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The garments/textiles industry is the second most polluting industry in the world. However, efforts to understand and curtail its adverse environmental impacts have not been commensurate, and previous works have largely been fragmented and disjointed. This study aims to coduct a comprehensive and systematic green supply chain management (GSCM) investigation on this industry, where a multidimensional framework involving green supply chain practices and performance is developed, validated and applied.

A framework consisting of 12 constructs (8 on practices and 4 on performance) and their underlying measures were developed through an extensive literature review. A survey methodology was used to obtain responses from 403 garment-manufacturing firms in Bangladesh, one of the leading garment producers in the world. Confirmatory factor analysis and structural equation modeling were used first to validate the first- and second-order constructs and then test the hypothesized relationships.

Internal environmental management and cooperation with stakeholders were identified as necessary precursors for implementing the second-order green supply chain practices comprising green design, green purchasing, green manufacturing, green transportation, green facilities and end-of-life management. The implementation of green supply chain practices was found to have a (direct) positive impact on environmental, economic and operational performance and an indirect positive impact on organizational performance. Similarly, both economic and operational performance was found to impact organizational performance positively. Surprisingly, a negative relationship (albeit low) was observed between environmental and organizational performance. Also, garment-manufacturing firms were found to have been unable to translate their IEM capabilities into strategic and long-term cooperation with stakeholders.

The study fills a gap in the literature about applying/implementing GSCM in the garment industry. Future studies in the garment industry and elsewhere could utilize the framework to understand further the synergistic impact of green supply chain practices on performance.

The findings provide practitioners, policymakers and organizations associated with the garment industry with critical insights on the various opportunities and challenges in adopting GSCM. Also, the positive impact of green supply chain practices on performance could provide the impetus for manufacturing firms to adopt GSCM.

A comprehensive GSCM investigation on the garment industry has not been previously attempted and constitutes the novelty of this work. Also, Bangladesh is the second-largest garment exporter worldwide, making this study contribution even more valuable.

Effective environment and climate change management require supply chain-wide focus (from the initial design to the end-of-life management) as well as universal participation and commitment of firms. However, the environment-related role and contribution of different sized firms in the supply chain are unclear from previous research which this study seeks to clarify using the context of UAE's construction sector.

Using data collected from a structured survey (455 responses) and semi-structured interviews with 20 key supply chain stakeholders, this study analyses and understands hypothesized differences between small and medium firms (SMEs) and large firms on three key supply chain environmental sustainability aspects: the extent of green supply chain practices (GSCP) implemented, the strengths/influences of drivers and barriers affecting the implementation of GSCP, and the associated environmental, cost-related and organizational performance benefits derived from GSCP.

Large firms were found to show significantly greater levels of implementation of GSCP, greater internal drive for implementation and lower barriers to implementation than SMEs. SMEs though were found to be not too far behind large firms with regards to the environmental, cost-related and organizational performance benefits from GSCP implementation.

Findings from this study is useful for benchmarking the GSCP implementation of large firms and SMEs, influences of drivers and barriers affecting the implementation of GSCP and associated performance benefits derived from GSCP implementation. Policymakers and practitioners could use the study findings to develop suitable policies/interventions so as to ensure that all firms irrespective of their size can contribute equitably towards improving the environmental sustainability of supply chains.

This study is arguably the first comprehensive attempt to understand how various environmental sustainability aspects in supply chains are perceived and performed by SMEs and large firms.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

The purpose of this paper is to take the first step in solving environmental supply chain management issues. It proposes a green supply chain management (GSCM) model which would provide environmental benefits to the Thai rubber industry. To this end, a GSCM optimisation model was formulated, whereby the manufacturing processes of rubber products, along with their distribution and transportation, could be improved. The expected result is that total greenhouse gas emissions would be minimised and environmental performance maximised.

Linear programming was chosen as the mathematical programming for investigation into the problem of finding the association of quantity of rubber product flow between the supply chain entities (farmer, trader group, and factory) and the transportation mode and route, with a view to minimise total greenhouse gas emissions.

The results indicate that by using the proposed model, GHG emissions could be minimised to 1.08 tons of GHGs per ton of product.

A GSCM model developed in this research can be used as a decision support tool for Thai rubber policy makers. This would allow them to better manage the Thai rubber industry to achieve environmental benefit.

This research is among the first attempts to develop a GSCM model for the Thai rubber industry. It can contribute to providing a basis for a GSCM modelling framework, along with a formulation for research development in this area.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.