Unlocking the Commercialization of SAF Through Integration of Industry 4.0: A Technological Perspective

Abstract

Sustainable aviation fuel (SAF) has demonstrated significant potential to reduce carbon emissions in the aviation industry. Multiple national and international initiatives have been launched to accelerate SAF adoption, yet large-scale commercialization continues to face technological, operational, and regulatory barriers. Industry 4.0 provides a suite of advanced technologies that can address these challenges and improve SAF operations across the supply chain. This study conducts an integrative literature review to identify and synthesize research on the application of Industry 4.0 technologies in the production and distribution of SAF. The findings highlight that technologies such as artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, and 3D printing can enhance feedstock logistics, optimize conversion pathways, improve certification and compliance processes, and strengthen overall supply chain transparency and resilience. By mapping these applications to the six key workstreams of the SAF Grand Challenge, this study presents a practical framework linking technological innovation to both strategic and operational aspects of SAF commercialization. Integrating Industry 4.0 solutions into SAF production and supply chains contributes to reducing life cycle greenhouse gas (GHG) emissions, strengthens low-carbon energy systems, and supports the United Nations Sustainable Development Goal 13 (SDG 13). The findings from this research offer practical guidance to policymakers, industry practitioners, investors, and technology developers seeking to accelerate the global shift toward carbon neutrality in aviation.

1. Introduction

As global aviation continues to expand, so does its environmental footprint, particularly concerning greenhouse gas (GHG) emissions. Aviation currently accounts for 2–3% of global carbon dioxide emissions. Without targeted interventions, this share is expected to grow significantly over the coming decades due to increased air travel demand [1,2,3]. Figure 1 shows the carbon dioxide emissions generated by the aviation sector, considering different scenarios such as not doing anything regarding improving the aviation sector to reduce its emissions and making improvements in areas such as aircraft technology, operations, and SAF [4].

Sustainable aviation fuel (SAF), derived from renewable biomass, waste materials, or synthetic processes, has emerged as a promising solution to substantially reduce aviation-related carbon emissions [2]. SAF can reduce life cycle GHG emissions by 50–90% compared to conventional fossil-based jet fuels [5]. Promoting SAF production aligns with the United Nations Sustainable Development Goal (SDG) framework, which is designed to address interconnected global challenges arising from issues such as inequality, poverty, climate change, and environmental degradation [6].

Specifically, there are 17 Sustainable Development Goals (SDGs), including SDG 1—No Poverty; SDG 2—Zero Hunger; SDG 3—Good Health and Well-Being; SDG 4—Quality Education; SDG 5—Gender Equality; SDG 6—Clean Water and Sanitation; SDG 7—Affordable and Clean Energy; SDG 8—Decent Work and Economic Growth; SDG 9—Industry, Innovation, and Infrastructure; SDG 10—Reduced Inequalities; SDG 11—Sustainable Cities and Communities; SDG 12—Responsible Consumption and Production; SDG 13—Climate Action; SDG 14—Life Below Water; SDG 15—Life on Land; SDG 16—Peace, Justice, and Strong Institutions; SDG 17—Partnerships for the Goals. To date, these 17 goals have been supported by 169 targets, 4063 events, 1364 publications, and 8521 actions [7].

The SDG frameworks are of great significance as they offer a practical framework for aligning governments, policymakers, businesses, society, and individuals toward common objectives. Numerous scholars have examined the SDGs from various perspectives to highlight their importance [8,9]. Notably, SDG 13 emphasizes the importance of undertaking measures to combat climate change and mitigate its subsequent impacts [10]. According to Church and White [11], climate change refers to alterations in weather patterns and extreme weather phenomena, such as heatwaves and floods. It also involves the rise in the Earth’s temperature, changes in rainfall, and an increase in sea level.

Among the most impactful developments of SDGs is the growing interest in digital technologies, especially in the Industry 4.0 context [12]. Industry 4.0 is characterized by its integration of modern technologies. These include artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, big data analytics, cloud computing, augmented reality (AR), virtual reality (VR), and robotics [13,14,15,16,17,18,19]. Figure 2, under Section 2.2, distinguishes five principal Industry 4.0 technologies along with their main subcomponents. Disruptive technologies such as blockchain, AI, big data analytics, and Internet of Things (IoT) have enhanced operational efficiency through stronger connectivity and scalable solutions across sectors. These technologies appear promising when applied to supply chain integration and SAF production. They have significant potential in commercializing SAF production, expediting development, improving fuel quality, simplifying certification procedures, and improving infrastructure compatibility [20].

There is a large body of literature that has demonstrated the transformative role of Industry 4.0 technologies in achieving SDGs. For instance, based on a review of 473 articles, Hariyani et al. [21] illustrated how AI, cloud computing, and remote sensing could be used to achieve SDG 13 through automation and predictive analysis. Similarly, Varriale et al. [22] conducted a systematic literature review of 578 SDG-related literature. Their study identified 11 digital technologies that are applied in 17 industries to accomplish all 17 SDGs. Their results highlighted AI as a powerful tool in addressing SDG 13.

Moreover, Raman et al. [23] discussed how digital twins, AR, and VR are revolutionizing energy systems. Based on a review study of 508 publications, their paper proved that smart grids and AI-driven models are effective in attaining SDG 13. Magazzino and Zakaria [24] demonstrated how artificial neural networks (ANNs), which is a key technique in AI, could capture complex, non-linear interactions among sustainable indicators, resulting in significantly enhancing the success in reaching SDG 13.

Researchers have also shown significant interest in examining SDG 13 in developing and emerging economies. As one of the most prominent emerging economies, China is confronting serious climate challenges. Leveraging on a fuzzy analytical hierarchy process (AHP), Solangi and Magazzino [25] conducted a comprehensive analysis of financial risk, social benefits, and economic viability of its SDG 13 initiatives. Kartal et al. [26] showcased how energy and environmental policies could help five leading emerging economies to achieve their SDG 13. Based on data spanning from 2000 to 2020, their research disclosed diverse outcomes across the countries. For instance, in Brazil, gross domestic product (GDP) and foreign direct investment (FDI) increased the ecological footprint, indicating a negative impact on the environment, whereas renewable energy use (REU) reduced the footprint, supporting environmental sustainability.

GHG emissions associated with the combustion of fossil-based fuels are believed to be the major factor contributing to climate change, and renewable energies have been introduced as green alternatives to combat the issue [27]. Recognizing SAF as a practical solution to reduce emissions in the industry, several countries have established roadmaps to reach specific SAF production levels and significantly lower the carbon emissions generated by the sector. A list of SAF roadmaps can be found in

Table 1. Global SAF initiatives: A chronological overview.

Year	Initiative/Program	Country/Org	Objective	Reference
2021	SAF Grand Challenge Roadmap	US (DOE, DOT, USDA, EPA)	3B gallons by 2030, 35B gallons by 2050 Integrated multi-agency strategy focused on feedstock, technology, and supply chain development	[28]
2021	Canada Clean Fuel Regulations	Canada	Establishes SAF as a credit-generating option in fuel compliance markets Targets a 15% reduction by 2030	[29]
2021	Japan SAF Roadmap	Japan	Establishes SAF as part of national decarbonization strategy (No details have been released on design aspects) Targets 10% SAF by 2030 for international flights	[30]
2021	SAF Consortium Roadmap	New Zealand	Plans establishing a domestic SAF industry in New Zealand capable of reducing aviation emissions by up to 85% by 2050 Strong support for public–private collaboration	[31]
2022	National Sustainable Aviation Fuel Roadmap	United Arab Emirates	Aim to produce 700 million liters of SAF annually by 2030 Aims to implement a voluntary target for locally produced SAF to represent at least 1% of the total fuel used by national airlines at UAE airports by 2031	[32]
2023	Sustainable Aviation Fuel Roadmap	Australia	Focuses on production, supply chain development, and integration into the national aviation sector Supported by Queensland Govt., Qantas, Airbus, and LanzaJet	[33]
2024	Singapore Sustainable Air Hub Blueprint	Singapore	Achieve a 20% reduction in domestic aviation emissions by 2030, with a firm commitment to reach net zero emissions by 205 From 2026, flights departing Singapore will be mandated to use SAF	[34]

. In the United States, in alignment with international climate commitments to reduce GHG emissions in the aviation sector, a roadmap named the SAF Grand Challenge was proposed to promote SAF production [28].

Sustainable transitions, particularly in challenging sectors like aviation, require a pragmatic sustainability approach that goes beyond ambitious goals and idealistic frameworks [35]. According to D’Adamo et al. [35], a pragmatic sustainability approach can balance environmental, economic, and social goals through realistic and actionable strategies, rather than relying on abstract ideals or ideological approaches. Despite its recognized benefits, the widespread adoption of SAF faces several barriers. These include high production costs, stringent quality requirements, feedstock availability and sustainability, complex regulatory certification processes, challenges in distributing and integrating SAF into existing aviation infrastructures, lack of policy support, incentives, and mandates, and lack of awareness of the role of SAF as a sustainable fuel [36,37,38,39]. These barriers can be overcome through innovative approaches, such as adopting Industry 4.0 technologies to play a transformative role [14]. Decarbonizing aviation can be accelerated by incremental improvements and feasible innovation pathways, rather than pursuing perfection. Industry 4.0 technologies offer practical and scalable tools to address major barriers to commercializing SAF and achieve a pragmatic, sustainable progress within the aviation industry.

This research offers a unique contribution to the existing literature on SAF production and commercialization. It presents the first exploratory analysis of how Industry 4.0 technologies are integrated and applied within SAF roadmaps, specifically focusing on the SAF Grand Challenge in the United States. Prior studies have examined technological innovations associated with Industry 4.0 and their general applicability in renewable energy supply chains. However, no study targeted the role of innovations and the potential contributions of integrating Industry 4.0 technologies to advance SAF commercialization. This research addresses knowledge gaps by evaluating and providing resources related to current practices and potential technological applications, specifically assessing how Industry 4.0 technologies could simplify the SAF commercialization pathway, expedite regulatory approvals, enhance fuel quality, and improve overall SAF operations and supply chains. Particularly, it provides resources discussing the application of Industry 4.0 technologies in the context of SAF production, supply chain, or policy, particularly those addressing challenges related to the SAF Grand Challenge workstreams and their corresponding action areas. The goal of this study is to answer the research question: From a technological perspective, how could the technological components of Industry 4.0 help accelerate SAF production? Through detailed case analyses and synthesis of existing literature on SAF production and Industry 4.0 technologies, this study offers managerial implications on the practical integration of the technologies into SAF production systems. This work, therefore, provides valuable insights not only for academics seeking to advance understanding of potential opportunities for achieving sustainable SAF production, but also for the stakeholders and policymakers aiming to accelerate SAF adoption to achieve emissions reduction targets in the aviation industry.

The remainder of the paper is structured as follows: Section 2 expands on information regarding the technological aspects of Industry 4.0 and elaborates further on the SAF Grand Challenge. Section 3 discusses the research methodology. Section 4 discusses the workstream intersections between Industry 4.0 technologies and the SAF Grand Challenge. In conclusion, Section 5 provides a summary of the findings and outlines a perspective for future studies.

2. Background

This section provides an overview of the two key concepts explored in this research: the SAF Grand Challenge and Industry 4.0, along with their associated technologies.

2.1. Sustainable Aviation Roadmaps

Achieving deep decarbonization in the aviation sector is a central challenge in the global effort to mitigate climate change. The United Nations SDGs, particularly SDG 13 (Climate Action), emphasize the need for accelerating transitions toward low-carbon energy systems, including those used in hard-to-abate sectors such as aviation [40]. Given the industry’s dependence on highly energy-dense liquid fuels, SAF has emerged as a promising alternative capable of reducing life cycle GHG emissions by up to 80% compared to conventional jet fuels [41].

Over the past two decades, several global and national efforts have been launched to promote SAF development through policy incentives, R&D funding, certification frameworks, and private–public collaborations. Several landmark regional and global initiatives have been instrumental in laying the foundation for the worldwide development and adoption of SAF. The efforts were initiated with the ASTM D7566 Certification in 2009, which approved blending SAF with conventional jet fuel. The ASTM Certification provided the initial commercial pathway for the use of SAF consistent with existing aircraft and airport infrastructure [42,43]. In 2013, the International Civil Aviation Organization (ICAO) rolled out the Alternative Fuels Roadmap to provide guidance to member states on sustainable fuels, while also compiling global SAF progress metrics. The establishment of this roadmap laid the groundwork for subsequent regulations and contributed to the ICAO Expo’s development of the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) in October 2016. As a key part of CORSIA, when an airline surpasses its 2020 CO₂ emissions threshold, it must offset those emissions [44,45]. One of the most significant regional initiatives, launched by the European Union (EU) in 2020, is the ReFuelEU Aviation Initiative [46]. ReFuelEU Aviation sets binding SAF targets for airlines in the EU. This involves blending SAF, starting at a minimum of 2% by 2025 and increasing to 70% by 2050.

Additionally, multiple countries have considered whether to develop their own SAF roadmap with a focus on increasing SAF capacity and the building of SAF supply chains and supporting sustainable feedstocks, SAF production, and SAF blending at airports. A summary of the initiatives by countries is presented in Table 1. It is worth noting that several other countries not included in the table, such as France, the United Kingdom, Finland, Norway, and Sweden, have established ambitious objectives for producing SAF; However, they have yet to publicly disclose a comprehensive roadmap outlining their plans and expectations, encompassing not only mandates and GHG reductions expected, but also all relevant aspects of SAF production.

Building upon these global and national initiatives, the United States has emerged as a key player in advancing SAF through the launch of the SAF Grand Challenge. While many countries have outlined general goals or introduced mandates related to SAF production and use, the U.S. roadmap distinguishes itself through its depth, interagency coordination, and actionable framework. The SAF Grand Challenge offers a clear pathway for specific actions across the supply chain. It sets ambitious production targets and aims to spur collaboration and innovation at every step in the effort to derive SAF solutions. The following takes a deeper look at this initiative, its structure, and its relevance as a benchmark model for other national efforts aiming to scale SAF production and decarbonize aviation.

The SAF Grand Challenge is an effort that integrates resources from the U.S. Department of Energy (DOE), Department of Transportation (DOT), and the U.S. Department of Agriculture (USDA) alongside the Environmental Protection Agency (EPA) with the aim of accelerating SAF production and utilization [28]. The U.S. Biden administration designed this initiative to enhance the use of SAF towards decarbonizing the U.S. aviation industry. The SAF Grand Challenge sought to achieve an annual production of 3 billion gallons by 2030 and 35 billion gallons by 2050 [28]. This goal envisions achieving a complete substitution for conventional jet fuel utilized within domestic aviation. The move would reduce GHG emissions by at least 50% over their life cycle.

This initiative builds on the fact that aviation is one of the most challenging sectors to decarbonize because of its reliance on high-density liquid fuels [47,48]. SAF offers a feasible option for emission reduction in the near and intermediate terms. SAF can be produced from renewable or waste-derived streams and is fully compatible with existing aircraft and infrastructure. However, production at these levels necessitates multiple and coordinated changes in policies, investments, and collaboration along the entire supply chain. These changes will range from feedstock cultivation and fuel production to logistics, certification, and consumption.

To achieve the strategic objective, the SAF Grand Challenge Roadmap lays out six key action areas: (1) feedstock innovation, (2) conversion technology innovation, (3) building supply chains, (4) policy and valuation analysis, (5) enabling end use, and (6) communicating progress and building support. Each area includes specific workstreams designed to tackle multifaceted technical, economic, environmental, and institutional issues. For instance, feedstock innovation relates to increasing the recovery of sustainable biomass and waste resources. Workstreams under conversion technology seek to enhance the efficiency and effectiveness of viable jet fuel production. The roadmap also emphasizes the need to develop comprehensive and regionally optimized supply chains capable of large-scale operations in production and distribution.

While the roadmap is more of a policy and coordination-oriented plan, it implicitly suggests that innovation and technical progress must be at the center of fulfilling each of these goals. Workstreams include developing environmental models, optimizing resource flows, fuel quality analysis, and infrastructure capacity expansion. The roadmap emphasizes that the objectives will not be achieved without long-term coordination and collaboration across government agencies, industry partners, academics, and the community. This collaboration is meant to provide consistent cross-sector action that is reactive and proactive. The rationale is to incorporate the best available data, information, knowledge, and intelligence into the various processes and operations embedded in SAF supply chains. The roadmap also highlights the need for adaptive implementation through ongoing learning, system-level assessment, and feedback loops. In this way, the capability and willingness to implement new approaches, tools, and innovations in relation to feedstock management, fuel qualification, and supply chain modeling will reflect the need to continually develop competitive capabilities. As a result, although the roadmap focuses on policy and coordination, its structure leaves space for advanced and innovative approaches and technologies to shape and assist the broader decarbonization goal in aviation.

2.2. Industry 4.0

Industry 4.0 represents the Fourth Industrial Revolution. A group of German scientists introduced the concept around 2011 to describe emerging technological trends in manufacturing. It focuses on the integration of automation, the management of big data through cloud computing, high-capacity data exchanges that enable connectivity across manufacturing processes, and the capability to convert digital instructions into physical outputs [49]. A key feature of Industry 4.0 is the decentralization of decision-making, allowing cyber-physical systems to operate independently [50]. Many disruptive technologies have been suggested under Industry 4.0. However, scholars have yet to reach an agreement, due to different perspectives across research domains [16].

This paper builds on the paradigms introduced by Weyer et al. [51] and insights from Choi et al.’s [52]. Figure 2 maps a broad set of technologies and applications associated with Industry 4.0, including blockchains, AI, IoT, 3D printing, and digital twins. Then, the following sections further explore the specific technologies that fall within each of these broader categories.

2.2.1. Blockchains

Blockchains are decentralized ledgers secured through cryptography. They can process and verify transactional data within a distributed database system [53]. According to Pattison [54], the core feature of a blockchain is decentralization, which eliminates the need for a central authority and prevents any single entity from controlling the information. A blockchain operates on a peer-to-peer (P2P) network, requiring all relevant parties to agree on the validity of transactions [55]. This system is often governed by smart contracts, which are digital protocols containing predefined terms that are stored directly on the blockchain. These smart contracts enable the synchronized and immutable storage of information, providing a high level of security and traceability throughout the information flow [56].

Table 2. Key technologies in blockchains and their applications in supply chain management.

Technology	Definition	Applications
Distributed ledger technology (DLT)	A shared, immutable ledger that operates without a central authority, minimizing the need for intermediaries [57].	Enables the collection, storage, and sharing of critical information [58]. Strengthens input authenticity, supports ethical sourcing, and minimizes fraud [59,60]. Enables a data-driven evaluation of performance through improved transparency [61]. Strengthens cybersecurity; fosters reliable and efficient supply chain management [62]. Enables real-time tracking and tracing of goods to ensure efficient logistics and smooth supply chain flow [63].
Smart contract	A computer-driven, automated contracting system designed to streamline digital collaboration among multiple parties [64].	Ethereum is a type of blockchain that allows users to design their contracts with customized data structures and functions through unique digital addresses and application programming interfaces (APIs) [65]. Facilitates automation, synchronization, and transparency [66]. Supports the enforcement of legal agreements and reduces the need for intermediaries in transferring asset ownership [66,67]. Collaborates on a shared digital platform leading to significant reductions in operational costs [68]. Tracks changes in asset status throughout the logistics process [69,70].
Tokenization	A digital representation of physical or intangible assets in the form of cryptographic tokens that can be exchanged or traded [71].	Enables cross-border payments and facilitates seamless information exchange [72]. Enhances the visibility and traceability of transaction flow [73]. Collects financial information (e.g., ratio of on-time deliveries) and non-financial information (e.g., cargo and vehicle details) to aid financial institutions make informed decisions [74,75,76]. Enhances financial credibility, resulting in lower interest rates or expedited loan approvals [77,78]. Raises capital and secures financing for their operations [79].

summarizes the key technology components in a blockchain and their applications in supply chain management.

2.2.2. AI

AI is currently driving significant transformation in Industry 4.0 by enabling innovative and intelligent production, operational, and management systems [80]. Major companies, such as Amazon, Walmart, and Best Buy, have already extensively integrated AI into their supply chains. The most widely adopted disruptive technologies within the field of AI include machine learning (ML), natural language processing (NLP), computer vision, robotic process automation, and generative AI [81].

Table 3. Key technologies in AI and their applications in supply chain management.

Technology	Definition	Application
ML	Relies on algorithms to enable systems to learn representations of large, complex datasets [82].	Extracts valuable information to support practical supplier evaluation and selection [83]. Uncovers hidden patterns in inventory [84]. Enhances transportation and distribution efficiency through delivery route optimization [85]. Analyzes historical sales data for seasonal forecasting and strategic planning [86].
NLP	Leverages statistical and semantic analysis to break down the relationships between words and phrases, enabling the automated extraction of meaning from speech or text [87].	Automates the extraction of key information like supplier names, quantities, and contract terms from invoices, bills, and other documents [88]. Facilitates real-time translation and enables chatbot-assisted correspondence in global supply chains [89,90]. Utilizes sentiment analysis to evaluate feedback from customers and suppliers [91]. Interprets complex legal language in supply chain contracts [92]. Supports planning by analyzing news and operational data to detect compliance issues and risks [93].
Computer vision	Enables the interpretation and extraction of meaningful information from visual inputs like images and videos [94].	Enables sophisticated image recognition algorithms to monitor inventory levels and warehouse management [95]. Automates stock counting, inventory tracking, and replenishment forecasting [96]. Enhances surveillance and security systems [97]. Supports quality control with autonomous defect detection during production and packaging [98,99]. Enhances traffic signs recognition, license plate reading, vehicle tracking, and cargo loading [100].
Robotic Process Automation (RPA)	Uses programming interfaces and software robots to automate the repetition of routine tasks [101].	Automates repetitive tasks like order processing, data entry, predictive maintenance, logistics coordination, inventory updates, shipment scheduling and tracking, invoice processing, report generation, and communications [102]. Utilizes warehouse management through technologies such as Automated Storage and Retrieval Systems (AS/RS), Automated Guided Vehicles (AGVs), Collaborative Robots (Cobots), and Goods-to-Person (G2P) systems [103,104]. Identifies, classifies, and manages items, including tasks like truck loading/unloading, item sorting, and shelf replenishment [105,106]. Integrates with SAP, Excel, and web portals to provide visualization for RPA analytics [103]. Coordinates with AI techniques like fuzzy logic and artificial neural networks to extract information from documents, streamlining procurement and simplifying RPA workflows [102].
Generative AI (GAI)	Refers to systems that can create new content, such as text, images, videos, software code, or simulations, by learning patterns from existing data [107].	Generates Python 3 code from verbal descriptions to simulate queuing systems and inventory processes [108]. Negotiates with suppliers by analyzing inputs and adapting customized negotiation strategies for mutual benefits [109]. Utilizes ML techniques to track users’ website behavior and purchase history to offer personalized shopping recommendations [110]. Provides comprehensive analysis including disruptions, managerial directives, cost-efficiency measurements, service quality, route optimization, and weather [111]. Supports sustainable and ethical business practices by minimizing carbon emissions through resource and warehouse optimization [112,113]. Monitors regulation, media, social and environmental impacts to prioritize fair trade and ethics [114,115]. Recommends actions like audits, alternative suppliers, or communication to address potential violations of fair-trade principles, environmental regulations, or labor laws [116,117].

summarizes the key technologies in AI and their applications in supply chain management.

2.2.3. Internet of Things (IoT)

According to Hassini [118], IoT refers to a network of physical devices characterized by the following key features: (1) They are digitally integrated within the supply chain network; (2) this connectivity enables data storage, sharing, and analysis; (3) it supports both intra- and inter-organizational processes; and (4) it enhances coordination in supply chain planning, control, and management. IoT plays a foundational role in the context of Industry 4.0, serving as a critical enabler of the Fourth Industrial Revolution [119,120]. An IoT network in supply chain management typically includes the four essential layers summarized in

Table 4. Four layers in IoT and their applications in supply chain management.

IoT Layers and Their Features	Applications
Sensing Layer includes RFID tags, sensors, actuators, and other intelligent devices [123]. ○ A foundational layer. ○ Enables identification, tracking, and collection of data from physical objects [124].	Uses temperature sensors, multi-agent systems, and smart devices to facilitate collaborative operations while maintaining high standards of safety and security in warehousing [125]. Utilizes Ethereum-generated QR codes [126] to store and secure tamper-proof manufacturing, tracking, and shipping information through unique blockchain address [127]. Uses a wide array of sensors, including accelerometers [128], battery sensors [126], cameras [129], color sensors, gas sensors [130], GPS modules (NEO-M8N, u-blox AG, Thalwil, Switzerland), gyroscopes, light sensors, humidity sensors [131], moisture sensors, pH sensors, and temperature sensors to support the effective management of transportation, logistics, inventory, and manufacturing activities [132]. The collected data is transmitted and aggregated by a third-party computer, which can operate either as a remote server (cloud computing) or a local proximity device (edge computing). Cloud computing enables data storage and processing on centralized remote servers, which are well suited for large-scale, complex analyses but may introduce latency. In contrast, edge computing processes data closer to the source—such as within local gateways or on-site devices—allowing for faster response times and reduced bandwidth requirements. After initial aggregation, the results can be transmitted remotely for further in-depth analysis if needed [133].
Network Layer transmits information gathered at sensing layer via wired or wireless networks [134,135]. ○ Utilizes wireless networks and sensor-enabled RFID technology to provide real-time visibility.
Service Layer uses middleware to interact between data services and applications [123,136]. ○ Integrates cloud computing and edge computing into the IoT ecosystem, enabling sensing devices to interface with IoT applications [133,137].
Interface Layer facilitates user interaction with the IoT system [138,139]. ○ Presents data in an accessible format and functions as an interactive gateway between end-users and IoT-enabled devices [140].

[121,122].

2.2.4. 3D Printing

3D printing, also called additive manufacturing, enables the creation of complex physical objects from digital 3D models by using a variety of materials such as plastics, metals, ceramics, and even biomaterials [141,142]. It has brought unprecedented levels of innovation to manufacturing as it allows diverse material inputs to support individualized design [142,143].

Table 5. Key features in 3D printing and their applications in supply chain management.

Key Features	Applications
Mass customization	Disrupts downstream production and distribution operations [144]. Allows customer co-creation of personalized products, merging product design, production, and distribution processes [145,146].
Material efficiency	Utilizes diverse raw materials to benefit upstream partners [147]. Encourages smarter component design, recycled materials, and lower environmental impact [148]. Supports sustainable and eco-friendly supply chain by lowering global carbon emissions and promoting responsible resource utilization [143].
Manufacturing decentralization	Facilitates a made-to-order production model, reducing inventory levels [149]. Supports decentralization manufacturing near consumption [150], cutting lead times and reliance on centralized production [146].
Inventory and logistics costs reduction	Enables on-site production, reduces the need for extensive physical transportation [151]. Allows end users to produce items locally [152]. Transforms inventory management towards a leaner system with more raw materials and semi-finished components over large volumes of finished stock [153,154].

summarizes its key features and applications in supply chain management.

2.2.5. Simulations (Digital Twins/AR/VR)

A digital twin is a real-time virtual model of a physical object or system that is created using sensor data gathered from the real world [155]. Teams can visualize, analyze, and learn more about the behavior and performance of the physical counterpart throughout its life cycle with this data-driven model [156]. The ability to deliver continuous and real-time data is the most crucial advantage of a digital twin. It enables organizations to monitor system performance, anticipate potential issues, and make more informed, data-driven decisions [157]. Digital twins can also provide simulations of how products, processes, and systems could react under varying situations [158,159].

Augmented Reality (AR) and Virtual Reality (VR) are immersive technologies that enhance physical–digital integration in supply chains by providing real-time, spatially contextualized information [160]. AR overlays digital content onto physical environments through smart glasses or mobile devices, while VR creates fully immersive simulations of supply chain environments, enabling users to interact with complex systems virtually [160]. These technologies are increasingly applied to support end-to-end supply chain functions, including logistics optimization, warehouse management, training and workforce development, infrastructure design, and collaborative decision-making.

Table 6. Key features in digital twins and their applications in supply chain management.

Features	Applications
Virtual representation	Virtual representations of machines, production lines, or entire facilities enables intelligent task execution and process improvement [161,162]. Simulates products, processes, and systems, enabling responses to different situations [158].
Real time visibility	Enables virtual testing to reduce material waste and downtime, supports employee training and process validation [163].
Continuous monitoring	Mimic real-time operation for continuous monitoring [164,165]. Sends live data to cloud platforms for disruption prediction and scenario simulations [166]. Empowers manufacturers to assess risks, estimate costs, and refine processes before large-scale changes [167,168].
Immersive training simulations	Realistic VR flight and maintenance simulators, combined with AR-guided field training, prepare technicians, and operators without real-world risks [169].
Remote collaboration and stakeholder engagement	Immersive AR/VR experiences engage stakeholders by demonstrating their activities and opinions in an interactive way [170]. For instance, a VR tour of a SAF production site can show investors and regulators the supply chain and sustainability practices first-hand.

summarizes key features and applications of digital twins, AR, and VR in supply chain management.

3. Research Methods

The integrative literature review approach has been adopted as the primary research methodology [171]. An integrative review is particularly suitable for emerging topics that have not yet been extensively conceptualized, allowing researchers to synthesize insights from multiple disciplinary perspectives to generate initial conceptualizations and theoretical frameworks [172]. This approach offers an ideal methodological foundation for this study because the nexus between Industry 4.0 and SAF production is an emerging and multidisciplinary field.

The authors of this research chose the integrative literature review method because it is flexible and can produce new theoretical insights and practical frameworks instead of just summarizing the body of existing literature. A more focused but creative analysis is made possible by the method’s emphasis on synthesizing and critically evaluating literature that particularly supports the study objectives, as opposed to a systematic review’s pursuit of thorough coverage of all pertinent literature [173]. The current study investigates the applicability of Industry 4.0 technologies in SAF production systems, which are both emerging research domains.

An integrative literature review requires researchers to demonstrate excellent conceptual thinking [174], transparency, and skillful techniques to critically review a large body of literature. This method of literature review aims to collect, examine, and synthesize the current literature on a research topic to facilitate new perspectives or framework emergence, while generating new knowledge [173].

This study conducted an integrative literature review because it can successfully capture literature from multiple domains through holistic conceptualization and synthesis [175]. This integrative review uses the framework from Whittemore and Knafl [171] for data collection, analysis, and synthesis. This integrative methodology synthesizes existing literature from diverse but related fields, such as renewable energy, aviation fuel policy, and advanced digital technologies, to strengthen the framework provided by the SAF Grand Challenge for technological advancements. Each step of the literature search utilized the following databases and related search tools: EBSCOhost, Emerald, Wiley, Taylor and Francis, Google Scholar, ScienceDirect, Springer, and Web of Science. The Google search engine produced additional literature from white papers, industry reports, news articles, company websites, and brochures. These data sources are well recognized as a reliable foundation for academic research [176]. This paper only includes English language publications from 2011 to the present, as Industry 4.0 was invented in 2011. There are three significant steps in this process.

Step 1: Identify disruptive technologies in Industry 4.0. To begin this integrative review, we searched literature review articles with a high impact factor or citation (as shown in Google Scholar) from reputable journals using the following keywords: “Industry 4.0” and “literature review”; “Industry 4.0” and “review studies.”

Given the wide range of terminology being used in Industry 4.0, the selection process is not restricted to these keywords. Instead, we examined the titles, abstracts, and keywords of all full papers, or the full paper itself as needed. In addition, a supplementary backward snowballing exercise was conducted, where citations from key studies identified in the first stage were reviewed.

This thorough procedure aims to capture as many representative literature review articles as possible. After careful examination, we identified n = 11 literature review articles, and they serve as a foundation for this review. They cover a wide range of relevant work related to the topic and discuss how Industry 4.0 reflects from different perspectives. In the end, this review effectively identified five broad sets of disruptive technologies associated with Industry 4.0 from a supply chain perspective, as shown in Figure 1. These include blockchain, AI, IoT, 3D printing, and digital twins.

Step 2: Identify specific disruptive technologies in supply chains and their applications. Each broad disruptive technology in Figure 1 also includes specific technologies underlying it. Our goal is to provide an in-depth review of applications of these technologies in supply chains. To explore the technologies within each broader category, we used the following keywords:

• Keywords referring to blockchain: “Distributed Ledger technology (DLT)”, “Smart contract”, “Tokenization”.
• Keywords referring to AI: “machine learning (ML)”, “natural language processing (NLP)”, “computer vision”, “robotic process automation (RPA)”, and “generative AI (GAI)”.
• Other keywords: “Internet of Things (IoT)”, “cloud computing”, “edge computing”, “3D printing”, “additive manufacturing,” “simulations”, “Digital Twins (DT)”, “AR/VR”.

n = 121 articles were selected and provided a summary about the applications and functions of each technology in supply chains. For data analysis, we have utilized tables to list the key technologies, their definitions, features, and representative supply chain applications, as shown in Table 2, Table 3, Table 4, Table 5 and Table 6. The goal is to enhance the understanding of Industry 4.0 and its representative disruptive technologies, and to provide insights about their applications in supply chains.

Step 3: explore how disruptive technologies could be used to address the SAF Grand Challenge. There are six workstreams under the SAF Grand Challenge: “Feedstock Innovation”, “Conversion Technology”, “Building Supply Chains”, “Policy and Valuation”, “Enabling End Use”, and “Communicating Progress”. There are also key action areas under each work stream. For instance, the key action areas under each workstream include:

• Feedstock innovation: Resource market and availability analysis; increase sustainable lipid supply; boost biomass production and waste collection; improve feedstock supply logistics; improve feedstock handling reliability; enhance sustainability of biomass supply.
• Conversion technology: Decarbonize and scale fermentation-based fuels; enhance ASTM pathways; develop bio-intermediates; reduce risk and scale up; develop innovative pathways.
• Building supply chains: Establish regional coalitions; model SAF supply chains; demonstrate regional supply chains; develop production infrastructure.
• Policy and valuation: Improve environmental data and models; techno-economic feasibility analysis; contribute to SAF policy development.
• Enabling end use: Support evaluation and testing; adopt high-percentage SAF blends; explore synthetic jet fuels; adapt infrastructure.
• Communicating progress: Engage stakeholders; assess benefits and influence; track SAF Grand Challenge; share positive impacts.

A Boolean search was conducted using the following keywords: “Industry 4.0 in SAF” or “Industry 4.0 in biofuels” or “Industry 4.0 in renewable fuels” to identify publications related to at least one key action area under the SAF Grand Challenge workstreams about disruptive technologies in Industry 4.0. In total, there are n = 177 representative publications included.

The literature was analyzed and synthesized through qualitative content analysis to highlight how those disruptive technologies could be used to address the key action areas in the SAF Grand Challenge.

Content analysis systematically analyzes the meaning of both qualitative and quantitative data [177]. A successful content analysis is both rigorous and flexible [178]. It is rigorous because it follows an iterative process to reduce large volumes of information to extract key insights from each publication. It is flexible because it allows a combination of concept-driven and data-driven methodologies in coding the information [179]. In our study, we organized a large volume of information into themes and sub-themes that form the structure of this paper. The themes are the following: feedstock innovation, conversion technology, building supply chains, policy and valuation, enabling end use, communicating progress, and the sub-themes are the key action areas under each workstream.

A summary of the steps and criteria for implementing this approach as a framework is outlined in

Table 7. Steps and Criteria of the Integrative Literature Review.

Step 1: Identify disruptive technologies in industry 4.0 Criteria: Q1: Does the selected paper provide a literature review of various technologies in industry 4.0? Q2: Is the selected resource from a reputable journal with high impact factor or citation as indicated in Google scholar?
Keywords: “industry 4.0” and “literature review”; “industry 4.0” and “review studies”	Summarized: n = 11 represented literature review articles about Industry 4.0 Identified: 5 broad set of disruptive technologies associated with Industry 4.0 from a supply chain perspective, including blockchain, AI, IoT, 3D printing, and simulations, as shown in Figure 2.
Step 2: Disruptive technologies in supply chain and their applications Criteria: Q1: Does the selected article provide reviews that enhance understanding of Industry 4.0 and its representative technologies? Q2: Does the selected article provide knowledge that stresses the applications of Industry 4.0 in supply chain management?
Keywords used in blockchain: “Distributed Ledger technology (DLT)”, “Smart contract”, “Tokenization”. Keywords used in AI: “machine learning (ML)”, “natural language processing (NLP)”, “computer vision”, “robotic process automation (RPA)”, and “generative AI (GAI)” Other keywords: “Internet of Things (IoT)”, “cloud computing”, “edge computing”, “3D printing”, “additive manufacturing,” “simulations”, “Digital Twins (DT)”, “AR/VR”	Summarized: n = 121 representative publications on specific technologies under each disruptive technology. Presented and detailed: their definitions, key features and applications in supply chain, as illustrated in Table 2, Table 3, Table 4, Table 5 and Table 6
Step 3: Industry 4.0 disruptive technologies in SAF Grand Challenge Criteria: Q1: Does the selected article discuss the application of Industry 4.0 technologies in at least one of the following SAF Grand Challenge workstreams: “Feedstock Innovation”, “Conversion Technology”, “Building Supply Chains”, “Policy and Valuation”, “Enabling End Use” or “Communicating Progress”? Q2: Does the selected article discuss at least one key action areas under Grand Challenge workstreams in relation to disruptive technologies in Industry 4.0?
Keywords used in the intersection of Industry 4.0 and SAF Grand Challenge: “Industry 4.0 in SAF” or “Industry 4.0 in biofuels”, or “Industry 4.0 in renewable fuels”	Conducted content analysis: n = 177 representative publications reviewing Industry 4.0 technologies under each SAF workstream (included snowballing). Summarized: the represented disruptive technologies under each SAF Grand Challenge workstream and their corresponding key action areas as shown in Table 8.
Inclusion criteria: Databases: EBSCOhost, Emerald, Wiley, Taylor and Francis, Google Scholar, ScienceDirect, Springer, and Web of Science. Additional searches for white papers, newspaper articles, company websites, and brochures were performed using Google’s search engine English language only Articles, books, and grey literature, conference papers, newspaper articles, company websites, and brochures company websites, and brochures are included Included timeframe from 2011 to present as Industry 4.0 was invented in 2011.

. The summary of the results after implementing the framework can also be found in

Table 8. Overview of Industry 4.0 applications within SAF commercialization framework.

Workstreams	Key Action Areas	Industry 4.0 Technologies	Applications
Feedstock Innovation	Resource Market and Availability Analysis	AI, Big Data, Blockchain, Cloud Computing	Forecasting, traceability, market optimization, resource allocation
	Increase Sustainable Lipid Supply	AI, ML, Blockchain, IoT	Precision agriculture, lipid recovery, waste tracking, IoT monitoring
	Boost Biomass Production and Waste Collection	IoT, AI, Autonomous Robots	Biomass monitoring, waste sorting, collection optimization, autonomous handling
	Improve Feedstock Supply Logistics	AI, IoT, Blockchain, Edge Computing	Route optimization, real-time tracking, smart contracts, local analytics
	Improve Feedstock Handling Reliability	AI, Digital Twins, Robotics, Edge Computing	Predictive maintenance, handling simulation, automated preprocessing, on-site analytics
	Enhance Sustainability of Biomass Supply	AI, Blockchain, IoT	Life cycle assessment, sustainability tracking, environmental monitoring
Conversion Technology	Decarbonize and Scale Fermentation-Based Fuels	AI, ML, IoT, Blockchain, Edge Computing	Process optimization, fermentation control, emission tracking, real-time data
	Enhance ASTM Pathways	Digital Twins, AI, Blockchain, 3D Printing	Process simulation, rapid prototyping, compliance tracking, virtual testing
	Develop Bio-Intermediates	AI, Cyber-physical Systems, 3D Printing	Molecule screening, automated conversion, prototype catalysts
	Reduce Risk and Scale Up	AI, Digital Twins, Blockchain	Predictive maintenance, scale-up simulation, supply chain transparency
	Develop Innovative Pathways	AI, IoT, Automation	Reaction modeling, process automation, real-time monitoring
Building Supply Chains	Establish Regional Coalitions	Blockchain, Cloud, Smart Contracts	Stakeholder collaboration, secure data sharing, automated agreements
	Model SAF Supply Chains	AI, Big Data, IoT, Edge Computing	Demand forecasting, route optimization, real-time analytics
	Demonstrate Regional Supply Chains	AI, Digital Twins, 3D Printing	Pilot simulation, component prototyping, performance optimization
	Develop Production Infrastructure	AI, Robotics, Automation, Blockchain	Automated operations, infrastructure monitoring, transparent build-out
Policy and Valuation	Improve Environmental Data and Models	AI, Big Data, Blockchain, Cloud Computing	Data aggregation, LCA modeling, emission verification
	Techno-Economic Feasibility Analysis	AI, Digital Twins, Edge Computing	Cost modeling, scenario analysis, real-time feasibility
	Contribute to SAF Policy Development	AI, Blockchain, Cloud Computing	Policy modeling, transparent reporting, incentive tracking
Enabling End Use	Support Evaluation and Testing	AI, Digital Twins, Blockchain, Automation	Performance simulation, test automation, data logging
	Adopt High-Percentage SAF Blends	AI, Cyber-Physical Systems, Sensors	Blend optimization, engine monitoring, real-time adjustment
	Explore Synthetic Jet Fuels	AI, 3D Printing, Automation	Molecule design, catalyst development, experimental validation
	Adapt Infrastructure	IoT, Blockchain, AI	Flow tracking, infrastructure readiness, compliance monitoring
Communicating Progress	Engage Stakeholders	AI, Blockchain, Cloud Computing	Secure communication, trust building, multi-party data sharing
	Assess Benefits and Influence	AI, Digital Twins, Cloud Computing	Impact quantification, visualization, scenario simulation
	Track SAF Grand Challenge	IoT, Blockchain, AI, Cloud Computing	KPI monitoring, automated reporting, real-time dashboards
	Share Positive Impacts	AI, AR/VR, Blockchain, Sentiment Analysis	Immersive visualization, public engagement, verified impact

, in Section 3.

4. Discussion and Implications

This section examines the potential contributions of various technological dimensions of Industry 4.0 toward achieving the SAF Grand Challenge objective, specifically focusing on the commercialization of SAF. Each aspect of Industry 4.0 technologies is analyzed individually to identify its intersections and applications within the respective work streams and action areas outlined in section two, pertaining to the SAF Grand Challenge. Table 8 outlines a summary of the results from implementing the framework. In addition to the workstreams, the table presents the key action areas within each workstream. The third column lists the specific Industry 4.0 technologies associated with each key action area, along with our observations on their applications, presented in the last column. The following subsections will then provide a more detailed discussion of each of these connections, explaining how the enabling technologies can facilitate and/or advance the implementation of each workstream.

4.1. Feedstock Innovation

It is crucial to establish sustainable feedstock supply chains to scale SAF production while minimizing costs, environmental impacts, and disruption risks [180]. Feedstock availability, efficiency, and sustainability can be enhanced by leveraging Industry 4.0 technologies such as analytic support from AI, real-time monitoring through IoT devices, transparency using blockchain technology, and efficiency of logistics leveraging automation [181]. These technologies play a critical role in addressing each key action area under the feedstock innovation workstream, ensuring a resilient and scalable SAF supply chain.

4.1.1. Recourse Market and Availability Analysis

SAF supply chains present unique challenges in securing adequate, sustainable feedstocks and dealing with complex markets [28]. Emerging digital technologies, from big data analytics and AI to blockchain and cloud platforms, are being deployed to process and optimize feedstock availability, traceability, and market selection. For resource market analysis, big data analytics and AI-driven modeling tools can improve forecasting of feedstock supply and demand trends under varying SAF production scenarios [182,183,184]. C. Wu et al. [185] presents an ML framework that enables rapid estimation of fuel cost across randomized input scenarios for SAF production. Using this model, one can evaluate how variations in feedstock cost, supply volume, or technology performance impact SAF prices. As a producer of SAF, LanzaJet integrates AI and data analytics to refine its feedstock and production strategies [186]. The application of ML to datasets of agricultural yields and available waste enables the company to pinpoint optimal feedstock sources and enhance its conversion processes. Such a proactive approach furthers innovative SAF, enhances new plant site selection, alters fuel compositions, and improves overall profitability.

Blockchain-enabled market databases can provide transparent tracking of commodity and non-commodity feedstock sources, ensuring secure and verifiable data for stakeholders [187,188]. The Roundtable on Sustainable Biomaterials (RSB), in collaboration with Bioledger (Version 1.5, Bioledger Ltd., London, UK), piloted a project that involved creating a blockchain database to track the life cycle of used cooking oil (UCO) as a feedstock for biodiesel [189]. By 2020, in cooperation with industry partners, the system had claimed to track around 1.93 million liters of UCO along with proof of UCO’s verifiable origin, secure digital records, and claims of simplified auditing.

Leveraging cloud computing, AI-powered cloud platforms can integrate real-time market data and conduct analyses, allowing policymakers and industry leaders to anticipate shortages, optimize pricing, and manage resource allocation [190,191]. With feedstock markets proving volatile, SAF and low-carbon fuel companies are increasingly turning to cloud AI solutions to streamline operations. Biofuel refinery Imubit, for instance, uses an AI optimization system [192]. Its cloud-based platform identifies plant-level process data, market data like feedstock prices, demand in specific geographies, and competitive position. The company automatically adjusts the production parameters for ongoing optimal profitability. Their platform features real-time dashboards that enable operators to monitor key performance indicators (KPIs) and conduct scenario analysis.

4.1.2. Increase Sustainable Lipid Supply

According to the near-term plans specified in the SAF Grand Challenge, policy support and investment are essential to facilitate the production and utilization of expanded lipid-based feedstocks [28]. AI is transforming precision agriculture, which means utilizing more technology and innovative practices to improve agricultural productivity [193]. AI provides guidance by using analyzed geospatial data to supply real-time information about soil moisture, weather, and crop water requirements. Farmers can then manage their resources responsibly, optimizing agricultural production potential with less environmental footprint [194]. Similarly, there are blockchain tracking systems that provide transparency in waste lipid collection and processing, as well as to ensure sustainability of used cooking oils (UCOs), industrial byproducts, and other waste lipids [195]. A pilot study report by the Roundtable on Sustainable Biomaterials (RSB) [196] showcased how numerous transparency and accountability challenges within the UCO market could be addressed or vastly improved using a blockchain ledger. Another study by Gong et al. [195] examined UCO recycling in the UK and described a South European pilot that integrated blockchain with IoT sensors. The blockchain–IoT system authenticated each collection and traced the oil’s life cycle. This curtailed fraudulent collections and tax evasion associated with black market oil trading. ML algorithms can analyze data from waste treatment plants using sources such as UCO, identifying new opportunities for lipid recovery and conversion into SAF [197].

Investments alongside policy support are required for the production and utilization of expanded lipid-based feedstocks as highlighted in the SAF Grand Challenge [28]. AI can transform precision farming, which is the application of farming practices that utilize advanced technologies, to increase agricultural productivity [193]. Resource allocation is made more effective with the abundant use of AI. An example is the analysis of geospatial data that can provide real-time information on the soil moisture content, weather conditions, and the amount of water crops require. This aids in optimal resource allocation while improving harvest yields and crop health, while reducing the ecological footprint [194]. The collection and processing of waste lipids such as UCO or industrial byproducts can be verified for their sustainability using blockchain tracking systems, therefore ensuring transparency for lipid waste collection and processing [195]. There are new opportunities for recovering lipids from waste treatment facilities, and converting them to SAF, that can be discovered through analyses of UCO employing ML algorithms [197].

4.1.3. Boost Biomass Production and Waste Collection

For improving biomass and waste collection processes, IoT sensors in waste management facilities collect monitored data from municipal solid waste (MSW), and agricultural and forestry residues. This ensures efficient collection and sorting [198,199]. AI-driven route optimization tools can improve waste-to-biofuel supply chain logistics, reducing transportation costs and emissions [200,201]. A comprehensive analysis by Fang et al. [202] found that AI optimization reduces waste transportation distances by 36.8%, resulting in a 13.3% reduction in costs, and almost 28% in time savings in collection operations. Also, autonomous waste-sorting robots (ZenRobotics Recycler, ZenRobotics Ltd., Helsinki, Finland) can classify and separate biodegradable and non-biodegradable materials, increasing the efficiency of recyclable waste [203,204]. Koskinopoulou et al. [203] developed a low-cost computer vision module based on deep learning for identifying and sorting items. Also, location allocation of the potential biomass feedstocks for SAF can clarify and accelerate the adoption process by farmers and investors, by providing them with accurate information about the feasibility and profitability of the feedstocks [205,206].

4.1.4. Improve Feedstock Supply Logistics

To optimize feedstock logistics, AI-driven logistics platforms can streamline feedstock transportation, storage, and preprocessing by analyzing real-time data on weather, road conditions, and refinery demand [207]. FuelCab India (FuelCab Technologies Pvt. Ltd., Gurugram, India) is an AI-based logistics platform specializing in biofuels [208]. It connects biorefineries with feedstock suppliers, including farmers and waste generators. They forecast feedstock availability, recommend optimal routes, and predict market demand. To reduce delays, FuelCab’s logistics engine uses real-time data together with predictive analytics [208].

IoT-enabled monitoring systems can track biomass storage conditions. This ensures that feedstock remains stable and usable throughout the supply chain [209,210]. By monitoring and controlling stored biomass in real time, researchers emphasize being able to maintain desired specifications of biomass, such as temperature and humidity, and ultimately improve the effectiveness of downstream conversion [209] as well as its resiliency to uncertainties [211]. Blockchain operationalized smart contracts improve operational efficiency through the automatic transaction between stakeholders in a biomass supply chain. Stakeholders include feedstock suppliers, transporters, and biorefineries [187,212]. According to Andiappan et al. [212], with blockchain technology integration, stakeholders connected within a biomass supply chain can control the quality of the biomass and ensure compliance with sustainability standards. Hence, they can select suppliers based on verified quality. Edge computing for biomass feedstock logistics supports low-latency, real-time decision-making through local computation on devices like harvesters and drones [213]. This reduces network data transmitted, simplifies bandwidth requirements, and enhances resilience and autonomy, especially in low-connectivity areas.

4.1.5. Improve Feedstock Handling System Reliability

Digital twins and AI-driven material behavior models can simulate solid feedstock characteristics to inform handling efficiency enhancement and reduce processing downtime [214,215]. In biomass supply chains, AI models and digital twins can simulate how heterogeneous feedstocks will behave under handling, for example, the way moisture content, particle size, or density would clog a conveyor or hopper [216]. The integration of real-time sensor data with advanced simulations enables digital twins to forecast issues (such as bridging in hoppers) and enable predictive maintenance, thus minimizing downtime [217]. Robotic automation in biomass preprocessing facilities can ensure uniform feedstock quality, thereby reducing inefficiencies in SAF conversion processes [181,218]. Edge computing solutions enable real-time analysis of feedstock composition. This allows processing facilities to dynamically adjust pretreatment methods for optimal fuel yield [219]. Within SAF supply chains, edge-computing gear continuously monitors feedstock characteristics and adjusts operations in real time. Hence, nothing goes to waste [219]. Preprocessing plants can now use line conveyors and grinders with a mix of NIR scanners (DA7250, Perten Instruments, Springfield, IL, USA), cameras, and moisture probes that read each batch while it moves. This allows the system to log moisture, ash, particle size, and other characteristics [220]. Rather than transferring that data to a cloud system, local units can run their own AI models (edge computing) and decide whether the material meets specifications [220].

Robotic preprocessing in biomass preprocessing facilities can ensure uniform feedstock quality. This reduces inefficiencies in SAF conversion procedures [181,218,220]. One example is smart sorting setups now used for city trash and farm waste [220]. Modern preparation lines utilize optical and chemical sensors guided by AI to recognize materials such as plastics, metals, and papers by matching their shape, color, and spectral fingerprint. Guided by those readings, robotic arms or pneumatic grabs can swiftly pull away the unwanted pieces, leaving a steadier, cleaner biomass stream for the next step.

4.1.6. Enhance Sustainability of Biomass and Waste Supply Systems

AI-powered life cycle assessments (LCAs) can evaluate the social and environmental impacts of biomass collection and conversion. These tools help ensure sustainable feedstock supply chains [59,221,222]. For instance, Ghoroghi et al. [223] showed that machine learning tools now help set up scenarios and streamline inventory records during an LCA. In a real-world test, G. Guo et al. [221] trained a gradient-boosting tree to forecast how much bio-oil a given feedstock would produce. The model then fed those predictions into a follow-up LCA. They found that every kilogram of bio-oil emitted 2.05 kg of CO-equivalent emissions.

Blockchain-enabled sustainability tracking verifies compliance with renewable energy policies and emission reduction targets. This ensures that feedstock sourcing remains ethical and environmentally responsible [224]. For example, Energy Web Foundation and Rocky Mountain Institute are developing a new blockchain registry called SAFc, tailored for the aviation industry [225]. Fuel producers will mint SAF digital certificates at production so that airlines and companies can claim them to track CO₂ reduction [225]. Additionally, IoT-based environmental monitoring systems track carbon sequestration and land use impacts. This provides data-driven insights for sustainable feedstock cultivation [226,227]. As one illustration, Dryad Networks (Dryad Networks GmbH, Berlin, Germany) uses distributed sensors to measure variables like soil moisture, fuel moisture, and tree growth. Their roadmap explicitly includes dendrometer sensors to gauge carbon sequestration in individual trees [228].

4.2. Conversion Technology

Developing and deploying SAF conversion technologies requires improvements in process efficiency, scalability, and infrastructure compatibility. Industry 4.0 technologies such as AI-driven process optimization, automation, IoT-enabled monitoring, blockchain for regulatory compliance, and additive manufacturing for catalyst development can provide several benefits. They include streamlining fermentation efficiency, validating ASTM pathways, developing bio-intermediates, mitigating risks, and innovating processes [229]. These technologies can play a key role in accelerating SAF deployment by reducing costs, improving efficiency, and enhancing scalability.

4.2.1. Decarbonize, Diversify, and Scale the Current Fermentation-Based Fuel Industry

AI-powered process control and enzyme optimization driven by ML can reduce the carbon intensity of starch ethanol production while maximizing yield in the fermentation process [230,231,232]. Owusu and Marfo [233] showed that combining a neural network with an ant colony optimizer models both enzymatic hydrolysis and fermentation stages at the same time. The system produced accurate forecasts of sugar and ethanol level reductions.

IoT-enabled bioreactors (BIOSTAT^® B-DCU, Sartorius Stedim Biotech GmbH, Göttingen, Germany) monitor fermentation conditions in real time, adjusting temperature, pH, and nutrient supply dynamically to enhance microbial efficiency without requiring additional corn planting [234,235]. For example, Baicu et al. [236] presented an IoT bioreactor proof of concept with a microcontroller connected to digital temperature and turbidity sensors. This system preserved the optimal yeast culture conditions by transferring data to the cloud and utilizing a Peltier module for precise heating or cooling. Edge computing solutions let fermentation plants process data on site, which speeds up processes and lets them make decisions in real time [237]. Additionally, blockchain-based carbon tracking systems can help monitor and verify emission reductions in ethanol-to-SAF conversion, ensuring regulatory compliance [188]. Blockchain tracks every ethanol milestone (corn harvest, fermentation, alcohol-to-jet upgrade) to create a clear low-carbon auditable record. Research shows this ledger can improve feedstock traceability by about 30%, cutting the odds of double-counting or errors [238].

4.2.2. Enhance Production and Reduce Carbon Intensity of Existing ASTM-Approved Pathways

To advance ASTM-qualified pathways, digital twins and AI-driven process simulations can model SAF conversion under different process conditions. This helps optimize efficiency and reduce costs before full-scale deployment [239,240,241]. Within an EU project, ORLEN and Yokogawa collaboratively developed a virtual plant model for green-hydrogen-and-carbon-dioxide-based synthetic aviation fuel [242]. The digital twin will simulate process economics and emissions for various modes of operation to identify the most promising route for production. 3D printing and additive manufacturing enable rapid catalyst and reactor component prototyping. This improves the efficiency of hydro-processed ester and fatty acid (HEFA), Fischer–Tropsch (FT), and alcohol-to-jet (ATJ) pathways [198,243]. Compliance monitoring with blockchain technology streamlines the regulatory approvals process by ensuring the accuracy of fuel performance data documentation. This accelerates the commercialization of ASTM-qualified SAF [244]. In a shipping industry pilot, COSCO Shipping and the Global Sustainable Bioenergy Network used blockchain to issue verifiable green certificates tied to specific biofuel procurements [245]. Each Proof of Sustainability certificate was recorded on-chain, allowing auditors, customers, and regulators to track carbon intensity and sustainability from source to combustion. This approach simplifies auditing and provides transparent proof of compliance for ASTM-approved SAF and renewable fuels.

4.2.3. Develop Bio-Intermediates and Pathways Compatible with Existing Capital Assets

For developing bio-intermediates and compatible pathways, AI-driven predictive modeling can identify optimal bio-intermediate formulations that are compatible with existing refineries. This reduces the need for costly infrastructure overhauls [246,247]. Comesana et al. [248] noted that ML models can predict relevant biofuel production pathways and screen large libraries of candidate molecules (jet fuel precursors) by their boiling point, energy content, and other characteristics. This enables rapid down-selection of promising formulations. Cyber-physical systems and automated processing units dynamically adjust reaction conditions. This optimizes the conversion of biomass, industrial waste gases, and other alternative feedstocks into SAF-compatible intermediates [249]. For example, a project led by the IEA investigated the conversion of biomass into bio-oils using fast pyrolysis or liquefaction [250]. These intermediates are then co-processed in existing refinery units. This method uses current equipment, such as hydrocrackers or FCC units, to upgrade bio-crude on site. Doing so eliminates the need for new investments while producing low-carbon jet and diesel fuels. 3D-printed biocatalysts and enzyme reactors improve the efficiency of biochemical conversion processes. This ensures higher yields at lower costs [251]. For instance, Schmieg et al. [252] utilized 3D-printed hydrogel lattices to entrap enzymes for continuous conversion. They achieved stable reactor operation for 72 h with consistent product formation.

4.2.4. Reduce Risk During Operations and Scale-Up

To mitigate scale-up and operational risks, AI-driven predictive maintenance reduces equipment failures by analyzing sensor data from reactors, distillation units, and processing plants to anticipate potential disruptions [13,253]. Shell used ML to monitor sensor signals, detecting deviations in temperatures and valve positions [254]. In 2020, the AI system identified 65 control valves needing repair, which traditional inspections had missed. Digital twins can simulate SAF production scenarios. This helps identify bottlenecks and optimize process design before large-scale implementation [240,255]. For example, Sierla et al. [256] outlined a semi-automated workflow that interprets a brownfield plant’s piping and instrumentation (P&ID) diagram and sensor data to construct a validated simulation model. Blockchain-based smart contracts can enhance supply chain coordination. This helps to reduce transaction delays and financial risks in feedstock procurement, processing, and SAF distribution, when upscaling the SAF production [239]. The Roundtable on Sustainable Biomaterials (RSB) and Bioledger piloted a blockchain database to track biofuel transactions and certifications. The implementation recorded each feedstock delivery and processing step under configurable smart contract rules that met the EU Renewable Fuel standards [196]. The pilot showed that a blockchain ledger can enforce compliance and prevent fraud, ensuring every transaction detects tampering.

4.2.5. Develop Innovative Unit Operations and Pathways

To achieve innovative unit operations and expand pathways, AI-enabled reaction modeling can help identify new SAF production methods that enhance feedstock flexibility while improving conversion efficiency [181]. For example, ML models can predict optimal reaction networks and catalytic conditions. In one study, researchers built ML models to predict key physical properties such as the boiling point and heat of combustion for thousands of organic molecules [248]. Shell used ML to monitor sensor signals, detecting deviations in temperatures and valve positions [257]. IoT sensors in SAF refining facilities can provide real-time insights into reactor conditions. This helps optimize energy use and reduce operational costs [258]. Advanced automation in SAF conversion plants improves process reliability. This allows SAF production to scale up efficiently without sacrificing quality or sustainability [259]. Neste has launched a Fintoil biorefinery in Finland that produces drop-in biofuels by utilizing pine residues [260]. They are utilizing Emerson automation capabilities to achieve peak performance. Neste’s newly established Fintoil biorefinery in Finland produces drop-in biofuels derived from pine residues. The system employs an Emerson automation suite to optimize performance [260].

4.3. Building Supply Chains

Industry 4.0 technologies are key to establishing optimum, scalable, low-emission SAF supply chains. Agility of stakeholder collaboration, selection of optimal supply chain modeling, risk avoidance, and large-scale production infrastructure development will be realized through Industry 4.0 technologies. Regional stakeholders build coalitions created on blockchain and deployed on cloud computing. This enables transparent collaboration and secure transactions between feedstock suppliers, refiners, and airlines. For example, blockchain smart contracts are programmed to automate agreements and real-time sustainability certification to build trust between SAF producers and users. Cloud-based platforms provide real-time information about feedstock availability, refinery capacity, and airport demand. This facilitates data exchange and decisions. AI-driven approacheswill enhance the accuracy of policy analysis and investment expectations regarding SAF. This allows stakeholders to evaluate the effect of regulatory incentives on SAF acceptance in different areas. Also, digital twins can help simulate a prospective SAF supply chain and its performance before implementing and locating its operational components.

4.3.1. Establish Regional Stakeholder Coalitions

This action area is about creating partnerships between feedstock providers, refiners, airlines, and policymakers to help actively develop supply chains for SAFs. Also, blockchain can improve SAF traceability when documenting its entire travel from feedstock to distribution by 30% [238]. This can reduce fraud and lead to higher regulatory compliance. For instance, Shell’s Avelia platform uses blockchain to provide clear and transparent tracking of SAF’s environmental attributes and to prevent double-counting of emissions credits [261]. By embedding agreed trust rules into the blockchain, all supply chain partners (from farmers and refineries to airlines and shippers) co-define and validate each transaction [196]. This effectively builds a system of consensus and embedded trust that aligns the coalition’s incentives. Cloud-based platforms provide a common real-time information backbone for SAF ecosystems. This implementation enables the ease of exchange of critical metrics such as fuel and inventory levels, fuel volume, and emissions intensity across value chain partners [262]. Microsoft Cloud Logistics, working with DB Schenker, exemplifies this in SAF supply chains [263]. Their pilot integrates SAF procurement, routing, and sustainability dashboards across companies to support coordinated decision-making and emissions tracking. According to IATA, a robust traceability framework, such as blockchain on a cloud platform, is essential for ensuring accurate SAF accounting in mixed fuel batches [264].

4.3.2. Model SAF Supply Chains

To develop and disseminate data and analytical tools, big data and AI are deployed to optimize feedstock sourcing, refinery site selection, and transportation logistics [20,265,266]. This ensures cost-effective and low-carbon supply chain pathways. In a case study related to the central Vietnam region, Duc and Nananukul [267] used an integrated methodology combining ML algorithms and optimization models to optimize the performance of a biomass supply chain. For example, airlines and fuel distributors can apply AI-based demand forecasting models to stabilize SAF supply, while also reducing transportation constraints [268]. Utilizing IoT sensors and edge computing facilitates the collection of real-time information from farms, refineries, and airports, can provide data on feedstock inventory, processes for fuel blending, and the efficiency of distribution [269]. With the data collected from sensors, edge computing ensures immediate data processing at the source [209]. This enables faster adjustments in logistics and refinery operations.

4.3.3. Demonstration of Regional SAF Supply Chains

As part of feedstock-to-fueling demonstration projects, Industry 4.0 can enable the de-risking and maturation of SAF production technologies before commercial deployment. AI-driven process optimization refines pilot plant operations, ensuring biomass-to-fuel conversion processes reach maximum efficiency before scaling. Biomass-to-fuel pilot facilities (e.g., NREL’s Integrated Biorefinery Research Facility) provide scaled-down biorefinery units such as hydrolysis and fermentation tanks. These units allow operators to gather process data and tune conditions before full-scale SAF production [270]. H. Wang et al. [271] built a validated artificial neural network (ANN) model of a pilot-scale (ton/day) entrained-flow gasifier. They trained the ANN on simulated pilot data and then used it in multi-objective optimizations to find operating conditions that maximize carbon conversion and hydrogen output. Cyber-physical systems in automated SAF processing can further enhance reliability by self-learning and adjusting processing parameters dynamically [249]. This minimizes inefficiencies during scaling. Additionally, 3D printing (additive manufacturing) accelerates technology deployment by enabling the rapid development of custom reactor components, catalysts, and fuel processing equipment tailored for SAF conversion pathways [272]. Borges et al. [273] demonstrated 3D printing a micro-structured catalytic stirring system for biodiesel production. It shows how 3D printing enables high mechanical strength and reusability. These capabilities accelerate technology deployment for custom components in biofuel production, including SAF.

4.3.4. Develop a Production Infrastructure to Support SAF Deployment in the Industry

Industry 4.0 technologies provide automated solutions for refining, blending, and distribution to ensure successful investment in commercial-scale SAF production infrastructure [249,266,274]. Honeywell deployed its automation suite at a large SAF biorefinery to provide real-time monitoring and control of complex conversion processes [275]. The system optimized performance and minimized downtime. Industrial robotics improve operational efficiency by automating critical fuel processing, storage, and blending systems to ensure consistent SAF quality [276]. Schneider Electric notes that modern biorefineries track Renewable Identification Numbers (RINs) and automate batch operations via integrated supervisory control data acquisition (SCADA) and distributed control system (DCS) platforms (Emerson Electric Co., St. Louis, MO, USA) [277]. This enables consistent blend quality and automated sampling. AI-powered predictive maintenance enhances refinery and distribution reliability by detecting potential failures, thereby reducing downtime and increasing SAF output [278]. In their study, Arinze et al. [279] noted the increasingly broader possibilities for facilitating reliability and cost savings in predictive maintenance of energy infrastructure like oil and gas facilities, mainly due to AI applications. Additionally, blockchain-based carbon tracking systems provide transparent carbon accounting and regulatory compliance for airlines, refineries, and policy regulators, ensuring CORSIA and other SAF mandates are met [280,281]. Yi [188] highlighted blockchain’s use in biofuel supply chains for transparency and traceability, which can extend to SAF.

4.4. Policy and Valuation

To maximize the social, economic, and environmental benefits of SAF under a multidimensional valuation framework, prioritizing robust policy and valuation is vital. In addition, the use of Industry 4.0 technology and advances in these technologies can help support better data-driven policy making and more evaluative economics. It can clarify the valuations of SAFs by providing data processing through AI-based analytics, blockchain protocols for transparent data sharing, and digital modeling methods. These technologies supplement three primary focus areas in the policy and valuation analysis workstream as they provide accurate, evidence-based conclusions for use by policymakers at the federal, state, and international levels.

4.4.1. Improve the Environmental Data and Models for SAF

Under the creation of more robust environmental and socio-economic datasets and analytical tools, big data, IoT, and AI provide capacity for real-time environmental assessments. This will help policymakers understand SAF’s carbon mitigation dimensions, land use changes, and economic value [282]. AI models used for real-time predictive modeling are also being used for dynamic LCA that combine instant feedback on feedstock sourcing, energy intensity and carbon emissions [222]. It is important to note that the enhanced accuracy ability is due to the advanced data integration. AI can also model the source of feedstocks and emissions dynamically and will provide more accurate and confident decisions [283].

Additionally, blockchain-based sustainability tracking ensures transparent verification of SAF’s environmental benefits, helping stakeholders comply with regulatory frameworks like CORSIA and the Renewable Fuel Standard (RFS) [188,196]. Furthermore, by applying blockchain to carbon tracking, stakeholders can transparently account for emissions reductions [281]. Cloud-based policy simulation platforms also allow regulators to test different policy scenarios. This will optimize incentive structures and carbon pricing mechanisms for SAF expansion. By running policy simulations in the cloud, authorities can test carbon pricing rules, subsidy levels, blending mandates, and other levers on a virtual SAF market. For example, a European project (PolicyCLOUD) demonstrated a serverless cloud framework that lets analysts simulate and compare alternative policies before implementation [284]. Similarly, the DOE/NREL Engage tool is a cloud-based energy simulator with a browser-based interface that manages data and scenarios in the cloud [285]. This system enables stakeholders to model grid and fuel transitions collaboratively.

4.4.2. Conduct Techno-Economic and Production Feasibility Analysis

For techno-economic and production potential analyses, AI-driven economic modeling can assess cost-competitiveness, feedstock pricing trends, and infrastructure investment needs across different SAF pathways [185]. A framework using ML for techno-economic evaluations of SAF is proposed by C. Wu et al. [185]. They used a stochastic analysis to investigate the likelihood of loss, demonstrating AI’s ability to comprehend and control the economic risks related to SAF routes. In another study, He et al. [20] underlined the role of ML in optimizing transesterification processes and predicting biodiesel yield when performing techno-economic analyses. They emphasized that ML models can predict fuel properties with high accuracy (R² = 0.85 to 0.99) and contribute to cost reduction by shortening technology development time and supporting process optimization. Digital twins simulate SAF production at varying scales to identify bottlenecks and cost reduction opportunities before real-world implementation [286,287]. Muldbak et al. [288] developed a digital twin of a pilot bioproduction plant that uses two-way data exchange to link real-time plant data with simulation models. The implementation enabled virtual testing of process changes, prediction of production bottlenecks, and suggested optimizations prior to physical implementation.

Policymakers can gain real-time cost and performance data through edge computing-enabled refinery sensors, enhancing their economic feasibility assessments [133]. This allows policymakers and analysts to use live data streams for immediate refinement of economic models, thereby improving their accuracy. By incorporating up-to-date operational conditions without delays, these sensors improve the accuracy of feasibility assessments. Meanwhile, automated data processing platforms streamline the integration of economic, environmental, and supply chain variables. This integration helps identify the most viable SAF production models [38]. For instance, a DOE-sponsored project is releasing a web-based toolkit that unifies techno-economic analysis (TEA) and life cycle assessment (LCA) models for multi-input biorefineries [270]. Ficili et al. [289] describes automated platforms operating with massive datasets to improve supply chain optimizations. Similar opportunities exist for SAF, if these platforms can integrate feedstock prices, environmental costs, and supply chain features into techno-economic analyses.

4.4.3. Contribute to SAF Policy Development

Informing SAF policy development requires AI-enabled policy evaluation tools to analyze historical data and forecast the long-term impacts of SAF incentives, subsidies, and regulatory frameworks [290]. In climate finance research, ML methods have successfully uncovered non-linear policy effects and provided predictive insights to anticipate impacts of financial and regulatory policies, such as sequencing of green credits or tax rules [291]. Yar et al. [292] discuss the impact of AI advancements on public policy by providing opportunities for decision-making improvements and data-driven insights, especially through enhanced policy prediction capabilities.

Blockchain-enhanced governance platforms ensure transparency in SAF credit trading systems, tax incentives, and compliance tracking [164,293]. This approach helps with mitigating fraud and ensuring equitable access to incentives. In practice, a blockchain-based certificate scheme records each credit transaction (issuer, buyer, quantity) on-chain, providing regulators and auditors a tamper-proof trail [294,295]. Also, smart contracts can auto-issue and settle SAF or biofuel certificates when fuel is produced or blended [294]. Swinkels [296] explores trading voluntary carbon credits on a blockchain-based exchange, which is analogous to SAF credit trading. Additionally, cloud-based collaboration networks can enable federal, state, and international policymakers to share best practices and coordinate regulatory frameworks for SAF expansion. For instance, the EU’s federated-system approach for the flexible and interoperable energy communities (FEDECOM) project is developing a cloud-based platform for integrated local energy systems and cross-border energy trading [297]. The system will provide centralized data, analytics, and communication for policymakers to harmonize standards and update regulations in real time.

4.5. Enabling End Use

Facilitating the widespread adoption and integration of SAF among civil and military users requires overcoming technical, regulatory, and logistical barriers. Industry 4.0 technologies can help accelerate SAF evaluation, support performance testing, sort distribution logistics, and enable infrastructure integration through AI-driven analytics, blockchain-enabled tracking, automation, and advanced manufacturing techniques. These technologies play a critical role in addressing the four key action areas in the enabling end use workstream, ensuring safe, efficient, and cost-effective deployment of SAF while meeting the aviation industry standards.

4.5.1. Support SAF Evaluation, Testing, Qualification, and Specification

For SAF evaluation, testing, qualification, and specification, AI can optimize fuel performance modeling to reduce the reliance on costly experiments [298]. In the aviation arena, He et al. [20] emphasized that ML-based technologies can shorten technology development time and support process optimization for certified aviation fuels. Baumann and Klingauf [299] used ML to develop fuel flow models based on full-flight data, which can be applied to SAF to optimize fuel performance and reduce physical testing. Digital twins simulate SAF combustion and engine compatibility under varying conditions [239]. This can help accelerate desirable certifications while minimizing testing costs for producers. Researchers at NREL use digital twins to study SAF combustion in jet engines, which is crucial for engine compatibility and certification [300]. Airbus, for instance, uses digital twins to plan, experiment, and run operations, such as simulating fuel performance and compatibility with the engine [301]. Besides, lab robots can mechanize fuel property analysis to ensure that SAF meets regulatory standards more effectively. Such automation lessens the scope for error and the need for constant testing, with each step and result traceable, a huge benefit for compliance with quality requirements by policymakers [302]. Blockchain digital certification systems can further enhance traceability and evidence of compliance [280]. This reduces the administrative burden for SAF producers seeking certification. The Energy Web Foundation and the Rocky Mountain Institute built a blockchain-based SAF certificate (SAFc) registry to increase transparency and promote the use of more sustainable fuels [244].

4.5.2. Facilitate the Adoption of Unblended and High-Percentage SAF Blends, Including up to 100% SAF

AI-driven chemical analysis enhances SAF blend optimization, ensuring fuel stability and performance across different aircraft models [20]. Jameel and Gani [303] demonstrated that an AI-driven hybrid model ensures the consistent achievement of critical end-product characteristics, such as viscosity and density, in fuel blending. This reduces off-specification fuel production. In another study, Liu and Yang [304] used an ANN model to predict the low flammability limit of SAF blends with 98.8% accuracy.

Cyber-physical systems (CPSs) in refinery operations allow real-time adjustments to SAF composition. This ensures that blends meet energy density, thermal stability, and viscosity standards. Digital optimization studies, such as those varying distillation cut points, demonstrate that computational control significantly increases SAF yield while adhering to operational limits [305]. By integrating sensors, controllers, and optimization algorithms in a cyber-physical setup, refineries can dynamically tailor SAF composition to meet specific energy density and stability requirements [303]. Advanced sensor networks in aircraft fuel systems can monitor SAF combustion efficiency in real time, supporting long-term performance data collection to evaluate SAF usage [306]. Airbus reported using multiple probes and sensors to gather in-flight emissions data from an A350’s engines operating on 100% SAF [307].

4.5.3. Explore Synthetic Jet Fuels That Enhance Operational Performance and Productivity

Exploring Jet A fuel derivatives for performance and productivity enhancements requires advanced AI-driven fuel formulation models that can formulate molecular structures with higher energy density and lower emissions [308]. Kuzhagaliyeva et al. [308] developed a deep learning framework to generate gasoline-like fuel mixtures with optimized combustion metrics. Their approach produced high-octane blends that reduce engine-out soot and improve efficiency. A study by Park and Kang [258] employed ANN to predict SAF’s volume swell characteristics. This is a crucial property for blend compatibility and optimizing SAF blends for various aircraft models. 3D printing can improve combustion efficiency and reduce wear on aircraft engines by creating customized fuel additives, often nanoparticle-based or novel organometallic compounds [309]. Additionally, 3D printing plays an essential role in creating customized engine hardware that works in tandem with fuel additives. For example, the GE Catalyst turboprop engine employed 3D-printed, optimized combustion components and fuel injectors to achieve an estimated 5% reduction in engine weight and an estimated 1% better fuel efficiency [310]. In addition, high-throughput experimentation platforms in laboratory facilities dedicated to SAF research are capable of accelerating testing of fuel properties [311]. This can enable the rapid development of next-generation SAF derivatives with superior performance characteristics. Such automation can greatly speed up the testing of fuel blends and catalysts to enable rapid optimization of high-performance SAF formulations [311,312]

4.5.4. Adapt Fuel Infrastructure to Support the Distribution and Use of SAF

IoT-enabled smart fuel monitoring systems can optimize storage conditions and blend precision in large-scale fuel depots, integrating SAF into the fuel distribution infrastructure. SAF supply chain distribution networks can be streamlined by AI-driven logistics platforms, which also lessen bottlenecks in airport delivery and blending. SAF disruptions in the fuel supply chain may be diminished by using blockchain-based fuel tracking systems to ensure real-time transparency and authentication of the SAF shipments. Predictive maintenance algorithms have the potential to significantly enhance the reliability of airports’ fuel storage and SAF pipeline networks, while also effectively minimizing disruptions in the SAF supply chain.

4.6. Communicating Progress and Building Support

The benefits of SAF, including environmental, economic, and climate advantages, must be clearly communicated and promoted through effective public engagement [313]. To ensure trust and transparency in SAF supply chains, data-driven communication strategies need to be employed [314]. Industry 4.0 technologies, such as AI-driven data analytics, blockchain transparency tools, and digital outreach platforms, can enhance stakeholder engagement, public trust, and progress measurements. These technologies improve each key action area under this workstream toward the commercialization of SAF by delivering accurate, real-time, and verifiable information.

4.6.1. Engage Stakeholders to Promote Awareness and Collaboration on Sustainable Feedstock Practices

For stakeholder outreach and engagement on sustainability, AI-driven data visualization platforms can present clear, interactive insights on the sustainability impacts of SAF. These include life cycle GHG emissions reductions, land use efficiency, and energy savings [315,316,317]. Suggested by Ahmed [315], AR and VR coupled with AI technologies can provide virtual training and collaborative environments for the stakeholders. Since 2015, blockchain technology has addressed the transparency limitations of traditional supply chain tracking by overcoming single points of trust and enhancing inter-organizational confidence [318]. Blockchain technology guarantees that SAF sustainability metrics can be tracked transparently so that stakeholders can validate the sustainability of feedstocks, carbon offsets for available resources, and compliance with environmental requirements [225,314]. Cloud-based collaboration tools facilitate the real-time sharing of knowledge among scientists, industry leaders, and policymakers in real time. The result is a more effective partnership for sustainable aviation initiatives that results in better outcomes. For example, LanzaJet [319] has expanded its partnership with Microsoft to optimize the production and supply chains of SAF aircraft using Azure’s cloud-based AI and ML tools (Version 2023, Microsoft Corporation, Redmond, WA, USA). Announced in April 2024, this partnership will allow stakeholders to share emissions, operations, sustainability metrics, and any other information auditable and in real time. This effort ensures trust, compliance with regulations, and global collaborations.

4.6.2. Carry out a Comprehensive Assessment of the Benefits and Influence of the SAF Grand Challenge

To support benefits assessment and impact analysis of the SAF Grand Challenge, big data analytics and AI-driven simulation models can evaluate SAF’s long-term economic, environmental, and policy impacts. [320,321]. For example, Okolie et al. [322] developed a data-driven techno-economic framework for SAF production to predict the minimum selling price of pyrolysis-derived SAF from feedstock and plant parameters. Providing important features such as scenario modeling of SAF adoption, digital twins can help stakeholders with a better understanding of expected emissions, costs, supply chain risks, and infrastructure needs [323]. Enderle et al. [323] mentioned that by incorporating uncertainties, the digital twin provides a risk-informed decision support in real time, such as how the optimal SAF choice may change when considering uncertainty bounds. Automated AI-generated reports can synthesize large datasets [324]. This will ensure that stakeholders and policymakers have access to science-backed, data-driven insights to guide decision-making. Emerging AI-driven reporting systems, such as natural language summaries and predictive visualizations, could further help synthesize these big datasets.

4.6.3. Track the Advancement of the SAF Grand Challenge Objectives

For measuring the progress of the SAF Grand Challenge, IoT-enabled data collection systems tracked SAF production volumes, GHG emissions reductions, and supply chain efficiency metrics in real time [325]. For instance, Emerson used gas analysis sensors across the SAF processes to overcome measurement challenges in SAF production [326].

Blockchain tracking systems secure the integrity of data and eliminate downstream manipulation. This strengthens the amenability for progress reports and sustainability claims [238]. Alevia, a blockchain-powered book and claim solution for aviation, enables freight forwarders to access and assign SAF environmental qualities to their shipping clients [327,328]. This facilitates the disclosure of emissions and encourages a wider adoption of SAF. The system enables freight forwarders to access and allocate SAF environmental attributes to their shipping customers, and allows both parties to disclose emissions while supporting broader SAF adoption. Cloud-based dashboards and AI-enabled benchmarking tools provide real-time updates on SAF deployment. This allows industry leaders to identify barriers or challenges to optimize the strategy for reaching their SAF production ambitions. Boeing [329] used a public SAF dashboard coupled with the U.S. inter-agency metrics site on a cloud-based platform to monitor possible SAF availability and detect imbalance in supply and demand.

4.6.4. Share the Positive Impacts of the SAF Grand Challenge with the Broader Community

To communicate public benefits effectively, AI-driven digital media tools can generate personalized educational content for different audiences. This spans policymakers, industry leaders, and the general public. For example, a report by Wheelock [330] showed how AI could create educational materials for aviation that could communicate public benefits effectively. Also, AR and VR experiences can bring SAF supply chains to life, allowing stakeholders to visualize the journey from feedstock sourcing to SAF-powered flights [170]. Additionally, social media sentiment analysis powered by AI can track public perception of SAF. This can help industry leaders address concerns and misinformation with fact-based, transparent communication [331]. Alahmari et al. [332] developed and tested an AI-based approach, word embedding and clustering, to study the service sector by taking important factors from both academic research and public opinion. Their results offered implications on how to render service economies more sustainably.

5. Conclusions

This review outlined the disruptive potential of Industry 4.0 technologies in aiding advancements towards the commercialization of SAF. It highlighted how the various technology dimensions play a critical role in overcoming the existing difficulties related to production efficiency, regulatory approval, supply chain sustainability, and stakeholder engagement. These dimensions are blockchain, AI, IoT, 3D printing, and simulations such as digital twins, AR, and VR. This analysis demonstrates that these technological dimensions offer powerful tools for accelerating SAF production and adoption. Moreover, this study aligns with SDG 13 as it clarifies pathways that can help the aviation sector accelerate its transition to carbon neutrality.

Overall results indicate that both AI-based analytics and IoT-based monitoring can meaningfully improve feedstock innovation by optimizing growing conditions, managing biomass sustainably, and improving logistics. In the area of conversion technology innovation, the study shows that digital twins and automation significantly reduce scale-up risks and improve operational efficiencies. This leads to acceleration in ASTM certification processes, which is critical for the rapid deployment of SAF, improving transparency and trust among stakeholders.

This research emphasizes the important role of Industry 4.0 technologies in policy formulation and valuation. It shows how AI-powered predictive models and scenario planning tools enable policymakers and industry stakeholders to make informed, data-driven decisions regarding SAF investments and incentives. Moreover, various ways that could employ AR and VR technologies to enhance stakeholder education and public outreach were presented.

Industry 4.0 integration into the SAF supply chain requires collaboration among its different stakeholders. Policymakers and regulatory agencies need to determine supportive incentives and certification frameworks that can accelerate the adoption. Technology developers and solution providers are also responsible for scalable, cost-effective tools that are also needed for SAF infrastructure. A key component of the implementation of these technologies is the suppliers of raw materials, refineries, and airlines that test, validate, and implement them. Researchers and investors should support the commercialization of SAF by providing expertise and funding, as well as innovation and feasibility assessments.

Despite the potential shown in the literature and case studies, there are technological gaps. These consist of the demand for more integration and interoperability of various digital technologies throughout the SAF production ecosystem, better cost-effectiveness of sophisticated sensors and digital infrastructure, as well as stronger frameworks for addressing cybersecurity and data privacy issues in IoT and blockchain applications. Filling these gaps will harness the complete potential of Industry 4.0 to achieve the ambitious targets of the SAF Grand Challenge initiative. Furthermore, digital platforms that connect feedstock suppliers, biorefineries, and airline customers can function as ecosystem-based business models that promote scalability and shared value. These digitally enabled business models can increase the added value across the SAF supply chain and offer economic incentives for broader adoption. Yet, many of these efforts are in their early stages of development, such as research, piloting, or demonstration. Future research should explore structuring and governing models that balance profitability, sustainability, and regulatory compliance. Moreover, future research can further explore technology readiness level assessments for the application of Industry 4.0 technologies in SAF production and its supply chains.

This review study is based on published works that may over-represent certain Industry 4.0 technologies, such as blockchain and AI, as well as regions with more advanced SAF programs and established related regulations, such as North America and Europe. Therefore, the generalizability of this proposed framework may be limited by not considering under-reported technologies or emerging efforts in other regions.

In conclusion, Industry 4.0 technologies play a strategic role in accelerating the commercialization of SAF. The aviation industry requires ongoing research and investments in emerging technologies, which can aid in fulfilling international climate commitments aligned with SDG targets. This review offers a reference point for policymakers, industry players, and technology developers to advance practical pathways for achieving a sustainable, reliable, and cost-effective scale-up of SAF production.