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This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

The global research evidences indicate that the technology adoption in case of agribusiness has a potential to enhance the performance and bring operational efficiency. India is the world’s largest producer as well as consumer of milk but struggles with yield per cattle, overall productivity, low rate of technology acceptance and adoption, health detection of milching units, animal data recording and presence of dairy products in the global market. The purpose of this study is to focus on identifying the challenges of technology adoption in dairy farms and constructing a hierarchical model using soft systems methodology.

This study uses nominal group technique-based discussion with domain experts and personal interviews with dairy farm owners/managers for the identification of challenges, fuzzy interpretative structural modeling as well as FMICMAC to develop a hierarchical model of challenging elements and to divide the identified elements into four categories based on the dominance of driving-dependence power.

This research has developed a list of 12 challenges affecting the technology adoption in a dairy farm business unit, identified through the personal interviews with 60 dairy farms across three highest milk-producing states of India in terms of annual milk output – Haryana, Punjab and Uttar Pradesh. Lack of government support followed by lack of educational opportunities in dairy-based education were found as the most crucial and high driving challenges, whereas high cost, huge investment and low acceptance of decision-maker were found as the most dependent challenges of technology adoption.

This research is one step ahead of interpretive structural modeling that considers the fuzzy-based dominance in the model to showcase the degree of relationship along with its existence, but it lacks to statistically validate the findings using techniques like SEM.

This paper has developed a list of challenges in adoption of technology along with their inter-relationships to highlight the required focus challenge that drives or is dependent on the other challenges. The goal is to bring performance improvement and assist Indian dairy farm business stakeholders or decision-makers in formulating strategic and action plans and help policy planners to make favorable policies based on the understanding of contextual relationship between challenges.

In Indian context, dairy is an important part of agriculture sector, and milk is an essential item that facilitates income generation to small and rural households and a source item for several other businesses and activities. The results of this research suggested the policy planners and government to ensure subsidized and insured technologies, training support and facilities, educational opportunities and efforts for promotion of technology adoption among dairy farmers. The suggestions are purely on the basis of the relevance of challenges in the hierarchy and can play a significant role in improving the level of technology adoption and can ultimately uplift the social and economic well-being from micro-level of farmers to macro-stage concerning economic development of India.

To the best of the authors’ knowledge, this study is purely original and outcome of the research conducted by authors.

This study explores value-added food products in smallholder dairy farming in developing countries by analyzing external pressures, supply chain learning, farmer innovation, education level, and food safety compliance.

We employed a quantitative approach by surveying 418 smallholder dairy farmers in three districts of Pakistan using interviewer-administered questionnaires. Data analysis involved confirmatory factor analysis and structural equation modeling.

The results indicate that external pressure significantly affects value-added smallholder dairy farms. This relationship is mediated by supply chain learning and farmers' innovative behavior, and moderated by farmers' education level and compliance with food safety standards.

Further research is required to explore the drivers of value addition at the supply chain level.

This study contributes to the understanding of smallholder dairy farming dynamics and provides practical implications for improving value addition by managing the interplay between antecedents and promoting best practices in the industry.

Milk is a perishable food product, one of the primary sources of nutrition. Reports worldwide indicate numerous food frauds and foodborne diseases associated with adulterated milk products. These safety concerns highlight the importance of a visible milk supply chain, which can be achieved by cutting-edge technologies. However, these technologies come with high costs. So, this study aims to propose a framework that integrates blockchain, Internet of Things (IoT) and cloud to enhance visibility with reduced cost in an Australian milk supply chain (AMSC).

A design science research methodology is used, where a proof of concept is also developed at the retailer end to show how blockchain, IoT and cloud can improve visibility with reduced cost in an AMSC.

According to cost and visibility analysis, blockchain implementation in AMSC would generate a high return on investment (ROI). For the given case, ROI becomes positive for all stakeholders after 750 cycles. Integrating IoT, cloud and blockchain is more profitable than just using blockchain. Additionally, technology implementation may not benefit all stakeholders equally. For example, the retailer needs 10 cycles to benefit, but the transporter needs 50 in the given case.

The findings of this study assist milk industries in decision-making regarding technology implementation in their supply chain and motivate them to implement these technologies, resulting in improved trust and coordination among entities and consumers.

A cost and visibility analysis are performed to evaluate the impact of technology implementation on cost and visibility in an AMSC. A SOAR (Strength Opportunities Aspiration Results) analysis is also performed for the strategic planning framework.

This paper proposes an approach for prioritizing Risk Mitigation (RMTG) approaches in perishable food Supply Chains (SCs).

An integrative approach has been proposed, based on the risk typology and Supply Chain RMTG (SCRMTG) approaches literature review, integrating trending Failure Modes and Effects Analysis (FMEA), Quality Function Deployment (QFD) and Quadrant Analysis (QA). Risks are prioritized using Trending FMEA. SCRMTG approaches are prioritized by considering the prioritized risks using QFD and also based on their strategic importance and ease of Benchmarking via QA. The proposed approach has been examined in a dairy-manufacturing company.

Findings indicated supplying the imported machine parts, old machines and delayed new product introduction, respectively, as the most prominent supply, process and demand risks and multiple sourcing, upgraded machinery, hiring skilled staff and training, collaboration with downstream partners as the highly prioritized SCRMTG approaches based on the strategic importance and ease of benchmarking.

The results of this study increase the awareness of SC managers and provide the company with a framework of risk management and the insights to manage SCRs in the dairy industry more effectively and efficiently.

While the literature review indicates that only a few studies have been focused on prioritizing SCRMTG approaches concerning each type of SCRs, SCRMTG approaches are prioritized based on the SCRs type. Other innovations include QFD development based on the FMEA and SCRMTG approaches, considering the probability of risk occurrence, severity-impact cost and risk recovery duration in trending FMEA instead of the three risk factors in traditional FMEA.

This paper explores the steps/countermeasures taken by buying and distributing firms to address supply chain disruptions caused by COVID-19.

This study employs a multiple case study methodology and conducts 36 semi-structured interviews with senior managers of nine different firms producing, procuring or distributing products from China and other highly impacted South Asian regions (Pakistan, Sri Lanka, India).

Results reveal that buying firms are moving to agile production, focusing on tier-1 supplier risk, enhancing inbound material visibility and temporarily closing production facilities to respond to the challenges posed by COVID-19. Furthermore, distribution centres are modifying their inventory policies, evaluating alternative outbound routes and sources of supply to manage disruptions caused to their business operations amid COVID-19 outbreak.

Supply chain firms can use the countermeasures provided in this study to mitigate the impact of COVID-19 and make the best out of this pandemic.

This study contributes to the supply chain literature by exploring the countermeasures taken by firms to mitigate the impact of COVID-19. In particular, this study explores such countermeasures from the perspective of two different entities (buyers and distributors) along the supply chain. Firms can use the countermeasures highlighted in this study to mitigate the impact of COVID-19 on the supply chain.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

This study aims to examine the impact of Lean 4.0 practices on value co-creation in the dairy ecosystem.

Data collection were carried out through a questionary application with 126 professionals linked to the dairy ecosystem, including milk producers, milk cooperatives and milk transporters. The data were analyzed using Cluster Analysis, Mann-Whitney test and Chi-Square test.

A strong relation was found between the use of Lean 4.0 tools and the increase in operational performance, in addition to milk quality. Moreover, it can be noted that the use of digital technologies from Industry 4.0 has a strong relation with dairy production optimization, in other words, it is possible to be more efficient in the dairy process via Lean 4.0 adoption.

The study is limited to analyzing the Brazilian dairy ecosystem. The results presented may not reflect the characteristics of the other countries.

Once the potential empirical impacts of the relation between Lean 4.0 and value co-creation are elucidated, it is possible to direct strategies for decision-making and guide efforts by researchers and professionals to deal with the waste mitigation present in the dairy sector.

Lean 4.0 proves to be a potential solution to improve the operational performance of the dairy production system. Lean 4.0, linked to value co-creation, allows the integration of the production sector with consumers, through smart technologies, so new services and experiences can be provided to the consumer market. Additionally, the consumer experience can be stimulated based on Lean 4.0, once the quality specification is highlighted based on data science and smart management control.

To the best of the authors’ knowledge, this is the first study that analyzes the interrelationship between the Lean 4.0 philosophy and the value co-creation in the dairy ecosystem. In this sense, the study reveals the main contributions of this interrelation to the dairy sector via value co-creation, which demonstrates a new perspective on the complementarity of resources, elimination of process losses and new experiences for the user through digital technologies integrated with the Lean Thinking approach.

This study aims to identify the psychological factors predicting sociopreneurial intention (SEI) and gain insight into the conversion of SEI to sociopreneurial behaviour (SEB) in the presence of facilitating events (FE) as a moderating factor.

Hypotheses are statistically tested using a partial least square structural equation modelling (PLS-SEM) based on purposive survey data (n = 110) from the leaders of dairy cooperatives in Indonesia. The measurement items are specifically developed for this research after thoroughly analysing the questionnaire items provided by prior studies.

The findings support the hypotheses that empathy (EM), perceived social responsibility (PSR) and self-efficacy (SEFF) are strong predictors of SEI. Moreover, although SEI can be directly converted to SEB, FE significantly moderated that conversion.

Future research should also involve the impact of SEB on the community and the sociopreneurs.

This research empirically examines the influence of external factors in converting SEI into SEB, which prior studies overlooked. Moreover, it involves the leaders of dairy cooperatives in Indonesia who perform SEB to help smallholder farmers’ communities facing dairy farming issues such as capital, milk productivity, land size and others as research participants.