ABSTRACT
In the 21st century, global supply chains have experienced severe risks due to disruptions caused by crises and serious diseases, such as the great tsunami, SARS, and, more recently, COVID-19. Building a resilient supply chain is necessary for business survival and growth. Similarly, there is increasing regulatory and social pressure for managers to continuously design and implement sustainable supply chain networks, encompassing economic, social, and environmental components. Hence, a panacea approach is required to establish a compromise position between resiliency concerns and sustainability responsibilities. To address this, this work presents a hybrid integrated BWM-CoCoSo-multi-objective programming model (BC-MOPM) formulated to deliver a compromise between resilience and sustainability supply chain network design (RS-SCND). First, a thorough literature review analysis is conducted to explore the relationship and correlation between resilience and sustainability to develop a framework for the resiliency and sustainability criteria, in a supply chain context. Second, four objectives were formulated, including the minimisation of total cost and environmental impact and the maximisation of social and resilience paradigms. A real two-tier supply chain network is deployed to evaluate the applicability of the developed BC-MOPM. Furthermore, sensitivity analysis is conducted to establish the relative importance of the identified criteria to prove the model's robustness. Results demonstrate the capability of the BC-MOPM in revealing trade-offs between the resiliency and sustainability aspects. © 2023 Elsevier B.V.
ABSTRACT
Investment in education technology (EdTech) is a complex decision problem for universities during the post-Covid era. With the objective to assess the quality and adoptability of education supply chain, a novel analytical evaluation model approach is proposed, based on quality function deployment and combinative distance-based assessment. To deal with uncertainty in the evaluation process, fuzzy theory is integrated into the model. To establish the house of quality matrix, technology-based stakeholders' requirements were identified and classified in four dimensions: economic and financial, technology adoption, sustainability, competencies. Moreover, nine supplier criteria were assumed. Based on expert evaluations, the results suggest that financial credit and supplier collaboration are the most prominent attributes to evaluate suppliers, while environmental commitment is sorted as the least important criterion. The results reveal that the three dominant suppliers, which provide the best response to the identified criteria, are providers of cloud service technology. © 2022
ABSTRACT
It is important to define optimal supply chain strategies that can respond to real vaccination needs in different disasters, especially in the event of a pandemic. The distribution of medicines and vaccines is more critical when they can decay and must arrive at their final destination as fast as possible. In this paper, to overcome these problems and respond to the pandemic of COVID-19 needs, we introduced a bi-objective model for the distribution of COVID-19 vaccines. The objectives are to minimize cost function and to minimize the maximum traveling time of the vaccines to treat targeted populations in different time phases. The bi-objective model is solved with the well-known multi-objective augmented epsilon-constraint method. Besides, we bring numerical results and the appliance of our proposed model. By solving the proposed model, we can find the optimal network of the vaccines and open needed facilities in several locations. Finally, we give the decision-maker several possible answers to choose according to his preferences. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The need for a good supply chain distribution of vaccines in disasters is critical in today’s world. This paper targets the vaccines’ allocation in several periods for various and separated demand zones. To discover and make the al-location network more efficient and help the policymaker decide more wisely, we implement a smart contract to suggest product allocation in different cases taking into account dynamical changes. Our smart contract tries to decrease the uncovered demands and facilitate the governments’ prediction of the demands and evolution of a disease in demand zones. In this manner, several reactions of the smart contract are considered through different scenarios that can happen in the pandemics. The cases are sudden increase, sudden decrease, and steady demands. Moreover, we discuss the validity of the proposed smart contract and its benefits. Finally, we discuss the future aspects of our work and other objectives that can be useful in the proposed problem. © 2021, IFIP International Federation for Information Processing.
ABSTRACT
Purpose In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging "recipes" of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing. Design/methodology/approach A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0. Findings The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0. Originality/value Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.