Stages Of Supply Chain Research Articles

Flexible Biosensors for Food Pathogen Detection

AbstractFood contamination poses a significant threat to public health, the economy, and human health worldwide, occurring at any stage of the food supply chain, from farm to fork. Efficient and effective real‐time monitoring methods for the early identification and rapid detection of pathogen contamination are critical to preventing possible food safety issues. In the past decade, flexible electrochemical biosensors have rapidly expanded in the detection of foodborne pathogens, owing to their ability to function well at biological interfaces that may be soft, intrinsically curvy, irregular, or deformable. The most important features of flexible sensors are their flexibility, multifunctionality, low cost, and good compatibility with solution processing methods. These properties enable flexible sensors to be used in various applications, including smart packaging. This review focuses on the materials, structures, and fabrication methods of flexible biosensors, as well as their applications in food safety monitoring. It examines recent advances in electrochemical immunosensors and aptasensors for pathogen detection, with a focus on their key components and their application in food analysis, while critically examining their current trends, particularly in conjunction with nanomaterials such as carbon nanotubes, graphene, gold nanoparticles, Indium tin oxide, etc. Finally, current issues and prospects for the future are discussed.

An Updated Isotopic Database of Fertilizers Used in Intensive Organic Farming: A Case Study on Protein Hydrolyzed Derivatives and Chelated Nutrients

The global demand for organic food products has rapidly increased over the last years, becoming an emerging niche market targeting the high-income segment. The higher retailing price for organic food products may increase the risk of fraudulent practices at the different stages of the food supply chain, and consequently, substantial control is needed. Currently, the authentication of organic food products, such as those of plant origin, remains a key challenge in analytical chemistry. While stable isotopes have emerged as a powerful tool for this purpose, most studies have focused on crops, missing the agricultural inputs used for fertilization that influence the isotopic values of the crops. In this study, we aimed to isotopically characterize commonly used fertilizers, soil conditioners, and micronutrient fertilizers in intensive organic agriculture in the largest organic production region in the world (Almería, Spain). Our goal was to clarify the limitations that nitrogen isotopic fingerprinting presents for the fertilizer input industry and to characterize the organic inputs. The conventional fertilizers analyzed in this study showed low δ15N values compared to their organic counterparts, except for some plant-based fertilizers, protein hydrolyzed fertilizers, and chelated nutrients. Both protein hydrolyzed fertilizers and micronutrient fertilizers presented a wide range of variability in their δ15N values, including some very low or even negative values, more similar to those of conventional fertilizers. The results of this study highlight the challenges of authenticating organic foods in agriculture when using nitrogen isotope analysis.

Biological control of green mold in simulated post-harvest chain of citrus Fruit: Efficacy of Candida oleophila strain O and molecular insight into elicitation of host immune system

Managing post-harvest decays in citrus fruit without relying on conventional pesticides presents a significant challenge in modern Plant Pathology. This study aimed to evaluate the efficacy of the biological control agent Candida oleophila strain O in controlling green mold caused by Penicillium digitatum throughout various stages of the post-harvest supply chain. Using a series of in vivo experiments, different scenarios of P. digitatum infections in clementine tangerine, orange, and lemon fruit were examined, with treatments applied before, during or after infection. The study simulated typical conditions of the citrus supply chain, including picking, processing in packinghouses, and transportation, as well as cold storage and shelf-life phases. Results indicated that C. oleophila exhibited significant efficacy in reducing green mold symptoms, even at shelf-life temperatures, making it a practical alternative to conventional fungicides. Additionally, the study provided insights into the molecular mechanisms underlying the defensive response of citrus fruit to C. oleophila treatment, with up-regulation of defense-related genes observed across different fruit types. Overall, this study underscores the potential of C. oleophila as a sustainable and effective solution for managing post-harvest decays in citrus fruit within the complexities of the supply chain.

Routing and facility location optimization in a dairy products supply chain

Routing and facility location optimization is an important aspect of supply chain management. This is because the problem of determining which facilities within a supply chain echelon should cost-effectively supply products to facilities in the next echelon is usually encountered by supply chain managers and analysts. In this paper, a linear programming model for supply chain routing and facility location optimization has been proposed. The model can be solved using the PuLP optimization library implemented in Python programming language. The model has been applied to a small dairy products supply chain to determine the most cost-effective routes for products in the supply chain. The optimization results proffered the optimal contribution of each factory warehouse to each distribution warehouse in order to satisfy distribution warehouse demand while minimizing costs. The results of the study indicated that the model is efficient in solving the routing and facility location optimization problem usually encountered in supply chain management. Therefore, this study will aid supply chain analysts and managers in determining the most cost-effective route for distributing products between echelons or stages of supply chains under study.

Effect of blockchain technology adoption on construction supply chain: a structural equation modelling (SEM) approach

Purpose The construction industry is frequently scrutinised by the public for a variety of issues, including waste, inefficiency, narrow profit margins, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency issues, coordination challenges, communication issues and fraud. The purpose of this paper is to assess the effect of blockchain technology adoption on the construction supply chain. Design/methodology/approach This study used a quantitative research approach through a questionnaire survey that was conducted among professionals in the Nigerian construction industry using the snowball sampling method, which resulted in a selection of 155 respondents. The collected data were analysed using partial least squares structural equation modelling, enabling a thorough assessment of the proposed relationships and offering valuable insights specific to the construction industry. Findings The study’s findings validated the conceptual framework established. The results indicated that implementing blockchain across all stages of the construction supply chain has the potential to improve the construction process. The study also revealed that blockchain technology will significantly affect the construction supply chain in a positive manner. Research limitations/implications This research was carried out in the South-western region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method. The study holds implications not only for local construction practices but will also contribute to the broader discourse on national construction sector challenges and possible solutions. Practical implications The findings of this study will be immensely beneficial to both professionals, practitioners and stakeholders in the Nigerian construction industry in learning about the potential of blockchain technology application in improving the construction supply chain. Originality/value The study in this paper constructed and evaluated a conceptual framework by exploring the connections between the variables. The results have significant implications for the construction sector, as they provide avenues for enhancing the construction process and the overall supply chain. These findings are valuable for researchers examining the potential effects of blockchain technology on the construction supply chain.

Stage-wise green supply chain management and environmental performance: Impact of blockchain technology

In the era of globalisation, the use of technology and concerns for sustainability is eminent in the supply chain management practices. The current study focuses on sustainable and green supply practices in different stages of supply chain management and how they can be facilitated by blockchain technology (BT). The study has addressed the existing gaps in the area namely, the lack of research assessing stage-wise green supply chain for environmental performance focusing on BT. The current study aims to assess the impact of BT on different stages of the green supply chain and a firm's environmental performance. The study also focuses on analyzing the impact of green supply chain stages on environmental performance. The study uses PLS-based structural equation modelling approach to investigate the hypothesised relationships between BT adoption and stage-wise green supply chain practices. The data was collected from individuals from medium-sized enterprises from the manufacturing industry in India. The findings reveal a positive association between blockchain adoption and green supply chain management practices leading to enhancement in environmental performance. Furthermore, the study indicates a positive relationship between blockchain integration and different stages of the green supply chain, underscoring its multi-faceted impact on environmental performance. The findings imply that the BT adoption can facilitate the realization of sustainable supply chain practices and performance improvement.

Reducing food loss through sustainable business models and agricultural innovation systems

PurposeThis study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.Design/methodology/approachThis study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.FindingsFirst, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.Originality/valueThis study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

Enhancing frameworks for utilising Sankey diagrams in modelling the water-energy nexus and circularity

Understanding the connection between water supply and energy consumption in a circular economy is crucial for managing the demands of these resources. Sankey diagrams have the potential to enhance the visualisation of resource flows. However, there is a knowledge gap regarding the effective use of Sankey diagrams to capture the water-energy nexus and circularity of water. This study focused on effectively utilizing Sankey diagrams to model the nexus of water and energy flows and to assess the circularity of water. A comprehensive literature review was conducted on major studies on water material flows and modelling. This was supported by a review of approaches for modelling circularity, which have evolved from the material circularity index. The newly developed framework in this paper models water resource flows at all stages of the water supply chain, embedding the nexus as an interaction and considering resource intensity and material circularity index at each node. A city-level case study in South Africa was then used to apply the recommendations, model circularity, and the water-energy nexus using Sankey diagrams. In terms of circularity, the results indicate that water loss in the study area was very high, accounting for up to 40% of the total water supply and the water recycle rate was still very low, ranging from 3% to 7%. In terms of the nexus, the residential sector was found to have the highest energy intensity for water supply, primarily due to energy demand for water heating. Exploring the use of renewable or solar energy for water heating will be of interest and value. The new approach presented in this study allows for the simultaneous consideration of objectives, enabling a more robust analysis of energy-effective water use and water circularity along the water supply chain.

Strategic mapping of onion supply chains: a comprehensive analysis of production and post-harvest processes in Northwest Ethiopia

IntroductionOnion is a significant vegetable crop in Ethiopia, serving as a source of income for smallholder farmers. However, various challenges in the production and post-harvest handling processes impede a consistent supply and marketing of the crop. This study focused on a comprehensive analysis of the onion supply chain, ranging from production to post-harvest handling, to address the existing production and marketing processes. The research initiative was undertaken to map the onion supply chain from farmers to consumers, intending to establish an improved marketing system in northwest Ethiopia.MethodsThe study area covered three onion-producing districts of the Amhara Region. Data collection involved interviews, observations, and market assessments using a structured questionnaire. Sampling followed a multistage technique. The analysis included descriptive statistics and food system analysis to map the supply chain and estimate marketing margins.Results and DiscussionPost-harvest loss in onion production presents a major obstacle in the farming sector, impacting its growth. The study identified six alternative channels for onion marketing where different actors are involved throughout the supply chain. Key stakeholders include farmers, local collectors, brokers, transporters, wholesalers, retailers, and consumers. Packaging and sorting activities are implemented at different supply chain stages to minimize post-harvest losses. Farm-level activities, including curing, sorting, grading, and ventilating, are crucial for reducing losses. The perishable nature of onion bulbs and the existing production and handling challenges exacerbate post-harvest losses. Efforts to address this challenge demand a comprehensive approach, integrating interventions across the value chain, from improved cultivars and storage infrastructure to enhanced market access strategies. Hence, stakeholders and governmental organizations are urged to promote onion value-addition technologies, including the establishment of processing industries. Such endeavors promote collaborative efforts across the onion supply chain, ensuring sustainable benefits for producers and traders.

Wasted Food, Wasted Resources? A Critical Review of Environmental Impact Analysis of Food Loss and Waste Generation and Treatment.

Food loss and waste (FLW) comes with significant environmental impacts and thus prevents a sustainable food system transition. Here we conducted a systematic review of 174 screened studies that assessed the environmental impacts of FLW generation and treatment. We found that the embodied impacts of FLW along the supply chain and impacts from FLW treatment received equal attention, but few studies have included both. The reviewed studies show narrow geographical (mostly conducted in industrialized countries) and food supply chain (mostly focused on the consumption stage) coverage. Life cycle analysis (LCA), material flow analysis (MFA), or their combination are the most commonly used to quantify FLW related environmental impacts. More method standardization, integration, and innovation and better FLW data with regional and stage resolution from a first-hand source are badly needed. Among the various proposed mitigation strategies covering technology, economy, behavior, and policy aspects, process optimization and waste management options are the most discussed. Our review calls for a more holistic environmental impact assessment of FLW generation and treatment and analysis of the trade-offs among different environmental impact categories and between supply chain stages, which would better inform relevant policy on effective environmental impact mitigation strategies toward sustainable food systems.

Supporting tactical harvest planning decisions of major fruits through a multi-objective modeling approach by using exact methods

An important stage of the agri-food supply chain is production (agricultural practices and harvesting), where relevant decisions are made in a short period of intensive activity. Regarding the major fruit supply chain, the complexity of these decisions increases because most fruit export companies manage several orchards with different fruit species and varieties. Moreover, the amount of labor and resources must be estimated at least two months in advance of the harvest so that they can be relied upon during the harvest season. In this study, a multi-objective modeling approach for supporting tactical harvest planning decisions of major fruits is developed, aiming to evidence its adequacy and to identify different harvest plans according to the prioritized objectives. Therefore, a multi-objective linear programming (MOLP) model, which considers three conflicting objectives, is proposed. The three objectives correspond to minimizing harvest costs, fruit loss and harvest days. For solving the MOLP model, five a posteriori multi-objective exact methods are compared using a synthetic instance based on a real case. The Pareto frontiers obtained by these methods are analyzed using three metrics related to accuracy, cardinality, and diversity criteria, so that the most suitable method can be selected. Thus, the selected method is applied to an actual case study of a Chilean fruit export company. In addition, the ε-constraint method is used in this case study to compare the obtention of non-dominated solutions in the convex and non-convex regions of the Pareto frontier. The results show that harvest plans vary significantly according to the prioritization of the objective functions. Thus, the percentage difference between each objective function’s worst and best values is around 497% when the harvest costs are minimized, around 1,448% when the fruit loss is minimized, and around 18% when the number of harvest days is minimized. In addition, the adequacy of the multi-objective approach rather than the mono-objective approach to support tactical harvest planning decisions is demonstrated. Furthermore, a scenario analysis for considering changes in the harvest periods is carried out by considering three possible scenarios. This analysis is required because these periods can change every year due to different factors, such as weather conditions and agricultural practices, among others. The results show that the harvest costs and fruit loss increase, on average, 87% and 100%, respectively, when the harvest period is short, that is, one day. Finally, managerial insights about the information obtained by the MOLP model are discussed.

An Assessment of Pandemic Era Inflation, 2021–2022

This paper examines the inflationary period of 2021 to the middle of 2022 primarily by employing US data but is suggestive of the global inflation experience given important cross-border linkages. Inflation is posited as a result of buyer firms, operating across multiple, global supply chain stages, who must shift their purchases to a reduced set of supplier companies. Supply is crimped because of pandemic impacts on capacity. Supply-induced shortages cause global demands to concentrate around fewer oligopoly producers who hike markups and prices. Empirical work is directed at understanding which specific factors that affect pricing are most responsible for the experienced inflation. The paper finds that markup increases represent a larger fraction of price increases than the other price components, suggesting that profit-push is the most important cause of the global inflation experience. Industrial data show that cuts to the aggregate productive capacity of firms did occur, setting the stage for shortages to develop, particularly after demand recovered from the pandemic lockdown.

Policy strategies for managing food safety risks associated with climate change and agriculture

Managing food safety risks associated with climate change and agriculture necessitates comprehensive policy strategies to safeguard public health, ensure food security, and sustain agricultural productivity. This review delves into the multifaceted challenges posed by climate change on food safety and explores policy approaches aimed at mitigating these risks. Climate change presents a myriad of challenges to food safety, including altered patterns of precipitation, temperature extremes, and increased occurrences of extreme weather events. These changes impact various stages of the food supply chain, from production to consumption, exacerbating existing food safety risks and introducing new ones. Addressing these challenges requires proactive policy strategies that integrate climate adaptation and food safety management. Policy strategies for managing food safety risks associated with climate change encompass a range of measures aimed at enhancing resilience, promoting sustainable agricultural practices, and strengthening food safety systems. One key approach involves the implementation of climate-smart agriculture (CSA) practices, which aim to improve agricultural productivity while minimizing environmental impacts and enhancing resilience to climate change. CSA practices such as crop diversification, soil conservation, and water management can help mitigate the impacts of climate change on food safety by reducing the risk of contamination and enhancing the quality of agricultural products. Furthermore, policies aimed at promoting sustainable food production systems can contribute to mitigating food safety risks associated with climate change. These policies may include incentives for farmers to adopt sustainable farming practices, support for organic farming, and regulations to reduce the use of chemical inputs in agriculture. By promoting environmentally friendly farming practices, such policies can help minimize the occurrence of foodborne pathogens and chemical contaminants in food products, thereby enhancing food safety. In addition to promoting sustainable agricultural practices, policy strategies for managing food safety risks associated with climate change also focus on strengthening food safety systems and enhancing surveillance and monitoring mechanisms. This includes investing in infrastructure and capacity building to improve food safety testing and inspection capabilities, as well as enhancing early warning systems for foodborne disease outbreaks. By strengthening food safety systems, policymakers can better detect and respond to emerging food safety risks exacerbated by climate change, thereby reducing the likelihood of foodborne illness and protecting public health. Furthermore, international collaboration and knowledge sharing are essential components of effective policy strategies for managing food safety risks associated with climate change. Given the global nature of both climate change and the food supply chain, coordinated action at the international level is crucial for addressing shared challenges and exchanging best practices. International organizations such as the World Health Organization (WHO), the Food and Agriculture Organization (FAO), and the World Organization for Animal Health (OIE) play a critical role in facilitating collaboration and providing technical assistance to countries in developing and implementing effective food safety policies in the context of climate change. Managing food safety risks associated with climate change and agriculture requires a multifaceted approach that integrates climate adaptation, sustainable agriculture, and robust food safety systems. By implementing proactive policy strategies that promote sustainable practices, strengthen food safety systems, and foster international collaboration, policymakers can mitigate the impacts of climate change on food safety and ensure the availability of safe and nutritious food for all.

A quality-based sustainable supply chain architecture for perishable products using image processing in the era of industry 4.0

In India, approximately one-third of agricultural produce is wasted every year due to the different issues present in the post-harvest supply chain stages. The supply chain management of perishable products becomes complex and challenging due to the inclusion of its perishability dynamics. Quality is the main factor that governs the buying or discarding of perishable products by consumers. Therefore, the main aim of this research work is to develop an accurate and efficient image processing model for the classification of the product (Tomato) based on its quality for managing the supply chain. There are three novelties in this research work. A four-stage supply chain architecture integrated with the image processing system at mandi, and warehouses is proposed (First). This image processing system is developed in two stages. In stage I, the acquired images of tomato during its life cycle are labelled with the help of machine learning algorithms (Second). This labelled data is used in stage II for the development of a classification model to segregate the product into various grades. For this, an optimized architecture of seven-layer Convolutional Neural Network (CNN) model is developed followed by optimization of its hyperparameters simultaneously using Design of Experiments (DOE) technique (Third). The optimized CNN model achieved maximum accuracy of 88.40% and reported an execution time of 7 min. Further, the results of standard hyperparameter optimization techniques like Grid search, Random search, Bayesian, and Hyperband are compared with the proposed DOE technique on the optimized CNN architecture. The work done in this paper enables the supply chain managers to take accurate and rapid decisions for pricing, procurement, storage, and transportation at various stages of the supply chain leading to Industry 4.0. This will result in reduced post-harvest losses and simultaneously achieve the benefits across social, economic, and environmental dimensions of sustainability leading to better supply chain management.

Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks

Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented by auction mechanisms and fuzzy optimizers that connect various supply chain stages. Specifically, edge computing offers powerful capabilities that enable real-time monitoring and data-driven decision-making in smart agriculture. We propose an edge computing framework tailored to agricultural needs to ensure sustainability through a renewable solar energy supply. Although the edge computing framework manages real-time crop monitoring and data collection, market-based mechanisms, such as auctions and fuzzy optimization models, support decision-making for smooth agricultural supply chain operations. We formulated invisible auction mechanisms that hide actual bid values and regulate information flows, combined with machine learning techniques for robust predictive analytics. While rule-based fuzzy systems encode domain expertise in agricultural decision-making, adaptable training algorithms help optimize model parameters from the data. A two-phase hybrid learning approach is formulated. Fuzzy optimization models were formulated using domain expertise for three key supply chain decision problems. Auction markets discover optimal crop demand–supply balancing and pricing signals. Fuzzy systems incorporate domain knowledge into interpretable crop-advisory models. An integrated evaluation of 50 farms over five crop cycles demonstrated the high performance of the proposed edge computing-oriented auction-based fuzzy neural network model compared with benchmarks.

The concept of food waste and food loss prevention and measuring tools.

Food waste (FW) has become a global concern, with an estimated 1.3 billion tonnes lost annually, costing about $1 trillion. Environmental and social consequences of FW are significant, contributing to 6% of European Unions' greenhouse gasemissions and affecting global food security. FW occurs is a complex issue occurring at various stages of the food supply chain (FSC) and is influenced by multiple factors such as infrastructure, available knowledge and socio-economic conditions. Developed countries FW is more prevalent at the consumption stage, whereas in the developing countries losses occur in agricultural production, post-harvest and distribution stage. Accurate quantification of FW across the supply chain is crucial and monitoring key performance indicators helps identify areas for improvement. The European Union mandates FW measurement, aligning with sustainable development goals, emphasizing the need for effective waste prevention measures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach was utilized to conduct a systematic literature review on FW key performance indicators (KPIs) and monitoring tools. The research identified 22 KPIs, categorized into three levels of the FSC: primary, secondary and tertiary. The most common KPIs included FW per capita, FW per portion and FW percentage. The study further discusses FW prevention measures and essential monitoring tools for addressing FW throughout the supply chain.

Enhancing pathogen identification in cheese with high background microflora using an artificial neural network-enabled paper chromogenic array sensor approach

Biohazards, which may occur at all supply chain stages, pose significant threats to food safety and public health. Addressing these concerns and enhancing food safety necessitates a nondestructive pathogen surveillance approach capable of continuously and simultaneously detecting multiple pathogens. Detecting and differentiating low concentrations of pathogenic bacteria amid high background microflora levels in foods is challenging, requiring technology with high sensitivity and robust discriminatory capability. This study introduces an artificial neural network-driven paper chromogenic array sensor (ANN-PCA) technique developed for the nondestructive, continuous, and simultaneous detection of Salmonella Enteritidis (SE) and Escherichia coli O157:H7 (Ec) from a high background microflora in shredded cheddar cheese. This method enables accurate detection of SE and Ec in monoculture and cocktail culture while distinguishing them from a high level of background microflora (∼7.5 log CFU/g), with accuracies ranging from 72 ± 11% to 92 ± 3%. In addition, SE and Ec were successfully identified at concentrations as low as 1 log CFU/g within one day, with an accuracy of 72 ± 11%. This approach exhibits promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in foods throughout the supply chain without enrichment, incubation, or other sample preparation steps.

Electric vehicle battery chemistry affects supply chain disruption vulnerabilities

We examine the relationship between electric vehicle battery chemistry and supply chain disruption vulnerability for four critical minerals: lithium, cobalt, nickel, and manganese. We compare the nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) cathode chemistries by (1) mapping the supply chains for these four materials, (2) calculating a vulnerability index for each cathode chemistry for various focal countries and (3) using network flow optimization to bound uncertainties. World supply is currently vulnerable to disruptions in China for both chemistries: 80% [71% to 100%] of NMC cathodes and 92% [90% to 93%] of LFP cathodes include minerals that pass through China. NMC has additional risks due to concentrations of nickel, cobalt, and manganese in other countries. The combined vulnerability of multiple supply chain stages is substantially larger than at individual steps alone. Our results suggest that reducing risk requires addressing vulnerabilities across the entire battery supply chain.

Building resilience in European food supply chains: results of a Delphi study

The COVID-19 pandemic and the war in Ukraine have negatively affected the sustainability of the food market and contributed significantly to the increase in agricultural prices. The goal of this article is to present the results of a European Delphi study on enabling factors and barriers to developing robustness within food distribution networks. The paper also aims to identify ways to ensure food security and build resilience in supply chains for European Union (EU) citizens, as well as strategies to protect the agricultural sector. The Delphi theses, the expected timing of their implementation and their impact on selected stages of supply chains were assessed. The paper employs a literature review, the Delphi method and STEEPED analysis to identify factors for increasing the robustness of European food distribution networks. The research findings highlighted crucial factors in enhancing food supply chain resilience, including financial aid to farmers, the use of advanced technologies (like AI, remote sensing, GIS, VR, or drones), and the encouragement of conscious consumption practices. The study revealed that severe weather, inadequate financial support for farmers and insufficient favourable legislation at the national level are the main barriers to achieving resilience in food supply chains. The main recommendations for building food resilience include encouraging localised food supply chains and, advocating for eco-friendly, sustainable production approaches and small-scale agriculture and reducing unnecessary food losses. The originality of the study is reflected in the presentation of the opinions of international experts on developing robustness within food distribution networks in view of the disruption caused by COVID-19 and the geopolitical situation.

A New Perspective for Construction Supply Chains in Digital Era

The construction industry is a highly labor-intensive industry with a low degree of automation and digitization. However, as in the manufacturing industry, a 'new order' is emerging for the way the construction industry works today. This study aims to present the transformation of construction supply chains (CSCs) in Industry 4.0 by considering each supply chain stage separately, where the main purpose is to determine the criteria for the integration of the CSC with the Industry 4.0 perspective. A literature review and a Delphi study were conducted, and thirteen criteria are proposed to show the integration of Industry 4.0 aspects and CSC stages. Furthermore, one of the multi-criteria decision-making (MCDM) methods, the Best-Worst method, is used to prioritize the criteria for guiding future implications during the transformation of construction supply chains. Among the six CSC stages, the design stage is the most important from the Industry 4.0 perspective. In addition, client collaboration by ICT (BIM) during the design phase is the most important sub-factors’ overall rank. Although there is a limited number of studies on the CSC, there are no empirical studies addressing the current digital age approach. As a result, there is a significant gap in the construction literature, and a thorough assessment of the building supply chain in an Industry 4.0 setting is lacking. To the best of the authors' knowledge, this is the first study to illustrate the transformation of CSCs in Construction 4.0 by looking at each supply chain stage separately.