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This chapter proposes a multiobjective model to design a Closed Loop Supply Chain (CLSC) network. The first objective is to minimize the total cost of the network, while the second objective is to minimize the carbon emission resulting from production, transportation, and disposal processes using carbon cap and carbon tax regularity policies. In the third objective, we maximize the service level of retailers by using maximum covering location as a measure of service level. To model the proposed problem, a physical programming approach is developed. This work contributes to the literature in designing an optimum CLSC network considering the service level objective and product substitution.

The earth’s carrying capacity cannot withstand the pace of consumption resulting from current economic models, mainly the linear economy (LE) built on a throwaway culture. In the last few decades, the concept of a circular economy (CE), aiming to design waste out of the economy and mimic ecosystems, emerged as a strong alternative to LE. Being at the heart of the economic landscape, supply chains (SCs) need to respond to the necessary shift to CE. In so doing, the planning and execution of circular supply chains (CSCs) require a broader comprehension of CE and more sophisticated and large-scale analytical decision models. This chapter surveys extant literature on available best practices and quantitative models for sustainable supply chains (SSCs) and offers a new definition of CSC. Mapping on the knowledge extracted from this classification, potential gaps and strengths in the literature are identified. Key research papers on the “closed-loop” and “open-loop” ends of CSCs are highlighted. Challenges in developing CSC performance indicators and prescriptive models are emphasized.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

In recent years, both academics and institutions have acknowledged the crucial role multinational enterprises (MNEs) can play in addressing the sustainability challenges, as formalized by the sustainable development goals (SDGs). Nevertheless, because of their extensiveness and their design as country-level targets, SDGs have proven challenging to operationalize at a firm level. This problem opens new and relevant avenues for research in international business (IB). This chapter attempts to frame the topic of extended value chain sustainability in the IB literature. In particular, it addresses a specific topic, that is, how sustainability and resilience-building practices interact in global value chains (GVCs). To do so, the present study develops the case of STMicroelectronics (ST), one of the biggest semiconductor companies worldwide.

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.

This chapter investigates the potential of integrating multiple criteria decision-making (MCDM) techniques with decision support systems of digital supply chain management (DSCM) to achieve optimal outcomes. Digital supply chain (DSC) employs digital technologies (DTs) such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to provide extensive datasets and valuable insights pertaining to supply chain operations. MCDM techniques employ these realizations to facilitate informed decision-making through the assessment of multiple competing criteria. Usually MCDM approaches are used in the academic research with comparatively lesser application in industry. We argue that MCDM methodologies can play an instrumental role in DSCM, specifically in the areas of supplier selection, demand forecasting, and inventory management. Nevertheless, the integration of MCDM like AHP, ANP, DEMATEL, etc., with decision support systems presents several challenges, including concerns regarding the quality of data and the intricate task of assigning weights to various factors.

COVID-19 not only has impacted adversely the health infrastructure, taking away lives of millions of people but it has also crippled the economy. The worst effected were food supply chain due to restrictions imposed on operations of shops and retail outlets. The consumers were suffering due to lack of supply. Similarly, agriculture produce were getting wasted due to lack of cold storage.

In this case we have proposed how a mobile-based application solution during COVID lockdown can successfully transform the livelihood of rural farmers in the state of Maharashtra (India), a state worst affected by the pandemic.

The technology-integrated supply chain model jointly developed by financial institutions, self-help groups (SHGs) and NGOs has enabled direct selling of fresh produces by rural women farmers to urban large residential societies at their doorsteps. It has provided a solution of municipality waste management by converting the waste to compost, getting them collected and used in the farmlands.

It will help the urban consumers to have the continuous supply of fresh vegetables and fruits available at their doorsteps, and keep a track of transport of foods from farm to fork. The farmers will be able to get better price for their produce. The model will also contribute towards circular economy (CE) through citizen partnership.

This study aims to identify the factors and propose a conceptual framework for the civil aviation industry from the sustainability perspective with the participation of international entrepreneurs. Based on the results of this study, international decision-makers and entrepreneurs in the civil aviation transportation industry will better understand their decision-making processes. A combination of interpretive structural modelling (ISM) and matrix-based multiplication applied to a classification (MICMAC) was used to classify practical factors to depict a conceptual model based on their level and classification in the sustainable supply chain (SSC) of the civil aviation transportation industry. In this study, special attention has been paid to the issue of sustainability as an essential mechanism for developing international entrepreneurship in the civil aviation transportation industry. The factor of flexibility in service production was identified as the driver factor; the factors of organisational commitment to a SSC were found to have the highest driver-dependent power that can attract international entrepreneurs in this field.