An ANP-based approach for lean and green performance assessment

Resources, Conservation & Recycling 143 (2019) 77–89

Contents lists available at ScienceDirect

Resources, Conservation & Recycling journal homepage: www.elsevier.com/locate/resconrec

An ANP-based approach for lean and green performance assessment Luana Marques Souza Farias, Luciano Costa Santos , Cláudia Fabiana Gohr, Lenilson Olinto Rocha ⁎

T

Universidade Federal da Paraíba, Programa de Pós-Graduação em Engenharia de Produção, João Pessoa, Brazil

ARTICLE INFO

ABSTRACT

Keywords: Lean manufacturing Green manufacturing Analytic network process Assessment

Simultaneous application of lean and green practices requires adequate methods to evaluate the contribution and effects of both paradigms on organizational performance. Thus, the objective of this study is to develop an integrated approach to evaluate the impacts of lean and green practices on organizational performance and prioritize improvements in the system. To this end, this research used the Analytic Network Process (ANP) for the operationalization of the theoretical framework and tested it through an application in a footwear company. By applying the assessment approach, it was possible to evaluate the lean and green systems individually and in an integrated way through the lean-green index, a performance measure developed specifically for this work. From a managerial perspective, this research provided a tool that enables companies to evaluate their lean and green systems and identify which practices should be prioritized to improve operational and environmental performance.

1. Introduction Companies from different industries have adopted the lean manufacturing approach, which allows better utilization of productive resources and substantial waste reduction (Womack et al., 1990; Jasti and Kodali, 2015). However, besides improving the use of productive resources, it is important that an organization make decisions to improve the use of natural resources as well, avoiding waste and minimizing the negative impacts of manufacturing activities. In order to support such decisions, the green manufacturing approach emerges as a management paradigm that applies tools and techniques to reduce waste and minimize the environmental impact of manufacturing processes, ensuring improved pollution control and reduction of consumption of natural resources (Garza-Reyes, 2015; Gandhi et al., 2018). Although the study of the relationship between lean and green is a recent theme (Caldera et al., 2017; Zhan et al., 2018), it has been found in the literature that the interest in the joint implementation of both paradigms has increased significantly (Prasad et al., 2016; Chugani et al., 2017). This trend is justified by the fact that both seek waste reduction and continuous improvement (Dües et al., 2013; Chugani et al., 2017). However, while the interactions between both approaches stand out, Chaplin and O’Rourke (2018) and Hallam and Contreras (2016) emphasize that lean and green are still seen as separate functions within organizations. In order to obtain the benefits of integration,

León and Calvo-Amodio (2017) defend the systemic implementation of lean and green practices. The systemic implementation, therefore, requires adequate metrics to evaluate the contribution and effects of both paradigms on organizational performance. In the case of lean and green performance assessment, the systematic literature review conducted by Garza-Reyes (2015) indicated that most assessment methods were designed for supply chains, and only the method developed by Verrier et al. (2014) was tested in manufacturing. In addition to this, León and Calvo-Amodio (2017) emphasized that the effects of lean and green practices on organizational performance have been neglected in the literature. One of the few studies with this purpose was conducted by Thanki et al. (2016), who evaluated the influence of lean and green practices on organizational performance criteria. However, in spite of their contribution, the authors did not consider interrelationships between lean and green practices. Aware of this gap, Thanki et al. (2016) suggested the use of the Analytic Network Process (ANP) as a way to include interrelationships between practices in lean and green assessment. The inclusion of interdependencies between practices in the assessment process recognizes the systemic nature of it and addresses the appeal of the current literature for greater integration between lean and green (Hallam and Contreras, 2016; León and Calvo-Amodio, 2017; Chaplin and O’Rourke, 2018). Considering the demand for research on this subject, this study aims

Corresponding author. E-mail addresses: [email protected] (L.M.S. Farias), [email protected] (L.C. Santos), [email protected] (C.F. Gohr), [email protected] (L.O. Rocha). ⁎

https://doi.org/10.1016/j.resconrec.2018.12.004 Received 30 July 2018; Received in revised form 18 October 2018; Accepted 6 December 2018 0921-3449/ © 2018 Elsevier B.V. All rights reserved.

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

to develop an ANP-based approach for lean and green performance assessment. In addition to making possible the prioritization of improvements in the system, ANP considers interdependence and feedback among elements. Feedback allows judgments in any direction within the network of relationships (Saaty, 1996) and it is an important aspect of ANP to capture the interactions between lean and green. From a managerial perspective, the main contribution of this research is to provide a tool that enables companies to evaluate their lean and green systems and identify which practices should be prioritized to improve operational and environmental performance. From the research literature perspective, this study helps to reduce the gap of integration between lean and green, in particular, when it comes to performance assessment. Moreover, unlike many studies focused on lean and green supply chains, this study differentiates from previous research since it was focused on lean and green operations, from the internal practices’ point of view (Chavez et al., 2013). The proposed approach was tested in a footwear manufacturing plant that has successfully applied lean and green practices. Results were consolidated by calculating the lean-green index (LGindex), which allows an integrated view of impacts on organizational performance. The lean-green index is another relevant contribution of this article since it provides a simple and unified performance measure that can be easily understood by managers and researchers for self-assessment and comparison purposes. The remainder of this paper is organized as follows: initially, the theoretical background is presented for the construction of the assessment approach. Next, the theoretical framework for lean and green performance assessment is described, as well as the procedures for its empirical application and calculation of the lean-green index. Then, the application of the framework, the calculation of the lean-green index and the sensitivity analysis are described. Subsequently, we discuss the main implications of the framework. Finally, the conclusions and suggestions for future studies are presented.

systems. This finding is aligned to León and Calvo-Amodio’s (2017) proposition towards a systemic view of lean and green, in which lean practices should not be implemented in isolation. When it comes to the links between lean and green practices and performance, some studies demonstrate empirical evidence of these relationships. Chiarini (2014), for example, identified how four lean practices (5S, cellular manufacturing, total productive maintenance, and setup time reduction) could help to reduce environmental impacts. Diaz-Elsayed et al. (2013), in another case study, used an optimization and simulation approach to assess the performance of a manufacturing system affected by the application of lean and green strategies. The relationships between practices and performance have also been investigated by survey studies with larger samples sizes. Rothenberg et al. (2001), for instance, examined the relationship between lean manufacturing practices and environmental performance in 31 automobile assembly plants in North America and Japan. Although they found some evidence to support the link between lean practices and resource efficiency, they did not include green practices in their research. Yang et al. (2011) expanded this view, including environmental management practices in their study. Using data collected from 309 manufacturing firms, they provided empirical evidence that environmental management practices represent an important mediating variable to resolve the conflicts between lean manufacturing and environmental performance. Likewise, Hajmohammad et al. (2013) confirmed that the impact of lean management on environmental performance is mediated by environmental practices. On the other hand, Hong et al. (2012) confirmed lean practices as an important mediator to achieve excellent environmental performance, but they did not include green practices in their study. More recently, Garza-Reyes et al. (2018) surveyed 250 manufacturing organizations to investigate the effect of lean practices on environmental performance. They found that the practices of total productive maintenance (TPM) and just-in-time (JIT) have the strongest significance on environmental performance. Finally, by conducting empirical studies in Chinese organizations, Zhan et al. (2018) found that the adoption of lean and green practices can lead to better organizational performance, especially when it is moderated by the Chinese practice of guanxi. Most of the previous studies demonstrate that lean and green practices may complement each other when the focus is on improving operational and environmental performance. This complementarity justifies the gap claimed in the literature for an integrated performance measurement system (Garza-Reyes, 2015; Carvalho et al., 2017; Ramos et al., 2018). The premise of it is that lean and green are mutually supportive in order to achieve better performance. The previous studies on lean and green allowed us to extract practices and performance criteria that have been used as variables in the current research. In addition, we investigated the existence of relationships between different lean and green practices and performance criteria, regarding environmental and operational dimensions. The results of this analysis are presented in Table 1, which shows the relationships between practices and criteria. As shown in Table 1, through a literature review on lean and green, we identified criteria of operational performance (productivity, profit, and inventory), environmental performance (environmental impacts and energy consumption), and criteria common to both dimensions (waste reduction, cost reduction, and quality). In addition, eight lean practices (VSM, SMED, KZ, 5S, TPM, SW, PP, and CM) and five green practices (EMS, LCA, 3R, DFE, and EEC) were selected. It is important to note that the so-called environmental management system is a general term for all certification and standardization systems, such as ISO 14001 and similar systems. The elements of Table 1 (criteria, practices, and relationships), identified from a literature review, were the basis for the development of the lean and green performance assessment approach proposed in this article. Although the current literature has proved the relationship between lean and green practices and performance, management tools to

2. Theoretical background The lean system encompasses a set of interlinked operational practices that aim to reduce or eliminate non-value-added activities throughout a product value stream (Hajmohammad et al., 2013; Jasti and Kodali, 2015). The literature demonstrates that the implementation of lean practices is associated with better operational performance (Chavez et al., 2013; Wickramasinghe and Wickramasinghe, 2017). On the other hand, current studies also show that the adoption of lean practices may lead to positive results for environmental performance (Rothenberg et al., 2001; Fercoq et al., 2016; Cherrafi et al., 2017). However, although the lean system has provided significant benefits regarding environmental performance, its scope is limited and it does not cover all resource flows from a life-cycle perspective, such as energy and water (Ball, 2015). With a focus on minimizing negative environmental impacts, the green manufacturing system appears as an appropriate approach (Verrier et al., 2014; Fercoq et al., 2016; Gandhi et al., 2018). As an improvement approach, the green manufacturing system is implemented through practices that limit or reduce the potential negative impacts of the production and consumption of goods and services on the natural environment, thus improving the company's environmental footprint (Galeazzo et al., 2014; Verrier et al., 2014). The study of the joint adoption of lean and green practices has also been discussed in the literature. Galeazzo et al. (2014), for example, investigated the synergistic effects of the interaction of lean practices with green practices. This interaction was investigated through qualitative research and generated results more relevant to the implementation process than to the performance evaluation, the focus of this paper. From the implementation point of view, Galeazzo et al. (2014) proposed that simultaneous implementation of lean and green practices is likely to be associated with higher operational performance when compared to the sequential implementation approach of both 78

Resources, Conservation & Recycling 143 (2019) 77–89

• • Energy consumption

•

• • • Environmental

•

• • Quality

Environmental impacts

• • Cost reduction

•

Operational and Environmental

Lean practices: VSM – value stream mapping; KZ – kaizen; 5S – housekeeping (seiri, seiton, seiso, seiketsu, shitsuke); SMED – setup time reduction (single minute exchange of die); SW – standardized work; PP – pull production; CM – cellular manufacturing; TPM – total productive maintenance. Green practices: EMS – environmental management system; LCA – life cycle assessment; 3R – reducing, reusing and recycling; DFE – design for the environment; EEC – environmental emission control.

• • • •

•

• •

•

•

• • • •

• •

•

•

• • • • Productivity Profit Inventory Waste reduction Operational

• • • •

• • • •

• • • •

• • •

•

•

• • • •

•

•

• •

•

• •

•

DFE 3R LCA EMS TPM CM PP SW SMED 5S KZ VSM Criteria Dimension

Practices

Table 1 Relationships between performance criteria and lean and green practices.

•

Prasad et al. (2016); Thanki et al. (2016). Garza-Reyes (2015); Thanki et al. (2016); Hallam and Contreras (2016). Miller et al. (2010); Vinodh et al. (2016). Miller et al. (2010); Dües et al. (2013); Pampanelli et al. (2013); Ball (2015); Kurdve et al. (2015); Prasad et al. (2016); Fercoq et al. (2016); Hallam and Contreras (2016); Thanki et al. (2016); Thanki and Thakkar (2016); Garza-Reyes et al. (2018). Diaz-Elsayed et al. (2013); Kurdve et al. (2015); Thanki and Thakkar (2016); Thanki et al. (2016); Hallam and Contreras (2016); Prasad et al. (2016). Yang et al. (2011); Diaz-Elsayed et al. (2013); Hajmohammad et al. (2013); Domingo and Aguado (2015); Thanki et al. (2016); Hallam and Contreras (2016); Vinodh et al. (2016). Pampanelli et al. (2013); Chiarini (2014); Domingo and Aguado (2015); Garza-Reyes (2015); Hallam and Contreras (2016); Vinodh et al. (2016). Miller et al. (2010); Hajmohammad et al. (2013); Verrier et al. (2014); Ball (2015); Thanki et al. (2016); Garza-Reyes et al. (2018).

EEC

References

L.M.S. Farias et al.

prioritize practices to achieve specific performance objectives seem to be still missing. Nevertheless, it can be found some few noteworthy initiatives in this way. Carvalho et al. (2017), for example, developed a mathematical model to support decision making in identifying the best set of green and lean supply chain management practices to improve their eco-efficiency. As it was focused in supply chains, the study of Carvalho et al. (2017) presented a too narrow set of lean and green practices to be considered in the context of internal manufacturing operations. Furthermore, even though the proposed mathematical model allowed the prioritization of practices, it did not consider the interactions that exist among lean and green practices. Focused on manufacturing operations, Ramos et al. (2018) proposed a checklist-based benchmarking method to assess cleaner and lean production practices. Although comprehensive regarding practices and performance indicators, the method proposed by Ramos et al. (2018) fails to relate practices to specific performance criteria. Another noteworthy initiative is the work of Bai et al. (2018), who developed multi-criteria decision-making model to support the evaluation of the investment in lean manufacturing practices in order to achieve environmental and operational goals. To implement their model, they used a three-parameter interval grey number with rough set theory and the “Interactive and Multicriteria Decision Making” method (TODIM). However, when the objective is to link integrated lean and green practices to performance criteria, the model of Bai et al. (2018) has two noticeable limitations. First, they did not include green practices in the model. Second, even though the joint approach of grey based rough set and TODIM is useful for dealing with psychological expectations, the model did not consider all possible interactions between practices. As recommended by the authors, future research may use ANP or Decision Making Trial and Evaluation Laboratory (DEMATEL) to incorporate inner and outer dependencies among practices. More closely aligned with the aim of this study, Thanki et al. (2016) used the Analytic Hierarchy Process (AHP) to investigate the influence of lean and green paradigms on the overall performance of small and medium-sized enterprises. Their model allows identifying the effect of lean and green practices on different performance criteria. However, AHP is conceptually limited to hierarchical relationships. As a result, the study of Thanki et al. (2016) disregarded the intricate interrelationships between practices and performance criteria. In order to capture the interrelationships existing within a lean and green system, we adopted the network perspective incorporated by ANP. As mentioned by Bai et al. (2018), DEMATEL would be another potential method to incorporate network relationships. Notwithstanding, DEMATEL does not consider hierarchical relationships, and consequently, it is not appropriate to link performance goals to enablers. On the other hand, ANP allows for more complex relationships and feedback among elements in the hierarchy, thereby fulfilling the purpose of this study. 3. Methodology 3.1. The integrated framework The basic assumption of this study is that lean and green can be seen as subsystems of an integrated management system. Thus, both subsystems can support each other and achieve performance objectives that are not conflicting, but complementary. In this way, once performance criteria, practices and their relationships were identified (Table 1), we developed an integrated framework to evaluate the impacts of lean and green practices on organizational performance (Fig. 1). In this framework, the criteria were divided into two levels (dimensions and determinants), and lean and green practices are considered as enablers of performance criteria. The lean and green performance assessment is a problem that has three basic characteristics: (i) it involves multiple criteria for evaluation, (ii) criteria and practices are interdependent, and (iii) the 79

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Fig. 1. Lean and green performance assessment framework.

evaluation is usually subjective since it depends more on the perception of decision makers than on objective measurement. Considering these three characteristics, ANP was the chosen technique for the operationalization of the framework. ANP is a multicriteria analysis technique that allows quantifying subjective judgments and evaluating the interdependencies among the elements of a system (Saaty, 1996). In the ANP methodology, problems are formulated as networks, and not only as hierarchies, such as in the AHP. The elements of the system are analyzed individually and in clusters, and their interdependencies are analyzed in pairwise comparison matrices. The results from an ANP model are displayed in supermatrices that allow establishing the impacts of the elements on the criteria and prioritizing decisions based on these impacts (Saaty, 1996). The application procedure was divided into seven steps: 1) formulation of the problem, 2) comparison matrix of performance dimensions, 3) pairwise comparison matrices of determinants in relation

to performance dimensions, 4) pairwise comparison matrices of enablers in relation to performance determinants, 5) pairwise comparison matrices between enablers, 6) supermatrix formation, and 7) calculation of the lean-green index. In addition, we performed a sensitivity analysis to verify the effects on the system related to the variation of weights in performance determinants. The application sequence is detailed in Section 4. 3.2. Empirical research In order to test the applicability of the ANP-based framework, we conducted an assessment study in a Brazilian footwear manufacturing plant. It is a large factory that employs around 7000 employees to produce 670,000 pairs of rubber sandals per day. The plant was chosen for having well-structured lean and green systems. For data collection, we initially conducted semi-structured 80

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Table 2 Saaty's fundamental scale. Source: Adapted from Saaty (1990). Value

Definition

Explanation

1 3 5 7 9 2, 4, 6, 8

Equal importance/contribution/relation Weak importance/contribution/relation Strong importance/contribution/relation Very strong importance/contribution/relation Extremely strong importance/contribution/relation Intermediate values

Two elements contribute equally. Judgment slightly favors one activity over another. Judgment strongly favors one activity over another. An activity is strongly favored and its dominance demonstrated in practice. The evidence favoring one activity over another is of the highest possible order of affirmation. When compromise is needed.

interviews with company managers to obtain an overview of the system and to identify the lean and green practices adopted. This preliminary analysis allowed us to adjust the generic framework to the application context. In the second phase of data collection, three respondents were selected for being the most appropriate people in the plant to evaluate the lean-green system: the production manager, the lean coordinator, and the environmental manager. Each of the respondents evaluated the issues related to his/her area, and the production manager evaluated the performance criteria at a strategic level. For this, an assessment questionnaire divided into five blocks was used, containing comparative questions in pairs. The classification of the comparative answers was based on the fundamental scale of Saaty (1990), used for both ANP and AHP (Table 2). It is a scale of 1–9 for comparing two components of a system in pairwise comparison matrices. On this scale, 1 implies equal importance and 9 implies stronger importance of the row element than the column element. When the column element has stronger importance than the row element, a reciprocal value is assigned, where 1 represents equal importance and 1/9 represents stronger importance. Data obtained by the questionnaire were calculated according to traditional ANP procedures. Thus, it was possible to obtain the weights of each relationship existing in the assessment framework by analyzing the comparison matrices and supermatrices generated, from which the results are obtained.

relationships (I) between components; Sikja is the relative impact of paradigm i on the enabler k of the determinant j of the dimension a. Then, the lean-green index (LGindex) is calculated by the product of Wia with the relative weight of each dimension (Ca ):

4. Application of the ANP framework As mentioned earlier, the practical test of the framework was performed in seven steps. The results of the application in a footwear manufacturing plant are presented below. STEP 1: Formulation of the problem The construction of the lean and green performance assessment started with the definition of elements that make up the evaluation system and its interrelationships. The framework presented in Fig. 1 was the result of the structuring of the theoretical problem. As the framework was constructed based on the literature, the formulation of the problem was concluded with its adaptation to the practices adopted by the company. Based on the initial interviews, it was identified that the lean and green practices that the company adopted were present in the original framework. However, the company did not use three practices designed in the framework: VSM, DFE, and LCA. Thus, these practices were excluded from the assessment procedure for application purposes. STEP 2: Comparison matrix of performance dimensions According to the proposed framework, operational and environmental performance dimensions are at the highest hierarchical level of the assessment system. Using a scale of one to nine, the relative importance of dimensions was assessed through a single question: “with regard to the importance to the company, compare the following dimensions of performance”. The results are shown in Table 3. Although simple in numerical terms, Table 3 clearly shows the company's strategy of integrating operational and environmental dimensions in a balanced way. According to the ANP methodology, the eVector (Eigenvector) represents the weighted priority of each dimension, and the inconsistency is determined by the consistency ratio (CR), an indicator recommended by Saaty (1996) to measure inconsistency in judgment. According to Saaty and Kearns (1985), inconsistencies below 0.20 are considered tolerable. STEP 3: Pairwise comparison matrices of determinants in relation to performance dimensions In order to capture the weighted priority (e-Vector) of each determinant for each performance dimension (operational and environmental), comparative questions were applied for each pair of determinants. In this evaluation, an example of a question was: “with regard to the importance to the company's operational performance, compare the

3.3. Quantifying the lean-green index (LGindex) After the matrices were obtained, the lean-green index (LGindex) was calculated, which represents the combined impact of lean and green systems on organizational performance. Initially, it was obtained the influence index (Wia ), which consists in the sum of the product of the relative importance of all weights involved. This index determines the influence of each paradigm i (lean or green) on each dimension a (operational or environmental). The equation for this index was based on the studies conducted by Meade and Sarkis (1999); Agarwal et al. (2006); Wong et al. (2014). Therefore: J

Kja

Wia =

D I Pja Akja Akja Sikja

(1)

j=1 k=1

(2)

LGindex = Wia × Ca

where, Pja is the relative weight of determinant j on the dimension a; D Akja is the weight for enabler k of the determinant j in the dimension a for the dependency relations (D) between components, that is, the dependence between the enablers in relation to each determinant, and I consequently to each dimension; Akja is the stabilized weight for enabler k of the determinant j in the dimension a for the interdependency Table 3 Pairwise comparison matrix of performance dimensions (CR: 0.00000).

Environmental performance Operational performance

Environmental performance

Operational performance

e-Vector

1 1

1 1

0.50000 0.50000

81

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Table 4 Pairwise comparisons of determinants for operational performance (CR: 0.09430). Operational performance

Inventory

Profit

Productivity

Quality

Cost reduction

Waste reduction

e-Vector

Inventory Profit Productivity Quality Cost reduction Waste reduction

1 1/5 7 5 3 5

5 1 7 5 5 3

1/7 1/7 1 1 1 1

1/5 1/5 1 1 3 1

1/3 1/5 1 1/3 1 1

1/5 1/3 1 1 1 1

0.06844 0.03681 0.24760 0.19010 0.25258 0.20448

Table 5 Pairwise comparisons of determinants for environmental performance (CR: 0.04407). Environmental performance

Energy consumption

Environmental impacts

Quality

Cost reduction

Waste reduction

e-Vector

Energy consumption Environmental impacts Quality Cost reduction Waste reduction

1 3 3 1 3

1/3 1 1/3 1/3 1/3

1/3 3 1 1 1

1 3 1 1 1

1/3 3 1 1 1

0.09401 0.41682 0.17512 0.13894 0.17551

Table 6 Pairwise comparisons of the influence of green practices on energy consumption (CR: 0.00675). Energy consumption

3R

EEC

EMS

e-Vector

3R EEC EMS

1 1 1/7

1 1 1/9

7 9 1

0.45070 0.49009 0.05921

Table 7 Pairwise comparisons between lean practices in relation to 3R (CR: 0.00942).

criterion productivity with the criterion profit”. The response obtained was represented by score 7 (third column of Table 4), which indicates that productivity was assessed as much more important than profit when considering operational performance. The relative weights of each determinant for each performance dimension can be seen in Tables 4 and 5. It is worth noting that not all determinants were evaluated in both dimensions. The relationships have been previously defined in the framework presented in Fig. 1, in which the operational dimension relates to six determinants, the environmental dimension relates to five determinants, and three determinants are common for both dimensions. STEP 4: Pairwise comparison matrices of enablers in relation to performance determinants The next step was to evaluate the lean and green practices (enablers) in relation to the performance determinants, according to the pre-established relationships in the assessment framework. Table 6 shows, for example, the comparison matrix of green practices in relation to the determinant energy consumption. In this example, the standard question was “how intensely does the practice X contribute to energy consumption compared to the practice Y?”. Matrices such as these were made for all performance determinants. The resulting data from these matrices are synthesized in the supermatrix shown in Table 8. STEP 5: Pairwise comparison matrices between enablers In order to capture interdependencies between lean and green practices, comparison matrices between enablers were constructed. Thus, lean practices were compared to each other in relation to each green practice, and vice-versa (green practices were compared to each other in relation to each lean practice). Table 7 shows, for example, the comparison matrix of lean practices in relation to the green practice 3R. In this case, the standard question was “compare the lean practice ‘A’ with the lean practice ‘B’ in relation to 3R”. Comparing 5S with CM, for example, the result obtained was score 5, indicating that the practice 5S has a stronger relationship with 3R than with CM. Analogous matrices were constructed for the other practices, and the results were synthesized in the supermatrix (Table 8).

3R

5S

CM

KZ

TPM

PP

SMED

SW

e-Vector

5S CM KZ TPM PP SMED SW

1 1/5 1/3 1/5 1/5 1/5 1/5

5 1 5 1 1 1 1

3 1/5 1 1/3 1/3 1/3 1/3

5 1 3 1 1 1 1

5 1 3 1 1 1 1

5 1 3 1 1 1 1

5 1 3 1 1 1 1

0.41076 0.06943 0.22515 0.07367 0.07367 0.07367 0.07367

STEP 6: Supermatrix formation After the construction of individual matrices, the ANP methodology proceeds with the formation of a supermatrix, in which the final scores can be evaluated globally. The supermatrix contains the e-Vectors obtained from the matrices previously elaborated. The previous matrices were considered valid for the construction of the supermatrix (Table 8) since all presented inconsistencies below 0.20, as recommended by Saaty and Kearns (1985). Then, the initial supermatrix is multiplied by an arbitrarily large number, so that the values remain stable and reach convergence, thus obtaining the final supermatrix as shown in Table 9. This supermatrix contains the relative priorities of each practice, that is, the final impact scores of each practice considering all criteria analyzed. The priorities of the lean and green practices in the supermatrix after convergence correspond to the stabilized values of the rows in Table 9. Thus, it can be verified that the practices with the greatest impact on the company’s performance are EMS, 3R, 5S, and KZ. These findings mean that the company must prioritize these practices to improve its organizational performance, considering the environmental and operational dimensions. If there is any change in the priority of determinants concerning operational and environmental performance, the priority of the practices may also change. The influence of the variation in priority of determinants on practices is described in Section 5, where the sensitivity analysis is conducted. STEP 7: Calculation of the lean-green index (LGindex) The final stage of the assessment process was the calculation of the lean-green index based on Eqs. (1) and (2) presented in Section 3.3. For this purpose, it was necessary to create first a pairwise comparison matrix (Table 10) to obtain the values of S, which correspond to the weights of each paradigm concerning the enablers. The influence indexes (Wia ) of each paradigm on the environmental and operational dimensions, calculated by Eq. 1, can be observed in 82

83

Determinants

Lean enablers

Green enablers

Cluster/Elements

Determinants

Lean enablers

Green enablers

Cluster/Elements

0.600 0.200 0.200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.584 0.135 0.281 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.600 0.200 0.200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

SW 0.451 0.490 0.059 0.027 0.102 0.283 0.554 0.034 0 0 0 0 0 0 0 0 0 0

Energy consumption

SMED

PP

0.037 0.248 0.190 0.253 0.204

0 0 0.175 0.139 0.175 Determinants

0 0 0 0 0 0 0 0 0 0 0 0.068 0

0 0 0 0 0 0 0 0 0 0 0.094 0 0.417

Inventory 0 0 1.000 0.065 0 0.178 0 0 0.460 0.300 0 0 0 0 0 0 0 0

0 0 0 0 0

0 0 0 0.411 0.069 0.225 0.074 0.074 0.074 0.074 0 0 0 0 0 0 0 0

0 0 0 0.065 0.065 0.421 0.192 0.092 0.092 0.073 0 0 0

EEC

0.091 0.091 0.818 0.367 0.076 0.279 0.233 0 0.044 0 0 0 0 0 0 0 0 0

Environmental impacts

3R

Environmental performance

Operational performance

Green enablers

Dimensions

Lean enablers

3R EEC EMS 5S CM KZ TPM PP SMED SW Energy consumption Inventory Environmental impacts Profit Productivity Quality Cost reduction Waste reduction

Table 8 Supermatrix before convergence.

0.900 0 0.100 0.034 0 0.306 0 0 0.421 0.239 0 0 0 0 0 0 0 0

Profit

0 0 0 0 0

0 0 0 0.424 0.072 0.153 0.100 0.056 0.036 0.158 0 0 0

EMS

0.900 0 0.100 0.146 0.082 0 0.294 0.170 0.309 0 0 0 0 0 0 0 0 0

Productivity

0 0 0 0 0

0.327 0.260 0.413 0 0 0 0 0 0 0 0 0 0

5S

0 0 0 0.481 0 0.405 0.114 0 0 0 0 0 0 0 0 0 0 0

Quality

Lean enablers

0 0 0 0 0

0.327 0.260 0.413 0 0 0 0 0 0 0 0 0 0

CM

0 0 1.000 0.026 0 0.273 0.295 0 0.295 0.111 0 0 0 0 0 0 0 0

Cost reduction

0 0 0 0 0

0.280 0.094 0.627 0 0 0 0 0 0 0 0 0 0

KZ

0.500 0 0.500 0.051 0.067 0.249 0.264 0.053 0.264 0.053 0 0 0 0 0 0 0 0

Waste reduction

0 0 0 0 0

0.202 0.097 0.701 0 0 0 0 0 0 0 0 0 0

TPM

L.M.S. Farias et al.

Resources, Conservation & Recycling 143 (2019) 77–89

84

Determinants

Lean enablers

Green enablers

Cluster/Elements

Determinants

Lean enablers

Green enablers

Cluster/Elements

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Energy consumption

PP

SW

0 0 0 0 0

0 0 0 0 0 Determinants

SMED

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

Inventory 0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

EEC

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Environmental impacts

3R

Environmental performance

Operational performance

Green enablers

Dimensions

Lean enablers

3R EEC EMS 5S CM KZ TPM PP SMED SW Energy consumption Inventory Environmental impacts Profit Productivity Quality Cost reduction Waste reduction

Table 9 Supermatrix after convergence.

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Profit

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

EMS

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Productivity

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

5S

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Quality

Lean enablers

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

CM

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Cost reduction

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

KZ

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0 0 0 0 0 0

Waste reduction

0 0 0 0 0

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050 0 0 0

TPM

L.M.S. Farias et al.

Resources, Conservation & Recycling 143 (2019) 77–89

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Table 10 Pairwise comparisons between paradigms and enablers.

Green enablers (EMS, EEC, 3R) Lean enablers (5S, CM, SMED, SW, PP, KZ, TPM)

Paradigms

Green (S1)

Lean (S2)

e-Vector

S1 S2 S1 S2

1 1/9 1 9

9 1 1/9 1

0.900 0.100 0.100 0.900

Tables 11 and 12. The values in the second column of these tables correspond to the weights of determinants found in Table 8. The values in the fourth column correspond to the weights of practices contained in Table 8. The values of the fifth column correspond to the stabilized weights of practices contained in the supermatrix after convergence (Table 9). The green paradigm appeared as the one of greatest impact on both environmental and operational performance, with influence indexes (Wia ) of 0.144 and 0.142, respectively. Once these indexes are obtained, the impact of each paradigm on organizational performance (LGindex) can be calculated (Eq. 2), as shown in Table 13. The results show that the green paradigm has a greater impact on the company’s performance, with a weight of 0.143 (14.3%). According to Table 13, lean and green systems have, jointly, an impact of 0.230 (23.0%) on organizational performance (under operational and environmental perspectives). Thus, although the lean system has a greater number of practices implemented in the company, the green system stands out when it comes to the influence on the organizational performance. These results

reflect the company's strong emphasis on safety and environmental management, with a strong focus on meeting environmental standards. In spite of the interesting results, it is noteworthy to mention that the assessment process using the proposed framework was conducted to test and illustrate its applicability. Therefore, the findings from the field are just valid for that company, from the interviewees’ perspective. 5. Sensitivity analysis When using ANP, the sensitivity analysis is an essential procedure since it allows testing the robustness of the model in relation to the variation in judgments (Saaty, 1996). In this study, a sensitivity analysis was conducted to identify the effects of changing priorities on performance determinants for prioritization of lean and green practices. In each graph in Fig. 2, the dashed vertical line in black represents the current weight of each determinant. The dashed vertical lines in red represent points at which the priorities of the practices change with the variation of the determinant weights. Upon analyzing Fig. 2(a), it is observed, for example, that the

Table 11 Influence indexes for environmental performance. Environmental performance

D I Pja Akja Akja Sikja

Determinants

Pja

Enabler

D Akja

I Akja

S1

S2

Lean

Green

Energy consumption

0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.094 0.417 0.417 0.417 0.417 0.417 0.417 0.417 0.417 0.175 0.175 0.175 0.139 0.139 0.139 0.139 0.139 0.139 0.175 0.175 0.175 0.175 0.175 0.175 0.175 0.175 0.175

3R EEC EMS 5S CM KZ TPM PP 3R EEC EMS 5S CM KZ TPM SMED 5S KZ TPM EMS 5S KZ TPM SMED SW 3R EMS 5S CM KZ TPM PP SMED SW

0.451 0.490 0.059 0.027 0.102 0.283 0.554 0.034 0.091 0.091 0.818 0.367 0.076 0.279 0.233 0.044 0.481 0.405 0.114 1.000 0.026 0.273 0.295 0.295 0.111 0.500 0.500 0.051 0.067 0.249 0.264 0.053 0.264 0.053

0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.031 0.165 0.084 0.201 0.159 0.031 0.103 0.048 0.027 0.159 0.103 0.048 0.201 0.159 0.103 0.048 0.027 0.050 0.165 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050

0.1 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.1 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.1 0.9 0.9 0.9 0.9 0.9 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.9 0.9

0.9 0.9 0.9 0.1 0.1 0.1 0.1 0.1 0.9 0.9 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.9 0.1 0.1 0.1 0.1 0.1 0.9 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.1

0.00070 0.00039 0.00011 0.00036 0.00027 0.00247 0.00225 0.00009 0.00063 0.00032 0.00685 0.02191 0.00088 0.01079 0.00420 0.00045 0.01204 0.00658 0.00086 0.00279 0.00051 0.00352 0.00177 0.00100 0.00070 0.00144 0.00176 0.00128 0.00033 0.00405 0.00199 0.00026 0.00112 0.00042 0.095

0.00629 0.00348 0.00101 0.00004 0.00003 0.00027 0.00025 0.00001 0.00563 0.00286 0.06169 0.00243 0.00010 0.00120 0.00047 0.00005 0.00134 0.00073 0.00010 0.02513 0.00006 0.00039 0.00020 0.00011 0.00008 0.01300 0.01584 0.00014 0.00004 0.00045 0.00022 0.00003 0.00012 0.00005 0.144

Environmental impacts

Quality Cost reduction

Waste reduction

Influence index (Wia )

85

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Table 12 Influence indexes for operational performance. Operational performance

D I Pja Akja Akja Sikja

Determinants

Pja

Enabler

D Akja

I Akja

S1

S2

Lean

Green

Inventory

0.068 0.068 0.068 0.068 0.068 0.037 0.037 0.037 0.037 0.037 0.037 0.248 0.248 0.248 0.248 0.248 0.248 0.248 0.190 0.190 0.190 0.253 0.253 0.253 0.253 0.253 0.253 0.204 0.204 0.204 0.204 0.204 0.204 0.204 0.204 0.204

EMS 5S KZ SMED SW 3R EMS 5S KZ SMED SW 3R EMS 5S CM TPM PP SMED 5S KZ TPM EMS 5S KZ TPM SMED SW 3R EMS 5S CM KZ TPM PP SMED SW

1.000 0.065 0.178 0.460 0.300 0.900 0.100 0.034 0.306 0.421 0.239 0.900 0.100 0.146 0.082 0.294 0.170 0.309 0.481 0.405 0.114 1.000 0.026 0.273 0.295 0.295 0.111 0.500 0.500 0.051 0.067 0.249 0.264 0.053 0.264 0.053

0.201 0.159 0.103 0.027 0.050 0.165 0.201 0.159 0.103 0.027 0.050 0.165 0.201 0.159 0.031 0.048 0.031 0.027 0.159 0.103 0.048 0.201 0.159 0.103 0.048 0.027 0.050 0.165 0.201 0.159 0.031 0.103 0.048 0.031 0.027 0.050

0.1 0.9 0.9 0.9 0.9 0.1 0.1 0.9 0.9 0.9 0.9 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.1 0.9 0.9 0.9 0.9 0.9 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.9 0.9

0.9 0.1 0.1 0.1 0.1 0.9 0.9 0.1 0.1 0.1 0.1 0.9 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.9 0.1 0.1 0.1 0.1 0.1 0.9 0.9 0.1 0.1 0.1 0.1 0.1 0.1 0.1

0.00138 0.00064 0.00113 0.00077 0.00092 0.00055 0.00007 0.00018 0.00104 0.00038 0.00040 0.00368 0.00050 0.00516 0.00057 0.00314 0.00117 0.00186 0.01307 0.00714 0.00094 0.00508 0.00093 0.00640 0.00322 0.00181 0.00126 0.00169 0.00206 0.00150 0.00038 0.00473 0.00233 0.00030 0.00131 0.00049 0.078

0.01238 0.00007 0.00013 0.00009 0.00010 0.00492 0.00067 0.00002 0.00012 0.00004 0.00004 0.03309 0.00448 0.00057 0.00006 0.00035 0.00013 0.00021 0.00145 0.00079 0.00010 0.04569 0.00010 0.00071 0.00036 0.00020 0.00014 0.01518 0.01850 0.00017 0.00004 0.00053 0.00026 0.00003 0.00015 0.00005 0.142

Profit

Productivity

Quality Cost reduction

Waste reduction

Influence index (Wia )

energy consumption weight at 9.40% (actual weight) makes EMS a priority. As the weight increases, the priority for this practice decreases; by increasing over 20%, the priority changes to 3R. The next priority change occurs when energy consumption weight is over 70%, at which moment TPM takes priority over the others. On the other hand, EMS is the priority for any value of the determinant inventory (Fig. 2b). Analogous understanding can be found when analyzing the variation in priority of the others determinants. Sensitivity analysis makes the assessment dynamic and helps managers to anticipate the consequences of decisions on the lean-green system. Considering that business strategies may change over time, the proposed framework allows a continuous assessment and indicates the redefinition of priorities in lean and green practices.

6. Discussion The application of the assessment framework made it possible to test the ANP model and its relationships in practice. In the case of the studied company, the application revealed interesting results, revealing the lean and green practices with more influence on performance by analyzing the operational and environmental dimensions. From the practitioners’ point of view, the framework assists in identifying which lean and green practices should receive more attention and greater investment, depending on the performance criteria the company wishes to prioritize. In addition, the framework will also help managers to identify which practices can work together to improve performance in both lean and green systems and thus achieve higher levels of

Table 13 Lean-green impact levels on organizational performance (LGindex). Paradigms

Environmental performance

Operational performance

Weights (Ca) Lean Green Total

0.500 0.095 0.144

0.500 0.078 0.142

86

Lean-Green index (LGindex)

Normalized values for LGindex

0.087 0.143 0.230

0.378 0.622 1.000

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Fig. 2. Sensitivity analysis: (a) energy consumption, (b) inventory, (c) environmental impacts, (d) profit, (e) productivity, (f) quality, (g) cost reduction, (h) waste reduction.

operational and environmental performance. Another feature that makes the framework useful for performance management is its flexibility. From the generic framework presented in Fig. 1, each company can remove or add lean and green performance criteria and practices to adapt the evaluation to its context. Although the components and relationships of the framework are rooted in the current literature, most of them with empirical evidence, managers interested in modifying the original structure of the assessment system will be able to do so since this paper already provides a starting point for that. Therefore, the assessment procedure of the theoretical framework can be considered generalizable and at the same time adaptable for practical application. A potential barrier to the practical application of the framework is inherent to the ANP methodology. Although some companies may not have trained personnel to use ANP, there is a free software application

available on the internet (https://superdecisions.com) that implements the technique by assisting in structuring the problem and allowing automatic calculation. Software support greatly increases the usability of the framework and encourages managers to use it even without prior training. A relevant contribution of the proposed framework was the development of the lean-green index. The LGindex helps to unify the results of the assessment, allowing incorporating this metric to the performance measurement system of the company. Although ANP is useful for evaluating relationships and influences, the technique does not present a unified result. With the incorporation of the LGindex, companies can easily track the progress of their lean and green strategies. On the other hand, besides the unified monitoring, the detailing of the cause-effect relationships of ANP allows the identification of improvement actions necessary to increase local and global performance. 87

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al.

Sensitivity analysis is also another procedure that brings important practical contributions to the evaluation of lean and green systems. Besides adding the perspective of a continuous assessment, sensitivity analysis can be used as a planning tool since it allows anticipating the consequences of changing priorities in business strategy.

Acknowledgments

7. Conclusion

References

This research sought to propose an original approach for the assessment of lean and green practices using ANP as a fundamental technique. To that end, through a review of the literature, it was possible to identify practices that support the implementation of lean and green systems, as well as the related performance criteria under the operational and environmental dimensions. Finally, the relationships between performance criteria and practices were structured in a framework that was operationalized via ANP. The framework was tested in a footwear manufacturing plant, confirming that it is feasible to evaluate lean and green practices regarding the impacts on operational and environmental performance, and thus identify which practices should be prioritized. The assessment is unified through the lean-green index (LGindex), which brings a synthesized view of results and simplifies the monitoring of the progress of the system. Considering the literature on lean and green, there is a scarcity of assessment models involving both approaches. Therefore, it can be stated that this study contributes to the literature of the area since it proposes an assessment structure of both systems using ANP as an implementation tool and providing an index (LGindex) that enables the evaluation of lean and green systems in an integrated way. Compared to other similar assessment approaches, the framework proposed in this paper is different in some ways. Unlike the model proposed by Carvalho et al. (2017), it is focused on internal manufacturing operations, and not on supply chains, so it addresses a more appropriate set of practices and performance criteria for this context. It links practices to specific performance criteria and considers lean and green as components of the same integrated system. However, the most remarkable characteristic of the proposed approach is that it considers all possible interactions between practices and performance criteria, a gap left by Thanki et al. (2016); Carvalho et al. (2017); Ramos et al. (2018); Bai et al. (2018). Particularly, this article had an objective similar to that proposed by Thanki et al. (2016), which focused on small and medium-sized enterprises (SMEs). However, in addition to having a broader scope (focusing not only on SMEs), this study goes beyond the work of Thanki et al. (2016) in three ways: (i) it evaluates interrelationships between practices and performance criteria in a network, and not only in hierarchies, allowing feedback among the elements; (ii) it provides an instrument for a practical application, which enables companies to selfassess their performance; and (iii) it proposes a unified performance indicator (LGindex). Regarding the limitations of the research, it is important to consider that the existing relationships in the framework were based solely on literature analysis. Future studies could analyze the elements contained in the framework through factorial analysis to confirm the relationships obtained from the literature. Another opportunity for improvement in the framework is to measure the level of integration between lean and green practices applied to a company in order to identify synergies between both systems. Another path for the continuity of this research is to combine ANP with other multicriteria analysis techniques that take into account the relationships between the elements of a system, such as Graph-Theoretic Approach (GTA) or Interpretive Structural Modeling (ISM), in order to enrich the results generated. Regarding the practical application, future works could apply the focus group technique to the decision-makers to test the convergence in judgments. Furthermore, it is always noteworthy to recommend the extension of the number of practical applications to understand the limits of applicability of the proposed framework.

Agarwal, A., Shankar, R., Tiwari, M.K., 2006. Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach. Eur. J. Oper. Res. 173, 211–225. https://doi.org/10.1016/j.ejor.2004.12.005. Bai, C., Satir, A., Sarkis, J., 2018. Investing in lean manufacturing practices: an environmental and operational perspective. Int. J. Prod. Res. https://doi.org/10.1080/ 00207543.2018.1498986. Ball, P., 2015. Low energy production impact on lean flow. J. Manuf. Technol. Manag. 26, 412–428. https://doi.org/10.1108/JMTM-12-2012-0120. Caldera, H.T.S., Desha, C., Dawes, L., 2017. Exploring the role of lean thinking in sustainable business practice: a systematic literature review. J. Clean. Prod. 167, 1546–1565. https://doi.org/10.1016/j.jclepro.2017.05.126. Carvalho, H., Govindan, K., Azevedo, S.G., Cruz-Machado, V., 2017. Modelling green and lean supply chains: an eco-efficiency perspective. Resour. Conserv. Recycl. 120, 75–87. https://doi.org/10.1016/j.resconrec.2016.09.025. Chaplin, L., O’Rourke, S.T.J., 2018. Could lean and green be the driver to integrate business improvement throughout the organisation? Int. J. Product. Perform. Manag. 67, 207–219. https://doi.org/10.1108/IJPPM-01-2017-0008. Chavez, R., Gimenez, C., Fynes, B., Wiengarten, F., Yu, W., 2013. Internal lean practices and operational performance. Int. J. Oper. Prod. Manag. 33, 562–588. https://doi. org/10.1108/01443571311322724. Cherrafi, A., Elfezazi, S., Govindan, K., Garza-Reyes, J.A., Benhida, K., Mokhlis, A., 2017. A framework for the integration of green and lean six sigma for superior sustainability performance. Int. J. Prod. Res. 55, 4481–4515. https://doi.org/10.1080/ 00207543.2016.1266406. Chiarini, A., 2014. Sustainable manufacturing-greening processes using specific lean production tools: an empirical observation from European motorcycle component manufacturers. J. Clean. Prod. 85, 226–233. https://doi.org/10.1016/j.jclepro.2014. 07.080. Chugani, N., Kumar, V., Garza-Reyes, J.A., Rocha-Lona, L., Upadhyay, A., 2017. Investigating the green impact of Lean, Six Sigma and Lean Six Sigma. Int. J. Lean Six Sigma 8, 7–32. https://doi.org/10.1108/IJLSS-11-2015-0043. Diaz-Elsayed, N., Jondral, A., Greinacher, S., Dornfeld, D., Lanza, G., 2013. Assessment of lean and green strategies by simulation of manufacturing systems in discrete production environments. CIRP Ann. Manuf. Technol. 62, 475–478. https://doi.org/10. 1016/j.cirp.2013.03.066. Domingo, R., Aguado, S., 2015. Overall environmental equipment effectiveness as a metric of a lean and green manufacturing system. Sustain 7, 9031–9047. https://doi. org/10.3390/su7079031. Dües, C.M., Tan, K.H., Lim, M., 2013. Green as the new lean: how to use lean practices as a catalyst to greening your supply chain. J. Clean. Prod. 40, 93–100. https://doi.org/ 10.1016/j.jclepro.2011.12.023. Fercoq, A., Lamouri, S., Carbone, V., 2016. Lean/green integration focused on waste reduction techniques. J. Clean. Prod. 137, 567–578. https://doi.org/10.1016/j. jclepro.2016.07.107. Galeazzo, A., Furlan, A., Vinelli, A., 2014. Lean and green in action: interdependencies and performance of pollution prevention projects. J. Clean. Prod. 85, 191–200. https://doi.org/10.1016/j.jclepro.2013.10.015. Gandhi, N.S., Thanki, S.J., Thakkar, J.J., 2018. Ranking of drivers for integrated leangreen manufacturing for Indian manufacturing SMEs. J. Clean. Prod. 171, 675–689. https://doi.org/10.1016/j.jclepro.2017.10.041. Garza-Reyes, J.A., 2015. Lean and green: a systematic review of the state of the art literature. J. Clean. Prod. 102, 18–29. https://doi.org/10.1016/j.jclepro.2015.04.064. Garza-Reyes, J.A., Kumar, V., Chaikittisilp, S., Tan, K.H., 2018. The effect of lean methods and tools on the environmental performance of manufacturing organisations. Int. J. Prod. Econ. 200, 170–180. https://doi.org/10.1016/j.ijpe.2018.03.030. Hajmohammad, S., Vachon, S., Klassen, R.D., Gavronski, I., 2013. Lean management and supply management: their role in green practices and performance. J. Clean. Prod. 39, 312–320. https://doi.org/10.1016/j.jclepro.2012.07.028. Hallam, C., Contreras, C., 2016. Integrating lean and green management. Manag. Decis. 54, 2157–2187. https://doi.org/10.1108/MD-04-2016-0259. Hong, P., Roh, J.J., Rawski, G., 2012. Benchmarking sustainability practices: evidence from manufacturing firm. Benchmarking Int. J. 19, 634–648. https://doi.org/10. 1108/MRR-09-2015-0216. Jasti, N.V.K., Kodali, R., 2015. Lean production: literature review and trends. Int. J. Prod. Res. 53, 867–885. https://doi.org/10.1080/00207543.2014.937508. Kurdve, M., Shahbazi, S., Wendin, M., Bengtsson, C., Wiktorsson, M., 2015. Waste flow mapping to improve sustainability of waste management: a case study approach. J. Clean. Prod. 98, 304–315. https://doi.org/10.1016/j.jclepro.2014.06.076. León, H.C.M., Calvo-Amodio, J., 2017. Towards lean for sustainability: understanding the interrelationships between lean and sustainability from a systems thinking perspective. J. Clean. Prod. 142, 4384–4402. https://doi.org/10.1016/j.jclepro.2016.11. 132. Meade, L.M., Sarkis, J., 1999. Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach. Int. J. Prod. Res. 37, 241–261. https://doi.org/10.1080/002075499191751.

This study was partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance code: 1600604.

88

Resources, Conservation & Recycling 143 (2019) 77–89

L.M.S. Farias et al. Miller, G., Pawloski, J., Standridge, C., 2010. A case study of lean, sustainable manufacturing. J. Ind. Eng. Manag. 3, 11–32. https://doi.org/10.3926/jiem.2010.v3n1. p11-32. Pampanelli, A.B., Found, P., Bernardes, A.M., 2013. A lean & green model for a production cell. J. Clean. Prod. 85, 19–30. https://doi.org/10.1016/j.jclepro.2013.06. 014. Prasad, S., Khanduja, D., Sharma, S.K., 2016. An empirical study on applicability of lean and green practices in the foundry industry. J. Manuf. Technol. Manag. 27, 408–426. https://doi.org/10.1108/JMTM-08-2015-0058. Ramos, A.R., Ferreira, J.C.E., Kumar, V., Garza-Reyes, J.A., Cherrafi, A., 2018. A lean and cleaner production benchmarking method for sustainability assessment: a study of manufacturing companies in Brazil. J. Clean. Prod. 177, 218–231. https://doi.org/ 10.1016/j.jclepro.2017.12.145. Rothenberg, S., Pil, F.K., Maxwell, J., 2001. Lean, green, and the quest for superior environmental performance. Prod. Oper. Manag. 10, 228–243. https://doi.org/10. 1111/j.1937-5956.2001.tb00372.x. Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48, 9–26. https://doi.org/10.1016/0377-2217(90)90057-I. Saaty, T.L., 1996. Decision Making With Dependence and Feedback: the Analytic Network Process. RWS Publications, Pittsburgh 370 pp. Saaty, T.L., Kearns, K.P., 1985. Analytic Planning: the Organization of Systems. Pergamon Press, Oxford 208 pp. Thanki, S.J., Thakkar, J.J., 2016. Value-value load diagram: a graphical tool for leangreen performance assessment. Prod. Plan. Control 27, 1280–1297. https://doi.org/ 10.1080/09537287.2016.122064. Thanki, S., Govindan, K., Thakkar, J., 2016. An investigation on lean-green

implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach. J. Clean. Prod. 135, 284–298. https://doi.org/10.1016/j.jclepro.2016.06. 105. Verrier, B., Rose, B., Caillaud, E., Remita, H., 2014. Combining organizational performance with sustainable development issues: the lean and green project benchmarking repository. J. Clean. Prod. 85, 83–93. https://doi.org/10.1016/j.jclepro.2013.12. 023. Vinodh, S., Ramesh, K., Arun, C.S., 2016. Application of interpretive structural modelling for analysing the factors influencing integrated lean sustainable system. Clean Technol. Environ. Policy 18, 413–428. https://doi.org/10.1007/s10098-015-1025-7. Wickramasinghe, G., Wickramasinghe, V., 2017. Implementation of lean production practices and manufacturing performance: the role of lean duration. J. Manuf. Technol. Manag. 28, 531–550. https://doi.org/10.1108/JMTM-08-2016-0112. Womack, J.P., Jones, D.T., Roos, D., 1990. The Machine That Changed the World. Rawson Associates, New York 323 pp. Wong, W.P., Ignatius, J., Soh, K.L., 2014. What is the leanness level of your organisation in lean transformation implementation? An integrated lean index using ANP approach. Prod. Plan. Control 25, 273–287. https://doi.org/10.1080/09537287.2012. 674308. Yang, M.G., Hong, P., Modi, S.B., 2011. Impact of lean manufacturing and environmental management on business performance: an empirical study of manufacturing firms. Int. J. Prod. Econ. 129, 251–261. https://doi.org/10.1016/j.ijpe.2010.10.017. Zhan, Y., Tan, K.H., Ji, G., Chung, L., Chiu, A.S.F., 2018. Green and lean sustainable development path in China: guanxi, practices and performance. Resour. Conserv. Recycl. 128, 240–249. https://doi.org/10.1016/j.resconrec.2016.02.006.

89