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Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Photogrammetry systems are widely used in industrial manufacturing applications as an assistance measurement tool. Not only does it provide high-precision feedback for assembly process inspection and product quality assessment, but also it can improve the flexibility and robustness of manufacturing systems and production lines. However, with growing global competition and demands, companies are forced to enhance production efficiency, shorten production lifecycle and increase product variety by incorporating reconfigurable factory design that can meet challenging timeline and requirements. Although dynamic facility layout is widely investigated, the position selection for the photogrammetry system in dynamic manufacturing environment is usually overlooked. In this paper, dynamic layout of the V-STARS photogrammetry system is investigated and optimised in a digital-twin environment using deep reinforcement learning. The learning objectives are derived from the field of view (FoV) evaluation from point clouds 3D reconstruction, and collision detection from the digital twin simulated in Visual Components. The application feasibility of the proposed dynamic layout optimisation of the V-STARS photogrammetry system is verified with a real world industrial application. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

The adoption of simulation models as Digital Twins (DTs) has been standing out in recent years and represents a revolution in decision-making. In this context, we note increasingly faster and more efficient decisions by mirroring the behaviour of physical systems. On the other hand, we highlight the challenges to ensure the simulation models validity over time since traditional validation approaches have limitations when we consider the periodic update of the model. Thus, the present work proposes an approach based on the constant assessment of these models through Machine Learning and control charts. To this end, we suggest a monitoring tool using the K-Nearest Neighbors (K-NN) classifier, combined with a p-control chart, to periodically assess the validity of DT simulation models. The proposed approach was tested in several theoretical cases and also implemented in a real case study. The findings suggest that the proposed tool can monitor the DT functioning and identify possible special causes that could compromise its results. Finally, we highlight the wide applicability of the proposed tool, which can be used in different DT models, including near/real-time models with different characteristics regarding connection, integration, and complexity. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability). [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

The function of the smooth muscle cells lining the walls of mammalian systemic arteries and arterioles is to regulate the diameter of the vessels to control blood flow and blood pressure. Here, we describe an in silico model, which we call the 'Hernandez-Hernandez model', of electrical and Ca2+ signaling in arterial myocytes based on new experimental data indicating sex-specific differences in male and female arterial myocytes from murine resistance arteries. The model suggests the fundamental ionic mechanisms underlying membrane potential and intracellular Ca2+ signaling during the development of myogenic tone in arterial blood vessels. Although experimental data suggest that KV1.5 channel currents have similar amplitudes, kinetics, and voltage dependencies in male and female myocytes, simulations suggest that the KV1.5 current is the dominant current regulating membrane potential in male myocytes. In female cells, which have larger KV2.1 channel expression and longer time constants for activation than male myocytes, predictions from simulated female myocytes suggest that KV2.1 plays a primary role in the control of membrane potential. Over the physiological range of membrane potentials, the gating of a small number of voltage-gated K+ channels and L-type Ca2+ channels are predicted to drive sex-specific differences in intracellular Ca2+ and excitability. We also show that in an idealized computational model of a vessel, female arterial smooth muscle exhibits heightened sensitivity to commonly used Ca2+ channel blockers compared to male. In summary, we present a new model framework to investigate the potential sex-specific impact of antihypertensive drugs.