Due to globalization, supply chain networks are moving towards higher complexity and becoming vulnerable to various kinds of attacks such as counterfeiting, information tampering, and so on. Appropriate approaches are necessary to tackle different types of attacks and to ensure the required supply chain security. In this paper, we have addressed the product counterfeiting issue using Physical Unclonable Function (PUF) enabled Radio Frequency Identification (RFID) tag. In this research, as a preferred alternative to the traditional centralized databases, blockchain technology has been leveraged to support anti-counterfeiting. Applying blockchain technology to supply chains can add many useful features such as decentralization, immutability, transparency, traceability, non-repudiation, complicated record-keeping, and so on. We have also used a reputation-based consensus algorithm for the blockchain which is less resource-intensive and thus will not indirectly impose additional cost on supply chain products. In the same research direction, we have devised our system architecture that is suitable for lightweight supply chain devices. The proposed three protocols namely: registration protocol, verification protocol, and transaction protocol along with the blockchain technology help to transfer the ownership of the authentic product and keep the sensitive supply chain information safe. An encryption-based secret sharing technique has also been introduced to assist data protection.
keywords: Blockchain | PUF | RFID | Secret sharing | Supply chain security

Environmental sustainability analyses of end-to-end food supply chain (SC) operations need to be per- formed regularly to accommodate reconfiguration opportunities arising in the global business landscape. This research scrutinises the pertinent literature and identifies the challenges of data availability, data obsolescence, computational complexity, and data specificity that associate to well-established environmental assessment methodologies, and proposes a stepwise approach that considers key players and processes for generating “close to real-time snapshots” of the main environmental hotspots for the focus firm. The applicability of the proposed systematic approach is demonstrated via an implementation at a resource-intensive sector of significant scale, i.e., the UK poultry industry. Overall, this research con- tributes to the SC environmental sustainability management domain by guiding the mapping and identification of environmental hotspots across end-to-end networks of operations in the form of a stepwise framework, and through articulating several research propositions.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Environmental sustainability | Hotspots analysis | Food supply networks | Supply chain management | Systematic approach

Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multi- objective Mixed Integer Linear Programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented ϵ-constraint method is used to solve the problem. For large-scale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.
Keywords: Sustainable closed-loop supply chain | Multi-objective programming | Supply chain pricing | Augmented ϵ-constraint | Lagrangian relaxation | CO2 emissions

In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts.
Keywords: Closed-loop supply chain network | Sustainability | Data envelopment analysis | Stochastic programming | Multi-choice goal programming | Lagrangian relaxation