Synergizing TQM, JIT, and Green Supply Chain Practices: Strategic Insights for Enhanced Environmental Performance

Abstract

Background: This study examined the role of total quality management, just-in-time, and green supply chain management practices to improve environmental performance. Methods: Data from 207 manufacturing industry respondents from a developing economy were tested through a quantitative method using PLS-SEM with the help of SmartPLS to validate the measurement model. Results: The results show that just-in-time significantly impacts total quality management and green supply chain management practices. Similarly, total quality management significantly affects environmental performance. However, just-in-time insignificantly affects the environment. Likewise, total quality management is insignificant and negatively affects green supply chain practices. Conclusion: This research provides practical insight to practitioners for understanding and implementing practices in their supply chain networks. These findings support the strategic use of just-in-time and total quality management to promote green supply chain practices as a core skill to improve environmental performance. The findings are also helpful for supply chain practitioners, policymakers, and industrialists. This research enriches the literature in the supply chain.

1. Introduction

In recent years, rising globalization, industrialization, and environmental consciousness have created the need for transformation in supply chain practices and management approaches in order to achieve environmental sustainability [1,2]. Exemplary manufacturing companies embrace a proactive approach that harmonizes their operations with the long-term aspirations of their ultimate customers and the exigencies of immediate market demands. The emphasis on environmental protection puts pressure on manufacturing firms to contribute towards the environment through complementing green practices [3]. As consumers increasingly demand environmentally friendly goods and services, manufacturers must adapt their production processes to meet the expectations of these eco-conscious customers who want to buy green products and services [4,5]. Experimentation and observation have shown that green supply chain strategies boost environmental and business performance. Comprehending the prerequisites for executing green supply chain patterns to meet customer expectations is essential. Total quality management (TQM) and just-in-time (JIT) can help organizations reduce waste and adapt environmental sustainability programs to improve environmental performance. JIT practices in manufacturing firms have gained tremendous importance due to their philosophy of components arriving at the right time and right place with the right quantity. On the other hand, the quilt component and customer satisfaction are taken care of using the total quality management approach [6]. Thus, combining these practices can help establish green supply chain practices. For this reason, manufacturing firms are utilizing the TQM practices that facilitate adopting green practices and help cater to customer demands for a cleaner environment.

Countries like China are taking initiatives regarding the GSCM practices that support the SDGs before 2030 and carbon neutrality before 2060. Similarly, non-governmental institutions are also pushing for the adoption of green supply chain management (GSCM) practices (GSCMPs), including customers, suppliers, and competitors [7]. Pakistan ranked fifth for climate vulnerability, having witnessed 152 extreme weather events and suffered a loss of USD 3.8 billion from 1999 to 2018. So, conducting research in this area is crucial. Environmental deterioration has become an alarming issue for developing countries, and irresponsible manufacturing practices make the problem bigger. It was also mentioned by Yasir et al. (2020) that only a few companies in developing economies are focusing on environmental issues due to the lack of environmental consciousness and the absence of a green business strategy [8]. Research indicated that SMEs in the Asia–Pacific region are responsible for 50% of industrial pollution, and this will further increase due to their feeble internal competence and lack of capacity to improvise [9].

Research shows that GSCM strategies boost business performance. Research by Jackson and Lavelle (2018) highlighted the influence of JIT on environmental sustainability, emphasizing the need for further investigation into its combined effects with TQM [10]. Smith and Williams (2019) urged the association between TQM and environmental performance, motivating the exploration of its alignment with JIT in sustainability [11]. In today’s dynamic business environment, achieving environmental sustainability has become a paramount concern for organizations worldwide. JIT and TQM have gained significant attention for enhancing operational efficiency and product quality. Previous research has also highlighted the synergistic effects of TQM, JIT, and GSCPs on environmental performance and sustainability in manufacturing firms. Green et al. (2019) empirically assessed these practices’ complementary impact on US manufacturing companies’ environmental performance [4]. The findings revealed that JIT and TQM are directly and positively associated with green supply chain management practices. When combined, they provide a more significant impact on environmental performance than if implemented individually. This complementary relationship was further supported by Agyabeng-Mensah et al. (2021), who examined the synergy between GSCPs, JIT, and TQM on operational and business performance in Ghanaian manufacturing firms [12]. Their research demonstrated that the combination of these practices significantly improves both operational and business performance, with the synergy between GSCPs and TQM creating more value than the synergy between GSCPs and JIT. Furthermore, each practice complements others, and focusing on their overall effect on sustainability is essential. Therefore, this study may help in the focus on sustainability and environmental responsibility in supply chain management. The need for this study is highlighted by the growing awareness of the environmental impact of supply chain operations and the demand for more sustainable practices.

Further, to emphasize the goals of this paper and its motivation, this study aims to explore the integration of TQM, JIT, and GSCMPs to enhance environmental performance in manufacturing firms, particularly in developing economies. The goal is to provide a deeper understanding of how these practices, when combined, can not only improve operational efficiency but also contribute to sustainability goals by reducing waste and minimizing environmental impact. The motivation behind this research is driven by the urgent need for manufacturing companies, especially in regions with high environmental vulnerability like Pakistan, to adopt more sustainable practices due to rising pressures from governments, non-governmental institutions, and increasingly eco-conscious consumers. This study seeks to fill the gap in the literature by examining the combined effects of TQM, JIT, and GSCMPs in a developing economy context, thus broadening the applicability of these practices beyond developed nations. By investigating how these integrated strategies can improve both business and environmental outcomes, this paper aims to offer valuable insights that can help organizations enhance their sustainability efforts while maintaining competitive advantages. Hence, this research will seek an answer to the question given below:

RQ: To what extent do JIT and TQM influence GSCMPs and Environmental Performance (Evp)?

The structure of this study is as follows: Section 2 provides a literature review grounded in prior research. Section 3 outlines the methodology, while Section 4 focuses on data analysis and testing of both direct and indirect hypotheses. Finally, Section 5 covers discussions, research implications, and recommendations.

2. Literature Review

Complementarity Theory is a concept that has been applied in various fields, including mathematics, physics, and psychology [13]. This complementarity is based primarily on coordination mechanisms derived from the conjunction of highly structured, evolved proclivities. Complementarity Theory enhances theoretical discussions by providing a framework for understanding the synergistic effects of integrating various environmental practices within supply chain management. This study’s theoretical foundation is rooted in the work of Milgrom and Roberts (1995) [14], which posits that a firm’s capacity to develop and implement diverse environmental practices simultaneously serves as a source of sustainable competitive advantage, driven by their super-additive effect. In addition, Complementarity Theory suggests that different supply chain practices can interact with each other in synergistic or antagonistic ways. Synergistic interactions occur when different practices reinforce and enhance each other’s effectiveness, leading to improved outcomes. Antagonistic interactions occur when different practices conflict or interfere with each other, hindering overall performance. Complementarity Theory suggests that these practices can work together to achieve environmental goals in the context of lean and green supply chain practices. Lean practices can minimize resource consumption and pollution, such as just-in-time inventory management and waste reduction. Green practices like recycling and using renewable materials can further reduce environmental issues [15]. Multiple benefits are associated with environmentally sustainable practices, such as a firm’s reputation, customer loyalty, reduced cost, and profit maximization. These sustainable practices improve environmental performance and are essential for environmental sustainability. Çankaya and Sezen (2019) argued that sustainability can be achieved through the combination of lean practices, such as JIT and TQM [16]. JIT aims to reduce waste and increase efficiency by receiving inventory only as needed for production [17]. At the same time, TQM focuses on continuous improvement and process management to deliver high-quality products and enhance customer satisfaction [18]. Complementarity theory suggests that combining TQM and JIT practices has a significant impact on environmental practices. Dadi and Azene (2011) reported that TQM and JIT work together to boost performance cost-effectively [19]. Successful TQM and JIT plans have prepared businesses to accept environmental sustainability plans and practices. Therefore, the combination of environmental practices, TQM, and JIT techniques can aid in the achievement of environmental sustainability.

A substantial body of evidence supports the association between TQM and JIT practices [20,21]. They emphasize that JIT is closely intertwined with TQM, both aiming to meet customer expectations. Previous research has also found that TQM and JIT procedures complement each other, resulting in improved performance. Folinas et al. (2017) discovered that JIT enhances the efficiency of TQM implementation [22]. Furthermore, Modgil and Sharma (2016) identified a significant and positive correlation between TQM and JIT practices [23], and Chen (2015) observed a robust positive relationship, particularly among quality manufacturers in China [24]. He also established a notable positive link between TQM and JIT practices. Within Muthoni’s structural model (2015), JIT is evaluated as a prerequisite for TQM, with the findings supporting this relationship [25]. Consequently, we propose the following:

H1: 

JIT significantly and positively affects TQM.

Supply chain practices are helpful for firms as they are cost-effective and time-effective in reducing waste, cutting transportation time, sharing environmental risks, and enhancing resource efficiency. Green and Inman (2005) view JIT as an improvement strategy applied to manufacturing, distribution, and supply processes to eliminate waste and suggest that spontaneous practices should encourage and support the adoption of GSCM practices. Collaborative practices are necessary for JIT-selling and JIT-purchasing, involving close interaction with customers and suppliers [26]. Established collaborative organizations, particularly those involved in JIT components like JIT-selling and JIT-purchasing, should be integrated with the sustainability of GSCMPs, which emphasizes initiatives such as customer collaboration and green procurement. JIT is considered a precursor to GSCMP in our research, indicating the following:

H2: 

JIT significantly and positively affects GSCMPs.

JIT lean manufacturing can enhance environmental performance. However, it was found that businesses that extensively adopted JIT practices had a weaker connection between environmental performance and green practices. Setyadi (2019) revealed a strong and positive correlation between investments in JIT processes and overall performance [27]. Similarly, Hashmi et al. (2021) and Rashid et al. (2024) argued that appropriate inventory techniques significantly influence performance [28,29].

H3: 

JIT significantly and positively affects EVP.

TQM helps firms deliver products and services that precisely meet customer requirements. Additionally, with customers increasingly seeking eco-friendly products and services, a TQM focus on customer satisfaction makes it easier to implement ecological sustainability, leading to the development of environmentally friendly goods and practices. Garza-Reyes et al. (2018) assert that quality management is an “essential precursor” to the successful implementation of various GSCM practices [30]. Micheli et al. (2020) describe it as a multifaceted approach to ensure customer contentment and eliminate the production of defective goods and services. This necessitates a dual focus on customers and process control [31].

H4: 

TQM significantly and positively affects GSCMPs.

According to Hong et al. (2021), businesses tend to perform better environmentally when they adapt to the needs of customers and market demands [7]. Customers primarily drive the increasing demand for eco-friendly products and services. The customer-centric aspect of TQM plays a role in developing environmentally conscious products and services, notably when resources are used efficiently and waste is minimized [32,33]. Environmental challenges often benefit from the application of TQM principles.

H5: 

TQM significantly and positively affects EVP.

Research by Green et al. (2012) suggested that GSCMPs enhance environmental performance [32]. These practices can reduce the use of toxic and hazardous materials in manufacturing, decrease air emissions, and improve environmental performance by minimizing solid and water waste discharge. These patterns reduce ecological harm without compromising quality, energy efficiency, or cost reliability [6]. Previous research has empirically established a connection between environmental performance and GSCMPs [34], ascertaining that “organizations implementing GSCMPs will exhibit improved environmental performance.”

H6: 

GSCMP significantly and positively affects EVP.

The illustration below encompasses seven theories. In brief, the framework’s structure facilitates the examination of the impact of TQM and JIT practices on environmental sustainability, as demonstrated by the implementation of GSCMPs and improved environmental outcomes. There is a belief that a beneficial relationship exists between JIT and TQM, as well as GSCMPs and environmental sustainability, due to their complementary nature. It has been hypothesized that the combined effects of TQM, JIT, and GSCMPs will have a more significant influence on the individual environmental performance outcomes. There is an expectation that environmental performance is positively linked with adopting GSCMPs. While Figure 1 illustrates the conceptual model of this study, it also presents the direction of the hypotheses.

3. Research Method

This research utilized a deductive approach alongside a quantitative methodology with a positivism paradigm to collect data from our specified target population [35]. The quantitative–deductive approach is a well-established method that relies on numerical data and employs hypothesis testing to substantiate a theory. This approach is recognized as explanatory research and assists researchers in generating trustworthy knowledge when all parameters are well defined and valid. It effectively clarifies theoretical concepts and empirical findings [36]. The study adopted a causal research design to facilitate hypothesis testing and generate numerical outcomes. Consequently, this study employed a causal methodology to explore the causal relationships among the variables.

Data Collection

In this research, we employed organizational units of analysis to focus on managerial staff chosen according to knowledge-based criteria. This technique assists in deriving logical conclusions. We collected data to investigate the study variables from supply chain professionals in Pakistan’s manufacturing industry. The complexity of a model plays a crucial role in determining the minimum required sample size for a study [37]. More complex models with a higher number of predictors require larger sample sizes. Using a power level of 0.8, a medium effect size, and an α value of 0.05, with three predictors in the model, a minimum sample size of 77 was needed to test the research model reliably. This study includes a sample of 207 respondents, which exceeds the required size and is adequate for the analysis.

Due to the absence of a sampling frame, purposive sampling—a non-probability sampling method—was utilized [37]. Using informed judgment, participants were chosen from a targeted population. Given the study’s focus on evaluating environmental performance within the manufacturing sector of a developing economy, purposive sampling was applied to select sample units with specific characteristics (e.g., JIT, TQM). This approach enabled the deliberate and precise selection of sample units [38]. Further, purposive sampling in this study was driven by the need to select participants with specific characteristics relevant to the research objectives, ensuring that the sample was rich in information pertinent to the research questions [39]. However, it is important to recognize the potential biases associated with this sampling method. Purposive sampling can introduce selection bias, as it relies on the researcher’s judgment in selecting participants, which may lead to overrepresentation or underrepresentation of certain groups. This bias could influence the generalizability of the findings, as the sample may not fully reflect the broader population. To mitigate this, we made concerted efforts to ensure that the participants were as representative as possible of the relevant population within the constraints of the research objectives. Additionally, we acknowledge that the subjective nature of participant selection warrants careful interpretation of the results, with the understanding that these findings may not be universally applicable beyond this study’s specific context.

A questionnaire was selected as the research instrument due to its effectiveness in gathering numerical data. It comprised closed-ended questions addressing the study variables and was considered appropriate for application with a relatively large sample size. The questionnaire in the appendix employed a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5). Further, previously developed instruments were considered for this study as a basis for adapting new items or replication. This choice was made because these pre-existing items had already undergone review and validation [40]. An initial questionnaire was pre-tested with ten supply chain professionals to ensure clarity of context, language accuracy, and consistency. After completing the preliminary test, the questionnaire was converted into an online survey format, accompanied by a brief statement outlining the research objectives and guaranteeing the anonymity and confidentiality of respondents. Invitations were sent, and responses were collected between January 2023 and August 2023.

4. Data Analysis

The choice of analysis technique for this study was Partial Least Squares Structural Equation Modeling (PLS-SEM), primarily because it enables a comprehensive exploration of variance. The measurement model within the PLS-SEM framework evaluates the reliability of the research instrument. In contrast, the structural model facilitates hypothesis testing [37]. This study explicitly utilized PLS-SEM with Smart Partial Least Squares (PLS) version 3.0 to address concerns related to predictive purposes [41]. This finding justifies using Smart PLS as a non-parametric software for data analysis.

4.1. Demographic Profile

This study analyzed the demographic characteristics of the survey participants using IBM SPSS version 22. The demographic analysis categorized respondents based on age, gender, education level, job designation, and years of job experience. Regarding gender, 74.4% of respondents were male, while 25.6% were female. Likewise, 41.1% of respondents were between the ages of 31 and 40, 1.9% were between the ages of 41 and 50, and 1% were between the ages of 51 and 60. These age groups comprised the majority of respondents. A total of 56% of those surveyed were between the ages of 20 and 30. The following categories were the respondents’ educational backgrounds: 7.7% of students were matriculate, 44.97% were intermediate students, 44.97% were graduate students, and 2.4% were postgraduate students. Respondents had four categories of professional experience: 31.9% had less than five years of work experience, 21.6% had six to ten years, 21.3% had eleven to fifteen years, and 25.6% had sixteen years or more. Similarly, all employees were from the manufacturing sector with different proportions, like Chemical/Plastic 16.1%, FMCG 15.3%, Textile 12.9%, Pharmaceutical 12%, Food and Beverages 11.6%, Automobile 10.8%, Cement/Steel 10%, and others 11.2%.

4.2. Common Method Bias

To address potential standard method bias (CMB) arising from the self-reported nature of the data [42], this study employed a combination of procedural and statistical approaches. Respondents were assured of anonymity and were explicitly informed that their responses had no right or wrong answers, emphasizing the importance of providing honest judgments. A comprehensive collinearity test was conducted. The results of this test revealed that all Variance Inflation Factors (VIFs) were less than or equal to 5 [42]. This outcome indicates that common method bias (CMB) in the data was not severe for this study.

4.3. Measurement Model

Because this study primarily concerns predictive purposes, it necessitated using latent variable scores for subsequent analysis. This study utilized the Smart Partial Least Squares method [37]. Two key aspects of validity were addressed when establishing the measurement model: convergent validity, which ensures that the items effectively measure the intended construct, and discriminant validity, which confirms that the items linked to each construct are separate from those of other constructs. Convergent validity is typically confirmed when loadings and average variance extracted (AVE) values are both equal to or greater than 0.5 and the composite reliability (CR) exceeds 0.7 [37],

Table 1. Items with their sources and convergent validity results.

Construct	Items	Std β	α	CR	AVE
JIT			0.747	0.842	0.569
	JIT1: Our crews practice setups to reduce the time required.	0.920
	JIT2: We tend to have small lot sizes in our master schedule.	0.50
	JIT3: We have small lot sizes in our plant.	0.592
	JIT4: We usually meet the production schedule each day.	0.911
TQM			0.720	0.821	0.538
	TQM1: We frequently are in close contact with our customers.	0.815
	TQM2: We are concerned about the number of parts in an end item.	0.597
	TQM3: Charts showing defect rates are posted on the shop floor.	0.787
	TQM4: We extensively use statistical techniques to reduce variance in processes.	0.715
GSCMPs			0.900	0.933	0.781
	GSCMP1: Cross-functional cooperation for environmental improvements.	0.970
	GSCMP2: Environmental audit of suppliers’ internal management.	0.608
	GSCMP3: Design products to reduce the consumption of material/energy.	0.970
	GSCMP4: Eco-labeling of products.	0.935
EVP			0.846	0.886	0.577
	EVP1: Improvement in an enterprise’s environmental situation.	0.931
	EVP2: Decrease in frequency of environmental accidents.	0.656
	EVP3: Decrease in consumption of hazardous materials.	0.842
	EVP4: Reduction of solid wastes.	0.931
	EVP5: Reduction of effluent waste.	0.520
	EVP6: Reduction of air emissions.	0.563

Note: The results are from SmartPLS output.

presents evidence that the loadings, AVE, and CR values exceeded the threshold values, demonstrating that this study faced no issues in establishing convergent validity.

To assess discriminant validity, all Hetrotrait–Monotrait (HTMT) ratios should be ≤0.9 [37]. The HTMT ratio analysis results ranged from 0.355 to 0.857. Since all values were below 0.9, discriminant validity was confirmed for this study.

4.4. Structural Model

This study utilized a bootstrapping procedure with 5000 resampling iterations to verify hypotheses under specific conditions. The results showed that the hypotheses were supported when the beta value matched the direction of the hypothesis, the t-value was greater than or equal to 1.645, the p-value was less than or equal to 0.05, and the bias-corrected confidence interval did not contain a zero within its lower and upper bounds. Before hypothesis testing, this study ensured that multicollinearity was not an issue by examining Variance Inflation Factor (VIF) values [42]. The requirement was that all VIF values be less than or equal to 3.3 [43]. This study found that all VIF values were below 3.3, indicating that multicollinearity did not exist.

The analysis revealed that JIT and TQM accounted for 40% of the variance in GSCMPs. Additionally, JIT, TQM, and GSCMPs collectively explained 57% of the variance in EVP. After the hypotheses were supported, this study reported the effect size (f²). There are three categories for f²: 0.02 (small), 0.15 (medium), and 0.35 (large) [44,45]. The Q² or cross-validated redundancy for the two endogenous latent variables in the research model was calculated to test predictive relevance. A number larger than 0 indicates predictive relevance [42]. The values of GSCMPs and EVP were 0.201 and 0.322, respectively. These values are more significant than zero, indicating the appropriate predictive capability of the structural model.

5. Discussion

JIT and TQM practices were hypothesized in H1. The standardized coefficient of 0.403, which is statistically significant at the 0.01 level, confirms that JIT procedures have a significant impact on the implementation and improvement of TQM practices. This finding is crucial for organizations seeking to optimize production processes and enhance quality. The significant relationship between JIT and TQM is consistent with previous research, which has consistently shown that JIT systems can improve quality by reducing inventory levels and increasing the focus on quality control. Our findings further support this notion by demonstrating that JIT procedures can have a direct and positive impact on TQM practices. This highlights the importance of integrating JIT and TQM strategies to achieve optimal production outcomes. In comparison to previous studies, our research provides a more comprehensive understanding of the relationship between JIT and TQM. For instance, two studies study by Kannan and Tan (2005) and Mas’ udin and Kamara (2018) found a positive correlation between JIT and TQM but did not examine the direct impact of JIT on TQM practices [46,47].

The empirical results of our study significantly suggest a positive and substantial association between JIT and GSCMPs (H2). The standardized coefficient analysis yielded a strong coefficient of 0.959, indicating statistical significance at the 0.01 level. These findings illustrate the significant importance of JIT methods in developing and expanding GSCM practices, emphasizing JIT’s important role in promoting environmental sustainability within the supply chain. The substantial association between JIT and GSCMPs is consistent with previous research, which has consistently shown that JIT systems can improve environmental sustainability by reducing waste and increasing efficiency. Our findings further support this notion by demonstrating that JIT procedures can have a direct and positive impact on GSCMPs practices. This highlights the importance of integrating JIT and GSCMPs strategies to achieve optimal environmental sustainability outcomes. In comparison to previous studies, our research provides a more comprehensive understanding of the relationship between JIT and GSCMPs. For instance, a study by Ye et al. (2022) found a positive correlation between JIT and SC disruptions but did not examine the direct impact of JIT on GSCMPs. In contrast, our study used a more robust methodology to analyze the standardized coefficient, providing a more precise measure of the relationship between JIT and GSCMPs [48].

The findings also showed that the relationship between JIT practice and environmental performance (H3) was not significant, suggesting that JIT procedures do not directly influence environmental performance within the scope of the research model. This finding suggests that the implementation of JIT practices alone is not sufficient to improve environmental performance. This result could be attributed to several factors. First, JIT primarily focuses on improving operational efficiency by minimizing inventory and streamlining production processes, which may not directly translate into environmental improvements such as waste reduction or energy efficiency. While JIT can contribute to sustainability indirectly by reducing waste through efficient inventory management, its primary goal is not centered around environmental objectives but rather cost reduction and productivity enhancement. Moreover, environmental performance in manufacturing often depends on broader green practices and management strategies, such as waste management, energy efficiency, and sustainable sourcing, which may not be directly addressed by JIT alone. Additionally, the lack of a significant relationship could be influenced by the context of this study, mainly if the research was conducted within an industry or region where JIT practices are not yet fully integrated with sustainability initiatives. In such cases, JIT may be perceived as a tool for operational improvement rather than a means for enhancing environmental performance. Therefore, further investigation into the combined effects of JIT and other green management practices is needed to determine whether their integration can more effectively contribute to sustainability outcomes. This result is inconsistent with the findings of previous studies that have reported a direct correlation between JIT and environmental performance. For instance, a study by García Alcaraz et al. (2022) found that JIT practices significantly influence environmental performance in the manufacturing industry [48]. Likewise, the analysis also revealed that the relationship between TQM and GSCMPs (H4) was non-significant, with a standardized coefficient of −0.007. This suggests that TQM practices do not have a significant effect on GSCMPs. Meanwhile, TQM practices significantly influence GSCMPs in manufacturing. These findings collectively suggest that TQM practices are insufficient for improving GSCMPs.

The findings prove the significant association between TQM and environmental performance (EVP). The coefficient value of 0.051, significant at the 0.01 level, underscores the critical role of TQM in nurturing and improving EVP within the organizational context. This is consistent with previous research, highlighting TQM’s importance in achieving sustainable outcomes. For instance, enterprises employing JIT tend to apply TQM with greater rigor than conventional establishments [22]. Similarly, Chen (2015) suggests that TQM can be considered a subset of JIT [24]. Our study reinforces these findings by demonstrating that a complete TQM framework encompasses all fundamental components of JIT, implying that JIT is an integral part of the TQM concept. Furthermore, our investigation reveals a significant impact of GSCMPs on EVP. The standardized coefficient of 0.795, which is significant at the 0.01 level, shows that eco-friendly supply chain management practices (SCMPs) significantly impact environmental performance (EVP). This aligns with previous studies highlighting the importance of integrating environmental factors into supply chain management for sustainable results. Our findings support that adopting eco-friendly practices within supply chain management can significantly improve environmental performance. Additionally, our study sheds light on the relationship between JIT and TQM. We discovered that simply implementing JIT does not lead to optimal plant performance, indicating that a comprehensive TQM framework is essential for achieving the best results. This supports the idea that JIT is a vital component of the TQM concept [24]. Our findings further emphasize that TQM initiatives should incorporate JIT principles to ensure optimal plant performance and positive environmental outcomes.

5.1. Research Implications

5.1.1. Theoretical Implications

Drawing on Complementarity Theory, this study explores the combined effect of GSCM practices on environmental performance, highlighting the interconnectedness and interdependence of these practices. This theoretical framework allows a deeper exploration of how different environmental initiatives within the supply chain can work together synergistically to enhance environmental performance rather than focusing solely on individual best practices. Likewise, this study shows how JIT and TQM can work together to make the environment more sustainable when used together. There are implications from this study; the research specifies the order in which these procedures should be implemented, with JIT acting as a prelude to TQM implementation. The relationship between these three practices and another performance variable, for instance, environmental performance organizationally, should be the subject of further investigation by researchers. Knowing the additional ramifications of their combined effects for future research projects might be beneficial.

Further, applying the Complementarity Theory in this context contributes to the ongoing debate and argument surrounding the effectiveness of integrating lean and green supply chain practices. It provides a lens through which to examine the interplay between different practices and their combined impact on environmental performance. It offers valuable insights for managers and researchers seeking to optimize sustainable supply chain strategies. This theoretical perspective enriches the discussion by emphasizing the interconnected nature of green supply chain practices and their potential to drive collective environmental performance improvements. In addition, to support their environmental sustainability strategies, practitioners can employ a combination of JIT, TQM, and GSCMPs established in this study. The findings support the well-known advantages of JIT and TQM. JIT programs are to reduce waste, which lowers production and delivery costs and boosts profitability. Similarly, TQM programs significantly emphasize initiatives focused on the customer’s needs. These initiatives result in the production and delivery of goods and services that satisfy the requirements of customers, which in turn increases profitability, market share, and sales. Earlier undertaking to follow GSC strategies, this study’s findings suggest that production managers should prioritize the development and efficient implementation of JIT and TQM programs. If JIT and TQM are not integrated into green practices, they might not obtain the full benefit they could. In addition, JIT and TQM lean procedures primarily drive environmental preservation. According to this study, companies attempting to achieve environmental sustainability must combine JIT, TQM, and GSCMP strategies.

5.1.2. Managerial Implications

The implications of our findings are significant for organizations seeking to improve their production processes and enhance quality. By implementing JIT procedures, organizations can create an environment that fosters quality control and improvement, ultimately leading to better product quality and customer satisfaction. Furthermore, our study suggests that organizations should prioritize the integration of JIT and TQM strategies to achieve optimal production outcomes. In terms of societal consequences, this paper proposes a managerial method to enhance the environment and foster environmental sustainability. Although the research was conducted in Pakistan’s manufacturing industry, the findings may apply to firms worldwide. Adopting JIT methods is expected to result in a more efficient use of resources. Similarly, establishing TQM practices would boost effectiveness by ensuring high-quality products and services that meet client expectations. Methods for the green supply chain will also spread gains in efficiency and effectiveness throughout the entire supply chain, including suppliers and the suppliers of suppliers, until they reach end customers. By promoting responsible resource management and reducing environmental impact, this improvement in effectiveness and efficiency fulfills the social paramount of environmental sustainability. Organizations can benefit from combining JIT, TQM, and GSCMPs to create a more sustainable future and address more extensive social and environmental issues.

5.1.3. Practical Implications

These findings offer important implications for supply chain managers and industry practitioners aiming to optimize production processes and enhance sustainability. The significant relationship between JIT and TQM underscores the need for integrated strategies, where JIT methods improve operational efficiency and quality control. Practitioners should focus on harmonizing JIT with TQM to achieve comprehensive improvements in production quality. Similarly, the strong association between JIT and GSCMPs highlights JIT’s critical role in promoting environmental sustainability through waste reduction and efficiency gains. However, this study also reveals that JIT alone may be insufficient to enhance environmental performance, emphasizing the need for combined green management initiatives. Additionally, while TQM directly impacts environmental performance, it does not significantly influence GSCMPs, suggesting practitioners should complement TQM with specific green practices to maximize sustainability outcomes. These insights encourage managers to adopt holistic approaches, integrating JIT, TQM, and GSCMPs to align operational efficiency with environmental goals for sustainable supply chain success.

5.2. Limitation

Even though this study’s goals were met, it is essential to remember the limitations to consider when interpreting the results. One notable limitation of this study is the reliance on data collected from Pakistan, which may constrain the generalizability of the findings to other geographical regions and contexts. While the data from Pakistan provide valuable insights specific to its economic, cultural, and industrial conditions, it may not fully represent the dynamics in different countries or regions with distinct social, economic, or regulatory environments. This is particularly relevant in the context of global supply chains, where regional variations can significantly impact the outcomes of supply chain management practices. It is important to recognize that while our study’s findings offer valuable contributions within the context of Pakistan, further research across multiple countries and regions with diverse socio-economic and political settings is required to assess the broader applicability of these findings. Additionally, future studies could explore the influence of cultural factors, regulatory frameworks, and market maturity in other countries to provide a more comprehensive understanding of the research topic and its implications for global supply chain practices. Further studies in different international contexts will help to establish the robustness and generalizability of the conclusions drawn in this paper. In addition, survey-based investigations have also been criticized for their inherent risk of non-response and standard method biases. However, studies show that these biases have little effect on the validity of results. It was hard to achieve great responses in survey-based procedures and SCM studies. This study’s low response rate was another drawback. PLS Structural Equation Modelling (PLS-SEM) was the technique used, and the sample size remained sufficient to support it. We acknowledge that a higher response rate would increase this study’s trustworthiness, despite our belief that the sample is representative of Pakistani businesses. Furthermore, even though this study’s primary goal was to establish connections between environmental performance and TQM, JIT, and GSCMPs, it is essential to remember that other factors, like organizational culture, characteristics of the industrial sector, and market dynamics, may also have an impact on how much performance is affected by the constructs examined in this study. Comprehension of the topic and additional considerations should be incorporated into future research.

5.3. Future Research

Our research shows that businesses with deep-rooted JIT programs and TQM programs are more likely to adopt eco-friendly patterns and enhance their performance regarding the environment. Three strategic paramounts exist: low price, excellent quality, and quick response. The significance of incorporating environmental sustainability as a further strategic requirement may assist in combining a focus on customers, cheap cost, excellent quality, reactivity, and sustainability. Furthermore, the impact of organizational and environmental factors like culture, market dynamics, and industrial sector characteristics should be the subject of future research. A better understanding of the issue would be gained by examining how these contextual factors affect the effectiveness of JIT, TQM, and GSCM strategies in enhancing environmental performance.

5.4. Conclusions

This research has investigated the combined effect of JIT, TQM, and GSCM practices on environmental performance in manufacturing. The findings indicate that JIT and TQM practices significantly contribute to the advancement of GSCM practices, which in turn leads to improved environmental performance. Implementing JIT and TQM can effectively foster green supply chain practices and achieve enhanced environmental performance. These findings are valuable for supply chain practitioners, policymakers, and industrialists in developing and implementing strategies for sustainable GSCM. The findings also provide practical insights for practitioners to better understand and implement these practices within their supply chain networks. Overall, this research contributes to the expanding body of knowledge on the connection between SCM practices and environmental performance with the support of the Complementarity Theory, which is rarely used in supply chain management.