Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective
13 Apr 2021-Vol. 5, Iss: 2, pp 22
TL;DR: The five Vs of big data are proposed as a platform for determining the role of GSCM practices in improving SCF implementation and a hybrid self-assessment model that measures the environmental maturity of SCF is presented.
Abstract: This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.
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01 Jan 1994TL;DR: In this paper, a natural resource-based view of the firm is proposed, which is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development, and each of these strategies are advanced for each of them regarding key resource requirements and their contributions to sustained competitive advantage.
Abstract: Historically, management theory has ignored the constraints imposed by the biophysical (natural) environment. Building upon resource-based theory, this article attempts to fill this void by proposing a natural-resource-based view of the firm—a theory of competitive advantage based upon the firm's relationship to the natural environment. It is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development. Propositions are advanced for each of these strategies regarding key resource requirements and their contributions to sustained competitive advantage.
902 citations
TL;DR: The fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection, which exhibits a 5% improvement compared to the fuzzy AHP clustering method and 10% improvementCompared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.
Abstract: Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.
11 citations
TL;DR: In this paper , a systematic overview of innovation research strands revolving around AI is provided, where the authors identify technological, social, and economic reasons leading firms to embrace AI to innovate and also identify firms' product innovation, process innovation, business model innovation and social innovation.
Abstract: This study provides a systematic overview of innovation research strands revolving around AI. By adopting a Systematic Quantitative Literature Review (SQLR) approach, we retrieved articles published in academic journals, and analysed them using bibliometric techniques such as keyword co-occurrences and bibliographic coupling. The findings allow us to offer an up-to-date outline of existing literature that are embedded into an interpretative framework allowing to disentangle the key antecedents and consequences of AI in the context of innovation. Among the antecedents, we identify technological, social, and economic reasons leading firms to embrace AI to innovate. In addition to detecting the disciplinary foci, we also identify firms' product innovation, process innovation, business model innovation and social innovation, as key consequences of AI deployment. Drawing on the key findings from this study, we offer research directions for further investigation in relation to different types of innovation.
9 citations
TL;DR: A productivity evaluation model for nine DCs belonging to an international automotive vehicles and spare parts company is developed based on two multi-criteria decision making approaches: the Preference Ranking Organization Method for Enrichment of Evaluations II and data envelopment analysis (DEA).
Abstract: The current era of industrial economics necessitates warehouse and logistic distribution centers (DCs) to contribute productively toward an organization’s success. Playing such a critical productive role implies that logistics activities must be practiced effectively and efficiently. However, the indistinguishability between effectiveness and efficiency leads to a somewhat shallow interpretation, and consequently, a diluted evaluation. Hence, this paper aims to develop a productivity evaluation model for nine DCs belonging to an international automotive vehicles and spare parts company. The developed model was set up based on two multi-criteria decision making (MCDM) approaches: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness, while the DEA was utilized in order to measure the efficiency of the investigated DCs. The resulting hybrid model collectively creates what can conceptually and practically be considered a productivity evaluation model. The results also provide six different strategies through which distribution center locations can be evaluated in order to implement potential future initiatives.
8 citations
TL;DR: In this article , the authors address the issue of implementation of big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.
Abstract:
Purpose
The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.
Design/methodology/approach
Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.
Findings
The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.
Research limitations/implications
The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.
Practical implications
The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.
Social implications
The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.
Originality/value
This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.
3 citations
References
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TL;DR: In this paper, a natural resource-based view of the firm is proposed, which is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development, and each of these strategies are advanced for each of them regarding key resource requirements and their contributions to sustained competitive advantage.
Abstract: Historically, management theory has ignored the constraints imposed by the biophysical (natural) environment. Building upon resource-based theory, this article attempts to fill this void by proposing a natural-resource-based view of the firm—a theory of competitive advantage based upon the firm's relationship to the natural environment. It is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development. Propositions are advanced for each of these strategies regarding key resource requirements and their contributions to sustained competitive advantage.
5,339 citations
TL;DR: The authors found that environmental performance and economic performance are positively linked and that industry growth moderate the relationship, with the returns to environmental performance higher in high-growth industries, concluding that it pays to be green.
Abstract: Drawing on the resource-based view of the firm, we posited that environmental performance and economic performance are positively linked and that industry growth moderates the relationship, with the returns to environmental performance higher in high-growth industries. We tested these hypotheses with an analysis of 243 Finns over two years, using independently developed environmental ratings. Results indicate that “it pays to be green” and that this relationship strengthens with industry growth. We conclude by highlighting the study's academic and managerial implications, making special reference to the social issues in management literature.
4,227 citations
"Measuring the Environmental Maturit..." refers background in this paper
...The effective utilization of these resources leads to the successful implementation of competitive strategies, including those related to the environment [37,51]....
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TL;DR: The use of triangular fuzzy numbers for pairwise comprison scale of fuzzy AHP is introduced, and the use of the extent analysis method for the synthetic extent value S i of the pairwise comparison is used.
3,724 citations
TL;DR: The rating of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers and a vertex method is proposed to calculate the distance between two triangular fuzzyNumbers.
3,109 citations
"Measuring the Environmental Maturit..." refers methods in this paper
...The fuzzy TOPSIS was developed by Chen [140] to address the issue of uncertainty in MCDM problems....
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TL;DR: In this article, the authors present a framework for supply chain management as well as questions for how it might be implemented and questions for future research, and case studies conducted at several companies and involving multiple members of supply chains.
2,848 citations