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Article

Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model

by
Sumit Paul
1,
Syed Mithun Ali
1,*,
Mohd Asif Hasan
2,
Sanjoy Kumar Paul
3 and
Golam Kabir
4
1
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
2
Mechanical Engineering Section, University Polytechnic, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh 202001, India
3
UTS Business School, University of Technology Sydney, Ultimo, NSW 2007, Australia
4
Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(3), 1863; https://doi.org/10.3390/su14031863
Submission received: 30 December 2021 / Revised: 30 January 2022 / Accepted: 4 February 2022 / Published: 6 February 2022
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Abstract

:
Due to the increasing popularity of cost-based outsourcing and growing stakeholder concern about environmental, social, and technological issues, supply chain sustainability is vital in both developed and emerging economies. Bangladesh is an emerging economy and wood industry of Bangladesh is suffering from severe sustainability issues besides its growth. Hence, this article aims to examine the critical success factors (CSFs) for sustainability in the Bangladeshi wood industry, which is crucial to help supply chain managers engage in achieving sustainable development goals. This research investigated the CSFs and uncovered their interdependencies through the development of a methodology integrating a literature review, principal component analysis (PCA), interpretive structural modelling (ISM), and Matriced Impacts Croises Multiplication Appliquee aunClassement (MICMAC) techniques. PCA (n = 150) was used to identify and rank the CSFs for sustainability in the Bangladeshi wood industry while ISM (n = 9) and MICMAC were used to determine the driving and dependence power of the CSFs. The findings reveal that research and development, supplier relations, and using eco-friendly technology are the most significant CSFs of the Bangladeshi wood industry. Indispensable links revealing the driving and dependence power among the CSFs were also reported. To the best of our knowledge, this study is the first of its kind that examined the CSFs for supply chain sustainability in the Bangladeshi wood industry. The proposed methodology and findings will help managers in the Bangladeshi wood industry as well as other similar industries to understand the CSFs and reduce the complexity of decision-making in managing business process towards sustainability journey.

1. Introduction

Sustainability in the context of business refers to decisions that have an impact on the future conditions of nature, society, and business [1,2,3]. The concept of a sustainable supply chain is viewed as a measure that pertains to profit and loss—but also broader social and environmental concerns [4,5]. In other words, sustainability centres around reducing a business’s harmful impacts on people, society, and the environment while still enhancing value for customers, business partners, and stakeholders [6]. Aside from meeting social and environmental expectations, sustainability reduces cost and reputational risk, boosts revenue, and ensures long-term success [7].
There are many barriers to the successful implementation of sustainable supply chain management (SSCM), though these barriers vary in their significance [8,9]. Therefore, there is a strong need for organizations to identify the dominant factors (i.e., the critical success factors [CSFs]) to effectively pursue SSCM practices [10]. To help organizations adopt effective SSCM practices, this paper develops a methodology aimed at investigating CSFs for sustainability in the Bangladeshi wood industry and constructing a structural model to understand the interactive relationships among these CSFs.
The Bangladeshi wood industry mainly consists of paper and furniture manufacturing. Bangladeshi paper is manufactured using wood pulp, which is generally collected from forests [11]. It is growing every year. In fact, it is currently going through a transition period; demand for wood products is expanding due to their increased affordability and the country’s increasing population. The Bangladeshi furniture industry, which rapidly transformed from a cottage industry to a heavily mechanized mass-production industry in the early 1990s, mainly produces home and office furniture. According to the Bangladesh Furniture Industries Owners Association, Bangladeshi furniture manufacturers import about 60% of their raw materials. However, local manufacturers are currently expanding their reach in the international market. Bangladesh’s furniture export market has now expanded to 202 destinations in the world. It has grown to $74.9 million in 2019 from $31.4 million in 2013 [12]. Furniture industry has been included as one of the highest priority sectors in Bangladesh’s Export Policy 2018–2021 and declared as a “thrust sector”. The domestic furniture industry is characterized by the dominance of medium, small, and micro enterprises all over the country and some bigger competitors at major cities like Dhaka, Barisal, Bogra, Chittagong, Manikganj, Narayanganj, and Sylhet. Some prominent furniture factories are situated at Badda, Gandaria, Chankharpul, Madanpur, Mirpur, Shahjahanpur, Panthapath, Sutrapur, Gazipur, and Shyampur. According to some studies, there are around 81 medium sized enterprises, and around 71,000 micro and small enterprises and around 75,000 engaged enterprises in the industry. The sector contributes to around 9 percent of the manufacturing employments in Bangladesh. Almost 80 percent of the jobs are basic production works, and rest of the percentages are related to sophisticated skills like furnishing, marketing, assembling, and designing [13]. In general, according to the life-cycle approach, the most significant environmental impacts of the furniture industry can be divided into three groups: energy and raw material consumption; emission of chemical substances; and waste generation [14,15]. As a result, low consumption, low emission, and low pollution constitute the primary focus of low-carbon economic development. Hence, the low-carbon development of renewable wood resources is very important for sustainability in the wood industry. The wood industry involves several negative environmental externalities, including wastewater production and volatile organic compound generation. Due to these ecological issues—as well as deforestation—the wood industry is vulnerable to sustainability issues [16,17]. The sustainable development of the wood industry would entail an optimized wood-production cycle and improved waste-management procedures [18]. Overall, it is clear that an analysis of the CSFs for sustainability in wood industries is warranted. The global competition in the wood sector put considerable pressure on researchers to think about concerns over environmental conservation in the wood industry’s supply-chain-planning process. Achieving sustainability in the wood supply chain is complex and poses numerous challenges [19].
Bangladeshi wood industry uses both modern and indigenous technologies. Though the export-oriented and technology-intensive production processes, which includes sophisticated machineries, are being used for different operations, adopting modern technologies has been a major challenge for smaller enterprises. Moreover, many Bangladeshi firms lack required human and technical resources to maintain Enterprise Resource Planning (ERP) to ensure efficient production planning. With the emergence of E-commerce, information and communication technology is an important supply chain pillar, which connects consumers and firms with big consumers and retailers globally. This sector has been underdeveloped until now, which may improve in the near future. In addition to these, Bangladeshi furniture enterprises find it difficult to implement international certification systems [13].
Most of the SCM studies contain optimization of total cost equation to maximize the profit margin minimizing the variable demand and lead time uncertainty [1,2,3]. A recent study reviewed the assessment and optimization studies on wood supply chains that include at least one dimension of sustainability. They found that most of the papers included just one or two sustainability dimensions. Social responsibility was the least common dimension [20]. While these studies considered optimization models with deterministic parameters, none considered CSFs for SSCM in the wood industry. This study aims to fill this gap in the research.
This study incorporates principal component analysis (PCA), interpretive structural modelling (ISM), and Matriced Impacts Croises Multiplication Appliquee aunClassement (MICMAC) to evaluate the supply chain sustainability in the wood industry of Bangladesh. These three methods offer strong advantages. PCA has been used to identify and deconstruct CSFs using experts’ opinions. ISM has been used to determine the driving and dependence power of CSFs. MICMAC has been used to divide CSFs into four categories: dependent, independent, autonomous, and linkage [21]. The comparison of the current study with relevant studies have been included to highlight the contributions.
The rest of this paper is organized as follows. Section 2 gives the related materials and methods. Section 3 presents the results and discussion. Section 4 concludes this paper.

2. Materials and Methods

2.1. Conceptualization of Critical Success Factor

Despite the importance of supply chain sustainability in the wood industry, there is a dearth of research on the CSFs for sustainability in the wood industry [22]. Zineldin et al. [23] identified the factors that affect supplier–dealer relationships by interviewing 114 purchasing managers in the Swedish wood industry. Quesada et al. [24] identified and ranked the CSFs for sustainability in the U.S. furniture industry using the five-point Likert scale. Daian et al. [25] assessed the generation of wood waste by Australian furniture companies and its associated costs to enhance sustainability standards in the Australian manufacturing sector. Looking at the Amish furniture industry, Dewitt et al. [26] demonstrated a link between Porter’s cluster theory and supply chain management, providing evidence of their joint positive impact on firm competitiveness and performance. Moreover, Gomez et al. [27] identified the CSFs for sustainability in the U.S. pallet-manufacturing industry after surveying 1500 manufacturers. Quesada-Pineda et al. [28,29] analyzed and validated the CSFs that affect the supply chain of the U.S. pallet-manufacturing industry. Furthermore, they identified the external factors and business practices using the five-point Likert scale and PCA [24,25].
Penfield et al. [30] identified the characteristics of efficient hardwood sawmill supply chains through data envelopment analysis. Larsson et al. [31] conducted a literature survey to detail the potential of SSCM to solve the challenges of the Swedish wood-products industry. Thị Thu Sương [32] identified the factors that influence supply chain collaboration in the Vietnamese wood-processing industry using a questionnaire survey. Romagnoli et al. [19] reviewed used wood-based products, linking them with supply chain sustainability. Kogler et al. [33] found that the combination of unimodal and multimodal transportation eliminates ill-timed plans and bottlenecks, making the wood supply chain more resilient. Recently, Ebrahimi et al. [34] applied a bi-objective supply chain model to a build-to-order environment in order to maximize profits and minimize downtime in wood supply chains. Susanty et al. [35] investigated circular economy practices in the traditional Indonesian wood industry. Chu et al. [36] provided data on pollutants and emissions through a life-cycle analysis to promote sustainability in the Chinese wood industry. Ratnasingam et al. [37] identified the CSFs for sustainability in the Malaysian panel-based furniture-manufacturing industry by surveying 20 manufacturers using a questionnaire survey and PCA; however, they did not identify the interdependent relationships among the CSFs. Navarro et al. [38] identified sustainable suppliers in the wood fibre supply industry by integrating the analytic hierarchy process (AHP) with the technique for order of preference by similarity to ideal solution (TOPSIS) methods.
There are some existing studies on SSCM in emerging economies that incorporate PCA, ISM, MICMAC, and other mathematical methods. Rajput et al. [39] integrated PCA, ISM, and DEMATEL to identify IoT enablers in Industry 4.0 and the relationships between these enablers. Chowdhury et al. [40] employed ISM and MICMAC to identify the CSFs for sustainability in the Bangladeshi ready-made garments industry and their interrelationships—though the authors were careful to note that their results are specific to the industry and region of study. Devi et al. [41] employed PCA, ISM, and MICMAC to analyze IoT enablers in Industry 4.0. The comparison of the current study with relevant studies is shown in Table 1 to highlight the contributions.
This study identifies the CSFs for SSCM adoption in the Bangladeshi wood industry using both expert feedback and the existing literature. Therefore, we carried out a literature review in the initialization phase using several fundamental terms: “drivers for supply chain sustainability” OR “critical success factors of sustainable supply chain adoption” OR “CSFs in sustainable supply chain” OR “investigate and examine sustainable supply chain drivers.” We used two popular scholarly databases—“Google Scholar” and “ScienceDirect”—for this review. Table 2 details the 25 CSFs identified through the literature review. The CSFs have been contextualized for further analysis in the context of the Bangladeshi wood industry by considering input from industrial and academic experts.

2.2. Proposed Approach

Our proposed approach is illustrated in Figure 1. From the selected factors in Table 2, we identified 10 CSFs through PCA while considering expert opinions that were collected through a questionnaire survey. PCA is a popular machine learning technique used to identify underlying patterns in multivariate data [64]. It extracts important information from a set of data and, using orthogonal transformation, represents them as a new set of linearly correlated variables, which are called principal components. Through PCA, we excluded 15 factors for increasing the consistency of the Kaiser–Meyer–Olkin (KMO) measure of adequacy and Bartlett’s test of sphericity measurement.
After identifying CSFs for sustainability in the Bangladeshi wood industry and extracting them into four components, we employed ISM [65] and MICMAC to analyze the contextual relationships among the CSFs. ISM can be used as a systemic tool to identify contextual relationships; it is an interpretive tool, as the judgments of the groups decide the relationship between the variables. ISM is a structural method based on a mutual relationship from the complex set of elements; it is a modelling technique, as the specific relationships and overall structure are portrayed in a digraph model. It helps to impose order and direction on complex relationships. It is primarily intended as a group learning process—but individuals can also use it.

2.2.1. Identifying CSFs Using PCA

We developed a standard questionnaire based on the five-point Likert scale to identify the CSFs for sustainability in the Bangladeshi wood industry through PCA (Table A1 in Appendix A). We e-mailed the questionnaire to 150 experts—30 academics from Bangladeshi universities researching supply chains and 120 managers (30 quality managers, 50 supply chain managers, and 40 operational process managers) at 30 Bangladeshi paper mills and furniture-manufacturing companies (for expert profiles, see Table 3). After three months, we received responses from 93 of the experts (23 academics and 70 managers). Afterward, we consolidated the data and conducted a descriptive analysis in SPSS. PCA was employed to reduce the number of factors. The two most important results from PCA are total variance and the component matrix. Total variance computes explained variance, and the component matrix computes the weights of variables in a few easily interpretable factor components. PCA with varimax rotation was employed to divide the ten factors into four categories (14 factors were excluded for consistency of the PCA, and “Gender Diversity” was excluded for low factor loading). We employed varimax rotation because it maximizes the sum of the variance of square loading [62]. The minimum number of cases should be at least five times the number of items being analyzed [66].
In this study, we analyzed 93 experts’ responses against 11 factors. To quantify the degree of interconnections among the variables and verify the appropriateness of the analysis, we tested sampling adequacy using the KMO measure of adequacy and Bartlett’s test of sphericity. The KMO measure of adequacy was 0.948 (>0.5), and the significance value of Bartlett’s test of sphericity was 0.00 (<0.4); Cronbach’s alpha was 0.93, which is substantially greater than 0.7. These figures demonstrate high internal consistency for the obtained data [67]. Gender diversity was excluded, as it exhibited a factor loading below 0.6 after having a round-table discussion with 15 of our 150 experts.
Considering our experts’ input, we selected, ranked, and divided the CSFs into four categories using PCA. The rankings and categories are presented in Table 4 and Table 5, respectively.

2.2.2. Identification of the CSFs’ Driving and Dependence Power

Step 1: Developing a Structural Self-Interaction Matrix (SSIM): In order to develop an SSIM (Table 6), one professor from each university (total 5) and two industrial experts from furniture enterprises and two industrial experts from paper mills were invited for a round table discussion. The sample questionnaire for the discussion is in Appendix A After the round table discussion, according to the experts’ opinion, the SSIM was developed. We used the following four symbols to find the direction of interaction between two CSFs (e.g., i and j):
  • V-CSF: i facilitates reaching j
  • A-CSF: j facilitates reaching i
  • X-CSF: i and j facilitate reaching each other
  • O-CSF: i and j are not connected
Using these four symbols, we developed an SSIM for implementing SSCM practices throughout the Bangladeshi furniture industry, which is presented in Table 6.
Step 2: Forming the Reachability Matrix: We transformed the SSIM into an initial reachability matrix. It is a binary matrix that contains 0 and 1. It also follows certain rules, which are as follows:
Rule 1: For every V, 1 for (i, j) and 0 for (j, i)
Rule 2: For every A, 0 for (i, j) and 1 for (j, i)
Rule 3: For every X, 1 for (i, j) and 1 for (j, i)
Rule 4: For every O, 0 for (i, j) and 0 for (j, i)
Following these rules, we constructed an initial reachability matrix (Table 7) and the final reachability matrix (Table 8), which integrates the transitive relationships among the factors [66,68]. The driving power and the dependence power of each factor have also been calculated, as shown in Table 8. TMS has the highest driving power, and HS has the highest dependence power.
Step 3: Level Partitioning: From the final reachability matrix, we derived the reachability and antecedent sets for each factor. The reachability set comprises the factor itself and the other factors that it may facilitate. The antecedent set consists of the factor itself and the other factors that may influence it. We also calculate the intersection of these two sets for each CSF. If a CSF’s reachability and intersection sets are the same, the CSF is regarded to be at level 1 [55,69]. After the first iteration, we discarded the level-1 CSF and continued the same procedure until all of the CSFs’ levels were identified. The final partitioning result is shown in Table 9. In this case, HS is at level 1 and lies at the top of the ISM hierarchy. TMS is at the last level and lies at the bottom of the ISM hierarchy.
Step 4: Developing an ISM-Based Model: We identified seven levels from level partitioning in Table 9 with CSF 8 at level 1, CSFs 2 and 10 at level 2, CSFs 5 and 9 at level 3, CSF 4 at level 4, CSF 3 at level 5, CSFs 1 and 6 at level 6, and CSF 7 at level 7. After placing all of the CSFs at their level, we depicted a relationship between the CSFs as per the final reachability matrix (Table 8 using directional arrows, resulting in an ISM-based model, as shown in Figure 2).
Step 5: MICMAC Analysis: MICMAC effectively corresponds to cross-impact matrix multiplication applied to classification [5,7,9,34,70]. MICMAC was developed based on the multiplication properties of metrics. We employed it in this paper to gain further insight into our ISM-based model [55] and to evaluate the CSFs from the implementation perspective. It involves the classification of the identified CSFs based on their dependence and driving power, which we determined in the final reachability matrix.
MICMAC entails a graphical representation of the factors—based on their driving and dependence power—in four clusters: autonomous, dependent, linkage, and independent. Autonomous factors have weak dependence power and weak driving power. Dependent factors have strong dependence power but weak driving power. Linkage factors have strong dependence power and strong driving power. Independent factors have strong driving power but weak dependence power. Figure 3 illustrates the categorization of the CSFs into these four regions to evaluate the implementation of SSCM practices.
Figure 3 demonstrates that there is no autonomous CSF that shows that all ten of the considered CSFs are relevant to this study. We found CSFs 2, 5, 8, 9, and 10 (product quality, resource management, health and safety, supplier relations, and using eco-friendly technology, respectively) to be dependent and CSFs 1, 3, 6, and 7 (collaborative R&D with same-sector companies, transferring technology to suppliers and vendors, technology advancement and adaption, and top management support, respectively) to be independent. Although CSF 4 (information system) lies on the border separating linkage and independent CSFs, it may be a linkage CSF for all practical purposes [68,69,71]. Independent CSFs are viewed as a top priority for implementation in SSCM practices, as they pave the way for efficiently and effectively implementing linkage and dependent CSFs.

2.2.3. Model Validation

The SSIM, ISM, and MICMAC analyses were shared with all of the respondents, whom we asked to share their critical opinions on the study. The study was generally well-received and appreciated, though a few questioned the lack of an interaction between CSF 5 (resource management) and CSF 9 (supplier relations). We concluded that the lack of this interaction is because the Bangladeshi wood industry is in a development phase, transitioning from a non-sustainable to a sustainable path; this requires organizations to develop relations with new suppliers who are willing to follow a sustainable path and sacrifice their relationships with old suppliers who are not as eager to adopt SSCM practices. This interpretation convinced all the participants, validating the model.

3. Results and Discussions

Sustainability has become a global concern. To remain competitive in both domestic and international markets, SSCM practices are a must for Bangladeshi furniture companies. Some Bangladeshi furniture manufacturers and paper mills have international suppliers and customers who have begun to implement sustainability measures—this will inevitably lead to the further spread of SSCM practices throughout the Bangladeshi wood industry. Without understanding the CSFs, however, organizations cannot properly pursue a sustainable supply chain. Based on experts’ opinions, this study identified ten CSFs using PCA, as shown in Table 5. It then incorporated these ten CSFs into an ISM-based model (Figure 2) followed by MICMAC (Figure 3) based on the inputs from the ISM-based model. PCA helped to identify and rank the most important CSFs. ISM was used to uncover the relationships among those CSFs. Finally, MICMAC is helpful for managers to take appropriate actions at both the operational and strategic levels. Some of our most significant findings are as follows:
  • Research and development (CSF 1), supplier relations (CSF 9), and using eco-friendly technology (CSF 10) are the top-ranked CSFs. In Bangladesh, there is a low level of R&D, as it is an emerging economy. As a result, there is limited engagement with eco-friendly technology. Many companies are not interested in adopting an effluent treatment plant, as it would increase their operating costs. Additionally, good relationships with suppliers help organizations to ensure their long-term growth. The selected ten CSFs are divided into four categories: product quality and well-being of the organization, supplier relations, eco-friendly technology, and health and safety.
  • There are no autonomous CSFs (Figure 3), suggesting that all of the considered CSFs are relevant to this study.
  • The dependent CSFs (Figure 3) are product quality, resource management, health and safety, supplier relations, and using eco-friendly technology. These factors have weak driving power but strong dependence power. These CSFs certainly have associated costs in terms of resources, time, and focus that an organization may be unwilling to spend as part of its competitive strategy. To implement these CSFs, top management must actively support them.
  • The independent CSFs (Figure 3) are collaborative R&D with same-sector companies, transferring technology to suppliers and vendors, technology advancement and adaption, and top management support. These CSFs are vital to the implementation of dependent and linkage CSFs. As shown in Figure 2, top management support is the most important independent CSF in terms of pursuing SSCM practices, making it a driving force for the other three CSFs in this category. As the Bangladeshi wood industry is technologically lagging and more R&D efforts are required to substitute wood with non-wood fibres in papermaking to limit deforestation [11,72,73], top management support is necessary to foster it.
  • The only linkage CSF (Figure 3) is “information system.” With high driving power and high dependence power, it has a significant role in the implementation of a sustainable supply chain. Moreover, its presence in the linkage category suggests that the Bangladeshi industry is aware of the potential requirement of information systems in the various processes and purposes of a business.
Our findings will help managers focus on the most important factors to build a resilient and sustainable supply chain. They will help them identify the critical areas on which they should focus to more effectively incorporate SSCM practices. In turn, our results will play a role in the paradigm shift towards sustainability that the Bangladeshi wood industry is currently undergoing.
The factors with high driving power are strategically important, while those with high dependence power are performance-oriented and result-oriented. Continual improvement of the independent factors will help organizations achieve superior results [74]. Focusing on the CSFs, organizations can acquire a competitive advantage by manufacturing eco-friendly products. In turn, their customers and stakeholders will push other companies to implement similar procedures to remain competitive.

4. Managerial Implications

The present study has significant practical implications for industrial managers and government policy makers to ensure sustainability in the wood industry. The model presented here will help them understand the critical factors and their interrelationships in the context of the Bangladeshi wood industry. Moreover, it would be helpful for them to develop long-term and short-term plans to implement low-carbon economic development in the wood industry. Government plays significant roles in setting up policies and regulations to ensure low carbon economy, thereby enhancing sustainability in existing industrial sectors. These policies may encourage technological advancement, healthy and safe practices at factory workspace, collaborative R&D, etc. Governmental policies may support all stakeholders involved in the management to mitigate probable challenges. This study would encourage policy makers of the Bangladesh government to take necessary steps to change their policies to help enterprises move towards sustainability.
In developed countries, the government allocates required funds to encourage sustainable practices at the industries. In addition to these, the government also allocates significant funds for R&D activities to ensure use of advanced technologies, safety measures, low emission, and low pollution. However, in Bangladesh, there is very small or no funds for R&D activities. Advanced technologies help to increase the profit margin [4]. The presented study would help the decision makers to increase funds for technology development and R&D activities.
Training is required to make the human resources efficient. From the studies it is obvious that Bangladeshi wood furniture enterprises lack trained operators and human resources [16]. In addition to these, the enterprises find it difficult to accommodate international certifications and implement advanced technologies. The present study can encourage the managers and decision-makers to take necessary steps for train their workforces to improve quality of the products and reduce defective items [2].
In a nutshell, the major challenges for the wood industry of Bangladesh are mainly untrained workers, lack of R&D activities, health and safety issues, lack of advanced technologies. The managers can take initiatives to overcome the challenges by implementing sustainable practices in their enterprises.

5. Conclusions

By pursuing sustainability, organizations can achieve positive business growth and a competitive advantage over others [67,75]. Of course, implementing SSCM practices is a challenging and complex task that requires a thorough interpretation of the sustainability index across an entire supply chain. Due to the subjectivity of the sustainability index, sustainability assessments require a transparent approach [76,77].
In this paper, we used PCA to identify and rank the most significant CSFs for sustainability in the Bangladeshi wood industry. We then identified the relationships among these CSFs using ISM. This study—which constituted the first attempt to evaluate the CSFs for sustainability in the Bangladeshi wood industry—found that “top management support” is the most significant driving factor, leading to higher “research and development” and “technology advancement and adaption” in the pursuit of a sustainable supply chain. The Bangladeshi wood industry is growing but lacks the presence of new, green technologies, which has a severe, negative impact on the environment. Despite the recent growth of this industry, it remains vulnerable to national and international pressure pertaining to environmental protection and labour rights, both of which largely depend on the awareness of top management. Continual R&D and technological improvement are also important for improving product quality and addressing health and safety issues in this industry. Our proposed method will help company managers build a sustainable supply chain to compete in the international market.
This study has several limitations. First, it is highly dependent on experts’ opinions. Hence, it could be biased based on their personal beliefs [40]. In addition, our results are both industry- and location-specific. Future research could evaluate other sectors (e.g., automotive industry, ready-made garments industry) and emerging economies. This study could also be implemented with weighted ISM, total ISM, or structural equation modelling to get better results. Fuzzy MICMAC analysis could also be employed to improve the model.

Author Contributions

Conceptualization, S.P., S.M.A., G.K., S.K.P.; methodology, S.P., S.M.A., G.K., S.K.P.; software, S.P.; validation S.P.; formal analysis, S.P.; investigation, S.P., S.M.A., G.K., S.K.P.; resources, S.M.A., G.K., S.K.P.; data curation, S.P.; writing—original draft preparation, S.P.; writing—review and editing, M.A.H., S.M.A., G.K., S.K.P.; visualization, S.P.; supervision, S.M.A., G.K., S.K.P.; project administration, S.M.A., G.K., S.K.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Please respond to the following questionnaire. Please mark the factors on a five-point Likert scale: 1 (least important), 2 (not very important), 3 (important), 4 (very important), 5 (most important).
Table
Table A1. Questionnaire for PCA.
Table A1. Questionnaire for PCA.
Sl. No.Success Factor Description
1Recycling and waste management (RWM)Recycling focuses on the regeneration of renewable resources after use. The principles of waste management are elimination, reduction, reuse, recovery, and disposal.
2Using eco-friendly technology (ET)Using eco-friendly technology that conserves energy and reduces resource consumption.
3Sharing natural resources (NR)The long-term availability and sharing of natural resources.
4Product responsibility (PR)Product responsibility covers the entire life cycle, including the raw materials, product development, production, use, and recycling.
5Designing products for recyclability, reusability, and material recoverability (RRR)Designing for deconstruction to ensure that deconstruction processes retain maximum material value.
6Supplier environmental evaluation and feedback (SEEF)Suppliers are evaluated on several criteria: no child labour, working hours, no forced labour, no discrimination, employment compensation, freedom of association, use of eco-friendly materials, and health and safety practices.
7Using eco-friendly materials (EFM)Using eco-friendly materials.
8Managing hazardous substances (MHS)Managing wood-industry-specific hazardous chemicals, which are endorsed by the Occupational Safety and Health Regulations 1996 and have specific safety requirements.
9Support from customers (SC)Awareness among customers motivates manufacturers to produce green products.
10Training programs for supplier and vendors (SV)Training, implementing special projects, and monitoring performance.
11Collaborative R&D with same-sector companies (R&D)Collaboration on green research and development with suppliers and companies in the same sector.
12Flexible working arrangements (FWA)Flexible working—also known as flextime or flexitime—refers to non-traditional working arrangements that consider employees’ personal needs, often involving working from home and flexible hours.
13Supplier evaluation and selection (SES)Environmental auditing and supplier evaluation to ensure that products conform to regulations.
14Supplier relations (SR)Supplier relations reflect the extent to which an organization has a collaborative relationship with its suppliers.
15Stability in policy and regulations (PS)Stability in policies and regulations, which is often tied to political stability.
16Health and safety (HS)Manual lifting, forceful gripping, wood dust, and hazardous substances are the reported common risk factors in the wood industry. Inhaling hazardous substances and getting caught in moving parts are common injuries in the industry. The most prominent prior risk is inadequate personal protective equipment.
17Gender diversity (GD)Fair representation of people of different genders at various stages of the supply chain.
18Government legislation (GL)Regulations pressure suppliers and organizations to use eco-friendly products and processes to improve operational performances.
19Top management support (TMS)Top management support is crucial to the successful implementation of SSCM.
20Resource management (RM)Optimal resource usage helps firms achieve economic sustainability in the supply chain.
21Supplier audit (SA)Supplier audits have the potential to enhance supply chains’ environmental and social performance.
22Product quality (PQ)Sustainable supply chains can be achieved by producing high-quality products with a longer product life.
23Technological advancement and adaption (TAA)Willingness to research developing technologies and adopt new technologies in the pursuit of greater sustainability.
24Technology transfer (TT)Transferring technology to supply chain partners helps firms achieve long-term sustainability.
25Information system (IS)Information systems impact SSCM on various processes, such as planning, sourcing, and delivering, and at various levels, including tactical operations and organizational strategy.

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Sustainability 14 01863 g001 550
Figure 1. Proposed Approach.
Figure 1. Proposed Approach.
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Figure 2. ISM Model for sustainable supply chain in the wood industry.
Figure 2. ISM Model for sustainable supply chain in the wood industry.
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Figure 3. Driving power and dependence power diagram.
Figure 3. Driving power and dependence power diagram.
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Table
Table 1. Comparison of relevant studies with this study.
Table 1. Comparison of relevant studies with this study.
AuthorsMethods UsedRegion of StudyMain Contribution
Zineldin et al. [23]SurveySwedenIdentifying factors influencing supplier–dealer relationship
Quesada et al. [24]Survey using five-point Likert scaleUSAIdentification and ranking of CSFs for sustainability
Daian et al. [25]SurveyAustraliaAssessment of generation of wod waste to enhance sustainability standards
Dewitt et al. [26]Case studyUSADemonstration of a link between Porter’s cluster theory and supply chain management
Gomez [27]SurveyUSAIdentification of the CSFs for sustainability in the U.S. pallet-manufacturing industry after surveying 1500 manufacturers
Ratnasingam et al. [37]Survey and PCAMalaysiaIdentification of the CSFs for sustainability in panel-based furniture-manufacturing industry
Navarro et al. [38]Integration of AHP and TOPSISUSAIdentification of sustainable suppliers in the wood fibre supply industry
Rajput et al. [39]Integration of PCA, ISM, and DEMATELIndiaIdentification of IoT enablers in Industry 4.0 and the relationships between these enablers
Chowdhury et al. [40]Integration of ISM and MICMACBangladeshIdentification of the CSFs for sustainability in ready-made garments industry and their interrelationships
Devi et al. [41]Integration of PCA, ISM and MICMACIndiaAnalyze IoT enablers in Industry 4.0
This studyIntegration of PCA, ISM and MICMACBangladeshEvaluation of valuate the supply chain sustainability in the wood industry
Table
Table 2. CSFs for Supply Chain Sustainability.
Table 2. CSFs for Supply Chain Sustainability.
Sl. No.Success FactorDescriptionReferences
1Recycling and waste management (RWM)Recycling focuses on the regeneration of renewable resources after use. The principles of waste management are elimination, reduction, reuse, recovery, and disposal.[38,39]
2Using eco-friendly technology (ET)Using eco-friendly technology that conserves energy and reduces resource consumption.[5,7]
3Sharing natural resources (NR)The long-term availability and sharing of natural resources.[40,41]
4Product responsibility (PR)Product responsibility covers the entire life cycle, including the raw materials, product development, production, use, and recycling.[7,42]
5Designing products for recyclability, reusability, and material recoverability (RRR)Designing for deconstruction to ensure that deconstruction processes retain maximum material value.[43,44]
6Supplier environmental evaluation and feedback (SEEF)Suppliers are evaluated on several criteria: no child labour, working hours, no forced labour, no discrimination, employment compensation, freedom of association, use of eco-friendly materials, and health and safety practices.[8,45]
7Using eco-friendly materials (EFM)Using eco-friendly materials.[46]
8Managing hazardous substances (MHS)Managing wood-industry-specific hazardous chemicals, which are endorsed by the Occupational Safety and Health Regulations 1996 and have specific safety requirements.[47]
9Support from customers (SC)Awareness among customers motivates manufacturers to produce green products.[48]
10Training programs for supplier and vendors (SV)Training, implementing special projects, and monitoring performance.[49]
11Collaborative R&D with same-sector companies (R&D)Collaboration on green research and development with suppliers and companies in the same sector.[50]
12Flexible working arrangements (FWA)Flexible working—also known as flextime or flexitime—refers to non-traditional working arrangements that consider employees’ personal needs, often involving working from home and flexible hours.[7]
13Supplier evaluation and selection (SES)Environmental auditing and supplier evaluation to ensure that products conform to regulations.[36,51]
14Supplier relations (SR)Supplier relations reflect the extent to which an organization has a collaborative relationship with its suppliers.[52,53]
15Stability in policy and regulations (PS)Stability in policies and regulations, which is often tied to political stability.[54]
16Health and safety (HS)Manual lifting, forceful gripping, wood dust, and hazardous substances are the reported common risk factors in the wood industry. Inhaling hazardous substances and getting caught in moving parts are common injuries in the industry. The most prominent prior risk is inadequate personal protective equipment.[36,55]
17Gender diversity (GD)Fair representation of people of different genders at various stages of the supply chain.[56]
18Government legislation (GL)Regulations pressure suppliers and organizations to use
eco-friendly products and processes to improve operational performances.		[57]
19Top management support (TMS)Top management support is crucial to the successful implementation of SSCM.[58]
20Resource management (RM)Optimal resource usage helps firms achieve economic sustainability in the supply chain.[59]
21Supplier audit (SA)Supplier audits have the potential to enhance supply chains’ environmental and social performance.[60]
22Product quality (PQ)Sustainable supply chains can be achieved by producing high-quality products with a longer product life.[36]
23Technological advancement and adaption (TAA)Willingness to research developing technologies and adopt new technologies in the pursuit of greater sustainability.[59,61]
24Technology transfer (TT)Transferring technology to supply chain partners helps firms achieve long-term sustainability.[62]
25Information system (IS)Information systems impact SSCM on various processes, such as planning, sourcing, and delivering, and at various levels, including tactical operations and organizational strategy.[63]
Table
Table 3. Expert profile.
Table 3. Expert profile.
Organization Type/Pseudocode DesignationNo. of ExpertsAverage Work Experience (n)
AcademiaA001Professor4n > 15
Assistant professor2n > 5
Associate professor1n > 7
A002Professor3n > 10
Assistant professor1n > 5
Associate professor2n > 7
A003Professor4n > 15
Assistant professor3n > 5
Associate professor2n > 7
A004Professor2n > 15
Assistant professor1n > 5
Associate professor2n > 7
A005Professor2n > 12
Assistant professor1n > 7
IndustryFurnitureQuality managers20n > 15
Supply chain managers40n > 10
Operation process managers10n > 10
Paper millsQuality managers15n > 10
Supply chain managers10n > 10
Operation process managers30n > 10
Total 150
Table
Table 4. CFS ranking from PCA.
Table 4. CFS ranking from PCA.
CSFsRotated Component Matrix
(Factor Loading Less than 0.1 is Omitted)		MeanStandard DeviationRank
Component 1Component 2Component 3Component 4
R&D0.818 3.850.7061
SR 0.857 3.840.6802
ET 0.9303.840.5952
HS 0.875 3.81.7113
TMS0.690 3.081.1064
RM0.689 3.021.0325
PQ0.681 2.401.5616
TT0.744 2.141.0897
TAA0.784 2.061.0518
IS0.770 2.061.0309
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.		Cronbach alpha—0.93
Kaiser–Meyer–Olkin Measure of Sampling Adequacy—0.948
Bartlett’s Test of SphericityApprox. Chi-Square—859.184
Df—55
Sig.—0.000
Table
Table 5. Categories extracted from PCA.
Table 5. Categories extracted from PCA.
Extracted ComponentsCSFs Selected from PCA
Product quality and well-being of the organizationCollaborative Research & Development with same sector companies (R&D)
Product quality (PQ)
Transferring technology to suppliers and vendors (TT)
Information System (IS)
Resource Management (RM)
Technology advancement and adaption (TAA)
Top management support (TMS)
Health and safetyHealth and safety (HS)
Supplier relationsSupplier relations (SR)
Environment friendly technologyUse of environment friendly technology (ET)
Table
Table 6. SSIM for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
Table 6. SSIM for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
CSFsETSRHSTMSTAARMISTTPQ
R&DVOVAXVXVV
PQXAVAAAAA
TTVVOAAVV
ISOVOAXV
RMOOOAA
TAAVOVA
TMSVVV
HSAO
SRO
ET
Table
Table 7. Initial Reachability Matrix for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
Table 7. Initial Reachability Matrix for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
CSFs1. R&D2. PQ3. TT4. IS5. RM6. TAA7. TMS8. HS9. SR10. ET
1. R&D1111110101
2. PQ0100000101
3. TT0111100011
4. IS1101110010
5. RM0100100000
6. TAA1111110101
7. TMS1111111111
8. HS0000000100
9. SR0100000010
10. ET0100000101
Table
Table 8. Final Reachability Matrix for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
Table 8. Final Reachability Matrix for the CSFs to Apply SSCM Practices in the Bangladeshi Furniture Industry.
CSFsR&DPQTTISRMTAATMSHSSRETDriving Power
R&D11111101119
PQ 01000001013
TT01111001117
IS01011001116
RM01001001014
TAA11111101119
TMS111111111110
HS00000001001
SR01000001114
ET01000001013
Dependence Power3945631106956
Table
Table 9. Level-Partitioning Results from the Final Reachability Matrix.
Table 9. Level-Partitioning Results from the Final Reachability Matrix.
CSFReachability SetAntecedent SetIntersection SetLevel
R&D 1,2,3,4,5,6,8,9,101,6,71,66
PQ 2,8,101,2,3,4,5,6,7,9,102,102
TT 2,3,4,5,8,9,101,3,6,735
IS 2,4,5,8,9,101,3,4,6,744
RM 2,5,8,101,3,4,5,6,753
TAA 1,2,3,4,5,6,8,9,101,6,71,66
TMS 1,2,3,4,5,6,7,8,9,10777
HS 81,2,3,4,5,6,7,8,9,1081
SR 2,8,9,101,3,4,6,7,993
ET 2,8,101,2,3,4,5,6,7,9,102,102
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Paul, S.; Ali, S.M.; Hasan, M.A.; Paul, S.K.; Kabir, G. Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model. Sustainability 2022, 14, 1863. https://doi.org/10.3390/su14031863

AMA Style

Paul S, Ali SM, Hasan MA, Paul SK, Kabir G. Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model. Sustainability. 2022; 14(3):1863. https://doi.org/10.3390/su14031863

Chicago/Turabian Style

Paul, Sumit, Syed Mithun Ali, Mohd Asif Hasan, Sanjoy Kumar Paul, and Golam Kabir. 2022. "Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model" Sustainability 14, no. 3: 1863. https://doi.org/10.3390/su14031863

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