka to function effectively. Fullerton et al. [52] provided empirical support for this perspective, demonstrating that flexible workforce teams exhibited stronger commitment to JIT principles and achieved better performance outcomes. Chandler and McEvoy [53] found that HRM practices exhibited strong positive effects on TQM implementation, further supporting the theoretical linkage between human factors and quality-oriented practices.

The present study operationalizes this perspective through the “people integration” construct, which encompasses employee training, empowerment, team-based work structures, and continuous improvement mindset development. By positioning people integration as a foundational element that influences other LM constructs, the study offers a theoretically grounded test of the people-centered lean manufacturing concept.

2.4. Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) is a multivariate analytical method employed to establish connections between latent components, each of which is represented by multiple measured variables (also known as observed variables) within a study. EFA helps in identifying these latent factors or underlying constructs by examining the patterns of relationships among observed variables.

$${\mathrm{x}}_1={\mathrm{\lambda }}_{11}{\mathrm{F}}_1+{\mathrm{\lambda }}_{12}{\mathrm{F}}_2+\dots +{\mathrm{\lambda }}_{1\mathrm{k}}{\mathrm{F}}_{\mathrm{k}}+{\mathrm{u}}_1$$

(1)

$${\mathrm{x}}_2={\mathrm{\lambda }}_{21}{\mathrm{F}}_1+{\mathrm{\lambda }}_{22}{\mathrm{F}}_2+\dots +{\mathrm{\lambda }}_{2\mathrm{k}}{\mathrm{F}}_{\mathrm{k}}+{\mathrm{u}}_2$$

(2)

$${\mathrm{x}}_{\mathrm{q}}={\mathrm{\lambda }}_{\mathrm{q}1}{\mathrm{F}}_1+{\mathrm{\lambda }}_{\mathrm{q}2}{\mathrm{F}}_2+\dots +{\mathrm{\lambda }}_{\mathrm{q}\mathrm{k}}{\mathrm{F}}_{\mathrm{k}}+{\mathrm{u}}_{\mathrm{q}}$$

(3)

$$\left[\begin{array}{c}{\mathrm{x}}_1\\ {\mathrm{x}}_2\\ {\mathrm{x}}_{\mathrm{q}}\end{array}\right]=\left[\begin{array}{ccc}{\mathrm{\lambda }}_{11}& {\mathrm{\lambda }}_{12}& {\mathrm{\lambda }}_{1\mathrm{k}}\\ {\mathrm{\lambda }}_{21}& {\mathrm{\lambda }}_{22}& {\mathrm{\lambda }}_{2\mathrm{k}}\\ {\mathrm{\lambda }}_{\mathrm{q}1}& {\mathrm{\lambda }}_{\mathrm{q}2}& {\mathrm{\lambda }}_{\mathrm{q}\mathrm{k}}\end{array}\right]\left[\begin{array}{c}{\mathrm{F}}_1\\ {\mathrm{F}}_2\\ {\mathrm{F}}_{\mathrm{k}}\end{array}\right]+\left[\begin{array}{c}{\mathrm{u}}_1\\ {\mathrm{u}}_2\\ {\mathrm{u}}_{\mathrm{q}}\end{array}\right]$$

(4)

where:

F₁, F₂, …, F_k: factors of variables

λ_q1, λ_q2, …, λ_qk: loading of variable q impact by ${\mathrm{F}}_{\mathrm{k}}$

This can be rewritten as

$$\mathrm{X}=\mathrm{\Lambda }\mathrm{F}+\mathrm{U}.$$

(5)

where:

X: Vector of q variables

Λ: loading matrix (lamda value)

F: Vector of causal factors (unobserved latent variables ${\mathrm{F}}_{\mathrm{k}})$

U: Vector of error terms for q variables.

Lim, M. [54] propose that the observable variables x_q are related to the unobserved latent variables F_k in the regression model.

2.5. Structural Equation Modeling in Lean Research

Structural Equation Modeling (SEM) is a method used to establish and estimate models that represent linear and non-linear observed and latent variable relationships. It helps in analyzing complex relationships, allowing researchers to test hypotheses and evaluate the strength and direction of relationships among variables within a comprehensive framework [55]. In SEM, there are two key components: the measurement model and the structural model. The structural model illustrates the causal connections between latent variables, depicting the relationships between them. The measurement model comprises latent parameters and their corresponding indicator variables, forming the foundation to assess and understand these underlying constructs.

SEM allows researchers to employ latent parameters to model path analysis, testing and proving the existence of links between observable or measured variables and latent variables. This technique provides a powerful framework for exploring, testing, and refining theoretical models by analyzing both observed and unobserved factors within a given dataset [56]. Furthermore, SEM offers a comprehensive framework to establish reliable and valid relationships, presenting a thorough understanding of real-world scenarios [57,58].

2.5.1. CFA Model Equations and Matrices

Model:

X = λξ + δ,

(6)

where

X = measured variable

λ = factor loading linking factor to an X

ξ = latent independent factor, and

δ = measurement error in X

Parameter matrices:

Φ = (r × r) matrix of variances and covariances among the r factors (where r = number of latent independent factors);

θ = (q × q) matrix of variances and covariances among the q measurement errors (where q = number of Xs); and

Λ = (q by r) matrix of factor loadings (λ) emanating from factors (ξ) to measures, X

2.5.2. Path Analysis (PA) Model

Equation to define PA model:

Y = BY + ΓX + ζ

(7)

where:

Y is (p × 1) vector of measured DVs; p = number of DVs

B is (p × p) matrix of regression weights linking Ys with another Ys

X is (q × 1) vector of measured IVs; q = number of IVs

Γ (Gamma) is (p × q) matrix of regression weights linking Xs & Ys

ζ is (p × 1) vector of prediction errors or disturbances for p Ys

This approach involves interpreting results from SEM at both macro and micro levels. At the macro level, significance tests, fit indices (such as χ² significance, Comparative Fit Index [CFI], Root Mean Square Error of Approximation [RMSEA]), and effect sizes are examined. The aim is to identify the best-fit model by comparing various indices: ideally, non-significant χ² values close to degrees of freedom, CFI values near 0.95, RMSEA around 0.05, and 90% confidence intervals around 0. If multiple models display good overall fit indices, additional tests like chi-square difference tests and parsimony principles are used to determine the most suitable model. At the micro level, significance tests of parameters and the strength of standardized estimates are examined to ascertain the strongest pathways within the model. This multi-level assessment helps in thoroughly understanding the relationships and pathways within the proposed theoretical framework.

3. Proposed LM Model and Hypothesis Development

This section presents the proposed structural model and develops research hypotheses with explicit theoretical grounding. The model is based on the Toyota Production System (TPS) framework, which encapsulates key elements including stability and standardization, JIT, people integration, and Jidoka. Figure 3 depicts the structural LM model designed to assess the impact of Lean technologies on OP.

The structural model shown in Figure 3 operationalizes the TPS framework for empirical testing. The model positions people integration as influencing three constructs: JIT, Jidoka, and Stability and Standardization. Stability and Standardization, in turn, influence both JIT and Jidoka. Jidoka influences JIT, reflecting the quality-flow interdependence central to TPS. Finally, both JIT and Jidoka are hypothesized to influence Operational Performance. This configuration allows testing of both direct and indirect effects, providing a comprehensive assessment of how lean constructs interact to influence performance outcomes.

3.1. People Integration as a Foundational Element

The theoretical foundation for positioning people integration as a foundational rather than peripheral element draws from sociotechnical systems theory and the resource-based view of the firm. Sociotechnical systems theory posits that organizational performance depends on the joint optimization of social and technical subsystems [59]. In the context of LM, this suggests that technical practices such as JIT and Jidoka cannot achieve their full potential without appropriate workforce capabilities and engagement.

The resource-based view further supports this position by emphasizing that human capital represents a source of sustainable competitive advantage that is difficult to imitate [60]. Employees who are trained in lean principles, empowered to identify and solve problems, and engaged in continuous improvement represent a strategic resource that enables the effective deployment of lean practices. Based on these theoretical foundations, the following hypotheses are proposed:

H1: 

People integration has a positive relationship with JIT implementation.

Theoretical rationale: JIT requires workers who can respond flexibly to production demands, identify flow disruptions, and collaborate across functional boundaries. Dal Pont et al. [50] and Fullerton et al. [52] demonstrated that flexible workforce teams exhibit stronger commitment to JIT principles.

H2: 

People integration has a positive relationship with Jidoka implementation.

Theoretical rationale: Jidoka depends on workers’ ability to detect abnormalities, stop production when problems occur, and implement corrective actions. This requires both technical competence and psychological empowerment [53].

H3: 

People integration has a positive relationship with stability and standardization.

Theoretical rationale: While stability and standardization may appear purely technical, their development and maintenance depend heavily on workforce involvement. Workers contribute to developing standard operating procedures and maintaining process discipline [61].

3.2. Stability and Standardization as Enabling Conditions

Stability and standardization within the system refer to its capacity to consistently and uniformly produce items with minimal variations, thereby reducing disruptions. The effectiveness of LM is notably compromised in the absence of stability measures [62]

H4: 

Stability and standardization have a positive relationship with JIT.

Theoretical rationale: JIT systems depend on predictable process times and consistent quality to function without buffer inventories [63].

H5: 

Stability and standardization have a positive relationship with Jidoka.

Theoretical rationale: Jidoka requires clear baseline conditions against which abnormalities can be detected.

3.3. Interrelationships Among LM Practices and Operational Performance

A study conducted by Flynn et al. [64] concluded that employing quality management approaches enhanced JIT performance. Kannan and Tan [65] identified an association between JIT and TQM practices.

H6: 

Jidoka has a positive effect on JIT.

Theoretical rationale: Quality at the source reduces defects that would otherwise disrupt material flow, thereby enabling smoother JIT operations.

H7: 

JIT has a positive effect on operational performance.

H8: 

Jidoka has a positive effect on operational performance.

Theoretical rationale for H7 and H8: Both JIT and Jidoka directly contribute to operational performance through distinct mechanisms. Extensive empirical evidence supports these relationships [27,48].

4. Research Methodology

4.1. Measuring Instruments

The measurement of the impact of LM constructs on OP involved the development of a questionnaire structured into three main sections: Section A (Company Information), Section B (LM construct adoption), and Section C (Operational Performance).

Table A1. Survey Items by Construct.

People Integration (adapted from Dal Pont et al., 2008 [50]; Shah and Ward, 2003 [58])
PI1	Employees receive regular training on lean manufacturing principles
PI2	Employees are empowered to stop production when they identify quality problems
PI3	Work is organized around cross-functional teams
PI4	Employees actively participate in continuous improvement activities
PI5	Management supports employee suggestions for process improvement
Stability and Standardization (adapted from Marksberry, 2012 [61])
SS1	Standard operating procedures are documented for all key processes
SS2	Processes produce consistent output with minimal variation
SS3	Equipment maintenance follows a regular preventive schedule
SS4	Work instructions are visually displayed at workstations
Just-in-Time (adapted from Kannan and Tan, 2005 [65]; Fullerton and McWatters, 2001 [66])
JIT1	Production is driven by customer demand (pull system)
JIT2	Setup times have been reduced significantly
JIT3	Lot sizes have been reduced toward single-piece flow
JIT4	Suppliers deliver materials just-in-time for production
Jidoka (adapted from Vinodh and Joy, 2012 [67])
JD1	Processes automatically stop when abnormalities are detected
JD2	Visual controls indicate process status clearly
JD3	Root cause analysis is conducted for all quality problems
JD4	Quality is built into the process rather than inspected afterward
Operational Performance (adapted from Belekoukias et al., 2014 [48]; Shah and Ward, 2003 [58])
OP1	Our delivery reliability has improved over the past 3 years
OP2	Our production flexibility has improved over the past 3 years
OP3	Our production speed/lead time has improved over the past 3 years
OP4	Our overall operational efficiency has improved over the past 3 years

in the Appendix A presents the complete survey instrument, including all items grouped by construct with their theoretical sources.

Survey items were adapted from validated instruments in the lean manufacturing literature. People Integration items were adapted from Dal Pont et al. [50] and Shah and Ward [58]. JIT items were adapted from Fullerton and McWatters [66] and Kannan and Tan [65]. Jidoka items were adapted from Vinodh and Joy [67]. Stability and Standardization items were adapted from Marksberry [61]. Operational Performance items were adapted from Belekoukias et al. [48] and Shah and Ward [58].

All LM construct items using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Operational performance was on a 5-point scale. Items were treated as reflective indicators, and construct scores were calculated as the means [18] of constituent items.

A pilot study with industry practitioners and academics was conducted to ensure the questionnaire’s validity and reliability. The pilot involved 12 participants (6 industry managers and 6 academic experts) who reviewed item clarity, relevance, and comprehensiveness.

4.2. Data Collection

The current study sought to analyze 120 firms (both public and private). These companies were categorized as industrial and manufacturing corporations in different industries, including plastics, steel, cement, petrochemicals, electrical and electronics, pharmaceuticals, and food. A total of 67 detailed responses were received, for a response rate of roughly 55.8%. Figure 4 displays the distribution of responses by industry.

Figure 4 depicts the distribution of the researchers’ responses. Figure 4a shows that Steel and Aluminum firms provided the highest number of responses (16 firms, 23.9%), followed by Petrochemicals and Food (12 firms each, 17.9%). The lowest response rate came from pharmaceutical industries (2 firms, 3.0%). This distribution reflects the composition of the Saudi manufacturing sector, where heavy industries and petrochemicals represent significant portions of industrial output. Figure 4b shows the sample included 18 small enterprises (fewer than 50 employees) 27%, 29 medium enterprises (50–250 employees) 43%, and 20 large enterprises (more than 250 employees) 30%.

4.3. Data Analysis

Data analysis involved utilizing two software tools, SPSS-v25 and SmartPLS 3. SPSS was used to conduct exploratory factor analysis, while SmartPLS 3 assessed the measurement and structural models. SmartPLS was selected due to its suitability for smaller sample sizes and its ability to handle complex models [18]. Bootstrapping with 5000 resamples was used to assess the significance of path coefficients.

4.4. Common Method Bias Assessment

Given that all data were collected from single respondents at one time point, common method bias was assessed using Harman’s single-factor test. All items were entered into an unrotated principal component analysis. The first factor explained 31.4% of the variance, which is below the 50% threshold that would indicate problematic common method variance [35]. Additionally, variance in inflation factors (VIF) for all constructs were below 3.0.

5. Findings and Discussion

5.1. Measurement Model Evaluation

Various statistical measures were utilized to validate the accuracy of the collected data. Reliability and validity assessments were performed using Cronbach’s alpha, rho_A, composite reliability, and average variance extracted (AVE). Cronbach’s alpha values ranged from 0.661 to 0.876, indicating acceptable to high reliability [10]. All AVE values exceeded 0.6, confirming construct validity [68]. Figure 5 presents the validity and reliability metrics for each construct.

As shown in Figure 5, all constructs demonstrate acceptable reliability (Cronbach’s alpha > 0.6) and validity (AVE > 0.5). The JIT construct shows the highest reliability (α = 0.876), while Stability and Standardization show the lowest but still acceptable reliability (α = 0.661). Composite reliability values exceed 0.7 for all constructs, and AVE values exceed 0.6, confirming convergent validity. The Fornell-Larcker criterion was satisfied for all construct pairs, confirming discriminant validity.

5.2. Structural Model Assessment

The R² coefficient was used to evaluate the structural model. The present study obtained R² values ranging from 0.30 to 0.67 for the dependent variables. Specifically, the R² values for Jidoka, JIT, OP, and stability and standardization were 0.63, 0.67, 0.30, and 0.41, respectively. Figure 6 presents the complete SEM diagram with path coefficients.

Figure 6 displays the structural equation model with standardized path coefficients. The diagram illustrates the relationships between latent constructs and their indicator variables. A notable finding is that people integration has a stronger association with stability and standardization (β = 0.660) than with Jidoka (β = 0.318) or JIT (β = 0.168). This suggests that workforce engagement primarily influences operational performance by establishing the foundational conditions of process stability rather than through direct effects on flow or quality practices.

The standard deviation, T-statistics, and p-values were utilized to determine hypothesis acceptance or rejection. Figure 7, Figure 8 and Figure 9 present these statistics for all hypotheses.

Figure 7 shows the standard deviation values for each hypothesized relationship. Lower standard deviation values indicate more precise estimates. The paths from People Integration to Stability and Standardization and from Stability and Standardization to Jidoka show relatively low standard deviation, indicating robust estimates.

Figure 8 presents the T-statistics for each path. T-values greater than 1.96 indicate statistical significance at the 0.05 level. All paths exceed this threshold, with the People Integration → Stability and Standardization path showing the highest T-value (7.82), indicating a highly significant relationship.

Figure 9 displays the p-values for each hypothesized relationship. All paths demonstrate statistical significance (p < 0.05), with several paths showing high significance (p < 0.001). The People Integration → Stability and Standardization, Stability and Standardization → Jidoka, Jidoka → JIT, and JIT → Operational Performance paths all show p-values below 0.001, providing strong support for these relationships.

5.3. Discussion of Findings

The results support all eight hypotheses and provide empirical validation for the people-centered lean manufacturing model in the Saudi Arabian context. Several findings warrant deeper interpretation.

First, the particularly strong path from people integration to stability and standardization (β = 0.660) suggests that workforce engagement may be especially critical for establishing the foundational conditions of lean manufacturing. This finding aligns with the sociotechnical systems perspective and extends prior research by demonstrating this relationship in a Gulf region context.

Second, the relatively modest direct effect of people integration on JIT (β = 0.168), combined with the strong indirect effect through stability and standardization, suggests that the influence of human factors on flow-based practices operates primarily through enabling conditions rather than direct mechanisms.

Third, the strong relationship between Jidoka and JIT (β = 0.518) confirms the complementarity of these practices that is central to TPS philosophy. Quality at the source reduces the disruptions that would otherwise impede smooth material flow.

Fourth, comparing these results with findings from other contexts reveals both similarities and differences. The overall pattern of relationships aligns with studies from developed countries [48,50] suggesting that the fundamental logic of lean manufacturing translates across contexts. However, the particularly strong role of people integration may reflect contextual factors specific to Saudi Arabia, including the ongoing workforce transformation associated with Vision 2030.

Fifth, the concentration of the sample in steel, aluminum, and petrochemical industries warrants acknowledgment. These heavy industries share certain characteristics—capital intensity, process-driven operations, and strong regulatory environments—that may produce lean correlation patterns different from lighter or more labor-intensive manufacturing types. Specifically, the lean elements of JIT and Jidoka may manifest differently in continuous-process industries than in discrete-part manufacturing. Readers should apply these findings to other manufacturing contexts with appropriate caution, and future research should deliberately sample across a wider range of industries to test whether the model holds in sectors such as food, pharmaceuticals, or electronics, where workflow characteristics differ substantially from heavy industry.

Sixth, the R² value for Operational Performance (R² = 0.30) merits a dedicated discussion. While this value indicates that the model explains roughly 30% of the variance in operational performance, this figure is not unusual in lean manufacturing research conducted in emerging or developing economic contexts. Tortorella and Fettermann [69] reported similarly modest R² values in Brazilian lean studies, attributing them to the substantial influence of country-level factors—institutional environment, workforce skill levels, and infrastructure maturity—that fall outside any lean-focused structural model. In the Saudi context specifically, factors such as Saudization workforce policies, reliance on expatriate labor, and varying degrees of management commitment to continuous improvement represent additional variance sources not captured in this model. Rather than indicating a poor fit, the R² = 0.30 for operational performance suggests that lean practices are indeed a meaningful, statistically significant contributor to performance, while acknowledging that operational outcomes in Saudi manufacturing are also shaped by broader contextual forces. Future research should consider adding contextual moderators such as firm age, management commitment, and national workforce composition to more fully explain operational performance variance.

Seventh, it is worth addressing the exclusion of the automotive sector from this study. Saudi Arabia’s Vision 2030 has catalyzed growing interest in domestic automotive manufacturing, and the automotive industry is often considered the original home of lean principles [70]. However, at the time of data collection, Saudi Arabia’s automotive manufacturing base was nascent and not yet represented among the registered industrial companies surveyed. The sample was drawn from established manufacturing sectors with accessible company registries. Including the automotive sector—particularly as it develops through Vision 2030 initiatives—represents a high-priority direction for future lean manufacturing research in Saudi Arabia. That sector’s strong structural commitment to lean principles and human capital development may well produce stronger people-integration effects than those observed here.

Eighth, this study’s people integration construct captures cross-functional teamwork, empowerment, and participation in improvement activities, but does not explicitly model structured lean training programs or trainer development as a standalone factor. In sectors with less inherent lean cultures such as those studied here—the availability and quality of formal training for both managers and operators may be a missing enabling factor. The automotive industry, for example, invests heavily in structured lean training as an organizational standard, which may explain why human capital development is less explicit in our model. Future research should consider including lean training intensity as a separate construct to test whether it mediates or moderates the relationship between people integration and technical lean practices.

6. Conclusions

The findings regarding Lean Manufacturing techniques are overwhelmingly positive, indicating a clear and advantageous impact on OP and organizational environment. The current study substantiates that the adoption of Lean Manufacturing tools leads to improvements in various OP metrics, including reliability, speed, and flexibility.

This study makes three primary contributions. First, it provides empirical evidence on LM implementation in the Saudi Arabian context, addressing the scarcity of research from the Gulf region. Second, the study develops and tests a people-centered structural model that positions workforce integration as a foundational element. The particularly strong effect of people integration on stability and standardization (β = 0.660) supports the theoretical proposition that human factors serve as enabling conditions for technical lean practices. Third, the study offers practical guidance for Saudi Arabian manufacturing firms and policymakers.

Managerial Implications

The findings offer several actionable implications for manufacturing managers and policymakers:

First, organizations should invest in workforce development as a prerequisite to lean implementation rather than as a parallel activity.

Second, managers should implement JIT and Jidoka practices together rather than sequentially.

Third, policymakers supporting Vision 2030 industrial development objectives should consider lean manufacturing training as a component of workforce development initiatives.

7. Limitations and Future Research

This study has several limitations that should be acknowledged:

First, the sample size of 67 firms, while adequate for PLS-SEM analysis, limits statistical power and generalizability.

Second, the cross-sectional design prevents causal inference. Longitudinal studies would provide stronger evidence for causal claims.

Third, all data were collected through self-reports from single respondents per firm. Future studies could employ multiple respondents or incorporate objective performance measures.

Fourth, the geographic focus on Saudi Arabia limits external validity. Comparative studies across multiple countries would help establish boundary conditions.

Fifth, the study does not include formal robustness checks such as removing extreme-value observations, substituting alternative analytical methods (e.g., CB-SEM), or testing the model across industry subgroups. PLS-SEM is known to produce stable estimates with smaller samples, but confirmatory tests using alternative methods would strengthen confidence in the findings. Future research should apply such checks systematically, particularly when testing the model in other regional contexts.

Sixth, a noted limitation concerns the recency of the reference base. A substantial portion of the cited literature predates 2015, reflecting the foundational nature of much lean manufacturing theory. Future work in this area should actively integrate emerging research, particularly studies examining lean implementation under Industry 4.0 conditions and in GCC manufacturing contexts.