Making dairy supply chains robust against corruption risk: a systemic exploratory study

Assessing Risks in Dairy Supply Chain Systems: A System Dynamics Approach

Due to the dynamic nature of the food supply chain system, food supply management could suffer because of, and be interrupted by, unforeseen events. Considering the perishable nature of fresh food products and their short life cycle, fresh food companies feel immense pressure to adopt an efficient and proactive risk management system. The risk management aspects within the food supply chains have been addressed in several studies. However, only a few studies focus on the complex interactions between the various types of risks impacting food supply chain functionality and dynamic feedback effects, which can generate a reliable risk management system. This paper strives to contribute to this evident research gap by adopting a system dynamics modelling approach to generate a systemic risk management model. The system dynamics model serves as the basis for the simulation of risk index values and can be explored in future work to further analyse the dynamic risk’s effect on the food supply chain system’s behaviour. According to a literature review of published research from 2017 to 2021, nine different risks across the food supply chain were identified as a subsection of the major risk categories: macro-level and operational risks. Following this stage, two of the risk groups identified first were integrated with a developed system dynamics model to conduct this research and to evaluate the interaction between the risks and the functionality of the three main dairy supply chain processes: production, logistics, and retailing. The key findings drawn from this paper can be beneficial for enhancing managerial discernment regarding the critical role of system dynamics models for analysing various types of risks across the food supply chain process and improving its efficiency.

Regulatory bark and legal bite: how corruption and country institutional environments influence international logistics performance

Purpose

This research uses theoretical perspectives from public choice and public policy to establish and test theory of the combined effects of institutional environments and bureaucratic corruption on international delivery performance.

Design/methodology/approach

A panel archival dataset is assembled from multiple public databases to test hypotheses based on public policy, public choice and supply chain theory using a fixed effects model.

Findings

The authors' theory demonstrates that institutional environments as constituted by the level of regulatory trade barriers and legal system effectiveness combined with bureaucratic corruption can influence the timeliness of international deliveries.

Research limitations/implications

This research extends public choice and public policy with the insight that regulatory institutions' bark is not bad without the bite of effective legal institutions. The research uses archival data collected in mass surveys with data aggregated at the country level that can be unduly affected by selection effects, perceptual data, and unobserved underlying mechanisms.

Practical implications

The results of this research can be used to inform supply chain managers working in trade compliance to be aware of the costs and effects on logistics performance that result from encountering different institutional environments and the concomitant corruption.

Originality/value

This is the first investigation of the complex and significant interaction effects of institutional environments and corruption on international delivery performance.

Food Supply Chain Safety Research Trends From 1997 to 2020: A Bibliometric Analysis

Background

The COVID-19 pandemic has exposed the fragility of the global food supply chain, strengthened consumers' awareness of the traceability system throughout the supply chain, and gradually changed consumers' consumption concepts and consumption patterns. Therefore, the aim of this study was to analyse the relevant literature on food safety in the food supply chain, examine its current status, hot spots, and development trends, and provide some suggestions for academics and relevant government departments in food supply chain safety research.

Methods

We collected the literature on the food safety research of the food supply chain from the Scopus database, used BibExcel to count the subject categories, published journals, geographical distributions, research institutions, authors, and keywords in the literature, and used Pajek software to analyse the keywords in the literature, perform co-occurrence analysis, draw related knowledge maps, and perform cluster analysis on primary keywords. Finally, to study the development trend, we used CorTexT software to illustrate the theme evolution path map in this research field.

Results

The keyword visualization network revealed the following key research topics: (1) food safety at the consumer end of the food supply chain, (2) food safety management in the food supply chain, (3) risk management of food safety in the food safety chain, and (4) food safety at the production end of the food supply chain.

Conclusions

After comprehensive discussion and analysis, we concluded that food supply chain management may be a hot topic in the future, especially in traceability management combined with the blockchain. It is necessary to explore in-depth how the blockchain can affect the food supply chain to provide a theoretical basis for managing the latter.

Modelling resilient fashion retail supply chain strategies to mitigate the COVID-19 impact

Purpose

This paper aims to develop supply chain strategies for the fashion retail supply chain (FRSC), likely to be disrupted by the current pandemic (COVID-19) under physical and online retail stores. The resilient retail supply chain design is proposed under budget allocation and merchandise capacity constraints.

Design/methodology/approach

This paper utilises the theory of constraint (ToC) and goal programming (GP) to address the COVID-19 impact on FRSC. The budgetary and capacity constraints are formulated with a constraint optimisation model and tested with six different priorities to deal with the physical and online stores. Next, all priorities are developed under different FRSC business scenarios. The ToC-GP-based optimisation model is validated with one of the Indian fashion retail supply chains.

Findings

The proposed optimisation model presents the optimal retailing strategies for selling fashion goods over physical and online platforms. The multiple scenarios are presented for developing trade-offs among different strategies to maximise the retailer's merchandise performance. This paper also highlighted the strategic movement from high merchandise density stores to low merchandise density stores. This implies a reduction of sales targets and aspiration levels of both online and physical fashion stores.

Research limitations/implications

The proposed model is validated with one of the fashion retailers in India. Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

Practical implications

This research helps fashion retail supply chain managers deal with consumer demand uncertainty over physical and online stores in pandemic times. Limitation: Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

Originality/value

This is the first study that considered the impact of COVID-19 on the retail fashion supply chain. The effect of physical and online platforms is mainly discussed from consumer marketing perspectives, but an inventory and resilience perspective is missing in earlier studies. The role of merchandise planning is highlighted in this study.

Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic

Purpose

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

Design/methodology/approach

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

Findings

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

Originality/value

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.