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Food supply chains face growing pressure to reduce waste, protect product quality, and improve sustainability performance. Yet many supply chain decisions remain reactive, even though food products are perishable, demand is volatile, and quality deterioration often begins before problems become visible. This article proposes that food systems require a more integrated predictive orientation rather than isolated technological improvements. The central problem is that demand forecasting, waste analytics, cold-chain monitoring, and sustainability assessment are often developed as separate capabilities. When these tools remain disconnected, prediction does not necessarily translate into coordinated action or measurable environmental improvement. The absence of an integrated model limits the ability of food supply chains to anticipate waste, preserve quality, and learn from sustainability outcomes. The objective of this article is to propose a conceptual model of Predictive Food Systems. The model connects demand forecasting, waste reduction and inventory intelligence, cold-chain intelligence, and sustainability performance within a closed-loop architecture. It argues that predictive value emerges when data from each component continuously inform decisions across the whole system. The article is based on a conceptual synthesis of 26 peer-reviewed journal articles published between 2017 and 2025. These studies cover food logistics, artificial intelligence, demand forecasting, dynamic inventory management, cold-chain monitoring, food waste accounting, and sustainability assessment. The article does not report new empirical data but uses existing evidence to construct an original systems-oriented framework. The proposed model specifies how demand prediction can reduce overproduction, how inventory intelligence can redirect or discount perishable stock, how cold-chain intelligence can preserve shelf-life, and how sustainability metrics can feed back into predictive systems. Three tables organise the model’s forecasting approaches, cold-chain technologies, and integrated components. The article concludes that predictive food systems can become a practical pathway for reducing waste, improving quality, and advancing sustainability performance.

Digital food systems promise to connect farms, processors, logistics providers, retailers, regulators, and consumers through continuous data exchange. In this vision, production conditions, quality records, traceability events, safety alerts, inventory signals, and sustainability indicators move across the food chain as shared informational infrastructure. Yet the operational reality remains far less integrated, with food data often fragmented across systems that cannot easily communicate. The central problem is that existing debates frequently treat interoperability as a technical matter of software connection, data standards, or platform implementation. Such accounts understate the systemic, organizational, and political conditions that make data exchange difficult even when digital technologies are available. The result is a persistent gap between the promise of farm-to-fork data continuity and the practical experience of disconnection. This article develops a theory of data frictions in digital food systems. Data frictions are conceptualised as points of resistance, incompatibility, and disconnection that interrupt data flow between heterogeneous food system actors. The objective is to explain why interoperability fails across farms, processors, retailers, and regulators despite growing investment in digital agriculture, traceability systems, blockchain platforms, and data governance frameworks. The article adopts a conceptual theory-building approach based on a synthesis of 26 peer-reviewed journal articles published between 2017 and 2025. It draws on information systems, food supply chain management, agricultural data governance, platform studies, and organizational theory. The synthesis does not present new empirical data but uses existing evidence to construct a theoretical explanation of interoperability failure. The theory identifies four interrelated categories of data friction: technical, semantic, organizational, and political. These frictions manifest differently across the farm-processor-retailer-regulator nexus but jointly undermine coordination, traceability, accountability, and digital transformation. The article argues that data frictions are not peripheral glitches but structural features of digital food systems, requiring governance innovation, shared standards, and incentive alignment across the chain.

Circular food systems require a shift from linear models of production, consumption, disposal, and remediation toward systems that continuously recover value from biological materials. This conceptual article argues that such a transition cannot rely on policy, behavioural change, or technological substitution alone. It also requires an intelligence layer capable of sensing waste generation, interpreting material quality, identifying recovery options, and coordinating decisions across fragmented food-system actors. Artificial intelligence offers this intelligence layer by enabling predictive, diagnostic, and decision-support capacities across the food chain. Rather than treating AI as a narrow automation tool, this article positions AI as cognitive infrastructure for circular food economies. Its role is to connect data on food loss, by-product composition, logistics, environmental impacts, recovery technologies, and stakeholder constraints. The article develops a conceptual framework for AI-enabled circular food systems. The framework integrates five functions: waste detection, valorisation pathway selection, resource recovery, sustainability-oriented decision support, and cross-actor coordination. These functions are presented as mutually reinforcing layers that allow food materials to be tracked, evaluated, redirected, and valorised before they become unmanaged waste. The article contributes to sustainability and circular economy scholarship by linking food-waste valorisation with material flow intelligence and multi-criteria decision support. It highlights how AI can support real-time evaluation of waste streams, match heterogeneous residues with suitable recovery pathways, and improve the coordination of producers, processors, retailers, consumers, waste managers, and policymakers. It also clarifies the risks of fragmented digitalisation when data systems are not aligned with circular outcomes. The proposed framework is intended for researchers, practitioners, and policymakers seeking to design circular food systems that are adaptive, evidence-based, and sustainability-oriented. It does not present new empirical data. Instead, it provides a conceptual basis for future empirical testing, digital platform development, policy design, and interdisciplinary research on AI-enabled food circularity.