Search results

Supply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's profits.

A two-stage mixed-integer non-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale problems.

The optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are analyzed.

As for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integer programming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines exponentially.

This paper innovatively designs the resilient supply chain network for perishable foods under dynamic competition. The retailer's dynamic competition and resilient strategies are considered simultaneously when designing supply chain network for perishable foods. In addition, this paper gives insights into how to obtain the optimal inventory period and compare the retailer's resilient strategies.

Natural disasters cause serious operational risks and disruptions, which further impact the food supply in and around the disaster-impacted area. Resilient functions in the supply chain are required to absorb the impact of resultant disruptions in perishable food supply chains (FSC). The present study identifies specific resilient functions to overcome the problems created by natural disasters in the FSC context.

The quality function deployment (QFD) method is utilized for identifying these relations. Further, fuzzy term sets and the analytical hierarchy process (AHP) are used to prioritize the identified problems. The results obtained are employed to construct a QFD matrix with the solutions, followed by the technique for order of preference by similarity to the ideal solution (TOPSIS) on the house of quality (HOQ) matrix between the identified problems and functions.

The results from the study reflect that the shortage of employees in affected areas is the major problem caused by a natural disaster, followed by the food movement problem. The results from the analysis matrix conclude that information sharing should be kept at the highest priority by policymakers to build and increase resilient functions and sustainable crisis management in a perishable FSC network.

The study suggests practical implications for managing a FSC crisis during a natural disaster. The unique contribution of this research lies in finding the correlation and importance ranking among different resilience functions, which is crucial for managing a FSC crisis during a natural disaster.

The main aim of this study is to explore the appropriate factors in measuring COVID-19-induced supply chain disruptions and the impact of these disruptions on the economic vulnerability of small-scale farmers in Sri Lanka.

The findings revealed that most of the farmers have continued to cultivate even during the pandemic despite several challenges which affected their economic status. Therefore, it is concluded that COVID-19-induced transportation and demand disruptions exacerbated the economic vulnerability of small-scale farmers over the disruptions in supply and production.

The findings of this study are crucial for formulating novel policies to improve the sustainability of the Sri Lankan agricultural sector and alleviate the poverty level of Agri-communities in the countryside. As farming is a vital sector in the economy, increased attention ought to be given on facilitating farmers with government-encouraged loans or allowances for their financial stability. Further, the respective government authorities should develop programs for importing and distributing adequate quantities of fertilizers among all the farmers at controlled prices so that they can continue their operations without any interruption. Moreover, the government could engage in collaboratively work with private organizations to streamline the Agri-input supply process. There should be a government initiative for critical consideration of the issues of farming families and their continued motivation to engage in agriculture. Thus, farmers' livelihoods and agricultural prosperity could be upgraded through alternative Agri-inputs and marketing strategies, providing financial assistance, encouraging innovative technology, etc.

Despite the significance and vulnerability of the vegetable and fruit sector in Sri Lanka, there is a limitation in the empirical studies conducted on the supply chain disruptions caused by COVID-19 measures and their implications on the farmers' livelihood. Furthermore, previous empirical research has not employed adequate quantitative tools to analyze the situation or appropriate variables in evaluating COVID-19-induced disruptions. Hence, the current study explored the appropriate factors for measuring COVID-19-induced supply chain disruption using exploratory factor analysis. Then, the impact of those factors on the economic vulnerability of the small scale farmers was revealed through the ordinal logistics regression analysis.

The cold supply chain industry is still emerging and digital transformation is in the nascent stage in this industry. This paper argues that there are various barriers to implementing blockchain technology in the cold supply chain and aims to develop and validate a model for overcoming key barriers to implementing blockchain technology in the cold supply chain.

The adoption of blockchain technology was proposed through interpretive structural modeling (ISM) and further it is validated using structural equation modeling (SEM).

In this study, ten key barriers to implementing blockchain technology in the cold supply chain were identified, modelled and analysed. Poor leadership style of top management was found to be the most important barriers to implementing blockchain technology in the cold supply chain. The results of SEM indicate that all the paths are supported. The findings showcase the barriers responsible for the lack of blockchain technology infrastructure that ultimately impacts the cold supply chains.

This study highlights the fact that the fate of blockchain technology infrastructure development depends on the leadership style of top management. Demonstrating good leadership style by top management can help overcome the barriers. A good leader pulls the entire team instead of pushing the team. A good leader can guide the entire team to improve IT governance, financial investment, digital footprint, digital readiness, skills and collaboration with service providers to implement blockchain technology. Not only that, a good leader provides mental strength to the team and helps overcome the fear of implementing blockchain in the cold supply chain. A good leader demonstrates good administrative skills and focus on security and privacy policies.

This is a novel contribution towards analysing the key barriers to implementing blockchain technology in the South African cold supply chain using the integrated ISM–MICMAC and SEM approach.

This study aims at modelling the enablers of dairy supply chain (DSC) in Indian context.

Interpretive structural modelling (ISM) approach has been used to model the enabler of dairy supply chain. The opinion has been taken from the industry experts and experienced academicians. Further, Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) used to classify the enablers based on driving and dependence power.

Findings show that stakeholder trust and top management support/leadership are the very crucial enablers in dairy supply chain; they are at a lower level of hierarchical structure and work as primary enablers to development of DSC. While customer satisfaction and financial performance are at top of the digraph, it shows these enablers are the outcome of a smooth supply chain. The MICMAC analysis suggests that the identified enablers are largely classified into dependent and independent enablers; there are no autonomous enablers in the dairy supply chain.

The study can aid businesses in the dairy processing industry in managing demand fluctuations, enhancing product quality, implementing effective information systems and adapting procedures, thereby enhancing supply chain performance.

There is very limited study on enablers of the dairy supply chain in general, while in the Indian context, there is no specific study on modelling the enablers of dairy supply chain.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

The study evaluates small marginal farmers’ (SMFs) potential behavior, attitude and trust in the adoption of innovative emerging technologies.

The study employed an agile multi-factor approach to conceptualize a digital marketplace to connect a supply chain ecosystem for stakeholders.

The empirical findings suggest that most SMFs are willing to embrace innovative technologies. Nonetheless, they lack the necessary technological oriented education, training and funds to innovate. However, their reluctance to adapt changes is attributable to their fear of losing past customs and practices; they were threatened by the reaction of intermediaries (arthyias) to the adoption of technologies, which could result in them suffering huge losses.

This innovative disintermediation business model has a significant potential to reduce information asymmetry, cost and hoarding – and can thus increase the SMFs’ profit margins. Agricultural technological innovations have a profound potential to impact their supply chain logistics positively by reducing the wastage of perishable food and thus enhancing the consumer experience.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

This 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.

This 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.

First, 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.

This 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.

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).

A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.

The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.

The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.