Data Governance: Exploring Complex Data Sharing in Food and Fashion Supply Chains

While the food and fashion supply chains might deliver different end products, their underlying reliance on data sharing to enhance efficiency, sustainability, and compliance presents undeniable parallels. This blog explores the three most significant similarities in data sharing between these sectors and the critical role of reflexive governance architectures in shaping responsible innovation for these environments.

1. Transparency and Traceability

The food and fashion industries face increasing consumer demand for transparency and traceability. The journey from farm to fork is scrutinised in the food sector to ensure food safety and quality, as reflected in practices and technologies aiming to trace ethical sourcing and production processes. Similarly, the fashion industry, driven by ethical concerns and environmental impact, now seeks to track the origin of materials and the sustainability of manufacturing processes. Data-sharing platforms enable both sectors to trace products back to their origins, ensuring compliance with ethical standards and regulatory requirements. By adopting blockchain technology, for instance, stakeholders can maintain immutable records of transactions and product journeys, enhancing transparency and building consumer trust.

2. Efficiency and Waste Reduction

Operational efficiency and waste reduction are critical challenges that both industries address through strategic data sharing. In food supply chains, data analytics help predict demand patterns, optimise inventory levels, and prevent overproduction and spoilage. Fashion retailers and manufacturers leverage similar data insights to forecast trends, manage stock levels, and reduce unsold inventory, which often leads to waste. Advanced algorithms and machine learning models, fed with data from various points in the supply chain, facilitate just-in-time production models in both sectors, minimising waste and maximising resource use efficiency.

3. Collaboration and Compliance

The complexity of modern supply chains needs collaboration across various stakeholders to meet compliance demands and achieve sustainability goals. In the food and fashion industries, regulatory pressures demand adherence to increasingly stringent standards on safety, labour practices, and environmental impact. Data sharing becomes essential in this context, allowing for a unified view of the supply chain and helping parties proactively identify and address compliance issues. Shared data platforms foster collaboration and streamline the audit processes by providing accessible, transparent data to all involved parties, including regulators.

Technology to enable all these benefits is already available and evolving very fast. However, one of the biggest challenges is data governance, which must give stakeholders confidence in data sharing.

Governmentality in the Digital Landscape

The role of governmentality in shaping ethical considerations within supply chains cannot be overstated. Drawing parallels between food and fashion supply chains, we examine how various stakeholders throughout the supply chain address technical and ethical challenges in the food supply chain that also apply to the fashion industry. The impact of AI-driven decision-making highlights the need for early ethical deliberation to mitigate inherent biases, ensuring that data-driven solutions enhance rather than hinder ethical business practices.

Trust frameworks offer a collaborative and participatory solution to ethical data governance challenges. Initially proposed within the context of food supply chains, these frameworks provide a structured approach to data exchange, defining rights, responsibilities, and normative standards in other contexts. As Brewer et al. (2021) advocate, trust frameworks can be tailored to accommodate the unique needs of supply chains, ensuring transparency, accountability, and data security.

Building on this foundation, reflexive governance architectures could serve as a catalyst for ethical scrutiny and capacity building within digital societies. As discussed in our paper, these architectures encourage stakeholders to reconsider underlying assumptions and institutional arrangements, supporting the development of more ethical data practices. As supply chains face the complexities of integrating AI and machine learning, the role of reflexive governance frameworks becomes crucial in addressing technical, ethical, and practical dimensions effectively, as shown in Table 1. Early participatory evaluation of those dimensions across the supply chain facilitates continuous dialogue and adaptation, which are essential for the sustainability and resilience of supply chains.

The intersection of data-sharing strategies in food and fashion supply chains illustrates the common challenges they face and the vast potential for cross-industry innovation. By embracing reflexive data governance, these sectors can enhance transparency and operational efficiency while cultivating a cooperative ecosystem that reshapes global supply chains toward greater sustainability and effectiveness.

More about Reflexible governance architectures here:

Manning, L., Brewer, S., Craigon, P., Frey, J., Gutierrez, A., Jacobs, N., Kanza, s., Munday, S., Pearson, S. and Sacks, J. (2023) Reflexive governance architectures: Considering the ethical implications of autonomous technology adoption in food supply chains. Trends in Food Science & Technology. Available at: https://doi.org/10.1016/j.tifs.2023.01.015