Simulating Resilience in the Milk Supply Chain: The role of Big Data

Purpose: Supply chains provide a way for organisations to partake in cost advantages and leverage on relationships within the network to provide quality goods and services. However, these supply chains face risks which can hamper their performance and one of such risks is the risk of disruption. In order to mitigate the risk of disruption in supply chains, literature suggests building resilience. With technological advancements, organisations are of the opinion that these technologies potentially carry specific advantages that have previously been difficult to access. One of those technologies is Big Data. This research, therefore, seeks to explore ways in which Big Data can be adopted by organisations in order to build resilience, with a focus on the milk supply chain.
Design/Methodology/Approach: The research adopts a mixed method: Interviews and Simulation. The interviews allowed the research to gather information on the challenges faced by supply chains that have encountered recent disruptions such as the COVID-19 pandemic and the simulation allows the research to examine different disruption types objectively.
Findings: The results suggest that Big Data can be adopted to build resilience by supporting collaboration, flexibility, supply chain design and data management. The research also found that demand disruption had the least impact on this milk supply chain and the associated cost. Additionally, the research found that inventory planning prevents stockout situations, keeps customer service levels high and improves resilience within the supply chain. The research also found that while Big Data offers several advantages, supply chains often encounter several challenges when trying to adopt Big Data
Originality/Value: The originality of this thesis stems from the fact that this research is one of the few empirical studies that identify how Big Data can be applied in a milk supply chain context. This research not only develops a resilience measurement tool, but also carries out simulated experiments leveraging real-world data to measure the effects of three disruption types on resilience. Through objective data analysis and documentation of the results, this study contributes to the literature on supply chain resilience by highlighting parameters that can aid an evidence-based assessment of resilience within milk supply chains leveraging big data analytics.