Showing papers by "Kannan Govindan published in 2023"

A hybrid approach using Z-number DEA model and Artificial Neural Network for Resilient supplier Selection

Abstract: Today's business environment has created a high level of uncertainty and disturbed procedures in supply chains. Suppliers have been often identified as the main source of risks in creating the massive levels of disruptions in supply chains. That is why resilient supplier selection can greatly reduce purchase costs and time delays and can create stability in business practices, thereby increasing competitiveness and customer satisfaction. Pharmaceutical companies play an important key role in the health of society, and these companies are frequently exposed to this disorder. Hence, this paper tries to propose a new integrated approach based on traditional (delivery, quality, price, technology level) and resilient criteria for supplier selection in pharmaceutical companies using the Z-number data envelopment analysis (Z-DEA) model and artificial neural network (ANN). In the proposed approach, expert opinions have been provided based on Z-numbers due to the inherent ambiguity and uncertainty in the evaluation process. This is the first study that evaluates the pharmaceutical industry based on traditional and resilience factors by presenting a methodological structure under the uncertainty environment. Here, a fuzzy mathematical model is used. A real case study is utilized to indicate the applicability of the proposed approach to resilient supplier selection in the pharmaceutical industry. Finally, the suppliers are ranked and the best supplier is selected regarding the reliable level of α. To indicate the features and capabilities of the selected approach, the performance analysis is presented in three parts. First, the obtained results are compared with a fuzzy DEA (FDEA) method in the form of validation and verification. Second, a sensitivity analysis is executed to show the effects of different criteria on ranking results, and the price index is identified as the most important evaluation criteria. Third, a predictive model is presented based on ANN that is able to detect the efficiency or inefficiency of suppliers with an 83% accuracy.

The role of reverse logistics in a circular economy for achieving sustainable development goals: a multiple case study of retail firms

Abstract: Contemporary business models need to re-imagine the production and distribution of goods and services embedding social, economic, and environmental goals concurrently. To this end, reverse logistics in streamlining a circular economy (take, make, use, reuse, repair, and recycle) catapulted to the top of the discussion for collecting and distributing products. Yet, little is empirically known about the role of reverse logistics in the circular economy. This study attempts to fill this knowledge gap in theory and practice through a multiple case study approach including 40 semi-structured interviews with reverse logistics specialists from the four largest retailing firms in the United Arab Emirates (UAE). Findings reveal multiple ways by which reverse logistics contributes to a circular economy: for instance, reverse logistics enables firms to develop a circular product design; the combination of reverse flow with the forward flow consolidates the high volume of products, thus mitigating waste; use of innovative tools (robots, autonomous bikes) in reverse logistics increases the used products’ return rate and thereby enhancing recycling; technological advances (e.g. big data and IoT) in reverse logistics help trace the product thus reducing waste. The paper offers several valuable insights for practitioners to build a circular economy via reverse logistics (waste reduction, responsible consumption, and sustainable logistics). Our study also contributes to multiple United Nations Sustainable Development Goals (SDG 11, SDG 12, and SDG 13).

Coordination in a closed-loop sustainable supply chain considering dual-channel and cost-sharing contract: Evidence from an emerging economy

Abstract: Due to the significant role of the reverse supply chain (RSCs) in collecting used products and achieving a sustainable environment, both scholars and industries have paid close attention to pricing in reverse and closed-loop supply chains (CLSCs). Moreover, with the rapid development of the Internet and e-commerce in the latest decades, researchers have examined the impact of constructing online return channels based on customer behavior. In this article, a game-theoretic approach was applied to find the optimal economic and environmentally sustainable solutions in a two-level CLSC with a dual collecting channel including the retailer’s traditional channel and the manufacturer’s online channel. The purpose of the current study is to optimise the selling price, acquisition prices, market demand, channels return rate, the portion of manufacturing new products, and cost-sharing contract (CSC) participation shares for each player. For this purpose, various policies, such as centralised and decentralised modes, different structures such as Nash bargaining power, manufacturer-leader Stackelberg, and retailer-leader Stackelberg have been considered. However, the main contribution of this work compared to the existing literature is considering two CSCs from both retailer and manufacturer points of view, with a real case analysis from an emerging economy. In addition, a comprehensive sensitivity analysis has been carried out to enhance the validation of the proposed model. The results indicated that the manufacturer-leader Stackelberg strategy leads to the lowest profit for the SC in both decentralised and cooperative policies. However, when the retailer and manufacturer have equal decision-making power (Nash strategy) and the retailer participates in the remanufacturing cost (i.e. cost-sharing type-2) both the economic and environmentally sustainable goals of CLSC were met.

Analysis of supplier evaluation and selection strategies for sustainable collaboration: A combined approach of best–worst method and TOmada de Decisao Interativa Multicriterio

Abstract: Sustainable collaboration between manufacturer and supplier has emerged as a crucial supply chain decision for increasing business efficiency. In this study, a theoretical framework is proposed that uses practice-based view to analyze key performance indicators (KPIs) for developing sustainable collaboration. Further, the current study proposes a novel three-phase supplier evaluation–selection model to assess the incumbent suppliers on basis of KPIs for showcasing the applicability of theoretical framework. The model uses best–worst method (BWM) in the first phase for generating weights of KPI, adopts the TODIM approach in the second phase for evaluation of suppliers, and develops a supplier classification grid in the third phase for analyzing the impact of each selection strategy to be adopted. The novelty of the study is in evaluation of suppliers based on the KPIs with consideration of desirability as well as the potentiality metric and in consideration of the selection strategies, namely, supplier retention, supplier development, and supplier switching. A case study of India's leading home appliances company is taken to demonstrate the application of the current study. The result of BWM reveals that in terms of supplier's potentiality, “quality” emerges as a strong KPI while KPI “information disclosure” gains more importance while considering supplier's desirability towards strengthening the sustainable relationship. The TODIM grid analysis result suggests that suppliers with high performance in both metrics clearly qualify as the best suppliers and must be retained, while the suppliers performing low in both areas must be switched. For suppliers with metric values in conflict with each other, a trade-off analysis is needed. Important research and managerial implications are drawn from the validation of the proposed framework, which can be useful for researchers and practitioners.

Sustainable hierarchical multi-modal hub network design problem: bi-objective formulations and solution algorithms

Abstract: Abstract This paper presents a bi-objective model for the design and optimization of a sustainable hierarchical multi-modal hub network. The proposed model focuses on sustainability by considering economic, environmental, and social aspects of the decisions in a hierarchical network. A case of Turkish network for freight transportation is used to validate the proposed model. To solve the small-sized problems, the augmented epsilon constraint method version 2 (AUGMECON2) is applied. It can be inferred from the Pareto-optimal set obtained by AUGMECON2 that the effect of increasing the number of hubs after a threshold is marginal. The current contribution proposes two multi-objective genetic algorithms (NSGA-II and NRGA), which incorporate LP solving and Dijkstra algorithm. The results show the superiority of NRGA compared to NSGA-II in terms of solution time. Also, we present an alternative, more efficient formulation to the problem. Based on the alternative formulation, in addition to AUGMECON2, we use two exact methods, including Torabi and Hassini (TH) method and augmented weighted Tchebycheff procedure (AWTP), to find Pareto-optimal solutions for small, medium, and large-sized problems (including the case study). The performance of the proposed solution methods is measured using some multi-objective indicators. The results show the superiority of AUGMECON2.

The contingency effects of dependence relationship on supply chain information sharing and agility

Abstract: PurposeSupply chain agility (SCA) is the primary strategy for reducing impacts and quick recovery when supply chains experience a disruption risk, such as the COVID-19 pandemic. This study will investigate how SCA can be achieved through supply chain information sharing (SCIS) under the different dependence relationships (DR) with suppliers or customers. The purpose of this paper is to investigate this issue.Design/methodology/approachBased on information process and resource dependency theories, this study constructs and empirically tests a proposed model of the relationships amongst the three dimensions of SCIS and the two areas of SCA and the contingency effects of two types of DR on those relationships. Using a dataset collected from 400 manufacturers in China, the authors tested this theoretical model using multi-group and structural path analysis.FindingsThe results of the structural path and multi-group analyses show that (1) all dimensions of SCIS are positively correlated with both areas of SCA and (2) dependence on the supplier and dependence on the customer have completely different impacts on the relationship between SCIS and SCA.Originality/valueThis study can improve the understanding of the multidimensional concepts of SCIS and SCA and relationships between them under two different DR conditions in the Chinese manufacturing setting. It contributes to IS and the SCA literature and provides theoretically driven and empirical explanations for the diverse dynamics between the dependence on the supplier and customer.

Redesign of the sustainable wheat-flour-bread supply chain network under uncertainty: An improved robust optimization

Abstract: With the passage of time, the grains supply chain may go out of optimality and be disrupted, so it is necessary to redesign the supply chain to avoid incurring irreparable costs. Because of the occurring grain supply chain sub-optimization event as time passes, this paper redesigns a wheat-flour-bread supply chain network considering sustainable development under uncertainty. For this reason, both facility and transaction networks are investigated, and newly redesigned networks are presented. First, a new definition of sustainable development pillars in supply chains is proposed, and then a four-objective mixed-integer linear programming model is presented. Due to uncertainties in the economic, environmental, social, and technical parameters of the presented model, a robust optimization method is developed to deal with them. Then, an augmented weighted Tchebycheff method, followed by the Lagrangian relaxation algorithm, is applied for solving the four-objective model. Next, a real-case study is performed to appraise the efficiency of the model. Finally, a sensitivity analysis is conducted on the critical parameters of the model to analyze the behavior of different objective functions. With the model’s solution, the redesign of the supply chain provides an enhancement in the economic, environmental, social, infrastructural, and final objective functions by about 18, 23, 31, 37, and 20%, respectively.

Nonparametric numerical approaches to probability weighting function construct for manifestation and prediction of risk preferences

Abstract: Probability weighting function (PWF) is the psychological probability of a decision-maker for objective probability, which reflects and predicts the risk preferences of decision-maker in behavioral decisionmaking. The existing approaches to PWF estimation generally include parametric methodologies to PWF construction and nonparametric elicitation of PWF. However, few of them explores the combination of parametric and nonparametric elicitation approaches to approximate PWF. To describe quantitatively risk preferences, the Newton interpolation, as a well-established mathematical approximation approach, is introduced to task-specifically match PWF under the frameworks of prospect theory and cumulative prospect theory with descriptive psychological analyses. The Newton interpolation serves as a nonparametric numerical approach to the estimation of PWF by fitting experimental preference points without imposing any specific parametric form assumptions. The elaborated nonparametric PWF model varies in accordance with the number of the experimental preference points elicitation in terms of its functional form. The introduction of Newton interpolation to PWF estimation into decision-making under risk will benefit to reflect and predict the risk preferences of decision-makers both at the aggregate and individual levels. The Newton interpolation-based nonparametric PWF model exhibits an inverse S-shaped PWF and obeys the fourfold pattern of decision-makers’ risk preferences as suggested by previous empirical analyses.

Designing supply chain strategies against epidemic outbreaks such as COVID‐19: Review and future research directions

Abstract: The supply chain is vulnerable to disruptions, which can lead to operational failure. Therefore, it is crucial to investigate and monitor the risks associated with such disruptions. Epidemics, including the profound impact of COVID-19, exemplify disruptive risks that engender complexities throughout the value chain. COVID-19 has caused significant disruptions to the global supply chain due to the preexisting fragile supply chains, resulting in the unavailability of essential raw materials, halts in production, and imbalances between supply and demand. It has forced the supply chain community to rethink their risk mitigation strategies. Despite its significance, there are a few studies on the existing options and strategies to mitigate the risks associated with epidemics such as COVID-19. This special issue explores state-of-the-art knowledge of various risks and their mitigation strategies, presenting optimized models and solutions. This editorial seeks to introduce valuable insights and illuminate promising avenues for future research by reviewing existing studies. The comprehensive understanding derived from this editorial and the accompanying articles of this special issue will benefit both researchers and practitioners, enabling them to navigate the multifaceted landscape of epidemic-induced disruptive risks in the supply chain and adopt suitable mitigation strategies. Furthermore, this special issue lays a solid foundation for implementing advanced methods for managing and mitigating the disruptive risks that epidemics pose to the supply chain.

Sustainable network design of bio-energies generation based on the municipal solid waste (MSW) management under uncertainty

Abstract: Municipal solid waste in a circular economy will be used as a resource and the design of a product-oriented waste management network is essential. Considering the significant added value of bioenergy, it is one of the most important outputs of the waste management network that the production of a diverse range of them needs further study. Focusing on this issue, a sustainable waste management network was designed to optimally extract various bio energies by considering all three dimensions of economic, environmental, and social sustainability under uncertain conditions. The proposed model in this research for the sustainable municipal solid waste network was a multi-objective possibilistic mixed-integer non-linear programming (MOPMINLP). For dealing with uncertainty in this network a combination of methods based on interactive fuzzy programming was used. The model was implemented after collecting data from Arad Kooh, Iran as a case study using GAMS software. The results of the proposed solution method were the amount of bio-energies generated by treatment technologies and the launched technologies throughout the defined time interval. The validation results of the proposed solution method confirm the research results. Keywords: Bioenergy, Sustainable network of MSW, Treatment technology, Uncertainty