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1 Introduction

The food consumed by individuals and organisations is a daily choice that has far reaching effects. Food consumption and waste can be linked directly to the 2nd and 3rd and indirectly to the 12th Sustainable Development Goals (SDGs) [1]. It is estimated that roughly one third of food produced for human consumption is either lost or wasted [2]. Wastage of food amounts to significant environmental damage, from pollution to the loss of the commodity and the energy required to create it [3]. The environmental impacts from reducing food loss and waste are far-reaching and profound, affecting SDG 6 (sustainable water management), SDG 13 (climate change), SDG 14 (marine resources), SDG 15 (terrestrial ecosystems, forestry, biodiversity), and many other SDGs, such as the Zero Hunger goal (SDG 2), calling for an end to hunger, the achievement of food security, improved nutrition and sustainable agriculture [4].

Meats are considered safety-critical foods; meaning they are susceptible to spoilage and pathogenic microorganisms which affect both food quality and safety [5]. Meat production like beef is over 30 times more carbon intensive than tofu [6], with green and blue water requirements exceeding that of most other foods [7]. With global meat consumption set to increase by 12% by the end of the decade [8], sustainable, safe and efficient meat production will be key for the attainment of SDGs.

Technological advancements in the storage, packaging, transportation, and sale of food products have contributed to increased safety and efficiencies in the food supply chain. Emerging digital technologies such as the Internet of Things (IoT), Big Data, etc. provide a growing range of opportunities to transform food systems. Of the various digital technologies in development, Digital Twin (DT) is well poised to underpin the solution of many of the food chain’s problems like loss and waste [9], increase yields and efficiencies and global meat consumption [8]. Furthermore, with supply chains moving to “value webs” characterized by complex, connected and interdependent relationships, DTs enhanced capabilities for learning and collaboration will be critical to enable real-time supply chain optimisation and resilience [10].

Whilst DT technologies are currently being trialled to enhance productivity, especially by large enterprises [11], their applications are usually limited to one or only a few areas of the supply chain, missing the more holistic opportunities and benefits DTs can offer for a “zero-waste” circular meat supply chain.

To address this gap, this paper proposes a conceptual framework of DT for a circular meat supply chain. This paper is organised with background information relating to the key areas in a circular meat supply chain in Sect. 2. Section 3 proposes a conceptual framework of DT for sustainable meat production within a circular meat supply chain, with a description of its major areas. The final section draws some conclusions and suggestions for future work.

2 Background

The circular economy is a model of production born from the idea of “zero waste” and extends this idea to reduce overconsumption and restore and regenerate ecosystems and natural capital [12]. A circular economy aims to maximise resourcefulness; reducing material resources taken, improving the efficiency of what is made and recycling what is disposed to feed back into the cycle [13].

Contrary to a “take–make–dispose”, linear food supply chain, a circular supply food supply chain (CFSC) does not assume infinite resources. A CFSC seeks to reduce the loss and waste produced by all stages, from production, storage, transport to end users. With the inevitable waste that does occur, the resource is reused to extract energy (e.g., use of anaerobic digestors) and the nutrients cycled back into the food production system, thus closing the loop.

In this paper, 8 key areas have been identified for a circular meat supply chain and are depicted in Fig. 1. These are: land management, animal management, food processing, food products and packaging, transportation, retail, household and hospitality, and waste management. Land management refers to any form of husbandry that concerns soil or plants and animal management that of livestock. Food processing is differentiated from products and packaging as the management of processes rather than the output of processed and packaged food products. Transportation encompasses the movement at any stage, of the food products. Retail concerns the sale of the commodity.

Fig. 1.
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Key areas in a circular meat supply chain.

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This is different from household and hospitality, which primarily concerns the consumption of the foodstuff. Finally, waste management covers any form of recycling of lost and wasted food offering the resource back to the cycle.

3 A Conceptual Framework of Digital-Twin for a Circular Meat Supply Chain

Digital Twins (DTs) are typically described as consisting of physical objects, their virtual counterparts, and the data connections in between [14]. DTs close the data feedback loop between the digital and the physical objects, so the data flows between the physical and a digital object in both directions. All these DT characteristics and relationships are made explicit in the proposed conceptual framework of Digital-Twin for a circular meat supply chain (Fig. 2).

As shown in Fig. 2, the Data between physical and digital assets has a two-way flow. In the Sensing stream, measured state data of Physical Objects flows from to the digital realm to build their corresponding Digital Objects. The latter generate the actionable data corresponding to strategy, decisions and actions in the physical realm, which flow back from the Digital Objects to the Physical Objects (Actuation).

In the physical realm, real-time state data of the Physical Objects in each area is collected by sensing Edge Devices. An array of sensors/transducers in a wireless sensor network (WSN) measure relevant physical parameters, which are transformed into electric voltages or currents. If necessary, interfacing circuits (e.g., Analogue-to-Digital Converter) convert analogue electrical signals into digital format. In each WSN node, a microcontroller (either a microprocessor or a single-board computer) collects and sends the digitalised sensor data to a transceiver. Wireless communication technologies like Bluetooth, Wi-Fi, GPRS and NFC provide connectivity capabilities for diverse edge devices.

Fig. 2.
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Proposed conceptual framework of digital-twin and implementation.

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A Gateway is usually located in the vicinity of the connected devices. Sometimes, a proxy server may collect and process data to send it the Internet by using MQ Telemetry Transport (MQTT) standards, or HTML or Extensible Messaging and Presence Protocols (XMPP). Nowadays, the use of Android smart devices and equivalent operating systems is increasing in popularity, as they can be employed as a gateway for 3G and 4G networks [15].

Given the large number of sensors, amount and variety of data collected across the different areas of the food/meat supply chain, Big-Data-type solutions are required for such large and complex datasets. Whilst structured data (e.g., temperature, location, etc.) would favour SQL databases (e.g., Oracle, MySQL, etc.) for Data Storage, the use of No-SQL databases (e.g., MongoDB, Cassandra, etc.) allows the inclusion of unstructured data (e.g., digital photos and video files of crops, animals, stocks, etc.).

Raw data can be stored in a data lake for subsequent Data Mining. Starting from descriptive analytics, data is sorted and cleaned to assess the status of the physical objects, which will inform the model(s) for their corresponding Digital Objects. Through AI, Machine Learning insights from collected, relevant data can be teased out to understand why any particular events or changes in the state of the physical objects happened. Combined with historical data, predictive analytics enable to forecast the likelihood of future events. Using prescriptive analytics, insights about what to do to achieve a particular outcome (e.g., maximise yield, etc.) can be obtained.

Informed by the real-time data collected from the physical objects, the master model underlying the DT’s Digital Objects is used to generate actionable data regarding the decisions and actions to be performed on the physical objects (Actuation), namely through actuators. Given the finite resources, (e.g., water, fuel, etc.), reinforcement learning could help with resource allocation for specific goals (e.g., crop yields, animal growth, in-time delivery to warehouses, retailers, etc.). A data warehouse could be used as a repository for this filtered, actionable data (e.g., digital objects’ data).

3.1 Proposed Conceptual Framework of DT Applied to a Circular Meat Supply Chain

The proposed conceptual framework of Digital-Twin applied to a circular meat supply chain is shown in Fig. 3. By integrating all areas, processes, objects and technologies, traceability, controllability and safety in the food supply chain can be improved: from feed crops, animals, processing, distribution, sale, use and disposal of the food products. Data and knowledge across the supply chain are linked (Fig. 4), so production, planning, resource use and logistics are optimised to reduce waste and costs.

Land Management.

The state and attributes of Physical Objects like soil pH, moisture content and nutrient levels (e.g., nitrogen (N), phosphorus (P) and potassium (K)) can be monitored at the Sensing stage of the DT, in addition to plant/crop growth and environmental conditions (solar radiation/light levels, temperature, humidity, etc.). With scarce resources like water, DTs can help with resource allocation (e.g., irrigation) to meet specific conditions (e.g., soil moisture) that can maximise yields (Actuation).

Animal Management.

Physical Objects in this area range from individual animals (e.g., level of exercise & rest and grazing patterns through GPS/location data) to environmental/housing conditions (e.g., temperature, humidity, light levels, etc.). DTs can provide insights into animal health and welfare by monitoring, predicting and influencing animal behaviour and environment conditions (Sensing). Combined with animal feed/crop data, DTs can help farmers and farm vets to maintain the optimum conditions for animal health and growth (Actuation).

Fig. 3.
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Proposed conceptual framework of DT applied to a circular meat supply chain.

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Food Processing.

DTs can enable the optimisation of the processes by which raw food materials are turned into final products. Namely, key environment (e.g., temperature) and operation conditions (e.g., machine blades, etc.) from processes like cutting and boning, chilling, rendering, etc.) are monitored (Sensing) and controlled (Actuation) to enhance quality and control (e.g. avoid over trimming, etc.). Combined with relevant operations and market data (e.g., raw material availability, customer demand, etc.), Big-Data, AI-powered DTs can add extra efficiencies in operations planning and scheduling via improved demand and supply forecasting.

Food Products and Packaging.

Enabled by innovations in sensing and IoT, DTs can exploit technological advances like smart packaging [16] to monitor and control the safety and quality of processed/finalised products. Dotted with integrated sensors and intelligent labels, smart packaging can measure (Sensing) markers of freshness and/or identify the presence of harmful components in food. For Actuation, smart active packaging is showing promising results in providing augmented functions, from warning users when spoilage occurs to preserve the product. Examples of the latter include CO2-emitting pads and antimicrobial preservative releasers that inhibiting microbial growth in meat and antioxidant releasers to reduce fat oxidation [16].

Transportation.

The route food takes from farm to fork is complex, unique and unpredictable. DTs can capture the unique history of a product as it travels through the supply chain, thus offering improved traceability and authentication. With regards Sensing, there are significant opportunities in the use of IoT-powered sensors in vehicles for food distribution and storage [17]. Combined with location (e.g., GPS) and smart packaging technologies, DTs could monitor of environmental conditions and quality evolution of food products during transport and storage, informing distribution and warehouse operations and planning (Actuation).

Retail.

Reasons for food waste in the retail stage typically include inappropriate storage facilities and conditions (e.g., fridge/freezer errors), controls and quality checks at shelving (e.g., products passing beyond ‘best before’ dates) and inaccurate stock forecasting, like overstocking. Powered technologies like IoT and smart(er) packaging (Sensing), DTs can offer a valuable opportunity for the retail sector to reduce waste by providing accurate use by dates to inform stock management and planning, with their consequent impact on pricing (e.g., lower/more competitive pricing) and operational efficiencies (Actuation).

Household and Hospitality.

Only in the UK, near 7.6Mt of food waste is generated every year by households and the hospitality sectors, up to 75% of which is avoidable [18]. The leading cause of waste generation in UK households is not using food in time, with 4.5Mt of still edible food products being thrown away and an associated loss of almost £14 billion [19]. With accurate, “real-time” use by dates, DTs can offer significant value to consumers and professionals for better meal and stock planning and ultimately less domestic and HFSS food waste.

Waste Management.

Food waste and by-products can be re-integrated in the supply chain in several ways, such as by redistributing surplus food and diverting into animal feed [19]. Namely, DTs can help managing and coordinating surplus redistribution (between donors and beneficiaries) and waste reintroduction (e.g., controlling environmental conditions to turn waste into compost).

Fig. 4.
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Integration of data & knowledge across the circular meat supply chain with the proposed framework of DT, showing sensing and actuation flows.

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4 Conclusions and Future Challenges/Work

Every year, food waste contributes to up to 10% of total greenhouse gas emissions and generates more than 900 million tonnes of residues. With a global population forecasted to reach 9 billion in the next decade, reducing food waste has never been more critical to tackle the current climate crisis and achieve UN’s SDGs: Whilst wastage occurring at all areas of the supply chain, most of the proposed technology-based solutions still treat each area as silos. To overcome this limitation, this paper proposes the first framework of Digital Twin that integrates key technological enablers across all the key areas in a “zero-waste” circular meat supply chain. Meat has been purposedly chosen, as it is one of the most energy and resource-intensive food products. With an ever-increasing global demand posed to reach an historical maximum in the next decade, maximising efficiencies across all the areas of the meat supply chain are critical.

This framework represents the starting point for new conceptual thinking in food supply chain that uses digital twinning to integrate all areas, processes, objects and technologies for a sustainable, “zero-waste” food supply chain. Whilst applied to the meat sector, the proposed conceptual framework is comprehensive and versatile, looking at key food supply chain areas and thus could be used and/or adapted to other food sectors with little or no loss of relevance and/or applicability.

Future work will focus on the implementation of this framework, with the generation of a simulation model as the first step towards the creation and application of the DT in the meat/food supply chain.