Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment

Ebrahimi, Sajad; Szmerekovsky, Joseph

doi:10.3390/biomass5040063

Open AccessArticle

Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment

by

Sajad Ebrahimi

^1,*

and

Joseph Szmerekovsky

²

¹

Management Department, Seidman College of Business, Grand Valley State University, Allendale, MI 49401, USA

²

Department of Transportation and Supply Chain Management, College of Business, North Dakota State University, Fargo, ND 58108, USA

^*

Author to whom correspondence should be addressed.

Biomass 2025, 5(4), 63; https://doi.org/10.3390/biomass5040063

Submission received: 18 August 2025 / Revised: 21 September 2025 / Accepted: 2 October 2025 / Published: 9 October 2025

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Abstract

Achieving decarbonization targets in the aviation sector requires transformative approaches to sustainable aviation fuel (SAF) production. In this pursuit, feedstock innovation has emerged as a critical challenge. This research uses the U.S. SAF Grand Challenge as a case study, focusing on its feedstock innovation workstream, to investigate how Industry 4.0 technologies can fulfill that workstream’s objectives. An integrative literature review, drawing on academic, industry, and policy sources, is used to evaluate the Technology Readiness Levels (TRLs) of Industry 4.0 technology applications across the SAF biomass supply chain. The analysis identifies several key technologies as essential for improving yield prediction, optimizing resource allocation, and linking stochastic models to techno-economic analyses (TEAs): IoT-enabled sensor networks, probabilistic/precision forecasting, and automated quality monitoring. Results reveal an uneven maturity landscape, with some applications demonstrating near-commercial readiness, while others remain in early research or pilot stages, particularly in areas such as logistics, interoperability, and forecasting. The study contributes a structured TRL-based assessment that not only maps maturity but also highlights critical gaps and corresponding policy implications, including data governance, standardization frameworks, and cross-sector collaboration. By aligning digital innovation pathways with SAF deployment priorities, the findings offer both theoretical insights and practical guidance for advancing sustainable aviation fuel adoption and accelerating progress toward net-zero aviation.

Keywords:

Industry 4.0; biomass feedstock; sustainable aviation fuel; technology readiness level

1. Introduction

The aviation sector’s decarbonization has become a focal point of global climate initiatives. Sustainable aviation fuel (SAF) has emerged as one of the most promising solutions to potentially mitigate greenhouse gas (GHG) emissions by up to 80% compared to conventional aviation fuel [1]. Recognizing this opportunity, numerous countries (Australia [2]; Canada [3]; the European Union [4]; Japan [5]; New Zealand [6]; the United Arab Emirates [7]; the United States [8]) have initiated plans or roadmaps to cut life-cycle aviation emissions. In particular, the United States has launched a SAF Grand Challenge that includes a dedicated “feedstock innovation” workstream outlining key action areas for scaling sustainable biomass supply [8]. An essential part of the roadmap is to understand how to manage biomass feedstock supply chains and procure sufficient feedstock, which is critical for reliable SAF production and meeting demand [9].

Despite growing momentum, significant challenges persist in scaling biomass feedstock supply chains. These include quality variability [10], inefficiencies in logistics [11,12], material handling problems [13], limited traceability [14], seasonal supply fluctuations [13,15], spatial and temporal mismatches [16,17], and the lack of real-time integration of agronomic data [18]. In general, biomass feedstock supply chains are geographically dispersed and resource-intensive, heavily relying on historical data and qualitative information. This makes accurate forecasting and optimization of feedstock resources challenging [17]. These challenges highlight a critical need for technological innovation at the nexus of biomass procurement and digital technology, a need that Industry 4.0 developments could potentially fulfill [19].

The advent of Industry 4.0, which includes a myriad of disruptive technologies, such as the Internet of Things (IoT), robotics, artificial intelligence (AI), big data analytics, cyber–physical systems (CPS) (integrated computer and physical systems environment), and blockchain, offers transformative potential to address the systemic inefficiencies [20]. While many of these technologies have shown promise in related sectors such as precision agriculture [21,22] and smart logistics [23,24,25], their application to the biomass feedstock supply chain for SAF remains inconsistent and underdeveloped. Some applications of the technologies, such as remote sensing-based crop modeling [26], have reached the pilot or demonstration stage, whereas others, such as integrated blockchain traceability frameworks [27], are still conceptual or limited to small-scale pilots. This disparity highlights the need for a structured assessment of technology maturity to guide policy, research, and investment priorities. Despite the emergence of the aforementioned technologies, there has been no comprehensive evaluation of how ‘ready’ such technologies are for integration into the biomass feedstock supply chains for SAF.

The goal of our study is to address this gap by providing an in-depth assessment of feedstock innovation development and implementation, focusing on the maturity of Industry 4.0 technologies that can be integrated into biomass feedstock supply chains. Using a structured approach founded on the Technology Readiness Level (TRL), we conduct the assessments on a case related to the promotion of SAF production in the US, where innovation in biomass procurement is the focus of the initiative. Beyond a literature review or simple case registry, we critically examine available information to map the maturity of these digital technologies for feedstock innovation.

2. Conceptual Background

This section outlines the conceptual and theoretical frameworks underlying our analysis. First, we outline the conceptual framework guiding our analysis—namely, the SAF Grand Challenge feedstock innovation roadmap—along with relevant Industry 4.0 technologies. Then, we detail the methodology used for assessing TRLs across the identified applications.

2.1. Feedstock Innovation

In 2021, the U.S. government launched an initiative that tasked multiple agencies (DOE, USDA, DOT, and EPA) with collaboratively planning how to scale SAF production and use in the United States [28]. This effort resulted in a 2022 roadmap outlining steps to achieve SAF commercialization and move the aviation sector toward carbon neutrality through SAF production. Feedstock innovation is one of six workstreams defined in the SAF Grand Challenge. This workstream includes conducting research and development (R&D) on sustainable feedstock supply, reducing biomass feedstock supply chain costs, and increasing yield rates, while simultaneously minimizing technological uncertainties and risks [8]. To achieve that goal, the SAF Grand Challenge roadmap proposes five action areas (summarized in Table 1). Table 1 lists the five feedstock innovation action areas along with their objectives, key actions needed, and expected impacts, as outlined or inferred from the SAF Grand Challenge feedstock innovation roadmap [8].

Together, these five action areas form a comprehensive strategy to address the challenges in SAF biomass feedstock supply chains. Collaborative research, development, and demonstration (RD&D) efforts focused on these themes can mitigate investment risks, promote regional supply chain development, and facilitate long-term SAF market growth [8].

2.2. Industry 4.0

Industry 4.0 refers to the integration of disruptive technologies into cyber–physical systems (CPS), seamlessly linking physical, digital, and even biological domains [29,30]. This enables rapid decision-making, process optimization, and task automation across various industries [31]. Recent reviews highlight that Industry 4.0 technologies are transforming renewable energy systems, primarily through improved monitoring, predictive maintenance, and optimized grid operations [32,33]. For example, AI and big data analytics enhance forecasting for power generation and improve load management. Meanwhile, IoT and edge computing (on-site data processing) facilitate decentralized, sensor-based control throughout energy networks.

Within biofuel systems, digitization efforts typically focus on three main areas—sensing, analytics, and traceability—as they directly address key supply chain issues, such as feedstock variability, logistics, and certification [34,35,36]. IoT sensors and geospatial tools, such as remote sensing and drones, have been utilized to track key agronomic parameters, including moisture content and yield indicators [37,38]. For instance, Ahamed et al. [37] reviewed remote sensing methods for biomass production, highlighting their utility in estimating crop parameters. Similarly, Gano et al. [39] demonstrated the effectiveness of multispectral drone imagery paired with machine learning (ML) to estimate biomass attributes. AI and big data analytics are crucial for predicting biomass yields, optimizing supply chain logistics, and informing decision-making systems [40,41]. Blockchain technology is increasingly acknowledged for its role in secure data exchange and provenance tracking in renewable energy systems, while cloud and edge computing provide scalable, distributed infrastructure for managing supply chain data [42]. While CPS, digital twins, and augmented/virtual reality (AR/VR) are core to Industry 4.0, these technologies have seen limited adoption in biomass supply chains, appearing mainly in conceptual frameworks or small pilot studies [43,44].

Based on the specific needs of feedstock systems, we identify four key Industry 4.0 technology categories as most relevant: (1) Sensing & Automation, (2) Analytics & Intelligence, (3) Traceability & Infrastructure, and (4) System Integration (see Table 2). These categories emerged because they are consistently highlighted in bioenergy studies, whereas other Industry 4.0 technologies (e.g., additive manufacturing) show minimal relevance in biomass procurement.

2.3. TRL Assignment

Extensive research on biofuel development consistently highlights the persistent challenge of scaling technologies from controlled laboratory settings to robust industrial deployment [45]. This transition is rarely linear: delays and failures often stem from insufficient understanding of where a given technology lies in its development pathway. Assessing these maturity gaps is therefore critical for guiding investment, prioritizing R&D, and ensuring that innovations advance toward reliable deployment in the biomass supply chain.

To address this challenge, the Technology Readiness Assessment (TRA) provides a systematic, evidence-based framework for evaluating the level of technological advancement. TRA helps decision-makers identify gaps in testing, highlight at-risk technologies, and ensure that critical innovations are sufficiently validated before large-scale adoption. At the core of TRA is the TRL scale, a widely adopted nine-point metric that describes stages of technological maturity. TRL, as defined in ISO 16290 [46], is a standardized metric for technology maturity that indicates how close a technology is to market deployment [47]. Using TRL assessments helps identify development hurdles for new concepts and allows comparison of maturity across different technologies, highlighting weaknesses or at-risk technologies that require additional attention before further development [48]. In general, a new technology progresses from basic research through development and ultimately to deployment. Early pilot projects demonstrate the technology’s feasibility in relevant or operational environments, serving as a bridge between laboratory success and market acceptance. The scale consists of nine levels and covers three main phases: (1) TRLs 1–3, which align with basic scientific research, (2) TRLs 4–6, with technology development and prototyping, and (3) TRLs 7–9, with system demonstration, commissioning, and operational implementation. This alignment helps managers tailor project teams and funding sources (e.g., research grants for low TRLs, development programs for mid-range TRLs, and commercialization investments for high TRLs) appropriate to each stage of innovation maturity [49]. Table 3 provides a definition of each of the TRL scales, as well as their applications.

According to DOE [51], the TRL framework does more than assigning numbers; it organizes technology evolution into six broader stages of development. The first, basic technology research (TRL 1–2), involves observing fundamental principles and formulating initial concepts. The second, research to prove feasibility (TRL 2–3), transforms these concepts into proof-of-concept studies. The third, technology development (TRL 4–5), advances components and systems through validation in laboratory and early relevant environments. The fourth, technology demonstration (TRL 6), moves innovations into pilot-scale testing under real-world conditions. The fifth, system commissioning (TRL 7–8), ensures that prototypes are fully demonstrated, tested, and qualified for operational deployment. Finally, system operation (TRL 9) reflects technologies proven in continuous, full-scale industrial use. From a chronological perspective, Figure 1 illustrates the six broader stages of development, grouped with their corresponding TRLs.

By situating each innovation within this continuum, TRL makes it possible to diagnose where technologies currently stand, what risks remain, and what steps are required to achieve commercial readiness. For this study, TRL serves as the key evaluative tool because it not only captures the relative maturity of Industry 4.0 technologies but also provides a transparent basis for identifying the pathways through which these technologies can support the feedstock innovation goals of the SAF Grand Challenge.

3. Methodology

3.1. Integrative Literature Review

This study employs an integrative literature review approach to collect and synthesize academic and gray literature related to the application of Industry 4.0 technologies in biomass feedstock innovation for SAF. Following the guidelines for integrative reviews, first defined by Torraco [52], we identified sources from academic databases, government agencies (e.g., DOE, USDA), industry reports, and reputable news outlets. Unlike systematic reviews, which aim to be exhaustive, the integrative review method is framework-driven and conceptually selective, emphasizing thematic relevance and analytical depth over comprehensiveness [53]. Therefore, to assess the RTL, it suffices to refer solely to a limited number of valid references that confirm the application level of the technology in SAF biomass feedstock.

3.2. Review Framework

The analytical framework for the review is structured around five key feedstock innovation action areas outlined in the SAF Grand Challenge:

Expand feedstock availability and diversity
Improve Logistics Systems
Enhance feedstock quality and stability
Develop real-time feedback quality monitoring
Advance forecasting and planning tools

Each article included in the review had to address at least one of these action areas and apply at least one relevant Industry 4.0 technology, listed in Table 2. Furthermore, studies had to provide sufficient information, either directly or inferentially, to allow estimation of the associated TRL (based on the scales provided in Table 3).

3.3. Literature Search and Selection Process

A targeted search was conducted to retrieve both peer-reviewed and gray literature published between 2010 and 2025, in English. The databases consulted include:

Academic Databases: EBSCOhost, Scopus, Web of Science, IEEE Xplore, ScienceDiGect, and Google Scholar.
gray Literature and Web Sources: U.S. Department of Energy (DOE), U.S. Department of Agriculture (USDA), Department of Transportation (DOT), Environmental Protection Agency (EPA), and company websites, technical blogs, and white papers.

3.4. Data Collection and TRL Assignment

The technologies are evaluated based on three categories:

(1): Application domain: Each technology is first categorized according to its primary application within the biomass supply chain and mapped onto one of the five SAF Grand Challenge feedstock innovation action areas. This mapping ensures that technologies are evaluated in relation to the strategic priorities of the challenge rather than in isolation. For example, UAV-based phenotyping tools are placed under “Expand feedstock availability and diversity,” while digital twin models of depot operations are placed under “Improve logistics systems.” This systematic categorization provides a structured way to organize heterogeneous technologies into comparable groups.
(2): Alignment with key actions needed: After placement within an application domain, each technology is further assessed for its relevance to the specific key actions defined in each action area. This step helps distinguish between peripheral technologies and those that directly contribute to stated objectives. For instance, a sensing platform that measures biomass moisture content is aligned with the action on “real-time feedstock quality monitoring,” while crop modeling tools projecting yield under variable climate conditions are aligned with “forecasting and planning.” This layer of alignment excludes tangential innovations and highlights those most relevant for addressing SAF feedstock bottlenecks.
(3): Reported maturity level: Assessed using the TRL framework and aligned with DOE’s six-stage continuum of technology development, ranging from basic technology research (TRL 1–2) through system operation (TRL 9). For example, if a source described only a lab-scale trial of a blockchain concept, it was categorized as research to prove feasibility (TRL 2–3), whereas a successful multi-site pilot project of an IoT sensor network was placed in technology demonstration (TRL 6). Similarly, prototypes validated under relevant conditions but not yet scaled were assigned to technology development (TRL 4–5), while technologies with fully qualified, operational deployment across industrial contexts were categorized as system operation (TRL 9). This approach ensured that maturity ratings captured not just numerical TRLs but their placement within broader developmental stages.

When assigning TRLs to identified technologies, the authors considered several key points to ensure consistency and minimize bias:

Direct adoption where available: When peer-reviewed articles, government reports, or industry documents explicitly mentioned TRL, those values were adopted without changes. This approach ensured that assessments already performed by reputable organizations were directly integrated into our framework.
Inclusion of gray literature: Supplementary searches were conducted using Google to capture gray literature, such as corporate pilot updates, government white papers, and technical briefs. Only documents that contained verifiable technical descriptions of scale, environment, and outcomes were deemed credible enough to inform TRL inference.
Inference when TRLs were not reported: In cases where TRLs were not explicitly provided, maturity was inferred from descriptive evidence such as scale of deployment (laboratory prototype, pilot-scale, or commercial facility), type of testing environment (controlled vs. operational), and repeatability of results. For instance, laboratory prototypes with small-scale validation were categorized at TRL 4–5, while technologies undergoing demonstration in relevant field conditions were considered TRL 6–7, and repeated deployment in operational contexts was classified at TRL 8–9. A conservative approach was followed, with ambiguous cases assigned to the lower maturity level to avoid overstating readiness.
Cross-checking and consensus: To enhance objectivity and replicability, TRL assignments were not made by a single researcher. Instead, both authors independently reviewed the evidence and made preliminary TRA/TRL judgments, which were then cross-checked and discussed. Final assignments were reached through consensus, ensuring that the classification reflected multiple perspectives and minimized subjective bias.

4. Results and Discussion

In this section, we present TRL assessments for each action area (Table 4, Table 5, Table 6, Table 7 and Table 8), followed by a discussion of key technologies and their maturity for that area.

4.1. TRL Estimation of Industry 4.0 Applications in Expanding Feedstock Availability and Diversity

Following a comprehensive investigation encompassing breeding, land use mapping, and the integration of biomass into crop systems associated with biomass feedstock, the results are presented in Table 4. The findings for each are discussed in the following. In the category focused on breeding, applications such as advanced genomic selection tools and high-throughput phenotyping, enabled by ML algorithms, sensor-based imaging, and big data analytics, enhance the ability to identify and develop biomass crops with improved yields, stress tolerance, and adaptability. Genomic selection refers to a breeding approach for plants or animals, in which the behaviors and performance of the offspring are predicted based on their DNA [54]. In this approach, rather than awaiting outcomes, scientists utilize statistical methods to predict the anticipated characteristics of offspring before they are born. Regarding high-throughput phenotyping, it is a technique employed for assessing plant characteristics through the use of advanced technologies, including sensors, drones, and cameras [55]. Life-cycle assessments (LCAs) can also benefit from the integration of data from IoT, digital twins, and AI [56]. LCA is a systematic process that evaluates the environmental impacts of a product, process, or service throughout its entire life cycle, from raw material extraction to production, use, and disposal or recycling [56]. When constantly supplied with data from the previously mentioned technology combination, it is known as dynamic LCA. This combination can be useful in collecting data from the plant and simulating the cultivation of emerging biomass feedstock under various scenarios, considering different uncertainties, which results in a robust measurement of their generated carbon footprint. These technology-enabled approaches can strengthen the sustainability, resilience, and productivity of biomass supply chains by ensuring a reliable flow of high-quality feedstock.

In the land use mapping category, applications like precision geospatial analysis and remote sensing, supported by drones, satellite imagery, and geographic information systems (GIS), enable the accurate identification of suitable cultivation areas, monitoring land use changes, and assessment of environmental impacts. These capabilities, driven by Industry 4.0 tools, support informed decision-making for biomass deployment while safeguarding environmental sustainability.

In the third category of actions needed within this action area, which involves integrating biomass into crop systems, applications such as intercropping models and rotational planning, powered by simulation software, IoT-enabled field sensors, and data integration platforms can help design cropping systems that incorporate biomass without compromising food production [57,58]. This integration, underpinned by digital technologies, enables optimization of land productivity, diversification of farm income, and improved soil health, contributing to the long-term sustainability of biomass supply chains.

Table 4. Summary of TRL estimation of Industry 4.0 technology applications in breeding (a), land use mapping (b), and integration of biomass into crop systems (c) (see Appendix Table A1 for detailed operational functions related to each case of application).

	Industry 4.0 Technology	Application	TRL	References
(a)	Artificial Intelligence (AI)	Genomic Selection	4–5	[59,60]
	IoT, Robotics & Remote Sensing (UAVs)	High-throughput phenotyping	6	[61,62]
	Digital Twins & IoT	Dynamic LCA	4–5	[63,64]
(b)	AI & Satellite Remote Sensing	Yield-Mapping and Land Optimization	6	[65]
(b)	Remote Sensing (Satellite Imagery & GIS) & ML	Biomass and Land use Mapping	4–5	[37,66]
(c)	Autonomous Robotics & AI	Inter-seeding Biomass Cover Crops	7–8	[57]
(c)	AI & Big Data Analytics	Biomass Cover Crop Decision Support	4–5	[58]

The TRL results for this action area reveal distinct patterns in technological maturity across the three focus areas, including breeding, land use mapping, and crop system integration. In breeding, AI-driven genomic selection is in the technology development phase, reflecting that algorithm development is progressing faster than multi-location validation and trait portability. A pilot in Brazil applied ML models to predict sugarcane biomass traits from genomic data, achieving 20–30% faster breeding cycles in controlled field trials, though scalability remains limited by dataset heterogeneity [59].

Conversely, high-throughput phenotyping using UAV/IoT sensing has progressed to the technology demonstration stage, indicating that integrated prototypes are being used under realistic conditions. For Digital Twins and IoT in dynamic LCA (TRL 4–5), a conceptual model for European wood biomass supply chains integrates IoT sensors with virtual simulations to evaluate carbon intensity [64]. This approach shows a potential 15% reduction in life-cycle emissions in lab-validated scenarios, although further field integration is needed.

For land use mapping, AI combined with satellite yield-mapping platforms is at the technology demonstration level. However, more generic biomass-type classifiers remain in the technology development phase owing to domain shift and labeling constraints that impact their ability to generalize. In remote sensing and ML for biomass land use mapping (TRL 4–5), a case study in northern China used Sentinel-2 and Landsat-9 imagery with random forest algorithms to map energy crop potential, identifying 10–20% more viable land than traditional methods, albeit with validation confined to small regional datasets [66].

In crop-system integration, autonomous inter-seeding of biomass cover crops is already in the system commissioning stage. This high level of maturity is supported by repeated operational trials and early commercialization efforts. Conversely, for AI and big data in biomass cover crop decision support (TRL 4–5), a web-based tool (SEABEM) leveraged ML on African field data to predict cover crop biomass yields, improving farmer decisions in pilot farms by 25%, though reliant on limited training data [58].

These patterns demonstrate that hardware-centric, narrowly scoped tools with well-defined feedback mechanisms are maturing at a faster pace than data-centric, system-level analytics that rely on interoperable datasets. In the near term, stakeholders should focus on deploying sensing and automation technologies to mitigate operational risks, while also instrumenting workflows to generate high-quality data. In the medium term, investments should be directed toward establishing common data standards, enabling cross-site benchmarking, and integrating analytics more closely with operational and economic constraints to advance lower-TRL models toward pilot readiness.

4.2. Readiness Levels of Industry 4.0 Technologies in Biomass Logistics Systems

Table 5 summarizes the TRL assessment for Industry 4.0 applications in three key logistics improvements: (a) designing regional depots, (b) advancing densification, and (c) optimizing transportation networks. The findings for each are discussed in the following. In the category of designing regional depots, applications such as pellet-delivery systems and pelletization are fundamental to improving the efficiency of biomass preprocessing and distribution, by extending sourcing distance beyond traditional limits and enabling the use of diverse or low-yield biomass types [67,68]. Industry 4.0 technologies, including discrete-event simulation, digital twins, and IoT-based monitoring, can optimize depot layout, coordinate material flows, and assess operational performance under varying demand and supply conditions [69]. These advancements enhance regional supply reliability and reduce logistical bottlenecks, supporting the scalability of biomass supply chains [70,71].

In the category of advancing densification, applications like pellet dimension monitoring are essential for ensuring consistent product quality, which directly affects handling, storage, and combustion performance [72]. Technologies such as computer vision, RFID-enabled quality tracking, and cloud-based analytics allow real-time measurement and control of pellet characteristics, enabling early detection of deviations and maintaining uniformity across large production batches [72,73]. This precision improves downstream processing efficiency and strengthens overall supply chain resilience.

In the category of optimizing transportation networks, applications like route optimization are essential for reducing delivery costs, minimizing emissions, and improving timeliness [74,75]. Industry 4.0 tools, including GIS-based routing software, AI-driven logistics planning, sensor-equipped transport vehicles, and digital twins, support dynamic decision-making by considering real-time traffic, weather, and supply conditions [76,77,78]. These innovations enhance supply chain flexibility and sustainability, ensuring that biomass reaches end users efficiently [75].

Table 5. Summary of TRL estimation of Industry 4.0 technology applications in designing regional depots (a), advancing densification (b), and optimizing transportation networks (c) (see Appendix Table A2 for detailed operational functions related to each case of application).

	Industry 4.0 Technology	Application	TRL	References
(a)	Digital Twin & Simulation	Advanced Pellet-delivery System	4–5	[13,75,79]
(b)	Computer Vision & Automation	Smart Process Control and Quality Monitoring	6	[80]
(c)	IoT, RFID & Cloud Analytics	IoT-based Tracking and Coordination	4–5	[19,81]
	AI & GIS	Route Optimization	4–5	[82]
	IoT & Digital Twins	Real-time LCA of Logistics Operations	4–5	[78,83]

The maturity levels in logistics applications differ based on the extent to which each tool is integrated with existing operations. The development of digital twin technology and discrete-event simulation for regional depots is still at an early stage, where a U.S.-based model simulated wood pellet supply chains, optimizing depot layouts and reducing costs by 10–15% in virtual scenarios validated against historical data, though full-scale deployment awaits primarily due to models lacking validated data on variable feedstock flows, seasonal shocks, and depot behaviors during disruptions [75]. Progress in densification, including pellet size monitoring, is currently in the demonstration phase. Vision systems are seamlessly retrofitted onto existing lines, providing immediate QA/QC results and requiring limited cross-site data sharing. IoT, RFID, and cloud tracking technologies are within the development level (TRL 4–5), where a prototype chipless RFID system for biomass logs in Australia enabled real-time provenance tracking in lab tests, improving traceability accuracy by 20%, but was limited by scalability in rural environments due to connectivity issues, device durability concerns, and data-sharing policies [19]. Concurrently, AI-enabled GIS route optimization remains at the development stage due to fragmented fleets, limited real-time signals related to weights, moisture content [84], and delays, as well as siloed dispatch systems, which diminish model effectiveness and external validity. Moreover, Industry 4.0 technologies, such as IoT and digital twins for real-time LCA (TRL 4–5) were piloted in Norwegian wood supply chains, simulating emissions reductions of 12% via symbiotic networks, though confined to conceptual validations [78].

Near-term improvements can be achieved by deploying inline vision systems for pellet QA/QC and implementing phased IoT/RFID tracking on high-volume routes, supported by dashboards and APIs for real-time load monitoring. Pilot depots streaming process and flow data into digital twins can validate cost and reliability impacts through controlled tests. Routing optimization should incorporate operational constraints, with dynamic rerouting adopted as sensor coverage expands. Prioritizing interoperability through shared data models, event logs, and minimal vendor lock-in will enable lower-TRL analytics to progress by utilizing data from higher-TRL sensing and automation hardware (from development to demonstration). This approach can also help connect logistics performance with sustainability outcomes.

4.3. TRL Evaluation of Industry 4.0 Tools for Enhancing Feedstock Quality and Stability

Table 6 summarizes the TRL assessment for Industry 4.0 applications in three key logistics improvements: (a) enhancing drying and stabilization methods, (b) setting quality benchmarks, and (c) devising storage solutions. The findings for each are discussed in the following. In improving drying and stabilization methods, applications such as smart drying and near-infrared (NIR) spectroscopy are vital for enhancing the efficiency and consistency of biomass moisture reduction. Smart drying systems, supported by IoT sensors, AI-driven control algorithms, and real-time monitoring, can adjust drying parameters dynamically to reduce energy consumption while maintaining quality [68,85]. NIR spectroscopy provides a rapid, non-destructive assessment of biomass composition and moisture content, enabling precise control over stabilization processes and ensuring readiness for long-term storage or conversion [86].

In setting quality benchmarks, applications like thermal monitoring and moisture monitoring play a crucial role in maintaining high product standards across the biomass supply chain [87]. Thermal monitoring, often enabled by infrared imaging and embedded sensors, helps detect overheating or microbial activity that may degrade biomass quality [88]. Moisture monitoring systems, integrated with cloud analytics platforms, allow continuous data collection and trend analysis to ensure compliance with quality specifications. In addition to conventional indicators of feedstock quality, there is a growing need for sustainability-oriented benchmarks that capture environmental performance [64]. Emerging Industry 4.0 technologies, such as IoT sensor networks and digital twins, enable the generation of real-time data on resource use, emissions, and process conditions across biomass supply chains. When linked with LCA, these technologies enable the creation of dynamic benchmarks that extend beyond physical fuel properties to include carbon intensity and other environmental metrics [63]. Together, these technologies provide an evidence-based approach to standard setting and verification [87].

In developing storage solutions, similar monitoring technologies applied in the context of storage facilities support the prevention of biomass degradation during extended holding periods. Smart storage environments equipped with environmental sensors, predictive analytics, and automated ventilation systems can maintain optimal temperature and humidity conditions, thereby reducing spoilage and loss [89,90,91]. These innovations help preserve the economic value of biomass and ensure supply consistency across seasons.

Table 6. Summary of TRL estimation of Industry 4.0 technology applications in improving drying/stabilization methods (a), setting quality benchmarks (b), and developing storage solutions (c) (see Appendix Table A3 for detailed operational functions related to each case of application).

	Industry 4.0 Technology	Application	TRL	References
(a)	IoT sensors & AI control	Smart drying	6	[92,93]
(b)	(Near-infrared) NIR Spectroscopy & ML	Predict Key Quality Parameters of Biomass	7–8	[94,95]
(b)	IoT & Digital Twins	Dynamic LCA for Environmental Benchmarking	4–5	[64]
(c)	IoT Thermal Monitoring	Thermal Monitoring	9	[96]
(c)	Sensor Networks & Tomography	Moisture Monitoring	4–5	[84]

Maturity in this action area is clearly tiered. Closed-loop smart drying is in the demonstration stage, already delivering repeatable gains in energy use. Predicting key quality parameters of biomass using NIR spectroscopy is at the system commissioning stage, validated by repeated use in QA/QC. For IoT and digital twins in dynamic LCA (TRL 4–5), a European wood biomass pilot utilized sensor data to simulate environmental benchmarks, resulting in a 10–15% reduction in simulated carbon intensity in controlled tests [64]. Storage-focused thermal monitoring is at the final stage, with proven deployments for early heat-spot detection and fire-risk mitigation. By contrast, for sensor networks and tomography in moisture monitoring (TRL 4–5), a U.K. study deployed capacitive and acoustic sensors in wood piles, mapping internal moisture distributions with 85% accuracy in lab prototypes, but still costly and unproven at scale [84]. Together, the results show that sensing/automation tied to immediate operational feedback advances faster than measurement concepts that require new hardware footprints and complex inference.

Practically, near-term value comes from standardizing intake and inline QA/QC with NIR, instrumenting dryers with closed-loop control (publishing key performance indicators (KPIs) such as kWh per percentage of moisture removed and rework rate), and deploying risk-based thermal surveillance in storage with clear alarm thresholds and response standard operating procedures (SOPs) [86,97]. Regarding the integration of digital twins with LCA, a broader adoption will depend on establishing standardized data protocols, interoperability, and cross-sector collaboration. In parallel, it appears necessary to focus research and development on moisture tomography at high-loss sites by conducting controlled pilot studies, comparing detection times with traditional probes, and measuring loss prevention to justify scale-up. All sensor outputs, such as IDs, timestamps, and moisture/temperature traces, should be routed into one shared data layer so that benchmarked datasets can support the development of lower-TRL concepts toward pilot readiness and facilitate cross-site learning.

4.4. Technology Readiness of Real-Time Monitoring Systems for Feedstock Quality

Table 7 summarizes the TRL assessment for Industry 4.0 applications in three key logistics improvements: (a) deployment of sensors and portable analyzers, and (b) setting quality establishment of real-time data integration platforms. The findings for each are discussed in the following. An analysis of biomass feedstock activities, including the deployment of sensors and portable analyzers and the establishment of real-time data integration platforms, is summarized in Table 7. In improving the preprocessing efficiency of biomass, applications such as automated material handling and sensor-based contamination detection help streamline biomass preparation steps before conversion [98]. Automated material handling, supported by robotics, conveyor automation, and IoT-enabled tracking systems, reduces manual labor, minimizes handling losses, and improves throughput [99]. Sensor-based contamination detection, integrated with AI-driven image recognition and spectroscopy systems, ensures only clean, quality feedstock proceeds to further processing, reducing equipment wear and improving overall system efficiency [100].

In enhancing feedstock uniformity, applications like particle size monitoring and blending optimization address the challenge of variability in biomass properties [101]. Particle size monitoring systems, often equipped with machine vision or laser-based measurement tools, help maintain consistency critical for downstream processing [102]. Blending optimization, guided by predictive analytics and process simulation models, enables precise mixing of different biomass sources to achieve desired quality parameters and improve conversion yields [103].

In streamlining logistics integration, applications such as supply chain synchronization and digital twin modeling improve coordination between preprocessing facilities and downstream processing plants [104]. Supply chain synchronization tools, powered by cloud-based platforms, facilitate real-time communication between stakeholders, while digital twin modeling creates virtual replicas of preprocessing operations to simulate and optimize logistical decisions [105]. These approaches reduce bottlenecks, improve inventory control, and ensure the timely delivery of preprocessed biomass to conversion facilities.

Table 7. Summary of TRL estimation of Industry 4.0 technology applications in deploying sensors and portable analyzers (a) and establishing real-time data integration platforms (b) (see Appendix Table A4 for detailed operational functions related to each case of application).

	Industry 4.0 Technology	Application	TRL	References
(a)	Portable NIR Spectroscopy	On-site moisture content prediction	9	[106,107]
(a)	Machine Vision (Digital Cameras & AI)	Real-time classification of biomass quality	6	[102]
(b)	IoT Sensors & Wireless Telemetry	Real-time moisture monitoring and logistical decision-making	7–8	[108]
(b)	Cloud-Based Analytics & IoT	Automatic sampling	9	[109]

Results in Table 7 disclose a maturity disparity between point-of-use sensing and the digital infrastructure. Portable analyzers and automated sampling have reached system operation, delivering decision-ready measurements supported by established SOPs and routine audits. Inline machine vision is appropriate during the technology demonstration phase, where accuracy and reliability are highly sensitive to factors such as dust, lighting, and sample presentation. However, the implementation of controlled enclosures and standardized illumination is advancing pilot projects [110,111]. Conversely, the digital infrastructure remains underdeveloped: real-time IoT telemetry for moisture and condition-aware decision-making is currently in the commissioning phase, whereas integration and traceability platforms are still in development. Pilot programs continue to demonstrate benefits for traceability and exception management [108], but challenges such as interoperability, inconsistent connectivity, diverse device protocols, and data governance issues have impeded the transition from prototypes to routine operational deployment [112,113].

In the near term, the priority should be placed on point-of-use testing at intakes and critical control points (CCPs) [114]. It is also important to standardize the calibration procedures and instrument high-loss nodes with simple alerts and operator standard operating procedures. A minimal viable data layer, comprising shared IDs, timestamps, and lot/batch links, can be established with offline-first buffering, enabling sites with weak connectivity to still capture signals [115]. In the medium term, to enhance platform maturity from technology demonstration to commissioning, it is essential to secure integrations to a few high-value gates (intake release, re-route, re-dry) and run controlled A/B pilots, where two parallel pilot projects under similar conditions with one key variable altered are compared regarding their performance [116]. Furthermore, curating labeled datasets enables analytics to transition from descriptive to predictive.

4.5. TRL Assessment of Industry 4.0-Enabled Planning and Forecasting Tools

Table 8 provides a summary of the TRL assessment concerning Industry 4.0 applications in two critical enhancements: (a) spatiotemporal forecasting, and (b) the integration of models with TEAs. The individual findings pertaining to each are elaborated subsequently. In the development of spatiotemporal forecasting tools, applications such as forecasting under uncertainty and accurate sensor-enabled yield forecasting strengthen decision-making processes within biomass supply chains [18]. Forecasting under uncertainty, facilitated by stochastic modeling and ML algorithms, enables stakeholders to anticipate variations in feedstock availability driven by weather conditions, market dynamics, or supply disruptions [117,118]. Accurate sensor-enabled yield forecasting, utilizing remote sensing technologies, UAV-based imaging, and IoT-connected field sensors, enhances the accuracy of biomass yield predictions, thereby supporting improved harvest scheduling and supply planning [119,120]. These forecasting capabilities contribute to risk mitigation, optimize resource allocation, and improve supply chain resilience [79].

Regarding the linkage of models to TEAs, applications such as integrating stochastic models with TEAs serve to bridge the gap between technical performance data and economic feasibility assessments. By synthesizing simulation outputs with cost–benefit models, stakeholders can evaluate the economic viability of various biomass production and processing scenarios across different contexts [114,115]. This linkage, supported by big data analytics and cloud-based modeling platforms, aids in identifying the most cost-effective strategies for scaling biomass supply chains, whilst accounting for uncertainties inherent in production, processing, and market demand.

Table 8. Summary of TRL estimates of Industry 4.0 technology applications in creating spatiotemporal forecasting tools (a) and linking models to TEAs (b) (see Appendix Table A5 for detailed operational functions related to each case of application).

	Industry 4.0 Technology	Application	TRL	References
(a)	Stochastic Modeling	Forecasting under uncertainty	2–3	[121]
(a)	IoT Sensors & ML	Accurate sensor-enabled yield forecasting	7–8	[122]
(b)	Simulations & Stochastic TEA	Linking stochastic models to TEA	4–5	[82,123]

The maturity of spatiotemporal forecasting tools exhibits variability when evaluated along the six-stage continuum. Sensor-enabled yield forecasting has advanced to the system commissioning phase, with qualified deployments in operational environments. Nevertheless, their applicability across different crops, soils, and seasons remains limited by domain shift and small training datasets. Conversely, stochastic and scenario-based forecasting under uncertainty remain in the research stage. Prototype systems provide probabilistic outputs and calibrated intervals; however, end-to-end pipelines for ingest, systematic backtesting to validate predictive accuracy against historical data [124], and reliability diagnostics are still in the research stage, rendering forecasts advisory rather than operational. Linkages to TEAs are situated within technological development. A U.S. gasification case study incorporated supply uncertainties, yielding probabilistic cost estimates with 15–25% variance in minimum selling price (MSP), validated in small-scale economic models [82].While Monte Carlo simulations and scenario linkages are available, they are predominantly conceptual or case-specific and lack standardized integration into operational decision-making processes.

Recent priorities include enhancing data infrastructure, such as implementing consistent identifiers, timestamps, and feature stores, as well as institutionalizing backtesting protocols that utilize accuracy metrics, including bias, MAE/MAPE, reliability diagrams, and lead-time reporting. To facilitate decision-making under uncertainty, forecasts should consistently deliver probabilistic outputs with calibrated intervals rather than mere point estimates. Medium-term initiatives involve conducting controlled A/B pilot studies linked to operational KPIs such as supply shortfalls, re-drying rates, inventory levels, and cost per ton delivered. Connecting forecasts to TEA necessitates the propagation of forecast distributions into economic models and the derivation of decision-ready indicators such as net present value (NPV) ranges, breakeven prices, cost-of-shortfall, and emissions per ton [123,125]. Integrating these processes at pivotal stages—namely, intake release, rerouting, and re-drying—will ensure that uncertainty is systematically incorporated into both operational and investment planning. Ultimately, to advance forecasting and TEA linkages towards technology demonstration and commissioning, robust data practices are vital, including maintaining versioned datasets to ensure reproducibility, documenting data lineage for transparency, testing scalability via multi-site deployments, and preserving human oversight throughout the process [126].

5. Conclusions

This study employed an integrative literature review to evaluate the potential of Industry 4.0 technologies in supporting the feedstock innovation objectives of the SAF Grand Challenge. The feedstock innovation workstream comprises five action areas: expanding feedstock availability and diversity, improving logistics systems, enhancing feedstock quality and stability, developing real-time feedstock quality monitoring systems, and advancing forecasting and planning tools. Utilizing TRLs, within the broader framework of TRA, we systematically evaluated applications throughout the biomass supply chain and aligned them with the DOE’s six-stage continuum of technology development. This dual methodology enabled us not only to quantify the maturity of these innovations but also to contextualize their readiness for deployment in real-world biomass logistics.

The results highlight evident asymmetries in maturity levels across feedstock innovation areas, with some tools already operational (e.g., thermal monitoring, portable analyzers), while others remain at early development stages (e.g., digital twins, stochastic forecasting, dynamic LCA). To align these findings with the SAF Grand Challenge roadmap, we summarize key technology gaps and policy implications below:

Expand Feedstock Availability and Diversity: Advanced genomic selection and AI-driven decision support remain at TRL 4–5 due to limited cross-site validation and heterogeneous datasets, while UAV/IoT phenotyping is at TRL 6, and autonomous inter-seeding has reached TRL 7–8. These gaps highlight the importance of supporting multi-location trials, enhancing access to standardized datasets, and promoting interoperable land use mapping approaches that promote sustainability. Moreover, Dynamic LCA models linked with IoT and digital twins are still in early development (TRL 4–5), highlighting the need for pilot studies that connect breeding choices and land use scenarios to carbon intensity metrics. Policy support should incentivize the development of standardized environmental datasets and promote interoperability between agronomic models and LCA frameworks.
Improve Logistics Systems: Digital twins for regional depots and AI-enabled routing are still in the TRL 4–5 range, limited by fragmented data and rural connectivity, whereas inline vision systems for pellet QA/QC are more advanced at TRL 6. Addressing these challenges will likely require better rural digital infrastructure, greater data interoperability, and opportunities to demonstrate integrated depot and routing models at a pilot scale. While logistics tools increasingly incorporate sustainability, real-time LCA of transportation operations using IoT and digital twins remains limited to pilots (TRL 4–5). Scaling up will require establishing common data standards for emissions reporting and providing incentives for fleet operators to adopt interoperable tracking platforms.
Enhance Feedstock Quality and Stability: Smart drying technologies are progressing at TRL 6, and NIR spectroscopy is now at TRL 7–8, but moisture tomography and dense sensor networks remain early-stage at TRL 4–5 with high costs of deployment. Continued work in this area will benefit from focused R&D in high-loss storage environments, the development of standardized QA/QC benchmarks, and wider integration of NIR and thermal monitoring practices. Dynamic LCA for environmental benchmarking remains at TRL 4–5, with only proof-of-concept pilots. Advancing this application will depend on protocols for real-time emission monitoring and alignment with SAF certification schemes.
Develop Real-Time Feedstock Quality Monitoring Systems: Portable NIR analyzers (TRL 9) and automated sampling systems (TRL 9) are commercially ready, but machine vision classification remains at TRL 6, and IoT telemetry for moisture monitoring is only at TRL 7–8, constrained by issues of interoperability and governance. Moving forward, progress will depend on clearer frameworks for data governance and cybersecurity, alongside initiatives that integrate analyzers, telemetry, and cloud platforms into end-to-end monitoring solutions.
Advance Forecasting and Planning Tools: IoT sensor-enabled yield forecasting has matured to TRL 7–8 with demonstrated operational use, while stochastic modeling (TRL 2–3) and TEA linkages (TRL 4–5) remain in early stages without standardized backtesting or reproducibility protocols. Bridging this gap will require greater emphasis on probabilistic forecasting practices, robust datasets and pilot projects, and the gradual integration of uncertainty measures into TEA models and investment decisions.

These findings reaffirm three principal messages. Firstly, opportunities for low complexity are presently available within operational or near-operational tools (e.g., handheld sensing, thermal monitoring) that can be promptly scaled to enhance supply chain reliability. Secondly, intermediate-stage bottlenecks in data integration and forecasting highlight not only technical deficiencies but also governance, calibration, and interoperability challenges that must be addressed as a priority to facilitate the acceleration of demonstration and commissioning processes. Thirdly, a phased approach is required: immediate successes derived from operational tools should lead to the creation of standardized datasets and validation environments, thereby actively encouraging less mature technologies (development and feasibility stages) to progress towards demonstration and commissioning. Furthermore, our assessment confirms what we observed in Section 2.2—that advanced digital technologies, such as CPS, digital twins, and AR/VR, remain in the early stages of development. Primarily, these technologies are at the 4–6 TRL, indicating they have significant potential but are mainly employed in experimental settings at this stage.

While the TRL framework provides a valuable lens for comparing technological progress, it also carries inherent limitations. Maturity assessments remain partly subjective, and TRL scales do not fully account for external dimensions such as policy readiness, cost-effectiveness, or end-user acceptance. Furthermore, certain TRL assignments relied on gray literature, including press releases and corporate reports, highlighting the importance of cautious interpretation of these findings. Future research could therefore benefit from combining TRL with complementary approaches such as multi-criteria decision analysis (MCDA) or innovation readiness indicators to provide a more holistic assessment of technology adoption potential. Another limitation of this study arises from using an integrative literature review, which is conceptually selective rather than exhaustive. Future studies could address this by employing systematic reviews, bibliometric analyses, or meta-analytical approaches to capture a broader and more comprehensive body of evidence. To enhance validity, the TRL assignments were conducted and cross-checked by the authors, who had extensive experience in biofuel supply chain research, where in some cases, interpretations had to be considered where the application level of the technology was not directly mentioned. Future studies can enhance the robustness and comprehensiveness of the assigned TRLs by engaging stakeholders through surveys to gather the results. Building on these methodological considerations, several broader research gaps also remain. Future work should examine data governance mechanisms to enable interoperability and security across biomass supply chains, develop standardized frameworks for quality monitoring and forecasting models, and investigate cross-sector collaboration to accelerate SAF deployment. Addressing these issues would not only advance the technical maturity of emerging solutions but also strengthen the institutional and policy environments required for SAF adoption.

In conclusion, this paper contributes a systematic, evidence-based approach for mapping technological pathways in biomass feedstock innovation for SAF. The insights provided here can support stakeholders, including researchers, policymakers, and investors, in prioritizing technologies for scale-up, identifying R&D gaps, and designing more resilient and efficient biomass supply chains for SAF. Continued collaboration between technology developers, biofuel producers, and government agencies will be essential to translate digital innovations into tangible progress toward aviation decarbonization.

Author Contributions

Conceptualization, S.E.; methodology, S.E. and J.S.; validation, J.S.; investigation, S.E. and J.S.; data curation, S.E.; writing—original draft preparation, S.E.; writing—review and editing, J.S.; visualization, S.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Detailed operational functions of Industry 4.0 technology applications in breeding (a), land use mapping (b), and integration of biomass into crop systems (c).

	Industry 4.0 Technology	Application	Operational Functions	References
(a)	Artificial Intelligence (AI)	Genomic selection	AI models predict plant performance (yield, traits) from genetic data, accelerating breeding cycles significantly compared to traditional approaches.	[59,60]
	IoT, Robotics & Remote sensing (UAVs)	High-throughput phenotyping	Drones and LiDAR-based IoT sensors collect plant traits rapidly and accurately in breeding fields, enhancing selection processes.	[61,62]
	Digital Twins & IoT	Dynamic LCA	IoT and remote sensing data feed into digital twin land use models to simulate feedstock scenarios, enabling dynamic LCA that benchmarks carbon intensity and land use impacts in real time for sustainable planning and certification.	[63]
(b)	AI & Satellite Remote Sensing	Yield-mapping and land optimization	An AI-driven platform (Crop AIQ) generates sub-field yield maps using satellite imagery, identifying marginal land suitable for energy crops, enhancing biomass availability.	[65]
(b)	Remote Sensing (Satellite Imagery & GIS) & ML	Biomass and land use mapping	Methods classify land use and estimate potential biomass yields using aerial and satellite data, supporting regional mapping of biomass production potential.	[37]
(c)	Autonomous Robotics & AI	Inter-seeding biomass cover crops	Autonomous robots (EarthSense robots) navigate between main crops to plant cover crops early, significantly enhancing biomass yields without interrupting main crop production.	[57]
(c)	AI & Big Data Analytics	Biomass cover crop decision support	Predictive analytics tool forecasts biomass production of cover crops based on local soil/climate data, supporting decision-making and feasibility assessment for biomass integration into existing cropping systems.	[58]

Table A2. Detailed operational functions of Industry 4.0 technology applications in designing regional depots (a), advancing densification (b), and optimizing transportation networks (c).

	Industry 4.0 Technology	Application	Operational Functions	References
(a)	Digital Twin & Simulation	Advanced Pellet-delivery System	Discrete-event simulation model tests distributed preprocessing depots for palletization, showing reduced costs and improved efficiency in biomass logistics.	[13,79]
(b)	Computer Vision & Automation	Smart process control and quality monitoring	Image-based pellet dimension monitoring tool for real-time quality control, improving product uniformity and densification efficiency.	[80]
(c)	IoT, RFID & Cloud Analytics	IoT-based Tracking and Coordination	An IoT-based platform integrates RFID, GPS, and cloud analytics for real-time tracking, coordination, and improved scheduling of biomass logistics.	[19,81]
	AI & GIS	Route Optimization	An AI-powered routing system utilizes GIS data dynamically adjusts transportation routes to minimize costs and enhance logistics efficiency.	[19]
	IoT & Digital Twins	Real-time LCA of Logistics Operations	IoT sensors linked with digital twins mirror logistics operations in real time, enabling dynamic LCA of transport routes and emissions to benchmark environmental performance alongside cost efficiency.	[78,83]

Table A3. Detailed operational functions of Industry 4.0 technology applications in improving drying/stabilization methods (a), setting quality benchmarks (b), and developing storage solutions (c).

	Industry 4.0 Technology	Application	Operational Functions	References
(a)	IoT sensors & AI control	Smart Drying	Real-time moisture and particle size monitoring with adaptive control algorithm to consistently optimize biomass drying. Also, AI-driven drying systems that uniformly reduce moisture to safe levels, preventing microbial decay and contamination.	[92,93]
(b)	(Near-infrared) NIR Spectroscopy & ML	Predict Key Quality Parameters of Biomass	NIR sensors and ML models rapidly classify biomass quality parameters (moisture, ash, energy content) in real time.	[94,95]
(b)	IoT & Digital Twins	Dynamic LCA for Environmental Benchmarking	Digital twins combined with IoT data streams can feed into LCA models, resulting in real-time sustainability assessments and more transparent supply chain reporting.	[64]
(c)	IoT Thermal Monitoring	Thermal Monitoring	IoT-connected thermal imaging cameras provide continuous, cloud-based monitoring of biomass piles, identifying overheating risks early.	[96]
(c)	Sensor Networks & Tomography	Moisture Monitoring	Integrated capacitive moisture sensors and acoustic sensors with tomography algorithms create real-time maps of moisture and heat within biomass storage piles.	[84]

Table A4. Detailed operational functions of Industry 4.0 technology applications in deploying sensors and portable analyzers (a) and establishing real-time data integration platforms (b).

	Industry 4.0 Technology	Application	Operational Functions	References
(a)	Portable NIR Spectroscopy	Onsite Moisture Content Prediction	Handheld NIR spectrometers tested on 817 industrial wood chip samples at biomass plant, achieving high accuracy (R² ≈ 0.89) for rapid on-site moisture content prediction.	[106,107]
(a)	Machine Vision (Digital Cameras & AI)	Real-Time Classification of Biomass Quality	Digital camera system combined with neural networks tested at a 500 kg/day pilot biorefinery to classify corn stover quality in real-time, identifying problematic feedstock early.	[102]
(b)	IoT Sensors & Wireless Telemetry	Real-time moisture monitoring and logistical decision-making	Corn stover harvesting equipment fitted with moisture sensors and wireless telemetry units, enabling real-time moisture monitoring and logistical decision-making during harvest.	[108]
(b)	Cloud-Based Analytics & IoT	Automatic sampling	Prometec Q-Robot, an automated truck sampler designed for precise and efficient sampling of materials like biomass, automatically samples biomass truckloads, instantly measures moisture, and uploads data to a cloud database in real-time for immediate operational decisions.	[109]

Table A5. Detailed operational functions of Industry 4.0 technology applications in creating spatiotemporal forecasting tools (a) and linking models to TEAs (b).

	Industry 4.0 Technology	Application	Operational Functions	References
(a)	Stochastic Modeling	Forecasting under uncertainty	Stochastic models (ARIMA, GARCH, Monte Carlo) predicted seasonal woody biomass availability, providing robust forecasts under uncertainty scenarios.	[121]
(a)	IoT Sensors & ML	Accurate sensor-enabled yield forecasting	IoT sensor networks combined with ML deployed for real-time monitoring and accurate yield forecasting in precision agriculture, showing improvements in resource use.	[122]
(b)	Simulations & Stochastic TEA	Linking stochastic models to TEA	Monte Carlo simulations incorporated uncertainties in biomass supply, cost, and pricing into TEAs of biomass gasification projects, producing probabilistic cost and feasibility outcomes.	[82,123]

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Figure 1. The six levels of technological development continuum are based on the TRLs [51].

Table 1. Feedstock innovation workstream and its corresponding action areas, their objectives, key actions needed to meet the objectives, and their corresponding expected impacts.

Action Areas	Objectives	Key Actions Needed	Expected Impacts
Expand feedstock availability and diversity	Increase supply and range of sustainable biomass (e.g., residues, energy crops, waste).	Breeding R&D; land use mapping; integrating biomass into crop systems.	Wider biomass portfolio; enhanced resilience and scalability of SAF supply.
Improve logistics systems	Lower cost and improved efficiency of aggregation, transport, and preprocessing.	Designing regional depots; advancing densification; optimizing transportation networks.	Reduced delivered cost and loss; year-round consistent biomass availability.
Enhance feedstock quality and stability	Ensure consistent feedstock characteristics; reduce degradation and contamination.	Improving drying/stabilization methods; setting quality benchmarks; developing storage solutions.	Higher conversion efficiency; more reliable SAF production.
Develop real-time feedstock quality monitoring systems	Enable in situ measurement of critical feedstock properties to maintain quality standards.	Deploying sensors and portable analyzers; establishing real-time data integration platforms.	Minimized variability; better quality control from field to biorefinery.
Advance forecasting and planning tools	Develop predictive models for supply, yield, and cost to support long-term planning.	Creating spatial–temporal forecasting tools; linking models to techno-economic analyses (TEAs).	Informed investment decisions; proactive infrastructure development.

Table 2. Industry 4.0 technologies and their applications in biomass feedstock supply chains.

Category	Technologies	Applications in Biomass Feedstock Supply Chains
Sensing & Automation	IoT; GIS/Remote Sensing; Radio Frequency Identification (RFID); Robotics.	Field monitoring of feedstock quality (e.g., moisture, biomass density); GPS tracking of logistics movement.
Analytics & Intelligence	AI; Big Data Analytics.	Yield forecasting; Supply–demand matching; Process optimization.
Traceability & Infrastructure	Blockchain; Cloud Computing; Edge Computing.	Secure feedstock provenance; Certification of low-carbon fuel; Decentralized data systems.
System Integration	CPS; Digital Twins; AR/VR.	Virtual modeling; Real-time control; Operator training; Conceptual use in plant management.

Table 3. TRL measures and their applications’ phases and stages, during technology adoption [49,50].

TRL	Definition	Description
TRL 9	Actual system operated over the full range of expected mission conditions	Technology is in its final form and operated under full mission conditions. Example: actual system with full range of wastes in hot operations.
TRL 8	Actual system completed and qualified through test and demonstration	Proven to work in final form under expected conditions. Developmental testing and evaluation with actual waste; ORR completed before hot testing.
TRL 7	Full-scale, similar (prototypical) system demonstrated in relevant environment	Major step from TRL 6. Prototype tested in the field with simulants; results analyzed for final system design. Design virtually complete.
TRL 6	Engineering/pilot scale, similar (prototypical) system validated in relevant environment	Engineering-scale models tested in relevant environment. Step up from TRL 5 with scaling factors defined. Prototype should represent operational system.
TRL 5	Laboratory-scale, similar system validated in relevant environment	Components integrated in lab to match final application. High-fidelity lab testing with simulants. Results analyzed for scaling to real operations.
TRL 4	Component and/or breadboard validation in laboratory environment	Basic components integrated to verify they work together. Lab-scale validation of key subsystems.
TRL 3	Analytical and experimental proof of concept	Active R&D begins. Lab studies validate predictions of technology’s basic functions and critical parameters.
TRL 2	Technology concept and/or application formulated	Applications identified but speculative. No experimental validation; only theoretical studies or analysis.
TRL 1	Basic principles observed and reported	Scientific research begins transition to applied R&D. Basic properties of technology studied.

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Ebrahimi, S.; Szmerekovsky, J. Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment. Biomass 2025, 5, 63. https://doi.org/10.3390/biomass5040063

AMA Style

Ebrahimi S, Szmerekovsky J. Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment. Biomass. 2025; 5(4):63. https://doi.org/10.3390/biomass5040063

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Ebrahimi, Sajad, and Joseph Szmerekovsky. 2025. "Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment" Biomass 5, no. 4: 63. https://doi.org/10.3390/biomass5040063

APA Style

Ebrahimi, S., & Szmerekovsky, J. (2025). Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment. Biomass, 5(4), 63. https://doi.org/10.3390/biomass5040063

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Smart Biomass Supply Chains for SAF: An Industry 4.0 Readiness Assessment

Abstract

1. Introduction

2. Conceptual Background

2.1. Feedstock Innovation

2.2. Industry 4.0

2.3. TRL Assignment

3. Methodology

3.1. Integrative Literature Review

3.2. Review Framework

3.3. Literature Search and Selection Process

3.4. Data Collection and TRL Assignment

4. Results and Discussion

4.1. TRL Estimation of Industry 4.0 Applications in Expanding Feedstock Availability and Diversity

4.2. Readiness Levels of Industry 4.0 Technologies in Biomass Logistics Systems

4.3. TRL Evaluation of Industry 4.0 Tools for Enhancing Feedstock Quality and Stability

4.4. Technology Readiness of Real-Time Monitoring Systems for Feedstock Quality

4.5. TRL Assessment of Industry 4.0-Enabled Planning and Forecasting Tools

5. Conclusions

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A

References

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