Food Supply Chain Security Research Articles

A Delphi fuzzy analytic hierarchy process framework for criteria classification and prioritization in food supply chains under uncertainty

Effectively managing food supply chains is critical for ensuring food security and safety. Identifying and prioritizing the criteria affecting food supply chain performance is imperative to manage uncertainty and avoid undesirable consequences in Supply Chain Management (SCM). This study aims to identify and prioritize critical criteria affecting food supply chains under uncertainty. We use the Delphi method to choose the predominating criteria classified into four groups: Resilience and Flexibility, Transparency and Traceability, Communication and Collaboration, and Regulation and Standardization. The fuzzy Analytic Hierarchy Process (FAHP) method is used next to prioritize the selected criteria based on their significance. The results show investment in research and development, transparency and traceability, risk-sharing and collaboration among stakeholders, are the top criteria for food supply chain security and safety. These findings contribute to formulating and developing effective strategies for securing food supply chains.

The impact of the Ukraine war on the global food supply chain security: a literature review

The impact of the Ukraine war on the global food supply chain security: a literature review

The Impact of the Ukraine War on the Global Food Supply Chain Security: A Literature Review

The Impact of the Ukraine War on the Global Food Supply Chain Security: A Literature Review

A Reliable Traceability Model for Grain and Oil Quality Safety Based on Blockchain and Industrial Internet

Gain and oil are important compounds in global food supplies, and ensuring the quality and safety of grains and oil is a critical issue in the food supply chain security. Data traceability is the key factor in quality and safety management. Currently, it is a big challenge to ensure the reliability of data and guarantee the efficient exchange of data in various highly heterogeneous systems. To address this challenge, we develop a reliable traceability model applied to the grain and oil industry. In this paper, we first analyze the characteristics of the whole chain traceability information flow, and then we propose the concept that the connector for blockchain and industrial internet is suitable for data traceability in the grain and oil industry. Based on this concept, a reliable traceability model of grain and oil quality and safety is constructed. Finally, a reliable traceability prototype system for wheat quality and safety was designed, and the system implementation of the model was validated. The overall advantage of the proposed model is that the traceability information is safe and credible, the interaction is concise and efficient, and the security and full-process traceability of cross-chain information interaction are guaranteed. This paper fills the gap in the application of research chain network in the field of grain and oil traceability. Reference to this model can also be used to implement and adjust the traceability system, which is adaptable to stakeholders in the grain and oil industry. The model and techniques in this paper not only demonstrate value in real-world applications but also inspire further research in the field.

Enhancing Food Supply Chain Security through the Use of Blockchain and TinyML

Food safety is a fundamental right in modern societies. One of the most pressing problems nowadays is the provenance of food and food-related products that citizens consume, mainly due to several food scares and the globalization of food markets, which has resulted in food supply chains that extend beyond nations or even continent boundaries. Food supply networks are characterized by high complexity and a lack of openness. There is a critical requirement for applying novel techniques to verify and authenticate the origin, quality parameters, and transfer/storage details associated with food. This study portrays an end-to-end approach to enhance the security of the food supply chain and thus increase the trustfulness of the food industry. The system aims at increasing the transparency of food supply chain monitoring systems through securing all components that those consist of. A universal information monitoring scheme based on blockchain technology ensures the integrity of collected data, a self-sovereign identity approach for all supply chain actors ensures the minimization of single points of failure, and finally, a security mechanism, that is based on the use of TinyML’s nascent technology, is embedded in monitoring devices to mitigate a significant portion of malicious behavior from actors in the supply chain.

Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems.

Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain. The aim of the proposed method is to help managers and operators in food enterprises to find accurate risk levels of food security in advance and to provide regulatory authorities and consumers with potential rules for better decision-making, thereby maintaining the safety and sustainability of food product supply. The verification experiments show that the proposed method has the best performance in terms of prediction accuracy up to 97.62%, meanwhile achieves the appropriate model parameters only up to 211.26 megabytes. Moreover, the case analysis is implemented to illustrate the outperforming performance of the proposed method in risk level identification. It can effectively enhance the RITS ability for assuring food supply chain security and attaining multiple cooperation between regulators, enterprises, and consumers.

Toward a Taxonomy of Food Supply Chain Security Practices

Supply chain security (SCS) breaches in the food supply chains are distressing respective firms across the globe. They also have the potential of inflicting pain on the society at large. Despite the abundance of practices that have been advocated to cope with SCS breaches, the literature lacks a conceptual taxonomy to organize them. Classifying practices into respective taxa can advance theory building and stimulate the testing of substantive hypotheses. Toward this end, we develop a taxonomy which conceptually rests on the human immune system and its attempts to protect the human body from pathogens. We deploy a metaphorical transfer process and postulate that SCS practices can be classified into four classes based on their intent: prevention, detection, reaction, and restoration. We then discuss the implications of the taxonomy and assert that our taxonomy can be exploited to a variety of domains in the realm of risk management.

A MANAGEMENT SYSTEM FOR PREVENTING INTENTIONAL FOOD CONTAMINATION BASED ON RISK ANALYSIS

In today’s globalized world the number of risks in supply chains are increasing and the threats are becoming more complex. All the risks and threats can have a severely negative impact on markets. Some of these threats are terrorism, piracy and theft. Within the food industry, Food Terrorism has gained relevance due to the extreme consequences it may have on both public health and the market. Food Terrorism refers to the intentional contamination of food in any link of the supply chain, which covers everything from production to consumption. The Food Defense refers to the analysis, control and improvement of prevention mechanisms of those attacks; that is, the Food Defense involves a Risks Management. In general terms, this paper focuses on a proposal formulation based on a risk analysis that allows establishing a management system for preventing intentional food contamination and the improvement of the security of the supply chain. Given the recent events in Paris, on November 13, 2015, consideration has also been given to the present situation and the importance of the issue of preventing terrorism.This initial study covers only the theoretical methodological proposal and focusses only on the companies of the alimentary sector of Guanajuato State in Mexico. However, it may extend into a research plan security of food supply chain.