Integration of Digital Twin Modeling and Intelligent Scheduling Frameworks in the Food Processing Industry - A Review

The food processing industry is one of the most complex manufacturing environments in terms of decision-making, production scheduling, and maintaining food safety [1]. Each market often demands unique product formulations, which leads to frequent changeovers and cleaning cycles to stay compliant and preserve product integrity [1]. These transitions can significantly impact operational efficiency — in fact, some large-scale plants experience 25–30% OEE losses solely due to cleaning and changeover activities. As product variety increases, so does the complexity of scheduling. Managing this manually can quickly become overwhelming — humans simply cannot evaluate every possible combination or scenario within a large-scale production environment [1]. Inefficient scheduling not only leads to production delays but also wastes resources and reduces throughput. This is where digital simulation and intelligent optimization models can make a substantial difference [1]. By simulating hundreds of potential production sequences, these models identify the most efficient plan, minimizing human error while maintaining compliance and productivity [1]. To achieve this, such systems must consider multiple interdependent factors, including: Market demand and customer-driven priorities, Material availability and supply variability, Operational efficiency, by minimizing cleans while ensuring product safety and regulatory compliance [1]. Beyond scheduling, maintaining food quality and safety is equally crucial — not just for consumer protection but also for public health and economic stability [2]. Traditional methods of food quality assessment, while effective, are often labor-intensive, destructive, and lack transparency or traceability [2]. Recent advancements in deep learning and computer vision now offer digitally intelligent, automated, and cost-effective solutions that enhance the precision, consistency, and speed of food quality monitoring [2]. As reviewed in the study [2], these technologies follow a typical workflow — beginning with data acquisition and preprocessing, followed by model selection, training, and validation. They are increasingly applied across various domains, such as image classification, object detection, image segmentation, and image generation [2]. Moreover, integrating these systems with the Internet of Things (IoT) and digital twin frameworks enables real-time monitoring, predictive maintenance, and proactive control of food processing operations [2]. The broader framework for such digital integration can be visualized through five interconnected modeling layers, linking consumer behavior and production dynamics [1]. Consumer preferences are represented at the sensory and sales levels, while production performance is modeled at the phenomena, unit-operation, and plant levels [1]. Together, these create a unified multi-scale model that connects what customers desire with how factories manufacture — ensuring a smooth flow of information and decisions across both domains [1]. Practical examples such as cream cheese fermentation and meat freezing demonstrate how these multi-layered models can guide decision-making in real industrial contexts as shown in the study [1]. While challenges remain in collecting and synchronizing data across different scales, the integration of simulation, IoT, and deep learning technologies shows tremendous potential to drive economic efficiency, food safety, and sustainable growth within the food processing industry [1,2]. This review contributes by establishing a structured framework that integrates digital twin modeling with intelligent scheduling to enhance decision- making, production efficiency, and food safety in a very complex environment. The proposed integration lays the framework across the industry hence therefore it bridges cognitive scheduling approaches and digital twin frameworks, highlighting how human decision-making and digital modeling can be integrated to improve production reliability, reduce changeovers, and support data-driven operations in complex food processing environments.