Transforming agrifood production systems and supply chains with digital twins

Tzachor A., Sabri S., Richards C.E., Rajabifard A., Acuto M.

Could computer simulation models drive our ambitions to sustainability in urban and non-urban environments? Digital twins, defined here as real-time, virtual replicas of physical and biological entities, may do just that. However, despite their touted potential, digital twins have not been examined critically in urban sustainability paradigms—not least in the Sustainable Development Goals framework. Accordingly, in this Perspective, we examine their benefits in promoting the Sustainable Development Goals. Then, we discuss critical limitations when modelling socio-technical and socio-ecological systems and go on to discuss measures to treat these limitations and design inclusive, reliable and responsible computer simulations for achieving sustainable development. Little is known about the potential of digital twins in the pursuit of sustainability. This study examines the likely benefits of digital twins in urban sustainability paradigms, their limitations when modelling socio-technical and socio-ecological systems and possible ways to attenuate them.

Tzachor A., Devare M., King B., Avin S., Ó hÉigeartaigh S.

Global agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes. Machine learning applications in agriculture can bring many benefits in crop management and productivity. However, to avoid harmful effects of a new round of technological modernization, fuelled by AI, a thorough risk assessment is required, to review and mitigate risks such as unintended socio-ecological consequences and security concerns associated with applying machine learning models at scale.

Pylianidis C., Snow V., Overweg H., Osinga S., Kean J., Athanasiadis I.N.

In the environmental sciences, there are ongoing efforts to combine multiple models to assist the analysis of complex systems. Combining process-based models, which have encoded domain knowledge, with machine learning models, which can flexibly adapt to input data, can improve modeling capabilities. However, both types of models have input data limitations. We propose a methodology to overcome these issues by using a process-based model to generate data, aggregating them to a lower resolution to mimic real situations, and developing machine learning models using a fraction of the process-based model inputs. We showcase this method with a case study of pasture nitrogen response rate prediction. We train models of different scales and test them in sampled and unsampled location experiments to assess their practicality in terms of accuracy and generalization. The resulting models provide accurate predictions and generalize well, showing the usefulness of the proposed method for tactical decision support.

Shoji K., Schudel S., Onwude D., Shrivastava C., Defraeye T.

Controlling the hygrothermal conditions around fresh fruit and vegetables is vital for their preservation. Therefore, cold chain stakeholders often measure temperature along the supply chain of fresh produce. However, the temperature is typically monitored only in one segment of the entire cold chain, namely from the supplier until the distribution center. Besides, such measured data are rarely used for decision-making because they are not translated into the impact on the quality of the products. We provide a solution by extending the monitoring until the retail stores and upcycling these thermal data into actionable metrics. To do so, we use physics-based digital twins, namely virtual representations of the food products. This study focuses on 331 cold chain shipments of cucumber, eggplant, strawberry, and raspberry imported from Spain to Switzerland. We followed these fruits through pre-cooling, thermally stable conditions at the distribution center, and the temperature ramp-up phase before arriving at the retail store. The temperature performance of each carrier and flow analysis of the shipment enabled us to map the complex logistic system better. The digital twins detected that the fruits lost 43 - 85% of their quality before being displayed at the retail store. This quality loss remains invisible to the retailer. Additionally, we found a strong correlation between fruit quality and shipment duration (i.e., for cucumber r = -0.95 ( P < 0.001)), which emphasizes the importance of shortening the shipment to prolong the freshness of the fruit. The digital twins have shown a large potential to help further maximize shelf life and uniform product quality.

Perno M., Hvam L., Haug A.

• Literature on digital twins in the process industry is fragmented and immature. • There is a lack of common understanding of what digital twins are. • Challenges to digital twin implementation are identified and categorized. • Enablers to digital twin implementation are identified and categorized. • A model connecting barriers and enablers for implementing digital twins is introduced. Since the introduction of the concept of “digital twins” (DTs) in 2002, the number of practical applications in different industrial sectors has grown rapidly. Despite the hype surrounding this technology, companies face significant challenges upon deciding to implement DTs in their organizations due to the novelty of the concept. Furthermore, little research on DT has been conducted for the process industry, which may be explained by the high complexity of accurately representing and modeling the physics behind production processes. To consolidate the fragmented literature on the enabling factors and challenges in DT implementation in the process industry, this study organizes the existing studies on DTs with a focus on barriers and enablers. On this basis, this study contributes to the existing body of knowledge on DTs by organizing the DT literature and by proposing conceptual models describing enablers of and barriers to DT implementation, as well as their mutual relationships.

Henrichs E., Noack T., Pinzon Piedrahita A.M., Salem M.A., Stolz J., Krupitzer C.

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

Blair G.S.

Digital twins emerged in the field of engineering but are now being applied in many areas of study. This article reflects on the enormous potential of digital twins of the natural environment and proposes an approach that builds on the massive legacy of process model understanding in this area combined with new insights from data understanding, including from AI/machine learning.

Lin L., Bao H., Dinh N.

• A two-tiered approach for digital twin development and assessment process. • Uncertainty quantification is a key to digital twin bottom-up assessment. • Software risk analysis is critical to digital twin top-down assessment. • Techniques in uncertainty quantification and software risk analysis are reviewed. A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMAC’s knowledge base. As a critical component in a NAMAC system, digital twins (DTs) are used to extract information from the knowledge base to support decision-making in reactor control and management during all modes of plant operations. With the advancement of artificial intelligence and data-driven methods, machine learning algorithms are used to build DTs of various functions in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the reactor digital instrumentation and control systems, uncertainty quantification (UQ) and software risk analysis is needed. As a comprehensive overview of prior research and a starting point for new investigations, this study selects and reviews relevant UQ techniques and software hazard and software risk analysis methods that may be suitable for DTs in the NAMAC system.

de Kerckhove D.

The personal digital twin extends to individual persons, a concept that originated in engineering to twin complex machines with a digital simulation containing a model of its functions to monitor its past and present behaviour, and repair, correct, improve or otherwise ensure its optimal operation. Several independent trends in technological developments are seen to converge towards the elaboration of the digital replication of individual human data and life history, notably in health industries. Among the main ones, we consider the ubiquitous distribution of digital assistants, the rapid progress of machine learning concurrent with the exponential growth of ‘personal’ Big Data and the incipient interest in developing lifelogs. The core hypothesis here is that among the psychological effects of the digital transformation, the externalization of cognitive faculties such as memory, planning and judgement, the decision-making processes located within the human person are also emigrating to digital functions, perhaps as a prelude to a later re-integration within the person via brain–computer interfaces. The paper concludes with ethical considerations about these ongoing developments. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Niederer S.A., Sacks M.S., Girolami M., Willcox K.

Mathematical modeling and simulation are moving from being powerful development and analysis tools towards having increased roles in operational monitoring, control and decision support, in which models of specific entities are continually updated in the form of a digital twin. However, current digital twins are largely the result of bespoke technical solutions that are difficult to scale. We discuss two exemplar applications that motivate challenges and opportunities for scaling digital twins, and that underscore potential barriers to wider adoption of this technology. Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.

Pylianidis C., Osinga S., Athanasiadis I.N.

• Digital twins have not been established in agriculture yet. • Agricultural digital twins could be used pervasively on different spatial and temporal scales, and with varying levels of complexity. • An application-based roadmap for the adoption of digital twins in agriculture is proposed. • Agricultural digital twins are challenged to capture the interactions between living systems and their environment. Digital twins are being adopted by increasingly more industries, transforming them and bringing new opportunities. Digital twins provide previously unheard levels of control over physical entities and help to manage complex systems by integrating an array of technologies. Recently, agriculture has seen several technological advancements, but it is still unclear if this community is making an effort to adopt digital twins in its operations. In this work, we employ a mixed-method approach to investigate the added-value of digital twins for agriculture. We examine the extent of digital twin adoption in agriculture, shed light on the concept and the benefits it brings, and provide an application-based roadmap for a more extended adoption. We report a literature review of digital twins in agriculture, covering years 2017-2020. We identify 28 use cases, and compare them with use cases in other disciplines. We compare reported benefits, service categories, and technology readiness levels to assess the level of digital twin adoption in agriculture. We distill the digital twin characteristics that can provide added-value to agriculture from the examined digital twin applications in agriculture and in other disciplines. Then, inspired by digital twin applications in other disciplines, we propose a roadmap for digital twins in agriculture, consisting of examples of growing complexity. We conclude this paper by identifying the distinctive characteristics of agricultural digital twins.

Neethirajan S., Kemp B.

Artificial intelligence (AI), machine learning (ML) and big data are consistently called upon to analyze and comprehend many facets of modern daily life. AI and ML in particular are widely used in animal husbandry to monitor both the animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and effective business decisions for the farmer. One particularly promising area that advances upon AI is digital twin technology, which is currently used to improve efficiencies and reduce costs across multiple industries and sectors. In contrast to a model, a digital twin is a digital replica of a real-world entity that is kept current with a constant influx of data. The application of digital twins within the livestock farming sector is the next frontier and has the potential to be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. The mental and emotional states of animals can be monitored using recognition technology that examines facial features, such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers to build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis of individual farms. Our goal in this review is to assess the progress toward the use of digital twin technology in livestock farming, with the goal of revolutionizing animal husbandry in the future.

Verdouw C., Tekinerdogan B., Beulens A., Wolfert S.

Digital Twins are very promising to bring smart farming to new levels of farming productivity and sustainability. A Digital Twin is a digital equivalent of a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Using Digital Twins as a central means for farm management enables the decoupling of physical flows from its planning and control. As a consequence, farmers can manage operations remotely based on (near) real-time digital information instead of having to rely on direct observation and manual tasks on-site. This allows them to act immediately in case of (expected) deviations and to simulate effects of interventions based on real-life data. This paper analyses how Digital Twins can advance smart farming. It defines the concept, develops a typology of different types of Digital Twins, and proposes a conceptual framework for designing and implementing Digital Twins. The framework comprises a control model based on a general systems approach and an implementation model for Digital Twin systems based on the Internet of Things—Architecture (IoT-A), a reference architecture for IoT systems. The framework is applied to and validated in five smart farming use cases of the European IoF2020 project, focussing on arable farming, dairy farming, greenhouse horticulture, organic vegetable farming and livestock farming. • Analyses how Digital Twins can advance smart farming. • Defines the concept and develops a typology of different types of Digital Twins. • Proposes a conceptual framework for designing and implementing Digital Twins. • Validated in a multiple case study as part of the European IoF2020 project.

Borowski P.

In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.

Crippa M., Solazzo E., Guizzardi D., Monforti-Ferrario F., Tubiello F.N., Leip A.

We have developed a new global food emissions database (EDGAR-FOOD) estimating greenhouse gas (GHG; CO2, CH4, N2O, fluorinated gases) emissions for the years 1990–2015, building on the Emissions Database of Global Atmospheric Research (EDGAR), complemented with land use/land-use change emissions from the FAOSTAT emissions database. EDGAR-FOOD provides a complete and consistent database in time and space of GHG emissions from the global food system, from production to consumption, including processing, transport and packaging. It responds to the lack of detailed data for many countries by providing sectoral contributions to food-system emissions that are essential for the design of effective mitigation actions. In 2015, food-system emissions amounted to 18 Gt CO2 equivalent per year globally, representing 34% of total GHG emissions. The largest contribution came from agriculture and land use/land-use change activities (71%), with the remaining were from supply chain activities: retail, transport, consumption, fuel production, waste management, industrial processes and packaging. Temporal trends and regional contributions of GHG emissions from the food system are also discussed. Data on GHG emissions from the food system are mostly scattered across sectors and remain unavailable in many countries. EDGAR-FOOD, a globally consistent food emission database, brings together emissions from food-related land use and land-use change, production, processing, distribution, consumption and residues over 1990–2015 at country level.

Zou Y., Wu J., Meng X., Wang X., Manzardo A.

Gong Y., Ma F., Wang H., Tzachor A., Sun W., Zhu J., Liu G., Schandl H.

AbstractThe intersection of artificial intelligence (AI) and industrial ecology (IE) is gaining significant attention due to AI's potential to enhance the sustainability of production and consumption systems. Understanding the current state of research in this field can highlight covered topics, identify trends, and reveal understudied topics warranting future research. However, few studies have systematically reviewed this intersection. In this study, we analyze 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling. These methods uncover patterns of topic interconnections and evolutionary trends. Our results identify 71 trending terms within the selected publications, 69 of which, such as “deep learning,” have emerged in the past 8 years. The word2vec analysis shows that the application of various AI techniques is increasingly integrated into life cycle assessment and the circular economy. The top2vec analysis suggests that employing AI to predict and optimize indicators related to products, waste, processes, and their environmental impacts is an emerging trend. Lastly, we propose that fine‐tuning large language models to better understand and process data specific to IE, along with deploying real‐time data collection technologies such as sensors, computer vision, and robotics, could effectively address the challenges of data‐driven decision‐making in this domain.

Pathak P., Damle M., Dange P., Pillai S.

Agricultural supply chains are undergoing a huge change due to the advancements in digital technology. These technologies have triggered the unknown concepts of sustainability, efficiency, and transparency. The integration of digital technologies like the big data analytics, blockchain, artificial intelligence (AI), and the Internet of Things (IoT) in agricultural supply chains is discussed in the chapter. IoT devices provide for precision farming and less resource waste. Blockchain technology improves transparency and traceability guaranteeing food safety. It also enables to confirm the calibre of agricultural products. Big data analytics is helpful in the better decision-making. All these technologies address the major issues facing the agriculture industry. The issues include resource management, climate change, and food security. But the technology adoption has its own challenges. It is necessary to address issues including data protection, farmers' digital literacy, and the large upfront costs associated with implementing new technologies.

Nunes L.J.

Background: This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. Efficient biomass logistics play a key role in supporting circular bioeconomy principles by improving resource utilization and reducing operational costs. Methods: Two optimization approaches are evaluated: a base MILP model designed for scenarios with single processing points and an advanced model that incorporates intermediate processing steps to enhance logistical efficiency. The models were tested using synthetic datasets simulating vineyard regions to assess their performance. Results: The models demonstrated significant improvements, achieving cost reductions of up to 30% while enhancing operational efficiency and resource utilization. The study highlights the scalability and real-world applicability of the proposed models. Conclusions: The findings underscore the potential of MILP models in optimizing biomass supply chains and advancing circular bioeconomy goals. However, key limitations, such as computational complexity and adaptability to dynamic environments, are noted. Future research should focus on real-time data integration, dynamic updates, and multi-objective optimization to improve model robustness and applicability across diverse supply chain scenarios.

Keefe D.H., Jang H., Sur J.M.

Shashidhar S., Sunil C.K., Vineetha B.

Gao Q., Chi C., Wang Z., Xu B., Zou Y.

Boyle N.B., Jenneson V., Okeke-Ogbuafor N., Morris M.A., Stead S.M., Dye L., Halford J.C., Banwart S.A.

Background: The global food system faces growing pressure from population growth, climate change, wealth inequity, geo-political instability, and damage to the ecosystems on which our food supply depends. Fragmentation of the priorities and needs of food system stakeholders—citizens, food producers, food industries, governments—compounds the problem, with competing or misaligned interests increasing the risk of failure to adequately meet the needs of those that form, and are served, by the food system. Growing consensus on the need for transformative system level change to address the problems facing the food system is yet to be significantly reflected in strategic action. Methods: The national food strategy of the UK is offered as an exemplar to discuss the need to promote more coherent and ambitious visions of transformative change that acknowledge the complexity of the food system as a whole. We draw upon cross-sectoral experience to distil the needs, priorities, and key food system tensions that must be acknowledged to promote transformative systems change that equitably delivers healthy sustainable diets, contributes to a resilient global food system, and protects the environment. Results: Greater coherence, ambition, and consideration of the food system as a whole are needed if a UK national food strategy is to contribute to significant transformative change. Conclusions: To promote this, we advocate for (1) a food system digital twin to model and test potential food system interventions or legislation; (2) a citizens’ forum to inform and co-develop a cohesive national food strategy; and (3) increased cohesion and integration of food system governance within government to drive a coherent, ambitious national food strategy.

Bilotto F., Harrison M.T., Vibart R., Mackay A., Christie-Whitehead K.M., Ferreira C.S., Cottrell R.S., Forster D., Chang J.

Vargas-Canales J.M.

This analysis aims to explore the urgent need to drive a major transformation of the agri-food sector. With this, it is intended to contribute to defining strategies for the future of agri-food systems. In this sense, the dynamics and importance of the agri-food sector are examined. Scientific and technological developments in the sector are described below. Subsequently, the implications of the link between health and agri-food systems are discussed. Next, alternatives are proposed to recover, heal and improve agri-food systems and the planet. Finally, some strategies are formulated to begin the great transformation of the agri-food sector, a transformation for the life and well-being of all. With appropriate planning and management, the great transformation of the agri-food sector can be achieved and the demand for healthy, nutritious and safe foods can be met.

Zaidi S.A., Khan S.A., Chaabane A.

Inayatulloh, Husni H.S., Hartono I.K.

Hakiri A., Gokhale A., Yahia S.B., Mellouli N.

The rapid growth of industrial digitalization in the Industry 4.0 era is fundamentally transforming the industrial sector by connecting products, machines, and people, offering real-time digital models to allow self-diagnosis, self-optimization and self-configuration. However, this uptake in such a digital transformation faces numerous obstacles. For example, the lack of real-time data feeds to perform custom closed-loop control and realize common, powerful industrial systems, the complexity of traditional tools and their inability in finding effective solutions to industry problems, lack of capabilities to experiment rapidly on innovative ideas, and the absence of continuous real-time interactions between physical objects and their simulation representations along with reliable two-way communications, are key barriers towards the adoption of such a digital transformation. Digital twins hold the promise of improving maintainability and deployability, enabling flexibility, auditability, and responsiveness to changing conditions, allowing continuous learning, monitoring and actuation, and allowing easy integration of new technologies in order to deploy open, scalable and reliable Industrial Internet of Things (IIoT). A critical understanding of this emerging paradigm is necessary to address the multiple dimensions of challenges in realizing digital twins at scale and create new means to generate knowledge in the industrial IoT. To address these requirements, this paper surveys existing digital twin along software technologies, standardization efforts and the wide range of recent and state-of-the-art digital twin-based projects; presents diverse use cases that can benefit from this emerging technology; followed by an in-depth discussion of the major challenges in this area drawing upon the research status and key trends in Digital Twins.

Qiao J., Zhang M., Qiu L., Mujumdar A.S., Ma Y.

Fresh foods are prone to deterioration in complex and variable condition's encountered in supply chains. Currently it is not possible for consumers to intuitively or visually know the quality of fresh food at the time of purchase or subsequent storage prior to consumption which can result in issues of food safety and even wastage. Therefore, it is important to devise technologies to monitor the quality of fresh foods during transportation and marketing as well as to be able to reliably predict its possible deterioration so as to provide visual warning to the stakeholders. In the context of digital and intelligent transformation, some emerging technologies have received more attention. Intelligent packaging and digital twin provide innovative ideas for providing visual feedback concerning product quality. Intelligent packaging, a packaging system for monitoring, displaying and tracing of food quality status in supply chain offers great advantages. Further, digital twin displays great potential to provide reliable and timely quality prediction and risk warning with strong support of blockchain, artificial intelligence and big data analysis. In order to better focus on the development and integration of these emerging technologies to achieve visualization requirements for stability, accuracy and timeliness, this review comprehensively discusses recent research and progress in application of intelligent packaging (indicators, sensors and data carriers) and Industrial 4.0 technologies (blockchain, artificial intelligence, big data analysis and digital twin) by focusing on data acquisition, traceability, processing and visualization in fresh foods supply chain. Moreover, challenges and potential of these technologies are presented. This will better help stakeholders to make optimal decisions throughout the supply chain of fresh foods to meet the challenges of food waste and food safety.

Yadav V.S., Majumdar A.

Digital Twin (DT) is a technology platform that is revolutionizing the supply chain digitization process by creating virtual representations of physical systems. Agro-food supply chain (AFSC) is one of the most important supply chains that can be made more efficient by widespread adoption of DT. However, the adoption and implementation of DT in AFSC is very limited due to various hindrances. Thus, it is imperative to identify and analyze the DT barriers thoroughly; and subsequently, develop strategies to overcome the dominant barriers for the successful implementation of DT in AFSC. This study identifies the barriers to DT implementation through a literature review and experts’ opinions. The interaction amongst the barriers is captured using the “Weighted Influence Non-linear Gauge System (WINGS)” method. Lack of technology infrastructure, technology immaturity, and high capital investment emerge as the dominant causal barriers. Furthermore, to overcome the identified barriers, a framework based on a triple helix approach is suggested. The findings of the study will be useful for government agencies, policymakers, agricultural institutions, and agro-food industry stakeholders to eliminate the obstacles to the successful implementation of DT in AFSC.