Trends in Sustainable Inventory Management Practices in Industry 4.0

Abstract

This study examines 52 recently published papers on sustainable inventory management in Industry 4.0, intending to bridge theory and practice through a comprehensive literature review. By analyzing the latest advancements discussed over the past two years, covering 2024 and 2025, we identify key trends shaping the field and highlight existing gaps that may require further exploration. Focusing on this time frame is particularly relevant because it reflects how companies have recently started using artificial intelligence more practically to support sustainability goals. During these years, AI has been applied to improve how inventory is tracked, how demand is predicted, and how resources are managed to reduce waste. These tools are making supply chains more efficient while helping organizations to lower their environmental impact. In this regard, our work aims to provide a deeper understanding of how sustainable strategies are evolving in response to technological innovations, offering insights for researchers and practitioners seeking to enhance efficiency and environmental responsibility in modern supply chains.

1. Introduction

With the advancement of Industry 4.0, sustainable inventory management has become increasingly crucial in improving supply chain efficiency and resilience [1]. As industries transition to more environmentally responsible operations, the integration of such digital technologies as automation, real-time data systems, and smart analytics is reshaping traditional inventory management practices. Apart from considerably streamlining operations, these tools are contributing to increasing the importance of logistics performance in global markets, influenced by shifting customer expectations, stricter environmental regulations, and intensified international competition [2]. Sustainable inventory management today goes beyond improving efficiency as it plays a direct role in achieving broader environmental goals. Specifically, it contributes to reducing material waste [3], minimizing carbon emissions across the supply chain [4], and optimizing resource utilization through more accurate forecasting and planning [5]. The strategic adoption of sustainable inventory practices is essential for ensuring long-term economic growth while mitigating environmental impacts. Analyzing how these practices have evolved, particularly through the use of digital and automated tools, offers essential insight into how industries are aligning sustainability with competitiveness in the modern industrial landscape [6].

For this reason, the main goal of this paper is to conduct a comprehensive review of relevant and updated research on sustainable inventory management in the context of Industry 4.0. This objective will be pursued through the following three key areas of focus.

• This study includes a structured search for the recent literature, specifically journal articles, conference papers, and book chapters, on sustainable inventory management and Industry 4.0 technologies from the last two calendar years (2024 and 2025).
• The identified articles are thoroughly screened, categorized, and reviewed to synthesize key managerial insights, relevant techniques, and emerging trends related to sustainable practices in inventory management in Industry 4.0.
• Research gaps and promising future developments are formalized by discussing areas where innovative approaches can contribute to improving sustainability in inventory management in the evolving digital transformation landscape.

The structure of this paper is as follows. Section 2 presents a comprehensive literature review, including an overview of sustainable inventory management in Industry 4.0, a detailed description of the methodology used to conduct this review, and a categorization of studies based on their thematic focus. Section 3 synthesizes the results derived from this research with an emphasis on sustainability aspects that can be crucial in management practices. Finally, Section 4 summarizes the main findings of this study and proposes potential directions for future research in sustainable inventory management within Industry 4.0.

2. Literature Review

2.1. Sustainable Inventory Management in Industry 4.0

As industries strive to balance operational efficiency with environmental responsibility, sustainability is a key organizational priority. As observed in [7], achieving a balance between operational goals and sustainability requires a fundamental shift in how supply chain practices are designed. This involves aligning economic, social, and environmental objectives. In the era of Industry 4.0, where digital advancements are reshaping production and logistics, companies are rethinking inventory management practices to not only reduce carrying costs but also to minimize waste, use resources more efficiently, and reduce environmental impact [8]. Recent studies reflect a shared understanding that sustainable inventory management plays a critical role in long-term profitability [9], helps organizations comply with environmental regulations [3,10], and supports growing customer expectations for responsible and transparent practices [11]. This subsection provides a conceptual overview of these developments, drawing from the current literature to show how digital tools enable companies to balance sustainability with operational performance better.

One of the fundamental principles of sustainable inventory management is minimizing excess stock while avoiding shortages. In this regard, Nobil et al. [12] consider the impact of shortages in raw materials on production decisions, integrating environmental concerns by factoring in carbon emissions and the potential for carbon trading. Singh et al. [13] present an optimal control-based inventory model that minimizes excess stock while preventing shortages in an imperfect production system, aiming to balance supply, minimize losses, and ensure efficient stock management. As discussed in [14], overstocking leads to higher storage costs, increased material waste, and unnecessary resource consumption, while understocking disrupts production and affects customer satisfaction. To address these challenges, businesses are adopting data-driven demand forecasting, automated tracking systems, and real-time monitoring to improve inventory accuracy and reduce inefficiencies [15,16]. Another key aspect of sustainable inventory management refers to the integration of environmentally friendly practices into supply chain operations [17,18], for example, by implementing energy-efficient storage solutions [19] and prioritizing suppliers with sustainable practices [20]. In such a direction, many companies are also exploring circular economy models, where returned or unused products are refurbished, reused, or repurposed instead of being discarded [21]. Reducing transportation-related emissions is another important goal. To such an aim, localized supply chains may help minimize long-distance transportation, improving supply chain resilience while mitigating low-magnitude disruptions, as underlined by McDougall and Davis [22]. In this context, collaboration among such stakeholders as manufacturers, suppliers, and regulatory bodies remains crucial for advancing sustainability efforts in inventory management [23].

Despite the discussed benefits, challenges remain in transitioning to sustainable inventory management, especially given the pressures retailers face in meeting customer expectations [24]. Notably, as discussed by Agyemang [25], initial costs for implementing green technologies and redesigning supply chain processes can be significant. Additionally, balancing sustainability with cost efficiency requires careful planning and investment in long-term solutions. Considering all of these aspects, the future of sustainable inventory management will likely focus on continuous improvement and innovation, for which embracing emerging technologies and refining logistics strategies will be instrumental to meeting sustainability goals while maintaining competitive advantages in the current global market scenario [26].

Broadly speaking, sustainable inventory management involves a careful balance between reducing excess stock and avoiding shortages while also addressing broader environmental goals. Current practices reflect a growing focus on minimizing waste, improving forecasting accuracy, and integrating eco-friendly solutions across supply chains. At the same time, businesses face ongoing challenges, including the cost of transitioning to green technologies and the need to align sustainability with operational efficiency. Looking ahead, continued innovation, collaboration, and the adoption of practical strategies will be essential for companies seeking to strengthen their environmental performance and competitiveness in a changing global market.

2.2. Methodological Approach Used for the Review

We will conduct a structured literature search and review on sustainable inventory management practices in Industry 4.0 based on the systematic approach proposed by Muka et al. [27]. The first step was to investigate whether the topic had already been reviewed, and we were able to check that several reviews have been developed since 2024 that are relevant to the subject of the present work. However, none of these works specifically focus on a broad analysis of sustainable inventory management in Industry 4.0. This motivates us in our the present review to propose a more comprehensive and unique categorization of papers while incorporating the most recent contributions in the field. To ensure a thorough and diverse collection of relevant studies, a literature search was carried out across four major scientific databases, namely Scopus, IEEE Xplore, PubMed, and Google Scholar, each of them offering distinct advantages in terms of indexing peer-reviewed journals, conference proceedings, and multidisciplinary research. Figure 1 outlines the publication items by the database. A total of 59 papers have been collected, all within the time range of 2024 to the present. These papers have been categorized by type (journal papers, conference papers, and book chapters). The bar chart illustrates their distribution across the databases, while the pie chart provides an overview.

An advanced search was performed in each database in March 2025 by selecting fields and entering the following keywords: “Sustainability” AND “Inventory Management” AND “Industry 4.0”. The added date range refers to items published from 2024 to the present. In IEEE Xplore and PubMed, the search was conducted using the “all field” category, meaning that the selected keywords were applied across the entire content of each document, including titles, abstracts, keywords, and full text when available, ensuring a broad and inclusive search. For Scopus, the search was restricted to article titles, abstracts, and keywords to balance relevance and comprehensiveness. Scopus indexes many sources, and this refinement helps focus on the most pertinent studies. In the case of Google Scholar, a more extensive and less curated database, the results were reviewed only up to the third page to ensure that only the most relevant and widely cited papers were considered [28].

After compiling all the references and abstracts into a single document, we conducted a thorough screening process to eliminate duplicates, reducing the total number of items for analysis from 59 to a smaller, refined set comprising 52 items. Next, we grouped these filtered papers based on their publishers and performed an in-depth analysis to determine their core subject matter. Each paper was assigned to one of six distinct topic categories (TCs) during the classification process. This process was conducted by carefully examining each paper’s abstract, objectives, methodologies, and main contributions. We employed a qualitative content analysis approach to identify the dominant theme of each work, considering both explicit keywords and the underlying research aims. Full texts were consulted to clarify the primary focus along with the abstracts. The six topic categories were derived based on recurring patterns across the literature, ensuring that each category reflects a coherent thematic area. This method allowed for consistency in classification while remaining flexible enough to accommodate interdisciplinary studies that overlap across themes. The distribution of the analyzed papers, both by publisher and their assignment to TC, is illustrated in Figure 2. This figure provides an overview of how the papers are grouped based on their source and how they have been categorized according to their primary subject matter. Since some papers address multiple topics, we categorized each based on its most relevant primary focus while acknowledging its potential significance in other areas. This approach ensures that the classification remains structured and meaningful while preserving the awareness of interdisciplinary connections. The following sections provide a detailed description of each TC.

• ${\mathrm{TC}}_1$ Industry 4.0 and digital transformation. Papers in this category underline that Industry 4.0 brings digital tools like smart sensors, automation, and real-time data analytics to inventory management, improving efficiency and reducing waste. With predictive analytics, businesses can optimize stock levels, prevent shortages, and reduce excess inventory, leading to a more sustainable supply chain.
• ${\mathrm{TC}}_2$ Sustainability and circular economy. Papers in this category discuss sustainable inventory management focusing on minimizing waste, reusing materials, and reducing environmental impact. Industry 4.0 technologies enable better tracking of product lifecycles, allowing businesses to extend product use, implement recycling strategies, and adopt circular economy practices that reduce resource consumption.
• ${\mathrm{TC}}_3$ Artificial intelligence and machine learning applications. Papers in this category focus on AI and machine learning transforming inventory management by predicting demand, optimizing stock levels, and reducing overproduction. These technologies analyze patterns in supply and demand, adjusting inventory dynamically to prevent waste and improve efficiency, making supply chains more sustainable.
• ${\mathrm{TC}}_4$ Supply chain and logistics management. Papers sorted into this category deal with the role of efficient supply chain and logistics operations for sustainable inventory management. Industry 4.0 enables automated inventory tracking, smart warehouses, and optimized transportation routes, reducing excess storage, cutting emissions, and ensuring a smoother, more sustainable flow of goods.
• ${\mathrm{TC}}_5$ Sustainable manufacturing and lean production. Papers in this category are focused on lean production combined with Industry 4.0 technologies, helping manufacturers reduce waste and energy consumption. Smart monitoring systems and automated inventory controls ensure that materials are used efficiently, minimizing overproduction and improving sustainability without compromising productivity.
• ${\mathrm{TC}}_6$ Blockchain, digital twins, and emerging technologies. Papers in this category refer to relevant technologies. Blockchain enhances transparency and security in supply chains, reducing fraud and ensuring ethical sourcing. Digital twins create virtual models of inventory systems, allowing businesses to test and optimize strategies before implementation. Emerging technologies like the Internet of Things (IoT) and 3D printing further improve significant sustainability aspects in inventory management.

2.3. Examination of Papers in the Designated Topic Categories

2.3.1. Industry 4.0 and Digital Transformation

Asrol [29] develops a systematic literature review on Industry 4.0 adoption in supply chain operations, using both qualitative and quantitative analyses, including Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric methods. The author identifies five key dimensions to assess Industry 4.0 readiness: technology and IT infrastructure, supply chain integration, manufacturing and inventory, leadership and human resources, and sustainability. While this study highlights the role of digital transformation maturity models in general supply chain operations, our review aims to take a more targeted approach to Industry 4.0 technologies, enhancing sustainable inventory management. Another interesting review has been developed by Valenzuela-Cobos et al. [30], exploring the adoption of Industry 4.0 in logistics management and its advancement in Latin America. This review mainly provides a regional perspective on the impact of Industry 4.0 on logistics flexibility and risk management by considering how optimizing material and information flow helps to enhance competitiveness.

In this context, Singh et al. [31] examine the relationship between Industry 4.0 and environmental sustainability, particularly in manufacturing contexts. The authors discuss the main environmental challenges posed by such advanced manufacturing technologies as Cyber-Physical Systems (CPSs), including pollution and increased resource consumption, exploring how integrating Industry 4.0 with sustainable development initiatives can influence economic and business changes while supporting Supply Chain Decarbonization (SCD) and achieving net-zero targets. Akhouri et al. [32] analyze the integration of Supply Chain Management (SCM) with Industry 4.0 technologies to enhance sustainability in Indian manufacturing. Several key SCM factors are the object of study, with a particular emphasis on collaboration, cost efficiency, and adaptive inventory management in a technologically evolving landscape. Gantayat et al. [33] observe that Industry 5.0 builds on Industry 4.0 by emphasizing sustainability, human-centricity, and human–machine collaboration to create more socially aligned industrial systems. Their research highlights the role of Large Language Models (LLMs) in achieving higher levels of innovation in sustainable industrial practices and human-centered processes.

Rehman et al. [34] investigate the impact of Industry 4.0 technologies with a specific focus on the international performance of Small and Medium Enterprises (SMEs), applying dynamic capabilities theory and institutional theory. Using data from 301 Malaysian exporting SMEs and structural equation modeling, the study finds that most Industry 4.0 technologies positively influence SMEs’ global performance by enhancing their ability to adapt and innovate at an international scale. An exploratory case study of an Indian automobile supply chain is developed in [35], where the need for collaboration between supply chain entities and support from large manufacturers is considered the main driver of sustainability and Business Model Innovation (BMI) in the whole automotive sector. Ahmed et al. [36] focus on the medical device industry of emerging economies, using Pakistan as a case study. An integrated Multi-Criteria Decision-Making (MCDM) approach is proposed to evaluate and rank Industry 4.0 technologies based on their effectiveness in enhancing the performance of marketing strategies. The study identifies Big Data, Virtual Reality (VR) and Augmented Reality (AR), IoT, and machine learning as the most impactful technologies within the reference context. Bakeries have been another sector recently analyzed in the literature [37] in order to evaluate the impact of digitalization to optimize sustainable inventory management and product quality while driving profitability.

2.3.2. Sustainability and Circular Economy

Kumar et al. [38] provide a categorized review of 121 papers on Industry 4.0 and Smart Manufacturing (SM) published between 2011 and 2022. This research identifies China, the USA, India, and Germany as leading countries in Industry 4.0 research, underlining that conceptual models are currently predominant while case studies and mathematical modeling are gaining popularity. This review also explores the integration of Lean Six Sigma with Industry 4.0 for efficiency improvements but notes gaps in understanding long-term impacts and applications beyond manufacturing. Concerning Lean Six Sigma and Industry 4.0 integration, a holistic study is proposed in [39]. This paper is motivated by the disconnection between quality management and circular economy principles, aiming to bridge the gap in assessing Industry 4.0 production value based on sustainability considerations. Specifically, an integrated approach using Lean Six Sigma and the Delphi technique is proposed to map key quality elements against sustainability factors through Adapted Interpretive Structural Modeling (AISM), facilitating a shift toward sustainable manufacturing ecosystems. Sasso et al. [40] also focus on the relation between lean management and circular economy to address complex market competition challenges. They suggest that lean practices promote a smooth transition towards circular economy by optimizing resource use and supporting recycling and remanufacturing, aided by Just in Time (JIT) and Value Stream Mapping (VSM) methodologies.

Considering this perspective, Marak et al. [41] explore the interconnections between Industry 4.0, the circular economy, and sustainable performance, addressing a research gap primarily focused on bivariate relationships among these concepts. In detail, this study presents a theoretical framework to clarify this complex interlinkage by examining the drivers that facilitate the implementation of Industry 4.0 technologies, circular economy strategies, and sustainability practices while identifying practical challenges that firms commonly face in integrating these elements to achieve sustainability goals. Saleh and AlShafeey have explored another interesting line of research in the direction of this integration [42], leveraging the use of sentiment analysis and association rule-mining techniques on 6759 abstracts from the Scopus database. In this regard, sentiment analysis reveals a predominantly positive perception of Industry 4.0’s impact across three sustainability dimensions: Environmental, Economic, and Social (EES). Association rule mining demonstrates the strongest link between Industry 4.0 and economic sustainability, followed by social and environmental sustainability, highlighting benefits such as automation of hazardous tasks, job creation, and emissions reduction through real-time monitoring, among others. An important finding of this research is that EES aspects are frequently analyzed together in the Industry 4.0 context. For example, Amir et al. [43] discuss the impact of the transition from Industry 4.0 to Industry 5.0 on EES sustainability. The authors highlight the metaverse’s role and emerging technologies such as blockchain, IoT, VR, and AR in transforming real-world interactions into virtual experiences, contributing to sustainability across various domains, including supply chain networks, e-commerce, smart manufacturing, inventory management, etc.

Relevant cases have been developed in various practical contexts. Singh et al. [44] apply a risk assessment framework based on MCDM to mitigate Supply Chain Risks (SCRs) and enhance sustainability in the context of Additive Manufacturing Technology (AMT). This study reveals that AMT adoption significantly reduces risks related to lead time fluctuations, waste generation, supplier dependency, inventory management, and logistics, proving to be a strategic approach for improved supply chain sustainability and innovation in green technologies. A Brazilian case on food-tech startups integrating Industry 4.0 technologies to advance the circular economy is proposed in [45]. The study analyzes eight startups using questionnaires, interviews, and content analysis, revealing their role as key players in driving sustainable transformation while offering insights on leveraging technology in the food sector. Zaki [46] focuses on the hospitality sector by assessing circular economic practices in green hotels in Saudi Arabia and Egypt and analyzing specific practices that can contribute to increased hotel performance.

2.3.3. Artificial Intelligence and Machine Learning Applications

Artificial intelligence (AI) is becoming increasingly popular in inventory management practices. In this field, a study of 123 SMEs was led by Rana et al. [47] through various analytical methods, stressing the direct connection between implementing AI-based approaches and obtaining gains in terms of security increase and cost reduction in Supply Chain 4.0. A systematic review of the integration of AI in SCM during the transition from Industry 4.0 to Industry 6.0 is carried out in [48], underlining its role in enhancing human–AI collaboration and sustainability. Using the PRISMA framework, this study analyzes the literature from 2010 to 2023 and identifies key areas where AI has significantly contributed to supply chain management, including improved accuracy in demand forecasting, enhanced inventory optimization, and data-driven decision-making. Additionally, the study highlights critical challenges such as cybersecurity vulnerabilities, ethical concerns, and the need for upskilling the workforce to fully leverage AI capabilities. The findings of our review confirm and expand upon these insights, with a growing emphasis on the practical implementation of predictive analytics and AI-driven tools aimed at advancing sustainable and resilient logistics strategies. Focusing on the particular case of sustainable innovation in the U.S. fashion industry, Faisal et al. [49] identified a research gap in integrating AI into existing apparel manufacturing frameworks and examined AI’s role in production streamlining and personalized consumer experiences by pointing out the importance of demand forecasting.

Traditional inventory models face significant challenges in accurate prediction due to growing demand uncertainty regarding the need to improve forecasting accuracy and optimize stock levels. Kumar et al. [50] discuss the critical role of inventory management in maintaining a smooth supply chain with a specific focus on backorder prediction. They explain how companies leverage machine learning techniques to predict backorders more precisely, intending to reduce financial pressure and disruptions in supply chain operations while positively impacting customer satisfaction. Similarly, Ogidan et al. [51] emphasize the role of machine learning in manufacturing and its impact on quality and productivity within the Fourth Industrial Revolution. The discussion includes manufacturing-related machine learning algorithms, implementation challenges, and future research prospects by reviewing practical applications on quality process control, predictive maintenance, inventory management, and supply chain optimization.

Paraschos et al. [52] integrate reinforcement learning with lean green manufacturing principles for optimizing decision-making in a multi-stage degrading manufacturing/remanufacturing system. The reinforcement learning-based approach integrates pull production, predictive maintenance, and circular economy strategies to minimize material consumption while extending product lifecycles, as validated through experimental analysis. In another study [53], the same authors explore the role of Industry 4.0 and smart manufacturing technologies in enhancing decision-making by collecting real-time data on machinery and production. The authors focus on process scheduling in manufacturing, emphasizing the importance of adapting operations to dynamic product demand and customer trends and proposing a reinforcement learning framework for optimized manufacturing and maintenance control.

2.3.4. Supply Chain and Logistics Management

Industry 4.0 technologies and sustainability practices in the logistics industry converge to shape the evolving framework of Sustainable Logistics 4.0, as discussed by Di Nardo et al. [54]. Through a comprehensive literature review and bibliometric network analysis of studies from 2013 to 2023 sourced from Scopus and Google Scholar, the authors formalize how digital interventions consistently contribute to the improvement of sustainability in logistics. As observed by Holloway [55], sustainable inventory management plays a vital role in modern supply chain and logistic management, balancing efficiency with environmental responsibility. While financial constraints and organizational resistance may pose challenges to technological innovation, strategic investments and collaboration can drive meaningful change in terms of resilience while complying with existing regulations.

For example, Pinagapani et al. [56] discuss the design and implementation of a material-handling prototype robot and monitoring system within the framework of Industry 4.0. The authors highlight the role played by Automated Material Handling Systems (AMHSs) in improving efficiency in inventory storage and warehouses by reducing human intervention, also exploring automated queuing technology for continuous stacking and unstacking, with the ultimate goal of optimizing material placement based on weight capacity. Choudhary et al. [57] analyze the role of Radio Frequency Identification (RFID) and next-generation IoT technologies in enhancing supply chain flexibility while addressing security challenges and in the presence of regulatory constraints. This research discusses traditional inventory models such as Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ) and how IoT-driven solutions improve real-time data transmission and enhance inventory mobility. Additionally, the impact of digitization, cloud computing, and drones is discussed concerning the future of logistics. Al-Okaily et al. [58] lead a literature review on the intersection of management practices and Industry 4.0 technologies in driving supply chain sustainability, synthesizing findings on how approaches like Total Quality Management (TQM) and JIT contribute to efficiency and waste reduction. At the same time, the study highlights how blockchain, IoT, and big data enhance transparency and decision-making, concluding that all of these elements collectively contribute to improving sustainability efforts by minimizing environmental impact. Understanding the context in which these technologies and management practices are implemented is crucial, as industry-specific challenges and market dynamics can significantly influence their effectiveness in driving sustainable supply chain transformation.

In relation to practical scenarios, Khan et al. [59] elaborate on the need for optimizing spare parts warehousing to support decarbonization efforts in the oil and gas sector, proposing a smart warehouse management system powered by Industry 4.0 technologies to enhance logistics efficiency and sustainability for a major Norwegian company. Kumar et al. [60] analyze the operational performance of the Indian Railway supply chain using Industry 4.0 technologies to enhance customer service and inventory management. The authors propose an MCDM-based supply chain model to assess service quality and identify inventory-related challenges, such as integration systems, financial behavior, and management perspectives, helping decision-makers design a procurement-driven supply chain strategy in the railway sector. Focusing on the specific case of the Indian supply chain landscape, Pandey et al. [61] identify and benchmark the following key sustainability enablers to build a Reliable Supply Chain (RSC): digitalization, decentralization, smart factory technologies, and data security. The authors use an integrated MCDM approach to analyze the influence of these enablers and their interrelationships, demonstrating the EES benefits of Industry 4.0 adoption. Another relevant case study refers to the horticulture sector. In this regard, Singh et al. [62] focus on automated production management through technologies that enable the real-time tracking of crop growth and yield predictions, essential for managing inventory efficiently. By integrating AI and machine learning, this study demonstrates how farmers can optimize harvesting schedules, mitigate overproduction or shortages, and synchronize their output with market demand while promoting a more sustainable supply chain in agricultural inventory management. Huerta-Soto et al. [63] analyze optimization strategies for the modernization of the dairy supply chain, showing that AI and machine learning are emerging as preferred tools for improving inventory planning and logistics concerning traditional mathematical modeling. However, while modernization offers efficiency gains, the study also highlights environmental concerns and economic challenges smaller farms face.

2.3.5. Sustainable Manufacturing and Lean Production

Sustainable manufacturing within Industry 4.0 has been extensively studied by Harikannan and Vinodh [64] through the review of 442 related articles published between 2009 and 2022. In their work, the authors develop a conceptual framework for manufacturing industries willing to adopt sustainable practices aimed at creating economic value through digital technologies. This framework is highly relevant in light of the current literature review, as many recent studies align with its principles by emphasizing the use of digital technologies to drive both sustainability and economic value in industry. One study [64] also discusses how to enhance production efficiency while addressing sustainability concerns in the manufacturing sector, which, due to its resource-intensive nature, significantly impacts EES aspects. The improvement of these EES aspects via sustainable production strategies is widely discussed by Ospino et al. [65], who led a bibliometric analysis of 68 articles from Scopus and Web of Science by using scientific software and the PRISMA method and investigating how Industry 4.0 technologies can contribute to Lean and Green production practices in response to climate change and resource depletion. EES dimensions in sustainable manufacturing are also the object of a work of research led by Ahmad et al. [66], who connect each of these dimensions with suitable Key Performance Indicators (KPIs), e.g., resource efficiency and pollutant emissions for environmental sustainability, remanufacturing productivity and technology investments for economic sustainability, and social fairness initiatives and community feedback for social sustainability. The same authors [67] develop another paper on sustainability across various sectors. In detail, they explore the role of smart sensing in aviation, energy grids, resource extraction, and urban development. These advancements directly impact inventory management by tracking resources in real-time and making supply chains more efficient. Specific applications include AI-driven mine planning, IoT-enabled forest monitoring, smart ocean communication networks, and precision agriculture.

Considering practical applications in global markets, Harikannan et al. [68] use Structural Equation Modeling (SEM) to analyze and interpret data from experts in India’s automotive component manufacturing sector, indicating a strong connection between Industry 4.0 and sustainable manufacturing for EES performance. In a similar context, Alkaraan et al. [69] observe how companies transition to a Green Servitisation-Oriented Business Model (GS-OBM) while improving Environmental, Social, and Governance (ESG) performance. Using results from the UK Innovation Survey between 2012 and 2021, the authors show that Industry 4.0 technologies help integrate green servitisation strategies into sustainable supply chains, with corporate governance further strengthening this connection. Raissi [70] deals with corporate social responsibility by using structural equation modeling on survey data from 175 companies in Saudi Arabia and showing that digitalization enhances environmental decision-making. The study highlights how Industry 4.0 helps companies to improve service quality and stay competitive while also pointing out the difficulties they face in using digital technology to support sustainability. Sasaki [71] focuses on the Southeast Asian wood industry, showing how Industry 4.0 technologies can enhance carbon storage in Harvested Wood Products (HWPs). The author reviews past supply chain practices and proposes policy recommendations to encourage early adoption of advanced technologies for optimizing forest utilization and management.

2.3.6. Blockchain, Digital Twins, and Emerging Technologies

Various works in the literature specifically focus on using emerging technology in the field of reference. Hariyani et al. [72] review the transformative role of blockchain technology in manufacturing management and industrial engineering by analyzing 480 peer-reviewed papers from the Scopus database. The results show that blockchain significantly improves sustainable supply chain management in terms of real-time traceability and smart contract automation, apart from providing interesting opportunities for integration with IoT to build smarter manufacturing systems. Another insightful review of 60 blockchain-based frameworks has been carried out in [73], exploring the use of this technology in food supply chains and highlighting key applications such as transparency, traceability, and security. Moreover, areas where blockchain’s potential is likely underutilized have emerged, for example food donation, waste management, and supply chain financing. The review also elaborates on a roadmap for future research on the role played by blockchains for sustainable food supply chains. Kulshrestha et al. [74] develop a review on Spare Parts Management (SPM) in the Industry 4.0 era and the related contribution of emerging technologies. They implement a content-based analysis of 118 papers published between 1998 and 2022, categorizing SPM research into the following categories: inventory management, spare types, circularity based on 6Rs, performance indicators, and strategic and operational aspects. An interesting result from this research refers to highlighted research gaps at the intersection of SPM and Industry 4.0, emphasizing the need for further exploration to maximize technological benefits. Findings from the current literature confirm this need, showing increased efforts to close these gaps through practical applications of AI, IoT, and predictive analytics in spare parts inventory and circular strategies.

While global supply chain expansion and lean strategies have improved efficiency, they have also increased fragility and complexity. In this regard, a study on the impact of Industry 4.0 technologies on the resilience of manufacturing companies against the COVID-19 pandemic has been led by Madrid-Guijarro et al. [75]. This study makes use of Partial Least Squares Structural Equation Modeling (PLS-SEM) involving 304 manufacturing firms to confirm the positive influence of Industry 4.0 technologies on supply chain resilience to COVID-19. Furthermore, to address supply chain challenges in Industry 4.0, other useful technologies are gaining traction, for example, digital twins (DTs), which are virtual replicas of supply chain systems used to enhance their resilience and visibility, as stated in [76]. This work uses case studies, literature reviews, and qualitative methods to thoroughly evaluate DTs’ benefits, even providing a conceptual framework to guide professionals in implementing this technology for stronger SCM. An important aspect concurring to achieve this goal is waste management optimization, as highlighted by Fatorachian and Pawar [77]. Also, in this case, it is demonstrated that the combined use of digital solutions considerably leads to waste reduction through real-time monitoring, predictive maintenance, and increased transparency, aligning supply chain operations with sustainability and Net Zero targets. For example, Gonzalez et al. [78] leverage fog computing for designing an inventory management system aimed at improving sustainability in supply chains while effectively addressing demand uncertainties through the integration of an attention mechanism. Rather than relying solely on cloud services, fog computing helps outperform conventional demand estimation methods based on historical data in different demand modeling scenarios. Another significant technology in this field has been explored by Alzahmi and Shamayleh [79], who examine 3D printing’s impact on spare parts management. By enabling local production, 3D printing reduces inventory costs and shipping delays, supporting a shift from “make to stock” to “make to order” models. However, challenges like high costs and size limitations currently hinder widespread adoption. The study highlights the need for further research on the integration of 3D printing with Industry 4.0 technologies as well as the development of regulatory frameworks, which are essential aspects for achieving broader implementation. Andres et al. [80] discuss the transition from Industry 4.0 to Industry 5.0, which emphasizes smart and interconnected logistics operations with a specific focus on inventory management and warehousing. The authors analyze enabling Industry 5.0 technologies through real-world case studies, assessing their current applications and identifying areas for future exploration to drive the practical implementation of Logistics 5.0.

3. Formalization of Results

In the present section, we will formalize the insights gathered from the literature review by organizing them into structured tables, each dedicated to one of the previously identified topic categories. For each TC, we will summarize key aspects, including the methods and topics explored in the literature, the benefits associated with these approaches, the challenges researchers have encountered, and the future trends anticipated in that specific field. This structured approach will provide a clearer understanding of the current research state and highlight potential future research directions.

Table 1. Summary of ${\mathrm{TC}}_1$ Industry 4.0 and digital transformation.

Areas	Key Findings
Methods and topics	- Systematic literature review on Industry 4.0 and supply chain operations [29].
	- Local Industry 4.0 adoption focusing on logistic flexibility and risk management [30].
	- IoT, CPSs and environmental sustainability in manufacturing contexts [31].
	- Integration of SCM with Industry 4.0 technologies in Indian manufacturing [32].
	- Industry 5.0 and human-centric industrial systems [33].
	- Empirical study on Industry 4.0 and SMEs’ international competitiveness [34].
	- Exploratory study on sustainability and BMI in Indian automotive [35].
	- MCDM approach for ranking Industry 4.0 technologies [36].
	- Digitalization impact on sustainable inventory in the bakery industry [37].
Benefits	- Enhanced supply chain efficiency, inventory management, and logistics [30,32].
	- Support for environmental sustainability and supply chain decarbonization [31].
	- Improved collaboration and cost efficiency in SCM [32,35].
	- Industry 5.0 advances sustainability and human–machine collaboration [33].
	- Digitalization promotes business model innovation in market competitiveness [34,35].
Challenges	- IT infrastructure readiness is critical for successful Industry 4.0 adoption [29].
	- Regional disparities in Industry 4.0 implementation [30].
	- High resource consumption and environmental impact of CPSs [31].
	- Collaboration barriers existing between supply chain entities [35].
	- Sector-specific variability in digital transformation effectiveness [36,37].
Future trends	- Growth of Industry 5.0 with sustainability and human-centric innovations [33].
	- Stronger alignment of Industry 4.0 with environmental sustainability goals [31,37].
	- Policies supporting regional digitalization efforts and SMEs [30,34].
	- Greater integration of AI, big data, and machine learning in supply chains [36].
	- Expanding digitalization strategies across diverse industries [32,37].

reports key managerial insights for sustainable inventory management in Industry 4.0, emphasizing how digital transformation enhances supply chain efficiency, reduces costs, and supports environmental sustainability. Integrating IoT, CPSs, and data-driven decision-making enables more agile and resilient inventory management. However, the table also highlights critical challenges, including IT infrastructure readiness, regional disparities in technology adoption, and the high resource consumption of digital solutions. Future trends in this category suggest that Industry 5.0 will drive further innovation by combining sustainability with human-centric approaches. At the same time, AI, big data, and machine learning will play an increasingly vital role in optimizing supply chains. Managers should align digital strategies with environmental goals while promoting collaboration across supply chain entities to overcome adoption barriers.

Table 2. Summary of ${\mathrm{TC}}_2$ sustainability and circular economy.

Areas	Key Findings
Methods and topics	- Review on Industry 4.0 and SM and related conceptual and mathematical models [38].
	- Lean Six Sigma and Industry 4.0 integration through AISM and Delphi technique [39].
	- Lean management and circular economy synergy via JIT and VSM [40].
	- Theoretical framework on Industry 4.0, circular economy and sustainability [41].
	- Sentiment analysis and association rule mining revealing with impact on EES [42].
	- Analysis of Industry 5.0’s impact on EES, blockchain, IoT, VR, and AR [43].
	- MCDM-based risk assessment framework for mitigating SCRs within AMT [44].
	- Case on food-tech startups integrating Industry 4.0 for circular economy [45].
	- Circular economy practices in green hotels in Saudi Arabia and Egypt [46].
Benefits	- Manufacturing efficiency integrating Lean Six Sigma and Industry 4.0 [38,39].
	- Circular economy transition eased by lean practices, resource use, and recycling [40].
	- Industry 4.0 drives sustainability through job creation and lower emissions [42].
	- Contribution to sustainability across supply chains and inventory management [43].
	- AMT reduces lead time fluctuations, waste, and supplier dependency [44].
	- Food-tech startups and hospitality leverage Industry 4.0 for sustainability [45,46].
Challenges	- Limited case studies and mathematical approaches in Industry 4.0 research [38].
	- Lack of integration between quality management and circular economy [39].
	- Unclear interconnection between Industry 4.0 and circular economy strategies [41].
	- Sector-specific challenges in applying Industry 4.0 for sustainability [45,46].
Future trends	- Increasing adoption of Lean Six Sigma within Industry 4.0 [39].
	- Stronger alignment of circular economy principles with Industry 4.0 [40,41].
	- Wider use of sentiment analysis and AI techniques for Industry 4.0 impact [42].
	- Expansion of Industry 5.0 with metaverse and immersive technologies [43].
	- Greater role of AMT in reducing SCRs and promoting green innovation [44].

synthesizes insights on sustainable inventory management in Industry 4.0, highlighting how the integration of advanced digital technologies with circular economy principles is transforming supply chain operations. The findings emphasize that Lean Six Sigma methodologies combined with Industry 4.0 technologies contribute significantly to enhancing manufacturing efficiency, reducing waste, and promoting sustainable inventory management. The adoption of JIT and VSM further supports the transition to circular economy models by optimizing resource utilization and facilitating recycling efforts. Additionally, Industry 4.0 is driving sustainability by creating new job opportunities, lowering emissions, and increasing supply chain resilience through digital monitoring and predictive analytics. However, Table 2 also highlights several challenges that managers must address. The limited availability of case studies and mathematical modeling approaches in Industry 4.0 research restricts the ability to develop generalizable frameworks for sustainable inventory management. Moreover, the lack of integration between quality management systems and circular economy strategies creates gaps in the implementation of sustainability initiatives across different sectors. The complexity of aligning digital transformation efforts with circular economy objectives remains an issue, as many industries struggle with sector-specific barriers that hinder the full realization of Industry 4.0’s sustainability potential. Future trends of research outlined in Table 2 suggest an increasing convergence between Lean Six Sigma and Industry 4.0, fostering more structured and efficient sustainability practices. The adoption of AI-driven sentiment analysis and association rule mining is expected to play a greater role in assessing the EES impacts of digital transformation. Additionally, the expansion of Industry 5.0 incorporating immersive technologies such as VR and AR may enhance sustainable inventory management through real-time visualization. AMTs will also play a growing role in mitigating SCRs, reducing lead time fluctuations while promoting green innovation across industries.

To capitalize on these trends, managers should prioritize digitalization strategies that align with circular economy principles, integrate sustainability-focused quality management frameworks, and foster collaboration across supply chain networks to address implementation challenges. AI-driven decision-making, predictive analytics, and digital twin technologies can further enhance the effectiveness of sustainable inventory practices by proactively addressing sector-specific barriers.

Table 3. Summary of ${\mathrm{TC}}_3$ artificial intelligence and machine learning applications.

Areas	Key Findings
Methods and topics	- AI-based approaches in Supply Chain 4.0 improve security and cost efficiency [47].
	- Systematic review on AI’s role in SCM from Industry 4.0 to Industry 6.0 [48].
	- Machine learning for backorder prediction and inventory optimization [50].
	- Reinforcement learning integrated with lean green manufacturing systems [52].
Benefits	- AI enhances demand forecasting, inventory management, and decision-making [48].
	- Machine learning reduces financial pressures and prevents supply disruptions [50].
	- Reinforcement learning improves scheduling by adapting to real-time data [53].
	- AI in fashion manufacturing enables personalized consumer experiences [49].
Challenges	- AI implementation faces cybersecurity risks and workforce skill gaps [48].
	- Machine learning requires overcoming technical and implementation barriers [51].
	- AI integration into traditional manufacturing may pose compatibility issues [49].
	- Reinforcement learning models need extensive real-world validation [52].
Future trends	- Increased adoption of predictive maintenance and smart inventory management [51].
	- AI and machine learning driving sustainability and circular economy practices [52].
	- Further exploration of AI–human collaboration in supply chain optimization [48].
	- Adaptive AI solutions tailored for complex manufacturing environments [53].

reports results from the literature on the application of AI and machine learning in sustainable inventory management within Industry 4.0. As it is possible to observe, AI-driven solutions are transforming supply chain operations by enhancing security, cost efficiency, and decision-making. Particularly, machine learning techniques improve demand forecasting, inventory optimization, and backorder prediction, reducing financial pressures and preventing supply disruptions. Reinforcement learning has also been integrated with lean green manufacturing systems, allowing for dynamic scheduling and real-time adaptability. These advances contribute to more resilient and efficient supply chains, supporting sustainability by minimizing waste and improving resource utilization. However, the adoption of AI-driven approaches presents several challenges that may limit its widespread implementation, such as, for example, cybersecurity risks, workforce skill gaps, and other technical barriers. Additionally, integrating AI into traditional manufacturing processes may pose compatibility issues, requiring significant investments in infrastructure and employee training. Reinforcement learning models, while promising, require extensive real-world validation before they can be reliably applied to industrial settings. In the future, AI-driven sustainability initiatives are expected to further support circular economy practices by optimizing resource flows and reducing waste. Future research will also explore AI–human collaboration in supply chain management.

Table 4. Summary of ${\mathrm{TC}}_4$ supply chain and logistics management.

Areas	Key Findings
Methods and topics	- Bibliometric network analysis on digital interventions in sustainable logistics [54].
	- AMHSs optimizes warehouse efficiency through automated queuing [56].
	- RFID and IoT enhance supply chain flexibility, security, and inventory mobility [57].
	- MCDM applied to supply chain performance evaluation in the railway sector [60].
	- AI and machine learning enable predictions for sustainable horticulture logistics [62].
Benefits	- Industry 4.0 enhances logistics efficiency, waste reduction, and transparency [58].
	- AI-driven warehouse management supports decarbonization efforts in industry [59].
	- Smart factory technologies improve supply chain reliability and sustainability [61].
	- AI and machine learning improve dairy supply chain inventory planning [63].
	- IoT solutions improve real-time data transmission and inventory management [57].
Challenges	- Financial constraints and organizational resistance hinder technology adoption [55].
	- Industrial challenges and market dynamics influence technology effectiveness [58].
	- Smaller farms face environmental challenges in supply chain modernization [63].
	- Integration of IoT and blockchain requires overcoming regulatory constraints [57].
	- Implementation and scalability issues for AI in agricultural supply chains [62].
Future trends	- Increased investment in AI-driven predictive analytics for sustainable logistics [54].
	- Expansion of decentralized supply chain to enhance resilience and efficiency [61].
	- Further exploration of AI-enabled automated inventory tracking [62].
	- Evolution of blockchain and IoT integration for transparent and efficient SCM [58].
	- Role of smart warehouse management in green supply chain initiatives [59].

collects the main insights on sustainable inventory management in Industry 4.0, with a focus on supply chain and logistics management. We can see that the integration of digital interventions such as AMHSs, RFID, IoT, and AI-driven predictive analytics is transforming inventory planning and logistics efficiency. In addition, smart factory technologies and AI applications in supply chain performance evaluation contribute to sustainability by optimizing resource allocation and reducing carbon footprints.

These findings align with and build upon the literature review focused on the previous period 2013–2023 [54], which highlighted how digital interventions have consistently contributed to improving sustainability in logistics through enhanced forecasting, inventory control, and decision-making processes. Our review confirms this trend, with a growing emphasis on investment in AI-driven predictive analytics for sustainable logistics. However, challenges like financial constraints, regulatory barriers, and organizational resistance may exist with scalability issues, especially in agricultural supply chains, further complicating implementation. In the future, increased investment in AI-driven analytics and blockchain–IoT integration will enhance transparency and efficiency. Managers should balance technology adoption with workforce readiness and regulatory compliance to maximize Industry 4.0’s benefits for sustainable inventory management.

Table 5. Summary of ${\mathrm{TC}}_5$ sustainable manufacturing and lean production.

Areas	Key Findings
Methods and topics	- Review and elaboration of a conceptual framework for economic value creation [64].
	- Bibliometric analysis using PRISMA on Lean and Green production [65].
	- EES sustainability dimensions linked to suitable KPIs measuring their impacts [66].
	- SEM applied to Industry 4.0 adoption in India’s automotive manufacturing [68].
	- ESG improvement through GS-OBM based on UK Innovation Survey data [69].
Benefits	- Smart technologies enhance efficiency while addressing sustainability concerns [64].
	- AI and IoT monitoring improve resource tracking in several sectors [67].
	- Industry 4.0 strengthens corporate sustainability strategies and service quality [70].
	- Digitalization in forestry and wood industries supports carbon storage in HWPs [71].
	- Industry 4.0 help integrate green servitisation strategies into supply chains [69].
Challenges	- Manufacturing’s resource-intensive nature makes sustainability goals complex [64].
	- Difficulties in digitalization for sustainability-driven decision-making [70].
	- Market-specific dynamics influence the efficacy of sustainable manufacturing [68].
	- Policy and cost barriers hinder the adoption of forest management technologies [71].
	- ESG-focused business models require corporate governance adjustments [69].
Future trends	- Increasing reliance on AI-driven decision support for sustainable manufacturing [66].
	- Smart sensing expansion in aviation, energy grids, sustainable urban planning [67].
	- Integration of digitalization and sustainable initiatives in corporate governance [70].
	- Development of policy incentives for early adoption of Industry 4.0 in forestry [71].
	- Data-driven frameworks for EES sustainability in production [64].

reports the key findings on sustainable inventory management within Industry 4.0 and lean production, emphasizing the role of smart technologies in improving resource efficiency and corporate sustainability. On the one hand, digitalization enhances transparency, supports green servitization strategies, and enables data-driven decision-making. On the other hand, the resource-intensive nature of manufacturing, specific market dynamics, and the existence of policy barriers represent complex challenges to widespread adoption. As future lines of research, AI-driven decision support systems integrated with smart sensing technologies will be critical for integrating sustainability into manufacturing operations, also taking into account digitalized corporate governance. Managers need to be equipped to face cost constraints and regulatory challenges while implementing Industry 4.0 innovations to achieve long-term sustainability goals.

Table 6. Summary of ${\mathrm{TC}}_6$ blockchain, digital twins, and emerging technologies.

Areas	Key Findings
Methods and topics	- Blockchain is reviewed with a focus on smart contracts and real-time traceability [72].
	- DT is explored to enhance resilience and visibility of supply chain systems [76].
	- A review of 3D printing in SPM highlights a shift toward localized production [79].
Benefits	- Blockchain and IoT improve supply chain sustainability and automation [72].
	- Digital solutions help reduce waste and align with sustainability goals [77].
	- Fog computing enhances inventory management through forecast accuracy [78].
	- 3D printing eases a transition from “make to stock” to “make to order” models [79].
	- Industry 4.0 contribute to resilience during crises like the COVID-19 pandemic [75].
Challenges	- Blockchain adoption still remains limited in underutilized management areas [73].
	- Despite its benefits, 3D printing adoption faces cost barriers and size limitations [79].
	- Infrastructure and regulatory gaps in integrating digital solutions [77].
Future trends	- Research on Industry 4.0 to 5.0 transition about smarter logistics/warehousing [80].
	- Expansion of blockchain applications in food supply chains is expected [73].
	- More integration of DT with supply chains will improve real-time adaptability [76].
	- 3D printing in SPM will advance with cost efficiency and regulations [79].

highlights how emerging technologies are shaping sustainable inventory management in Industry 4.0. Specifically, blockchain improves traceability and automation, helping businesses track products in real-time. DTs enhance supply chain resilience by providing better visibility and predictive capabilities, while 3D printing is making it easier for companies to shift from keeping large inventories to producing items on demand, reducing waste and storage costs. Despite these advantages, significant challenges include high implementation costs, regulatory uncertainties, and infrastructure limitations. In the future, blockchain is expected to play a bigger role in food supply chains, while DTs will continue to improve real-time decision-making. As 3D printing technology becomes more cost-effective, its adoption is likely to grow. Managers should explore ways to integrate these technologies into their operations, balancing sustainability and adaptability.

4. Conclusions and Future Research

This study comprehensively reviews recent advancements in sustainable inventory management within Industry 4.0, examining 52 papers published in 2024 and 2025. Our analysis bridges theory and practice by identifying key trends, benefits, challenges, and future directions in how digital transformation is driving sustainability in supply chains, providing valuable guidance for researchers and practitioners aiming to improve efficiency and environmental responsibility. The identified papers have been categorized and discussed in relation to the following topic categories: Industry 4.0 and digital transformation (${\mathrm{TC}}_1$), sustainability and circular economy (${\mathrm{TC}}_2$), artificial intelligence and machine learning applications (${\mathrm{TC}}_3$), supply chain and logistics management (${\mathrm{TC}}_4$), sustainable manufacturing and lean production (${\mathrm{TC}}_5$), and blockchain, digital twins, and emerging technologies (${\mathrm{TC}}_5$). The findings have been formalized and reported accordingly.

Broadly speaking, it is possible to observe a consensus in the literature about the evidence that Industry 4.0 is redefining sustainable inventory management by integrating several technologies and AI-driven analytics to enhance efficiency, reduce costs, and support the development of sustainability strategies in diverse management areas. Circular economy principles, Lean Six Sigma, and digitalization are promoting effective waste reduction and resource optimization, reinforcing the shift toward more adaptive and resilient supply chains. Despite these benefits, barriers persist to the adoption of Industry 4.0 technologies in making inventory management practices more sustainable. For instance, infrastructure gaps, regional disparities, as well as the high resource consumption of digital solutions, hinder widespread implementation. Furthermore, on the one hand, AI applications have the potential to improve forecasting and decision-making considerably; on the other hand, they raise concerns over critical aspects such as cybersecurity, workforce readiness, and integration with traditional systems. The existence of complex regulatory constraints and financial limitations further complicate the transition to digitalized supply chains as well as sustainability initiatives for inventory management.

Future research will likely focus on bridging these gaps by advancing AI–human collaboration and expanding blockchain–IoT integration for a higher degree of transparency. The transition to Industry 5.0 is expected to emphasize human-centric sustainability as well as the use of immersive technologies together with improved AI-driven governance. To fully capitalize on these advancements, managers will need to align digital strategies with environmental goals and invest in workforce training while creating collaboration across supply chain networks for the long-term sustainability of inventory management practices.