In the era of business sustainability, the modern supply chain is becoming complex due to several inhibitors such as uncertainty in the market, technological innovation, environmental protocols, cross-border trade regulations, and many stakeholders’ involvement. In the existing literature, minimal discussion to study the inhibit supply chain complexity (SCC) inhibitors for achieving sustainability. Therefore, the study analyses the inhibitors to SCC and supply chain sustainability (SCS) jointly. The combined examination of the underlying relationship for improving the Petroleum Supply Chain’s sustainable performance (PSC) is arguably one of the complex sectors with a significant impact on the environment and sustainability. The inhibitors to SCC and SCS are identified through extensive literature review and experts’ opinions. Through a structured questionnaire, data were collected from PSC experts. An integrated approach of analytic hierarchy process (AHP) and interpretive structural modelling (ISM) is proposed to prioritize and examine the underlying relationship between inhibitors. This study explores the driving and dependence power of the inhibitors. The results indicate that most of the SCS inhibitors, such as institutional pressures (laws and regulations), strategic lack of strategic supplier alliance, market threat, act as drivers of SCC inhibitors, such as technological complexity, horizontal complexity, and complexity of customers. The study’s findings would help the supply chain managers and the petroleum sector policymakers to make better decision to overcome the challenges for achieving sustainability in PSC.
Keywords: Petroleum supply chain | Environment and sustainability | Complexity | Business strategy | Interpretive structural modelling | Performance

This study aims to analyze the innovation strategies for the green supply chain management with QFD (quality function deployment) multidimensionally. The novelty of the study is to define the criteria of green supply chain for each stage of QFD and propose a hybrid model by IVIF (interval-valued intuitionistic fuzzy) DEMATEL (decision making trial and evaluation laboratory) and IVIF MOORA (Multi-Objective Optimization by Ratio Analysis) respectively. The results demonstrate that understanding the customer expectations with customer relation management is the most important innovation strategy for the green supply chain management in en- ergy industry with the consecutive stages of QFD whereas benchmarking the competitive market environment has relatively the last seat in the ranking. Hence, it is recommended that energy companies should have an effective customer relationship management. In this context, these companies should make a detailed analysis to learn what their customers directly expect from them. With the help of this issue, these companies should generate their product and services based on these expectations. Additionally, it is also stated that new service and product development is also essential for energy companies to improve their innovativeness. For this pur- pose, a research and development department should be created, and the qualified people should be employed. Additionally, different opinions should be collected from various parties, such as customers, employees, and suppliers. Since customers who are satisfied will prefer these companies, the energy companies can catch the opportunity to increase their market share.
Keywords: GSCM | Energy industry | Innovation | QFD | IVIF DEMATEL | IVIF MOORA

With the aggravation of the global environmental crisis, consumers are keen to use green products, and enterprises are more committed to technology investment and innovation to meet consumers’ green preferences. This study considers a supply chain system composed of one manufacturer and one retailer, where the manufacturer invests in green emission reduction technology (GERT) to reduce carbon emissions, and the retailer invests in information disclosure technology to transmit the higher greenness quality of products to consumers. We discuss the technology investment decisions and cooperation strategies between the manufacturer and the retailer, as well as the impacts of government regulations on supply chain members’ decisions. We consider three scenarios: decentralized, government intervention, and cost sharing and government intervention. We derive the optimal technology investment decisions of the manufacturer and retailer. In addition, we identify the conditions for the manufacturer to subsidize the retailer’s disclosure technology costs. The results show that a higher emission reduction subsidy encourages GERT investments and increases supply chain members’ profits. However, when the government sets a higher emission reduction standard, the subsidy neither increases the emission reduction nor benefits the retailer, and hurts the manufacturer’s profit. We also demonstrate that when there is cost sharing and government intervention, emission reduction and supply chain performance are always better than in the other scenarios.
Keywords: Emission reduction | Information disclosure | Technology investments | Government intervention | Sustainable supply chain management

Environmental concerns relating to production affairs have made various organizations use green practices in different processes of supply chain, because the green supply chain management (GSCM) is considered as an important organizational philosophy to decrease environmental risks and as a preventive approach in order to increase environmental performance and achievement of competitive advantages for organizations. The purpose of the present article is to design an interactive model for the practices of GSCM and its application to clustering oil industries for analyzing their green performance. Therefore, the literature was studied and a total of fifteen practices were obtained using experts’ opinions in academic and oil industry professionals. In next, the fuzzy interpretative structural modeling (FISM) approach was utilized so as to determine the relationship between the practices through considering the linguistic ambiguities of judgments and designing the structural model. The existing relationships within the structural model were studied and tested by means of structural equation modeling (SEM). After that, the relative importance of each practice was calculated by applying fuzzy analysis network process (FANP). In the next, the oil industries were categorized in two clusters using the K-means algorithm aggregated to the particle swarm optimization algorithm. Results of the present study showed that ‘‘legal requirements and regulations”, ‘‘intra-organizational environmental management”, ‘‘green design” and ‘‘green technology” are of root and influential practices with relatively more importance than others; in addition, it was cleared that the first cluster industries have high performance whereas the second ones have medium performance from the viewpoint of considering the practices of GSCM. Finally, the discriminant function designed to forecasting environment performance of the oil industries and member- ship to clusters for each of them.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Web International Conference on Accelerating Innovations in Material Science – 2020.
Keywords: Green Supply Chain Management | Oil industries | Fuzzy interpretative structural modeling | Kmeans | Particle swarm optimization | Clustering | Discriminant analysis