Browsing by Author "Kumar, Anil"

Now showing 1 - 15 of 15

Citation - WoS: 24
Citation - Scopus: 27
A Framework for Evaluating Information Transparency in Supply Chains
(IGI GLOBAL, 2021) Erhan Ada; Muhittin Sagnak; Yigit Kazancoglu; Sunil Luthra; Anil Kumar; Luthra, Sunil; Kumar, Anil; Ada, Erhan; Sagnak, Muhittin; Kazancoglu, Yigit
Private public profit and non-profit organizations and society as a whole currently face a significant reliable information necessity problem. Especially supply chains need trustworthy information to perform their activities successfully. This study aims to propose a framework and identify how reliability of information can be evaluated and measured through the concept of transparency. In this context dimensions such as comprehensiveness regularity timeliness content scope and user-friendliness are the pillars of the proposed framework. Selected criteria have been used as inputs to develop the information transparency level. The fuzzy analytic network process (ANP) is used to obtain weights of these inputs and data envelopment analysis (DEA) is used for the determination of the efficiency ranking for transparency. Results demonstrated that content scope and comprehensiveness dimensions have 75% impact on the transparency of data. The remaining 25% is affected by timeliness regularity and user-friendliness.
Citation - WoS: 53
Citation - Scopus: 66
An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling
(Springer, 2022) Rohit Agrawal; Vishal A. Wankhede; Anil Kumar; Sunil Luthra; Abhijit Majumdar; Yigit Kazancoglu; Luthra, Sunil; Kumar, Anil; Agrawal, Rohit; Wankhede, Vishal A.; Kazancoglu, Yigit; Majumdar, Abhijit
The world is moving into a situation where resource scarcity leads to an increase in material cost. A possible way to deal with the above challenge is to adopt Circular Economy (CE) concepts to make a close loop of material by eliminating industrial or post-consumer wastes. Integration of emerging technologies such as Artificial Intelligence (AI) machine learning and big data analytics provides significant support in successfully adopting and implementing CE practices. This study aims to explore the applications of AI techniques in enhancing the adoption and implementation of CE practices. A systematic literature review was performed to analyze the existing scenario and the potential research directions of AI in CE. A collection of 220 articles was shortlisted from the SCOPUS database in the field of AI in CE. A text mining approach known as Structural Topic Modeling (STM) was used to generate different thematic topics of AI applications in CE. Each generated topic was then discussed with shortlisted articles. Further a bibliometric study was performed to analyze the research trends in the field of AI applications in CE. A research framework was proposed for AI in CE based on the review conducted which could help industrial practitioners and researchers working in this domain. Further future research propositions on AI in CE were proposed. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 43
Citation - Scopus: 50
Analysing the adoption barriers of low-carbon operations: A step forward for achieving net-zero emissions
(Elsevier Ltd, 2023) Anil Kumar; Sunil Luthra; Sachin Kumar Kumar Mangla; Jose Arturo Garza-Reyes; Yigit Kazancoglu; Garza-Reyes, Jose Arturo; Kumar, Anil; Luthra, Sunil; Mangla, Sachin Kumar; Kazancoglu, Yigit
In November 2021 the 26th United Nations Climate Change Conference (COP26) was held in Glasgow UK the global leaders from nearly 200 countries stressed taking immediate action on the climate issue and how to ensure global net-zero emissions by 2030. It is possible to accelerate the transition to low-carbon energy systems the present study seeks to identify and analyse key barriers to Low Carbon Operations (LCO) in emerging economies. A critical literature review was undertaken to recognise the barriers linked to the adoption of LCO. To validate these barriers an empirical study with a dataset of 127 respondents from the Indian automobile industry was conducted. The validated barriers were analysed using Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. BWM is used to determine the priority ranking of barriers while the DEMATEL method is employed to elucidate the cause-effect inter-relationships among the listed barriers. The results suggest that ‘Economic’ is the most influential category of barriers followed by ‘Infrastructure’ and ‘Operational’. The results also show that the barriers ‘Economic’ ‘Environmental’ ‘Infrastructure’ and ‘Organizational Governance’ belong to the cause group. Some significant managerial implications are recommended to overcome these barriers and to assist firms in the successful adoption of LCO and achieving net-zero emissions. The work was carried out in the automotive industry in India but provides findings that may have wider applicability in other developing countries and beyond. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 26
Analyzing critical factors of strategic alignment between operational excellence and Industry 4.0 technologies in smart manufacturing
(Emerald Publishing, 2024) Melisa Ozbiltekin-Pala; Yigit Kazancoglu; Anil Kumar; Jose Arturo Garza-Reyes; Sunil Luthra; Ozbiltekin-Pala, Melisa; Garza-Reyes, Jose Arturo; Kumar, Anil; Luthra, Sunil; Kazancoglu, Yigit
Purpose: The manufacturing sector is highly competitive and operationally complex. Therefore the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies. Design/methodology/approach: First determining the critical factors based on literature review and expert opinions these criteria are weighted and analytical hierarchy process is run to calculate the weights of these criteria. Afterward the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study. Findings: As a result of AHP “Infrastructure for Right Methodology Techniques and Tools is in the first place” Organizational Strategy is in the second place while the third highest critical factor is “Capital Investment”. Moreover based on grey relational analysis (GRA) results the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies. Originality/value: This study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 7
Are we really addressing the roadblocks to adoption of renewable and sustainable energy technologies? Total interpretive structural modeling approach
(SPRINGER HEIDELBERG, 2024) Yigit Kazancoglu; Nazlican Gozacan; Sunil Luthra; Anil Kumar; Gozacan, Nazlican; Luthra, Sunil; Kumar, Anil; Kazançoğlu, Yiğit
Urban areas serve as a vital contribution to the global structural change towards renewable and sustainable energy technologies which also influence climate change. The aim of this paper is to identify the adoption roadblocks to renewable and sustainable urban energy technologies. This research has three parts: a mini-systematic literature study was conducted to identify the most prevalent roadblocks. Using total interpretive structural modeling (ISM) the relationships between the roadblocks and the source of causation were then examined. The roadblocks are classified based on their dependence and driving powers using MICMAC analysis in the third part of this research. The principal results and major conclusions demonstrate that all roadblocks are necessary for renewable and sustainable urban energy technologies. The roadblocks at level I are insufficient infrastructure lack of coordination among authorities lack of quality and reliable data and information and competition with non-renewable technologies, roadblocks in level II are lack of skilled and trained personnel limited public participation awareness and consumer interest and lack of standardized technology, roadblock in level III is high initial investment cost, and lastly roadblocks in level IV are lack of subsidies and financial support programs and absence of coherent related policies. Furthermore as a result of the MICMAC analysis none of the aforementioned roadblocks are classified as autonomous variables implying that they are all required. The dependent roadblocks to renewable and sustainable energy technologies are defined as lack of coordination among authorities lack of information and competition with non-renewable technologies. Moreover linkage roadblocks have high dependence and driving powers which are insufficient infrastructure limited awareness and consumer interest and lack of standardized technology. Lastly high initial investment costs lack of subsidies and financial support programs absence of coherent related policies and lack of skilled and trained personnel are the driving roadblocks with high driving power however not dependent.
Citation - WoS: 58
Citation - Scopus: 76
Behavioral factors on the adoption of sustainable supply chain practices
(Elsevier B.V., 2020) Anil Kumar; Md Abdul Moktadir; Syed Abdul Rehman Khan; Jose Arturo Garza-Reyes; Mrinal Tyagi; Yigit Kazancoglu; Khan, Syed Abdul Rehman; Moktadir, Md. Abdul; Garza-Reyes, Jose Arturo; Kumar, Anil; Tyagi, Mrinal; Kazançoğlu, Yiğit
Sustainable supply chain management (SSCM) has become a popular research topic among scholars as evidence suggests it has significantly contributed to achieve more environmentally conscious and socially responsible supply chains. Operational excellence (OE) on the other hand can be achieved by incorporating SSCM practices within existing supply chain operations. However due to human expertise involvement and commitment towards excelling at sustainable and operational performance the effective deployment of SSCM practices now depends on various human-based behavioral factors (BFs). Human behavior is dynamic in nature and hence has an effect on the implementation of SSCM practices. Nevertheless research on BFs in view of SSCM practices is limited. To fill this knowledge gap this study examines the nature of BFs for SSCM practices towards OE in supply chains particularly within the context of the footwear industry of Bangladesh. In the first phase the BFs were identified and determined through a literature review and empirical investigation. In the second phase the Hesitant Fuzzy DEMATEL method was used to establish the cause-effect relationships among the factors. The influence of group validation by experts and a literature survey along with managerial implications was discussed and explained in the third phase of the study. The results suggest that the factor ‘organization culture’ is the most influencing behavioral factor followed by ‘commitment from higher authority’. Both theoretical and practical contributions of the study are drawn from its findings helping footwear industry managers to more effectively adopt SSCM practices in the supply chain operations of their organizations to achieve OE. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 27
Citation - Scopus: 47
Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector
(MDPI, 2021) Yigit Kazancoglu; Muhittin Sagnak; Cisem Lafci; Sunil Luthra; Anil Kumar; Caner Tacoglu; Luthra, Sunil; Kumar, Anil; Lafcı, Çisem; Taçoğlu, Caner; Kazançoğlu, Yiğit; Sağnak, Muhittin
Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system and to maintain its processes effectively. In the healthcare sector the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials increased energy use and its environmental burden. In this context circularity and sustainability concepts have become essential in healthcare to meliorate the sector's negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector apply big data analytics in healthcare and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation and a proposal for a big data-enabled solutions framework to barriers to circularity using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings managerial policy and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.
Citation - WoS: 35
Citation - Scopus: 71
Blockchain technology for enhancing traceability and efficiency in automobile supply chain—a case study
(MDPI, 2021) Nesrin Ada; Manavalan Ethirajan; Anil Kumar; K. E.K. Vimal; Simon Peter Nadeem; Yigit Kazancoglu; K. Jayakrishna; Vimal, K. E. K.; Kandasamy, Jayakrishna; Kazancoglu, Yigit; Ada, Nesrin; Kumar, Anil; Nadeem, Simon Peter; Ethirajan, Manavalan
A robust traceability system would help organizations in inventory optimization reduce lead time and improve customer service and quality which further enables the organizations to be a leader in their industry sector. This research study analyzes the challenges faced by the automotive industry in its supply chain operations. Further the traceability issues and waiting time at different nodes of the supply chain are considered to be priority issues that affect the overall supply chain efficiency in the automotive supply chain. After studying the existing blockchain architectures and their implementation methodology this study proposes a new blockchain-based architecture to improve traceability and reduce waiting time for the automotive supply chain. A hyper ledger fabric-based blockchain architecture is developed to track the ownership transfers in inbound and out-bound logistics. The simulation results of the proposed hyper ledger fabric-based blockchain architecture show that there is an improvement in the traceability of items at different nodes of the supply chain that enhances the Inventory Quality Ratio (IQR) and the mean waiting time is reduced at the factory wholesaler and retailer which thereby improves the overall supply chain efficiency. The blockchain embedded supply chain is more capable to eliminate the risks and uncertainties associated with the automotive supply chain. The benefits of adopting blockchain technology in the automotive supply chain are also described. The developed blockchain-based framework is capable to get more visibility into goods movement and inventory status in automotive supply chains. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 29
Citation - Scopus: 33
Circular dairy supply chain management through Internet of Things-enabled technologies
(Springer Science and Business Media Deutschland GmbH, 2022) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Anil Kumar; Sunil Luthra; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Kumar, Anil; Luthra, Sunil; Kazancoglu, Yigit
Internet of Things-enabled technologies help to collect data and make it understandable especially in supply chain processes thus minimizing the problems that may arise in supply chains. It is extremely important to support this process with Internet of Things-enabled technologies especially in supply chains that are vulnerable to disruptions such as the dairy supply chain. Moreover dairy supply chains are the type of supply chains where the most waste is generated, evaluating this waste is very beneficial to the circular economy. Therefore monitoring data in dairy supply chains and using Internet of Things-enabled technologies prevent losses, it is critical to have Internet of Things-enabled circular dairy supply chains in operation. The aim of this study is to determine the success factors of Internet of Things-enabled circular dairy supply chains based on the various stages of these chains, we hope to match each dairy supply chain stage with a success factor of Internet of Things-enabled technology and determine a ranking for these factors. Hence six success factors of Internet of Things-enabled circular supply chains are weighted for each stage of the chain, Internet of Things-enabled digital technologies are then matched with each stage of the chain and the success factor is determined. The ranking of factors can then be drawn up through the integration of Step Wise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference Similar to Ideal Solution (TOPSIS). The outcome of this study will provide managers and policy makers with insights into Internet of Things-enabled circular dairy supply chains. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 68
Citation - Scopus: 79
Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management
(Emerald Publishing, 2025) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Sunil Luthra; Anil Kumar; Sezer, Muruvvet Deniz; Pala, Melisa Ozbiltekin; Luthra, Sunil; Kumar, Anil; Kazancoglu, Yigit; Ozbiltekin Pala, Melisa
Purpose: The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM). Design/methodology/approach: Ten different BDA drivers in FSC are examined for transition to CE, these are Supply Chains (SC) Visibility Operations Efficiency Information Management and Technology Collaborations between SC partners Data-driven innovation Demand management and Production Planning Talent Management Organizational Commitment Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia. Findings: The results show that Information Management and Technology Governmental Incentive and Management Team Capability drivers are classified as independent factors, Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility Data-driven innovation Demand management and Production Planning Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition Operations Efficiency Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM. Research limitations/implications: The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud implementing laws concerning government incentives reducing food loss and waste increasing tracing and traceability providing training activities to improve knowledge about BDA and focusing more on data analytics. Originality/value: The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 16
Investigating the role of knowledge-based supply chains for supply chain resilience by graph theory matrix approach
(SPRINGER, 2023) Muruvvet Deniz Sezer; Melisa Ozbiltekin-Pala; Yigit Kazancoglu; Jose Arturo Garza-Reyes; Anil Kumar; Vikas Kumar; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Garza-Reyes, Jose Arturo; Kumar, Anil; Kazancoglu, Yigit; Kumar, Vikas
Nowadays providing information flow at every phase of a knowledge-based supply chain with technologies has become a vital issue due to rapid population growth globalisation and increases in demand in the supply chain. Knowledge-based supply chains have a critical role in increasing resilience in supply chain processes with emerging technologies. Thus it is necessary to determine the critical factors that increase SC resilience. Therefore this study aims to determine SC resilience improvement factors in knowledge-based supply chains and investigate the importance level of determining factors using the Graph Theory Matrix Approach. The results suggest that the most important supply chain resilience improvement factor is Adaptive Capacity (F3) followed by Product Prioritization (F9) and Flexibility (F1) respectively. This study is expected to benefit managers and policymakers as it provides a better understanding of critical SC resilience improvement factors that play a role in knowledge-based supply chains. In order to increase resilience in the supply chain system thinking and solutions should be encouraged by businesses to increase collaboration with stakeholders. Businesses and governments should provide collaborative long-term solutions for the uncertain environment to ensure a sustainable and resilient environment.
Citation - Scopus: 35
Reinventing reverse logistics through blockchain technology: a comprehensive review and future research propositions
(Taylor and Francis Ltd., 2023) Ashutosh Samadhiya; Anil Kumar; Rohit Agrawal; Yigit Kazancoglu; Rajat Agrawal; Kumar, Anil; Samadhiya, Ashutosh; Agrawal, Rohit; Kazancoglu, Yigit
Blockchain Technology (BCT) has effectively evolved in reverse logistics (RL) to speed up its operation by decentralising tracing and monitoring the goods delivered to the end consumers. This study outlines the current research fashion of BCT applicability in RL from 2015 to 22. A wide range of 226 research papers is selected from the SCOPUS database to conduct the bibliometric and network analysis for offering a comprehensive literature review on research clusters and fashions of BCT in RL. Some primary research clusters such as infrastructure development sustainable manufacturing logistics circular supply chain and waste management are identified. The network analysis has helped identify pioneer authors journals and countries actively involved in BCT research in RL. The content analysis findings indicate the evolution of BCT in various themes of RL. The articles also develop a result systematisation framework to concisely offer the outcomes of this research. Further the current study provides recommendations for future research work for academic and industry practitioners based on the existing literature. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 39
Citation - Scopus: 51
Resilient reverse logistics with blockchain technology in sustainable food supply chain management during COVID-19
(John Wiley and Sons Ltd, 2023) Yigit Kazancoglu; Melisa Ozbiltekin-Pala; Muruvvet Deniz Sezer; Sunil Luthra; Anil Kumar; Ozbiltekin-Pala, Melisa; Sezer, Muruvvet Deniz; Luthra, Sunil; Kumar, Anil; Kazancoglu, Yigit
COVID-19 which is a global problem affects the all supply chains throughout the world. One of the supply chains most affected by COVID-19 is food supply chains. Since the sustainable food supply chain processes are complex and vulnerable in terms of product variety it has been negatively affected by the operational effects of COVID-19. While the problems experienced in the supply chain processes and raw material constraints caused stops in production the importance of new business models and production approaches came to the fore. One of the issues of increasing importance is the adoption of reverse logistics activities in sustainable food supply chains and increasing the resilience of food supply chains by integrating blockchain technology into processes. However adapting blockchain technology to increase the resilience of reverse logistics activities in the food supply chain has advantages as well as risks that need to be considered. Therefore it is aimed to determine these risks by using fuzzy synthetic evaluation method for eliminating the risks of blockchain adaptation for flexible reverse logistics in food supply chains to increase resiliency. The novelty of this study is that besides discussing about the benefits of BC-T it is to identify the risks it can create to eliminate these risks and to guide the establishment of resilience in reverse logistics activities of SFSCs. According to results the risks with the highest value among the subrisks are determined as data security risks. Data management risks are calculated as the risk with the highest value. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 15
Citation - Scopus: 15
The effect of green supply chain management practices on carbon-neutral supply chain performance: the mediating role of logistics eco-centricity
(EMERALD GROUP PUBLISHING LTD, 2024) Farheen Naz; Ashutosh Samadhiya; Anil Kumar; Jose Arturo Garza-Reyes; Yigit Kazancoglu; Vikas Kumar; Arvind Upadhyay; Upadhyay, Arvind; Garza-Reyes, Jose Arturo; Naz, Farheen; Samadhiya, Ashutosh; Kazancoglu, Yigit; Kumar, Anil; Kumar, Vikas
PurposeUsing the lens of the natural resource-based view (NRBV) theory this study investigates the effect of green supply chain management (GSCM) practices such as green manufacturing (GM) eco-design (ED) green purchasing (GP) and investment recovery (IR) on the carbon-neutral supply chain (CNSC) performance of firms through the mediating influence of logistics eco-centricity (LE).Design/methodology/approachA conceptual framework that hypothesizes the relationship between GSCM practices LE and the CNSC performance of firms is developed. Key GSCM practices are then identified using experts' opinions. Furthermore we collected responses from logistics companies to validate the conceptual framework using the partial least squares structural equation modeling (PLS-SEM) method.FindingsThrough this study we found that GSCM practices significantly improve a firm's CNSC performance and the relationships between GSCM practices and CNSC performance are positively mediated by LE.Practical implicationsThe implications of the study suggest that logistics managers can benefit from the findings of this study to comprehend the impact of various GSCM techniques on LE and CNSC from the viewpoint of the NRBV paradigm.Originality/valueThis research provides valuable perspectives for managers and supply chain (SC) practitioners in their quest for sustainable and environmentally responsible SC operations through an extensive and novel analysis of the connection between GSCM practices LE and CNSC performance.
Citation - WoS: 29
Citation - Scopus: 41
The role of agri-food 4.0 in climate-smart farming for controlling climate change-related risks: A business perspective analysis
(John Wiley and Sons Ltd, 2024) Yigit Kazancoglu; Çisem Lafci; Anil Kumar; Sunil Luthra; Jose Arturo Garza-Reyes; Yalcin Berberoglu; Garza-Reyes, Jose Arturo; Luthra, Sunil; Kumar, Anil; Lafci, Cisem; Berberoglu, Yalcin; Kazancoglu, Yigit
The impact of climate change including fires droughts and storms on natural resources and agricultural output is increasing. In addition to these problems resource depletion and greenhouse gas (GHG) emissions agriculture also contributes to global warming. To reduce the dangers of climate change farmers are using sustainable practices. This article aims to link agri-food 4.0 technology with climate-smart agriculture (CSA) to lessen the two-way interaction (both affecting and impacted) between the agricultural sector and global warming as well as dangers related to the agri-food business. In light of this information the research methodology of the paper is twofold. Initially related risks towards climate change and the CSA and agri-food 4.0 technologies to overcome these risks were determined through a literature review. Then risks and technologies are evaluated by adopting the TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) which is used for evaluating the criteria set with the related technologies to overcome climate change-related risks and provide a guiding map for academics and practitioners to eliminate risks associated with these climate change-related factors. According to the study's findings the highest-priority concerns in the agri-food industries that are connected to climate change include energy consumption food safety and GHG emissions. Internet of Things (IoT) bio-innovation and artificial intelligence are thought to be the most promising technological solutions to address these problems. © 2024 Elsevier B.V. All rights reserved.