Dairy Supply Chain Research Articles

Hot topic: Avian influenza subtype H5N1 in US dairy—A preliminary dairy foods perspective

In February and March of 2024, an unusual illness began affecting dairy herds primarily in Texas and neighboring states. The causative agent of this illness was ultimately confirmed in late March 2024 to be a strain of highly pathogenic avian influenza H5N1 belonging to clade 2.3.4.4b. In the months following the emergence of this viral disease in cattle, infections have spread to at least 191 herds in 13 states at the time of this writing in August 2024, primarily through cattle and human movement. Surprisingly, early examination of raw milk samples from clinically affected animals indicated that the virus had an affinity for the mammary tissue, and viral shedding into raw milk occurred at high levels, exceeding 108 log10 50% tissue culture infectious dose (TCID50) in some cases. These high viral loads coupled with evidence that farm cats who consumed raw milk from clinically ill animals were infected and exhibited high mortality rates, raised concerns about the safety of the US milk supply for human consumption. To date, 4 cow-associated human infections have been reported, all from farm employees with direct contact with infected animals. Several parameters ultimately affect the theoretical public health risk from consumption of dairy products manufactured from a milk supply containing H5N1, namely (1) initial viral load, (2) persistence of H5N1 in raw milk, (3) viral inactivation through processing practices including pasteurization, and (4) human susceptibility and infectious dose. In the short period since the emergence of this disease in dairy cattle in the United States, research has begun to answer these critical questions, although our knowledge is still quite limited at this time. Here we review the literature available from the current H5N1 outbreak in US dairy cattle, as well as selected relevant literature from previous research in other animal agriculture sectors, that affect our current understanding of the parameters associated with the food safety risk of this disease in the US dairy supply chain.

Impact of resource reconfiguration on the dairy supply chain resilience

Investment in food supply chain resilience, as a critical infrastructure, has become necessary for all governments. Disruption of food supply chains can lead to significant economic challenges. Building a resilient supply chain requires resources; however, it is difficult for firms to allocate resources to various resilience strategies. This study allocates the budget to resilient capacity, that is, absorptive, adaptive, and restorative capacity, to minimize supply chain costs and maximize service levels. We developed a novel multi-objective mixed-integer nonlinear programming method for problem formulation. The developed model was converted into an equivalent linear model. We used the Monte Carlo approach to generate the scenarios and the average sample approximation to determine the required scenarios. Finally, the Lexicographic max-min approach solves the model using actual data from a dairy supply chain. The analysis revealed that allocating 50% of the budget to restorative capacity and the remaining to adaptive and absorptive capacity optimizes supply chain performance. This study provides insights for managers to make better decisions with a knowledge-based background, allocate resources to various resilient strategies, and build a more resilient and efficient supply chain.

Assessing operational resilience in food supply chains: a discrete-event simulation approach based on a framework for integrated decision-making

ABSTRACT Food supply chains (FSCs) play a critical role in ensuring food security but face increasing vulnerabilities to disruptions. With the objective to mitigate these challenges, this study focuses on assessing operational resilience within the Austrian dairy supply chain during sudden demand spikes. For this purpose, we introduce a novel framework for integrated decision-making which encompasses strategic, tactical, and operational perspectives, augmented with modular building blocks for FSC modelling and simulation. Based on this framework, we develop a discrete-event simulation model to assess and quantify the impact of strategic, tactical, and operational interventions on operational resilience. Our analysis reveals the effectiveness of upstream buffer stocks, adapted reordering strategies, and operational adjustments in response to demand increases. However, no single intervention consistently outperforms others across all performance metrics, emphasising the importance of tailored intervention selection. Hence, the major contributions, i.e. (1) the framework for integrated decision-making and (2) the quantification of potential interventions to enhance operational resilience, are useful for improving the operational resilience of FSCs and increase their ability to supply essentials in the short term despite disruptions. The results assist FSC actors in making more informed decisions to enhance operational resilience.

A Review of Options and Costs for Mitigating GHG Emissions from the U.S. Dairy Sector

The U.S. dairy sector is a significant emitter of methane and nitrous oxide, with the US EPA estimating it produced around 90 Tg CO2 eq. in 2021. This paper reviews the literature on and evaluates various mitigation actions for reducing GHG emissions in the U.S. dairy sector, focusing on both direct and indirect emission sources. We conducted a narrative literature review based on the cradle to gate life-cycle assessment method, covering the entire dairy supply chain up until milk enters retail establishments, including dairy and feed producing farm practices, processing, transportation, and their associated emissions/costs. The papers included were selected over a three year process depending on discussions with experts and issues mentioned in the emerging literature. We review significant opportunities for the U.S. dairy sector to reduce emissions, particularly through improved enteric fermentation and manure management practices. Additionally, we cover the potential for mitigating indirect emissions from feed production, processing, and transportation, areas less frequently covered in existing studies. This review also covers a gap in the literature by integrating economic findings including cost analysis and incentives to mitigate based on the social value of emissions reductions.

Sustainability Assessment Tools for Dairy Supply Chains: A Typology

The dairy sector faces increasing pressure to adopt sustainable practices. Various tools have been developed to evaluate sustainability of the dairy supply chain. This paper provides an overview of these tools, highlighting their strengths and limitations regarding sustainability dimensions, indicators, and system boundaries. A systematic literature search identified 27 tools that were then categorized into a typology based on dimensions of sustainability, geographical applicability, and accessibility. In-depth analysis was conducted on six tools: Sustainability Assessment of Food and Agriculture (SAFA), Sustainability Monitoring and Assessment RouTine (SMART), Response-Inducing Sustainability Evaluation (RISE) 3.0 version, Swiss Agricultural Life Cycle Assessment (SALCAsustain), MOnitoring Tool for Integrated Farm Sustainability (MOTIFS), and Technology Impact and Policy Impact CALculations (TIPICAL). Assessment focused on the relevance of covered sustainability dimensions to the dairy sector, level of supply chain coverage, type of indicators, accessibility, and practicability. The review identified tools which integrate multiple sustainability aspects in a comprehensive way (SAFA and SMART) and tools offering accurate quantification of the impact on sustainability dimensions of the production system (SALCAsustain, RISE, MOTIFS, TIPICAL). Only two tools extend assessment past the farm gate (SAFA, SMART). Future users should select tools based on the specific objectives of measuring sustainability in dairy systems. This review contributes to the literature by addressing various aspects of sustainability assessment tools, by addressing the need for an integrated and comprehensive view, and by considering the entire dairy supply chain.

An integrated framework for prioritizing risk mitigation approaches – the case of dairy supply chain

An integrated framework for prioritizing risk mitigation approaches – the case of dairy supply chain

Investigation of Effect of Labour and Road Infrastructure on Fermented Dairy Production Capacity in Upstream Fermented Dairy Supply Chain

Scope of the study focuses on fermented dairy products because, they constitute very large and important part of human diet. And these products are very healthy and have some health benefits. 2731 fermented dairy processors in 227 sub-sectors in 48 cities have been in-vestigated to determine whether these processors in 48 cities are efficient in producing fer-mented dairy products. And input oriented CCR (Constant Returns to Scale) DEA (Data En-velopment Analysis) model is deployed to determine the level of efficiency of the dairy pro-cessors in producing fermented dairy products. According to results, İzmir ranks 1st, Balıkesir ranks 2nd, Şanlı Urfa ranks 3rd and Kahraman Maraş ranks 4th. While Ardahan is the lowest ranking city. And Generalized Linear Model has been utilized to find out the effect of the length of roads, the number of white-collar and blue-collar employees on fermented dairy production capacity. And results states that the number of blue-collar employees has a positive effect on fermented dairy production capacity at 0.001 significance. And the number of white-collar employees doesn’t have a significant effect on the fermented dairy production capacity while length of road has a positive effect on the fermented dairy production capacity at 0.10 significance level.

An AcciMap approach to analyse the Chinese melamine milk scandal

PurposeThe 2008 Chinese melamine milk scandal resulted in six reported fatalities and affected around 300,000 children, of whom 54,000 were hospitalised. Previous studies have used linear approaches to examine the root causes of the melamine milk scandal.Design/methodology/approachIn the present study, we applied a systems approach to the melamine milk scandal to identify the complex systems-level failures across the supply chain leading to the incident and why food fraud incidents such as this occurred in the dairy sector. Additionally, systemic failures associated with food fraud vulnerability factors were considered (i.e. opportunities, motivation and control measures).Findings48 contributory factors of influence were identified and grouped across six sociotechnical levels across the Chinese dairy system, from government to equipment and surroundings. Lack of vertical integration (processes and communication) contributed to the failure. When viewed from a broader perspective, the melamine milk scandal can be linked to a series of human errors and organisational issues associated with government bodies, the dairy supply chain, individual organisations and management decisions and individual actions of staff or processes.Practical implicationsThis approach is of value to policymakers and the industry as it supports public health investigations of food fraud incidents and proactive food safety management.Originality/valueTo the best of our knowledge, this is the first study to analyse a food safety or fraud incident using the AcciMap approach and the food fraud vulnerability assessment (FFVA) technique. AcciMap analysis is applied to both unintentional and intentional aspects of the incident.

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers and disposal centers). The exergetic model is based on the extended exergy accounting method, which calculates the consumed exergy in all six echelons by considering the equivalents of labor and capital exergy. This model aims to minimize both the economic and exergetic costs associated with the food supply chain. To solve the proposed mathematical model, an ensemble global–local search metaheuristic algorithm utilizing a combined whale optimization algorithm (WOA) and simulated annealing (SA), called CWOASA, is proposed. In the CWOASA algorithm, first, WOA is used to search the entire search space. Next, SA is applied to further improve the best solution obtained by WOA. A real dataset of a dairy supply chain is used to evaluate the efficiency of the proposed metaheuristic-driven exergy analysis model and claim its benefits over the existing methods. Simulation results revealed that the proposed method leads to a 6.74% reduction in the total exergy consumption of the supply chain. The proposed methodology offers valuable insights, shedding light on the prospective gains in terms of mitigated environmental impact for each incremental economic expenditure, thus laying the foundation for promising avenues of future research.

Feature extraction for artificial intelligence enabled food supply chain failure mode prediction

The Farm to Fork Strategy of the European Commission is a contingency plan aimed at always ensuring a sufficient and varied supply of safe, nutritious, affordable, and sustainable food to citizens. The learning from previous crises such as COVID-19 indicates that proactive strategies need to span numerous levels both within and external to food networks, requiring both vertical and horizontal collaborations. However, there is a lack of systematic performance management techniques for ripple effects in food supply chains that would enable the prediction of failure modes. Supervised learning algorithms are commonly used for prediction (classification) problems, but machine learning struggles with large data sets and complex phenomena. Consequently, this research proposes a manual approach to feature extraction for artificial intelligence with the aim of reducing dimensionality for more efficient algorithm performance, and improved interpretability/explainability for benefits in terms of ethics and managerial decision-making. The approach is based on qualitative comparative analysis informed by in-depth case knowledge which is refined through Boolean logic, yielding solutions that reflect complex causality as opposed to single failure point modes. Two case exemplars are presented to support the proposed framework for implementation: export readiness of dairy supply chains under the Russia-Ukraine war, and egg supply chain sustainability during COVID-19 lockdown in the United Kingdom.

Consumer Behavioural Patterns Towards Milk and Dairy Products: An Analytical Investigation of an Indian Dairy Supply Chain

Background: India has the most significant milk production and consumption level in the world. Fluid milk consumption will rise to 90 million metric tons in FY 2024 (Annual Report 202-23, United States Department of Agriculture). Milk is an essential component of daily diet in every age segment in India. Consumers’ behaviour and consumption patterns are based on their choices and preferences about product quality, price and preferences. This article critically investigates the consumer perception towards indigenous dairy markets and their consumption and behavioural pattern. Methods: Quantitative approach and Multiple Logistic Linear Regression Analysis (MLLRA) technique was used to test the hypothesis to establish a direct and indirect relationship among endogenous and exogenous variables. The sample was drawn from rural and urban regions of India’s top ten milk-producing states. Result: The research findings suggested a significant relationship between the direct and indirect role of consumers buying behaviour characteristics towards milk and dairy products and their satisfaction level. This research is limited to milk processors, dairy companies and dairy scientists in the field of marketing and dairy research. Dairy Farmers, Processors and Producers are vigilant about the quality of food products, especially milk delivered to their end users and their value-addition in the Milk Supply Chain (MSC) and logistics. In the past three years, during the unprecedented COVID-19, consumers’ consciousness level about health and well-being has been argued.

Impact of Raw Milk Quality on Dairy Products & Payment Systems

The trend of microbial quality of raw milk is affected by the seasonal variation in in milk production practices and ambient temperature with the season. A payment system, which includes testing for selected parameters, with subsequent rejection and/or penalties or bonuses, is considered functional to improving raw milk quality. In addition to the minimal legal requirements, milk may be graded (and paid) according to its “quality,” usually measured according to composition (fat, protein, lactose, other solids, free fatty acids), hygienic quality. Psychrotrophic microbes, particularly Pseudomonas spp., are found in the microbiota of chilled milk because they can grow at temperatures below their optimal growth temperature. Psychrotrophic counts ranging from 105 to 108 CFU/ml in refrigerated raw milk affect cheese quality, since the synthesized thermoresistant enzymes affect the nutritional value, sensory properties and texture. Therefore, stringent measures must be implemented throughout the dairy supply chain to ensure the microbial quality of raw milk is maintained at safe levels, thus safeguarding the integrity and safety of dairy products for consumers.

Carbon Footprint and Carbon Sink of a Local Italian Dairy Supply Chain

The dairy industry’s contribution to global warming has been thoroughly examined. However, it is important to raise public awareness of emission hotspots and the possibility of mitigation in dairy supply chains. This study assessed the Carbon Footprint (CF) of five dairy products through a cradle-to-grave Life Cycle Assessment approach and evaluated the carbon sink potential of some practices. The functional units were 1 kg of fresh raw milk, yogurt, fresh cheese, mozzarella cheese, and aged cheese. The data collected were related to an extensive dairy farm, a cheese-factory, two markets, a delivery service, and a court of consumers. The CFs were 4.39, 5.10, 9.82, 8.40, and 15.34 kg CO2 eq. for fresh raw milk, yogurt, mozzarella cheese, fresh cheese, and aged cheese, respectively. The hotspots of the dairy supply chain considered herein refer to farm activities and energy consumption, whereas conservative agriculture practices and rotational grazing sequestered 1.60 ± 0.80 kg CO2 eq. per kg of dairy product consumed. The CF was reduced by 0.14 kg CO2 eq. for 1 kg of dairy product delivered at home compared to direct purchasing at a market. The carbon sink capacity of dairy farms appeared as a primary mean for mitigating climate change in the dairy supply chain.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Improving Cold Chain Logistics and Temperature Control Measure in Aavin Dairy Supply Chain

Improving Cold Chain Logistics and Temperature Control Measure in Aavin Dairy Supply Chain

The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made significant contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these innovative devices have been pivotal factors for the implementation in dairy farms. In fact, pocket-sized devices enable non-expert users to perform quick, low cost and non-destructive analysis on various samples without complex preparation. As bovine colostrum (BC) quality is mostly given by the Ig G (IgG) level, evaluating the ability of portable NIRS tools to measure antibodies concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected as soon as possible after calving (<6 h) in primiparous and pluriparous Holstein cows farmed in 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA (R2CV ≥ 0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; R2V ≥ 0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design coupled with the increased interest in colostrum quality within the dairy sector may boost the collection of extensive BC data for management and genetic purposes in the near future.

Genomic characterization of Listeria monocytogenes and Listeria innocua isolated from milk and dairy samples in Ethiopia

Listeriosis caused by Listeria monocytogenes often poses a significant threat to vulnerable populations. Dairy products have been implicated in outbreaks of listeriosis worldwide. In Ethiopia, studies have identified Listeria spp. and L. monocytogenes in various dairy products, but the genetic diversity and phylogenetic relationships of these bacteria remain largely unknown in the low- and middle-income countries. Therefore, we conducted whole-genome sequencing on 15 L. monocytogenes and 55 L. innocua isolates obtained from different levels of the dairy supply chains across three regions in Ethiopia. Genomes were assembled and used for MLST genotyping and single nucleotide polymorphism (SNP) analysis to infer phylogenetic relationships. We identified a total of 3 L. monocytogenes (i.e., 2, 145, and 18) and 12 L. innocua (i.e., 1489, 1619, 603, 537, 1010, 3186, 492, 3007, 1087, 474, 1008, and 637) MLST sequence types among the studied isolates. Some of these sequence types showed region-specific occurrence, while others were broadly distributed across regions. Through high-quality SNP analysis, we found that among 13 L. monocytogenes identified as ST 2, 11 of them were highly similar with low genetic variation, differing by only 1 to 10 SNPs, suggesting potential selection in the dairy food supply chain. The L. innocua isolates also exhibited low intra-ST genetic variation with only 0–10 SNP differences, except for the ST 1619, which displayed a greater diversity.

A multi-objective robust optimization to design a sustainable dairy supply chain under uncertain product demands, economic and social consideration. A real case study from Bulgaria

The operation of dairy supply chains (DSCs) is associated with the generation of significant amounts of wastewater pollutants and CO2 emissions. The presence of uncertainty regarding various parameters of the considered DSCs has an additional impact on their sustainability. The most efficient way to improve the sustainability of DSCs is by applying mathematical modeling approaches for optimal design of sustainable DSCs operating under uncertain conditions. The present study proposes an optimal model for design of a sustainable DSC for the production of two types of dairy products with a technology selection, which includes economic, environmental and social impact models, as well as dairy production model. The optimization model takes into account uncertainty in products demands, raw materials and products prices and social costs. The effectiveness of the proposed approach is proved in a real case of a DSC from Bulgaria, which includes suppliers of two types of milk and dairy plants for the production of two types of dairy products and markets. The obtained optimal values of the economic, environmental and social costs show that the application of the optimization approach results in sustainable solutions that do not significantly change with an increase in the uncertainty level of consideration of the uncertain parameters.

Examining the carbon emissions from dairy supply chains: An in-depth review

Examining the carbon emissions from dairy supply chains: An in-depth review

Analysis of sustainable performance indicators in dairy supply chain using fuzzy-DEMATEL

Analysis of sustainable performance indicators in dairy supply chain using fuzzy-DEMATEL