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Engineering, Industrial
Digital twins have the potential to significantly improve the efficiency and sustainability of the agri-food supply chain by providing visibility, reducing bottlenecks, planning for contingencies, and improving existing processes and resources. Additionally, they can add value to businesses by lowering costs and boosting customer satisfaction.
- digital twin
- agri-food supply chain
- contributions
1. Introduction
In recent decades, various technologies have been implemented to improve the efficiency of the agri-food supply chain. New challenges are arising that require the use of innovative solutions due to evolving market demands, regulations, and cost-effectiveness. As a result, increasing efficiency through effective, integrated smart technologies and approaches like digital twins (DTs) has been actively addressed in recent years. A DT is a new notion that has emerged alongside the advancement of Industry 4.0. It provides virtual representations of physical systems during their lifecycle using real-time data from sensors, thereby enhancing decision-making processes. The DT can represent both living and non-living objects, as well as processes that can be analyzed and simulated to interfere with the course of evolution [1]. The use of reliable DTs could be one of the most crucial techniques for monitoring supply chain processes in a real-time. As a result, the ability to simulate multiple operations and predict critical situations in advance enables rapid response and process modification, as well as enhancing resilience.
A DT is a virtual copy of a physical system, including its environment and processes, that is kept up to date by sharing information between the physical and virtual systems. It is a tool that has a continuous link between its physical and virtual counterparts (the twin) [1,2]. It consists of three components: a digital definition of its counterpart derived from CAD, Product Lifecycle Management (PLM), etc.; operational and experiential data of its counterpart gathered primarily using Internet of Things (IoT) data and real-time telemetry; and information model (dashboards, HMIs, etc.) that corresponds to and displays the information to facilitate decision-making. A DT is continuously learning and updating itself by using sensor data or external entities. All aspects of human activity, including the livestock sector, logistics, the petrochemical industry, and manufacturing, can profit greatly from DT systems [3]. The design, management, maintenance, development, and all industrial aspects related to goods, services, equipment, operations, and activities, as well as human resource management, can all be optimized with the use of these tools. Moreover, it enables users to remotely manage and control components and systems, as well as assess and predict resource- and process-related changes through “what if” analysis. Thus, firms would be able to assess information regarding service quality, new product development [4], and timely delivery. Furthermore, the DT is used to aid in the identification of control parameters to meet target KPIs and enhance the existing operation in terms of increasing energy efficiency and savings, reducing the number of off-target end products, improving process consistency, and reducing downtimes during maintenance [5].
In the context of the supply chain, the DT is a simulation model of an actual supply chain that forecasts supply chain dynamics using real-time data and snapshots [6] and that can send and receive data in both directions in real-time [7]. Supply chain DTs differ from conventional simulation models in terms of update frequency, powerful analytics capability, and simulation capability, allowing for deep synchronization and dynamic interaction between the physical and virtual worlds [8]. Supply chain analysts can use its output to assess supply chain activity, predict unforeseen events, and implement corrective measures. It is also used to monitor and forecast real-time changes in orders, supply, demand, approvals, and so on. As a result, firms can effectively evaluate their supply chain and adapt to changes more swiftly.
Despite the importance of DTs in improving agri-food supply chain activities, from the literature review conducted, it has emerged that the scientific community does not have a common understanding of the concept. As a result, DTs have been presented in a variety of ways in the articles. In certain cases, distinguishing DTs from digital models and digital shadows has become more challenging [9,10,11,12,13,14].
The adoption of DTs in agri-food supply chains is crucial because it enables the early detection of risks and the quality monitoring of food items using statistical, data-driven, or physics-based models [15]. Given the rising concerns about monitoring real-time activities, the agri-food supply chain continues to struggle with assuring traceability.
2. Challenges in the Agri-Food Supply Chain
The agri-food supply chain is a complex network of stakeholders who share common goals such as ensuring food quality, food security, food safety, and sustainability. It is subject to greater uncertainty and risk than other supply chains, raising serious issues concerning its impact on the environment, society, and the economy [20]. Additionally, unprecedented occurrences such as the COVID-19 pandemic and Ukraine’s prolonged war, as well as economic sanctions, have highlighted the vulnerabilities of global supply networks [21,22]. These factors can result in problems related to unexpected delays, cost management, collaboration, data synchronization, rising freight charges, demand forecasting, digital transformation, port congestion, and the perishable nature of products [23].
The agri-food supply chain is one of the sectors that use advanced tools to evolve into a data-driven, intelligent, agile, and autonomously connected system [4]. Recent technology breakthroughs in cloud computing, IoT, big data, blockchain, robotics, and AI provide smart connected systems [20], allowing for the automation of this industry. Automation approaches are essential for developing supply chain DTs, which can lead to scalable and sustainable growth in the industry.
3. Supply Chain DTs
A DT is a dynamic, real-time depiction of the different agents in the supply chain network that forecasts supply chain dynamics using real-time data and snapshots. In logistics, the supply chain DT maps the data, state, relationships, and behavior of the system, mimicking its behavior using dynamic simulation capabilities [21,24]. Four areas of DT application have been identified in the supply chain, including network level (network management and transportation), site, manufacturing, warehousing, and cargo handling [24]. Network management is concerned with managing and monitoring valuable networks, while the transportation domain includes use cases involving the network-level transportation of products and commodities. Manufacturing is the most common application area on the site level, involving tasks related to the production of goods. Warehousing covers applications related to facilities that store, ship, and return goods and materials.
Supply chain optimization through DTs adds value to businesses by lowering costs and boosting customer satisfaction [25]. To do this, all aspects of the supply chain must be upgraded, including material flows, financial flows, and information flows. By simulating alternative scenarios and identifying risks and opportunities, DTs enable businesses to optimize levels of inventory, reduce costs, enhance collaborations, and improve supply chain efficiency [26,27]. Additionally, supply chain management based on DTs does not require physical proximity, meaning that actual product movement from source to the consumer is no longer dependent on the location of the parties performing control and collaboration [28].
DTs are becoming increasingly popular in the agri-food supply chain due to their benefits, such as improved product quality, resource utilization, maintenance, production planning, reduced losses, improved logistics, energy savings, and increased visibility [18,19,22,29,30,31]. They enable supply chain actors to control demand, understand demand patterns, monitor food quality and marketability, track goods during transportation, ensure traceability, and monitor environmental conditions [26,32,33]. In agriculture, the use of such tools can provide information on fertilizers, chemicals, seeds, irrigation management techniques, environmental protection, pests, climate, crop monitoring management solutions, market demands, and business changes [34]. In general, DTs in the agri-food supply chain provide simulation and optimization, livestock tracking and health management [2,35,36,37], collaborative planning and collaboration [8], crop monitoring and management [38,39], supply chain visibility and traceability [40,41], and predictive analytics and decision support [42] to help farmers and supply chain managers identify patterns and generate actionable insights.
This entry is adapted from the peer-reviewed paper 10.3390/logistics7020033
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