Logistics of temporary testing centers for coronavirus disease.

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability. This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem. We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted.

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations.

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?

Digitalisation is expected to transform end-to-end supply chain operations by leveraging the technical capabilities of advanced technology applications. Notwithstanding the operations-wise merits associated with the implementation of digital technologies, individually, their combined effect has been overlooked owing to limited real-world evidence. In this regard, this research explores the joint implementation of Artificial Intelligence (AI) and Blockchain Technology (BCT) in supply chains for extending operations performance boundaries and fostering sustainable development and data monetisation. Specifically, this study empirically studied the tuna fish supply chain in Thailand to identify respective end-to-end operations, observe material and data-handling processes, and envision the implementation of AI and BCT. Therefore, we first mapped the business processes and the system-level interactions to understand the governing material, data, and information flows that could be facilitated through the combined implementation of AI and BCT in the respective supply chain. The mapping results illustrate the central role of AI and BCT in digital supply chains' management, while the associated sustainability and data monetisation impact depends on the parameters and objectives set by the involved system stakeholders. Afterwards, we proposed a unified framework that captures the key data elements that need to be digitally handled in AI and BCT enabled food supply chains for driving value delivery. Overall, the empirically-driven modelling approach is anticipated to support academics and practitioners' decision-making in studying and introducing digital interventions toward sustainability and data monetisation.

Opportunities, challenges and trade-offs with decreasing avoidable food waste in the UK.

Around 6 million tonnes of edible food are being wasted (post-farm gate) in the UK each year. This fraction of edible wasted food is known as avoidable food waste. In a circular economy food is a valuable resource that must be captured at all stages of the food supply chain and, where possible, redistributed for consumption. This can prevent avoidable food waste generation, and dissipation of food's multidimensional value that spans environmental, economic, social, technical and political/organisational impacts. While the importance and benefits of surplus food redistribution have been well documented in the global literature, there are still barriers that prevent perfectly edible food from being wasted. This study looks at the main stages of the food supply chain, and amasses the opportunities, challenges and trade-offs associated with surplus food redistribution to the UK economy. It highlights points in the food system where interventions can be made, to improve food's circularity and sustainability potential. Stakeholder interrelations, regulatory and socio-economic aspects are discussed in relation to their influence on decreasing avoidable food waste. The main output from this work is a diagrammatic depiction of where challenges and trade-offs occur along the food supply chain, and how policy and socio-economic reforms are needed to maximise avoidable food waste prevention, and the surplus avoidable food redistribution in the food supply chain for social benefit.

Labeling Nutrition-Sensitive Food Chains: A Consumer Preference Analysis of Milk Products.

While nutrition-sensitive value-chain approaches are strongly advocated, studies on consumer preferences for such interventions are lacking. This study aims to fill this gap by examining a nutrition-sensitive chain labeling scheme, using the Ugandan dairy sector as a case. A survey was conducted among 250 consumers, primarily eliciting perceptions of the importance of a nutrition-sensitive chain label compared to nutrition claims/facts. In addition, a choice-based conjoint experiment was designed with nutrition label, brand, fat content, and price as attributes. Findings show that nutrition-sensitive chain labeling was more positively perceived by consumers than by nutrition claims/facts. Ordered logistic regression analysis indicated that BMI, nutrition knowledge, and label use influenced consumers' perceived importance of a nutrition-sensitive chain label relative to sex, age, children, and milk purchase frequency for nutrition claims/facts. This is confirmed by the higher utilities for the nutrition-sensitive chain label in our conjoint experiment. Future research should focus on the integration of nutrition-sensitive chain labeling with existing labels in a way that promotes candid interpretation by consumers. Industrial and policy actors in the agri-food sector can use these findings to innovate and regulate appropriate labeling schemes in the context of nutrition-sensitive value chains.

How to transition to reduced-meat diets that benefit people and the planet.

Overwhelming evidence shows that overconsumption of meat is bad for human and environmental health and that moving towards a more plant-based diet is more sustainable. For instance, replacing beef with beans in the US could free up 42% of US cropland and reduce greenhouse gas emissions by 334 mmt, accomplishing 75% of the 2020 carbon reduction target. We summarise the evidence on how overconsumption of meat affects social, environmental and economic sustainability. We highlight the social, environmental and economic effectiveness of a range of dietary interventions that have been tested to date. Because meat eating is embedded within complex cultural, economic, and political systems, dietary shifts to reduce overconsumption are unlikely to happen quickly and a suite of sustained, context-specific interventions is likely to work better than brief, one-dimensional approaches. We conclude with key actions needed by global leaders in politics, industry and the health sector that could help aide this dietary transformation to benefit people and the planet.

Applying Value Stream Mapping to reduce food losses and wastes in supply chains: A systematic review.

The interest to reduce food losses and wastes has grown considerably in order to guarantee adequate food for the fast growing population. A systematic review was used to show the potential of Value Stream Mapping (VSM) not only to identify and reduce food losses and wastes, but also as a way to establish links with nutrient retention in supply chains. The review compiled literature from 24 studies that applied VSM in the agri-food industry. Primary production, processing, storage, food service and/or consumption were identified as susceptible hotspots for losses and wastes. Results further revealed discarding and nutrient loss, most especially at the processing level, as the main forms of loss/waste in food, which were adapted to four out of seven lean manufacturing wastes (i.e. defect, unnecessary inventory, overproduction and inappropriate processing). This paper presents the state of the art of applying lean manufacturing practices in the agri-food industry by identifying lead time as the most applicable performance indicator. VSM was also found to be compatible with other lean tools such as Just-In-Time and 5S which are continuous improvement strategies, as well as simulation modelling that enhances adoption. In order to ensure successful application of lean practices aimed at minimizing food or nutrient losses and wastes, multi-stakeholder collaboration along the entire food supply chain is indispensable.