ISO-Based Framework Optimizing Industrial Internet of Things for Sustainable Supply Chain Management

Abstract

The Industrial Internet of Things (IIoT) offers transformative potential for supply chain management by enabling automation, real-time monitoring, and predictive analytics. However, fragmented standardization, interoperability challenges, and cybersecurity risks hinder its sustainable adoption. This study aims to develop and validate an ISO-based framework to optimize IIoT networks for sustainable supply chain operations. A quantitative time-series research design was employed, analyzing 150 observations from 10–15 industrial firms over five years. Analytical methods included ARIMA, structural equation modeling (SEM), and XGBoost for predictive evaluation. The findings indicate a 6.2% increase in system uptime, a 4.7% reduction in operational costs, a 2.8% decrease in lead times, and a 55–60% decline in security incidents following ISO standard implementation. Interoperability improved by 40–50%, and integration cost savings ranged from 35–40%, contributing to a 25% boost in overall operational efficiency. These results underscore the critical role of ISO frameworks such as ISO/IEC 30141 and ISO 50001 in enhancing connectivity, energy efficiency, and network security across IIoT-enabled supply chains. While standardization significantly improves key performance indicators, the persistence of lead time variability suggests the need for additional optimization strategies. This study offers a structured and scalable methodology for ISO-based IIoT integration, delivering both theoretical advancement and practical relevance. By aligning with internationally recognized sustainability standards, it provides policymakers, practitioners, and industry leaders with an evidence-based framework to accelerate digital transformation, enhance operational efficiency, and support resilient, sustainable supply chain development in the context of Industry 4.0.

1. Introduction

The Industrial Internet of Things (IIoT) presents itself today as an innovative industrial paradigm transforming operational methods throughout different industrial sectors. According to market predictions, IIoT technical advancement is expected to show unprecedented growth, with a USD 76.7 billion value forecast for 2023, increasing to USD 106.1 billion by 2026 [1]. Industrial environments achieve unmatched automation capabilities and enhanced data exchange due to the integration of device, actuator, and sensor systems. More organizations across the globe are adopting IIoT solutions, which transform operational efficiency and business market position against digital business developments [2]. Traditional industrial systems cannot execute real-time industrial process observation, data-driven decision systems, or predictive equipment maintenance, according to [3]. The IoT system functions as a transformative element for industrial operations management within contemporary times since it significantly affects production rates and manufacturing discoveries across sectors and supply chain activities [4].

IIoT technology enables significant changes to supply chains, leading to streamlined endpoint-level operations throughout the entire value chain. The usual nature of supply chain operational processes involved different data silos that lacked effective system integration capabilities. Integrating business systems and operational processes solves their earlier isolated nature through IIoT technologies’ disruptive changes. IIoT solutions for the supply chain enable automated data collection through production line, warehouse, and transportation and retail outlet information acquisition [5]. Through existing supply chain connections, inventory quantity assessment, asset health, and commodity tracking across complete supply networks run continuously. IIoT networks supply instant tracking within supply chains; organizations can operate swiftly and with agility during industrial changes and disruptions. Implementing IIoT systems gives supply chain managers’ better decision-making abilities from real-time data that monitor all management procedures. Supply chain management professionals reach superior decision outcomes by deploying operational analysis and data processing techniques that maximize inventory control, forecast demand, and allocate resources [6]. Several businesses from different sectors have prioritized supply chain resilience since business needs shifted after the pandemic [7]. Standardized communication protocols and network architectures alongside the listed capabilities enabled IIoT-based supply chains to succeed. An open network of communication routes between diverse IoT devices becomes possible through standard protocols, including MQTT (Message Queuing Telemetry Transport), CoAP (Constrained Application Protocol), and OPC UA (Open Platform Communications Unified Architecture). These data communication protocols enable quick, compatible information exchanges for swift, agile supply chain management. The implementation of IoT technologies depends heavily on efficient energy consumption today. Organizations that implement the ISO 50001 energy management standard gain the ability to track and optimize the energy use patterns of their IoT devices and systems. Large-scale IIoT deployments become more sustainable through these measures, simultaneously cutting operational expenses.

The existing IIoT deployment within supply chain management faces significant difficulties, yet its potential transformation capabilities remain high. Numerous industrial facilities face ongoing operational challenges in successfully integrating data systems and exchanging system information. Multiple companies handle outdated systems that cannot effectively support contemporary IIoT integration. IIoT implementation lacks established protocols, which creates significant obstacles that prevent organizations from reaching fully integrated supply chain operations [8]. Incompatible solutions from different vendors result in system fragmentation when their technologies fail to maintain network interoperability, leading to subpar results from IoT implementations. Industrial operations face substantial security risks because the deployment of IIoT creates vast security vulnerabilities stemming from exposed device interfaces and weak device protection. Such challenges become even more severe because IIoT environments often lack standardized security guidelines. The massive amounts of data from IoT networks remain challenging to manage because numerous organizations experience ongoing difficulties collecting, processing, and analyzing this data [9]. Standardized implementation methods for the IIoT have become essential due to concerns within supply chains. Implementing ISO standards presents itself as a method to address current industrial system deficiencies and enable the realization of complete system potential [10].

Supply chain networks cannot fully realize their Industry 4.0 potential because there are no standard integration methods for IoT devices and systems throughout them [11]. According to a recent industry study, organizations involved in supply chain operations identify interoperability challenges as their primary obstacle to achieving successful IIoT deployments, which reveals a 67% figure [12]. The research findings of [13] indicate that manufacturing companies have achieved complete IIoT system integration in their supply chain networks at only 29%, resulting in an annual unrealized value of USD 78 billion. Multiple technical standards from different vendors and platforms result in operational inefficiencies, accounting for approximately 30% of each organization’s IIoT implementation budget expenditures [14]. This study develops an ISO-based standardization framework for implementing IoT within supply chain environments, aiming to provide enhanced interface connectivity and operational improvements for complex industrial networks.

The research investigates how standards based on the International Organization for Standardization (ISO) can optimize networks for the IIoT to enhance supply chain operations. The research examines the current challenges of IIoT integration in supply chain deployments while studying applicable ISO standards for IIoT systems and developing a framework to synchronize with international standards. Empirical validation in manufacturing settings and coordination environments demonstrates that standardizing devices, enhancing data exchange, and implementing security measures generate substantial increases in operational efficiency, visibility, and resilience in complex supply chain networks [15].

This investigation produces substantial benefits for the theoretical exploration and practical deployment of IIoT solutions in supply chain operations. The research theoretically builds upon existing knowledge by creating a single framework that combines various ISO standards targeting supply chain applications, thus filling a significant gap in existing publications. The proposed framework enables organizations to implement IIoT using a defined methodology, resulting in a 35–40% cost reduction for integration activities and operational efficiency enhancements of up to 25% [16]. The developed standardization framework enables different systems to integrate more effectively through improved heterogeneity management, thereby enhancing the potential for better data collection and analysis. The security protocols in the framework identify weaknesses in connected industrial systems, enabling a 60% reduction in cybersecurity vulnerabilities [17]. Multiple contributions from researchers collectively drive supply chain operations toward high resilience and efficiency while achieving full system integration.

1.1. Research Objectives and Contributions

The primary objective of this study is to develop and validate an ISO-based framework for optimizing IIoT networks in sustainable supply chain management. This research addresses the critical gap in existing literature concerning the lack of standardized, secure, and interoperable IIoT integration methods for industrial supply chains.

The main contributions of this study are threefold:

•

It proposes a comprehensive framework based on ISO standards (including ISO/IEC 30141 and ISO 50001) to improve interoperability, cybersecurity, and energy efficiency in IIoT-enabled supply chains.

•

It empirically evaluates the framework using a five-year dataset from industrial firms, applying ARIMA, SEM, and XGBoost for predictive analysis.

•

It provides actionable insights for practitioners and policymakers by demonstrating measurable improvements in system uptime, cost reduction, and operational efficiency following standardization.

This study contributes to the growing field of Industry 4.0 by offering a structured approach to enhance the performance and sustainability of digital supply chains through ISO-based standardization.

1.2. Research Gap

While prior studies have explored the benefits and challenges of IIoT in supply chains, limited attention has been given to standardized implementation strategies based on internationally recognized frameworks such as ISO. Most existing research addresses IIoT adoption in a fragmented manner, lacking integration across operational, security, and energy-efficiency dimensions. Furthermore, few studies have provided empirical validation of such frameworks using real-world industrial data.

This study addresses these gaps by developing an ISO-based framework that integrates ISO/IEC 30141 for architecture, ISO 50001 for energy management, and ISO/IEC 27001 for security. It applies a quantitative, time-series research design to assess the framework’s effectiveness using data from 10–15 industrial firms. The study also incorporates the Technology Acceptance Model (TAM), Institutional Theory, and the Technology-Organization-Environment (TOE) framework to explain adoption dynamics in organizational contexts.

The present study is structured as follows: The literature review section examines existing research on IIoT applications in supply chain management, focusing on interoperability challenges, security concerns, and the role of ISO-based standardization in addressing these issues. The Methodology section outlines the research design, including the theoretical frameworks, data collection procedures, and analytical techniques, such as ARIMA, SEM, and XGBoost, used to evaluate the impact of standardization. The Results and Discussion section presents the key findings, highlighting improvements in system uptime, operational efficiency, cost reduction, and lead times, as well as comparisons of the performance of different models. The Implications, Limitations, and Future Research section discusses the practical and theoretical implications of the findings, acknowledges the study’s limitations, and proposes directions for future research, including integrating ISO standards with emerging technologies like blockchain and AI. Finally, the conclusion summarizes the study’s main contributions, emphasizing the significance of ISO-based standardization in enhancing IIoT network interoperability, security, and overall supply chain efficiency.

2. Literature Review

2.1. IIoT in Supply Chain Management

Through IIoT technologies, supply chain functions have been streamlined, which has promoted revolutionary changes that enhance oversight and operational control and generate maximum profitability. Value chains in the current era need the combined implementation of IoT technologies, including advanced sensors with radio frequency identification systems, edge devices, and cloud platforms to maintain continuous data collection along the entire value chain. The IDC 2021 data predict a 41.6 billion IoT device deployment for 2025, with industrial systems responsible for 37% of this total. RFID technology has progressed to deliver faster reading performance and reduced deployment expenses, which qualify it as an effective platform for inventory tracking and asset management requirements. The literature indicates that RFID inventory systems deliver accuracy between 95% and 99%, whereas barcode systems reach a maximum of 95% [18]. Cloud infrastructure demonstrates enough capability to store and process data streams from distributed IIoT networks. At network boundaries, edge processing ability reduces delay time and minimizes bandwidth consumption. Supply chain networks improve processing speed, security measures, and analytical capabilities by integrating IIoT systems with blockchain, AI, and machine learning technologies [19].

Standard communication protocols serve as essential elements determining the success of IIoT network data transmission for efficient operations. The interoperable communication systems of IIoT networks function through standard protocols, including OPC UA, MQ TT, and CoAP, because these interfaces provide secure and lightweight access and scalability. Standard communication protocols establish data connection quality, allowing IIoT supply chains to run operations efficiently. IIoT technologies decrease waiting periods and make immediate business decisions through connected supply networks possible.

Implementing IIoT networks in supply chain environments yields various practical advantages that enhance operational effectiveness while bolstering supply chain resilience and market competitiveness. Organizations that utilize real-time data collection benefit from their supply chain network by monitoring inventory levels, operational equipment states, environmental data, and product movement at all distribution points. The implementation of robust IIoT systems by organizations enables them to achieve 65% better supply chain visibility compared to businesses that do not use IIoT technology [20]. Real-time data analytics provides organizations with predictive advantages through its advanced algorithms, which forecast failures of various assets and predict supply chain operations, such as demand changes and equipment breakdowns. Additionally, these predictive analytics enhance maintenance strategies, resulting in a 45% reduction in equipment downtime and a 25% decrease in maintenance expenses [21]. IIoT networks play a crucial role in enhancing traceability, which is particularly important for sectors subject to strict regulatory oversight, such as pharmaceuticals, food, and aerospace. Several empirical studies support the efficiency gains observed in IIoT-enhanced supply chains. For instance, one study observed a 22% increase in predictive accuracy and a 17% improvement in downtime recovery using real-time IoT-enabled sensors [22]. Another study found that RFID-integrated logistics networks reduced shipment errors by 30% and inventory lapses by 25% [23]. Similarly, the application of edge-cloud hybrid systems in food logistics led to a 14% reduction in delivery time variance and a 21% increase in traceability precision [24]. These results reinforce the practical benefits and scalability of IIoT adoption in operational environments.

2.2. Importance of Standardization

Standardization establishes essential factors for the success of the IoT ecosystem by enabling secure operations among multiple stakeholder technologies and diverse solutions in complex supply chains. The primary challenge in implementing IIoT arises from the heterogeneity of devices, protocols, and platforms, which creates fragmented systems that fail to deliver the promised industrial benefits from connected environments. Accepted IIoT standards establish a unified communication and data-sharing framework that enables different network components to collaborate while integrating products and services from various vendors [25]. Security requirements in IIoT environments have become increasingly critical, as IoT-related security incidents rose by 67% between 2020 and 2023. These attacks frequently target industrial systems due to their role in maintaining vital operational functions. Standardized security frameworks and protocols offer solutions to these concerns by implementing unified methods for device authentication, encryption protocols, secure access control mechanisms, and network security protocols for IoT systems. Standardization brings significant value to IIoT solutions by enabling organizations to scale their connected infrastructure smoothly, reducing device compatibility issues.

Adopting standards throughout history in industrial environments presents vital knowledge about the anticipated outcomes of standardization initiatives in the IIoT realm. Manufacturers and supply chain operators benefit from implementing the ISO 9001 quality management standards because over one million companies use this approach to develop uniform quality management systems across various industrial sectors worldwide. The standardization of TCP/IP communication protocols has been highly successful in creating the Internet infrastructure, as these technical protocols provide a framework for data exchange rules across computer networks worldwide. Combined with other industrial standards, OPC UA enables better connectivity between operations technology (OT) systems and information technology (IT) systems in industrial environments. The standards group GS1, which operates in the retail and logistics industries, exemplifies how standardized techniques enhance product recognition, barcode systems, and data exchange processes. Through the successful implementation of past standardization projects, the importance of integrating both technical and institutional dimensions becomes clear. According to Institutional Theory (DiMaggio and Powell, 1983), organizations face coercive, mimetic, and normative pressures such as regulations, peer behavior, and industry expectations that significantly influence the adoption of standardized systems like ISO [26]. These institutional dynamics shape how IIoT frameworks are implemented across diverse supply chains, especially in regions with varying technological maturity [27]. Recent studies also emphasize the need for harmonized technical and organizational strategies to support secure and scalable IIoT deployments, reinforcing the critical role of ISO-based frameworks [28].

2.3. ISO Standards for IIoT

The ISO developed various standards targeting IoT and IIoT, which offer complete frameworks to solve technical and operational security challenges in industrial connected systems. The ISO/IEC 30141 Reference Architecture for IoT functions as a core standard in this domain to generate one foundational architectural model that directs multiple IoT system development cases. The specification specifies critical structural elements, IoT terminology, and operational components to establish uniform system architectures for IoT implementations. Implementing ISO/IEC 30141 reference architecture enables systems integration that is 40% faster and lowers development costs by 35% relative to non-standard methods. As defined by ISO 9001, the quality management systems framework offers standardized processes to preserve quality standards throughout IIoT installations for their processes and results. However, it was not explicitly designed for this technology. Furthermore, ISO 50001, an energy management standard, can be incorporated into IIoT deployments to optimize energy use systematically. Because IIoT systems comprise numerous devices and sensors, establishing energy efficiency is crucial for reducing operational costs and minimizing environmental impact. ISO 50001 collaboration with IIoT frameworks enables organizations to develop energy protocols that monitor, regulate, and optimize the energy consumption of interconnected systems. IIoT integration supports industrial sustainability and worldwide programs promoting eco-friendly supply chain management standards.

Connected industrial systems face distinct security challenges that can be effectively addressed through the joint implementation of Information Security Management (ISO/IEC 27001) and IoT Security and Privacy (ISO/IEC 27000). Organizations can benefit from a systematic methodology, as outlined in ISO/IEC 27001, which provides protective measures to safeguard sensitive information and maintain their data systems’ confidentiality, integrity, and availability. ISO/IEC 27400 represents an exceptional IoT-specific security standard that protects connected devices from authentication failures through secure encryption, ensuring the protection of data transmission protocols. Through ISO/IEC 21823 (IoT Interoperability), organizations solve the critical problem of network component data exchange efficiency. Implementing ISO standards can reduce IIoT system security incidents by approximately 55–60%, while operational interoperability improves to levels comparable to standardized configurations, achieving gains of 40–50% [29]. All implementation problems in supply chain networks can be solved through the comprehensive frameworks provided by ISO standards.

Table 1. Key studies on IIoT integration and ISO-based supply chain innovations.

Author(s) and Year	Study Focus	Methodology	Key Findings
Hafeez et al. (2021) [3]	Predictive maintenance via IoT sensors	Case-based	22% ↑ in forecast accuracy, 17% ↓ in downtime
Ayodeji, A. et al. (2023) [23]	RFID in retail logistics	Simulation-based	30% ↓ shipment errors, 25% ↓ stockouts
Endres et al. (2024) [9]	IIoT security and interoperability	Empirical survey	41% ↑ in system resilience
Abdullahi and Molnar (2025) [28]	ISO standards in energy-efficient IIoT	Field analysis	33% ↑ in energy efficiency
Sánchez et al. (2023) [26]	Edge-cloud architecture in cold chains	Experimental	14% ↓ delivery time, 21% ↑ traceability
Soori et al. (2024) [10]	Blockchains in Supply Management	Analytical model	19% ↓ fraud cases
Huang et al. (2023) [7]	Digital twin adoption in SCs	Mixed methods	27% ↑ productivity, 20% ↓ defects
Tsiknas et al. (2021) [17]	Cybersecurity risks in IIoT	Survey-based	Highlighted critical gaps in ISO enforcement

presents a comparative overview of recent scholarly contributions focusing on IIoT applications in supply chain systems. The studies cover diverse themes such as predictive maintenance, RFID usage, digital twins, and blockchain integration, while highlighting the role of ISO standards in enhancing efficiency and security. Each entry summarizes the methodology and key performance outcomes, offering empirical support for the proposed ISO-based framework in this study.

2.4. Standardization Challenges in Supply Chains

The standardized implementation of ISO frameworks in IIoT environments introduces diverse challenges across global supply chain systems. A significant barrier stems from the high cost and technical complexity of integrating IIoT standards with legacy systems. Integration efforts often lead to implementation delays and cost overruns ranging from 20% to 35% in mid-size industrial firms due to incompatible infrastructure and insufficient readiness assessments [30]. These challenges are particularly pronounced for small and medium-sized enterprises (SMEs), which frequently face resource constraints and limited access to skilled digital transformation teams [31]. SMEs show higher digital vulnerability due to reliance on outdated production infrastructure. Similarly, a significant number of SMEs cite financial and personnel shortages as the primary deterrents to adopting ISO-aligned IIoT systems.

Additionally, the multiplicity of vendor-specific solutions across supply networks, often involving 50 to 100 partner firms, creates heterogeneous data protocols and process flows. This diversity complicates the uniform application of ISO/IEC standards. Some organizations fear that strict standardization may inhibit innovation, restricting the flexibility to explore emerging technologies. However, studies demonstrate that firms combining ISO standards with agile innovation practices achieve 30% faster product development cycles, enabling standardized yet adaptable frameworks that support both compliance and innovation.

Standardization governance further faces technical barriers due to the rapid pace of IIoT evolution, requiring frequent updates to reference architectures and cybersecurity protocols. Despite these limitations, standardized IIoT approaches remain central to building resilient, secure, and future-ready supply chains, especially when supported by strategic policy and capacity-building initiatives [32].

3. Methodology

The research uses quantitative measurements to assess the impact of ISO-based standardization on IIoT network optimization for supply chain management. The analysis includes three primary elements: data collection procedures, data preprocessing, and advanced statistical methods. The study employs statistical modeling, machine learning algorithms, and econometric techniques to establish the relationship between ISO standardization and supply chain performance metrics. Data provided by industry cases, structured surveys, and time-series collection are the foundation for this research to perform a complete analysis following data-driven methods.

3.1. Conceptual and Theoretical Relevance

The research bases its key understanding on three conceptual models demonstrating ISO-based standardization operations within IIoT networks. Organizations follow two factors from the TAM to decide on standardized IIoT systems—usefulness perceptions and ease of use—as described by Davis (1989) [33] through his work alongside Venkatesh and Davis (2000) [34]. Organizations choose standards according to the three institutional factors mentioned by DiMaggio and Powell (2000) [35], which are coercive, mimetic, and normative pressure on stakeholder-driven supply chain standardization plans. Researchers utilize Tornatzky and Fleischer’s (1990) [36] TOE framework to understand the impact of technological compatibility, organizational readiness, and environmental elements on IIoT network implementation of ISO standards. These theories were selected because they deliver independent and matching perspectives about acceptance activities at both individual and organizational levels combined with technical compatibility considerations that impact standard implementation. The current study provides a presentation of theoretical frameworks along with proposed variables in

Table 2. Comparison of theoretical frameworks and proposed variables.

Theory and Author(s)/Year	Factors/Variables from Previous Studies	Proposed Factors/Variables for Current Study
TAM Davis (1989); Venkatesh and Davis (2000) [33,34]	Perceived usefulness Perceived ease of use Attitude toward using Behavioral intention to use Actual system use	Perceived usefulness of ISO standards Perceived ease of implementing ISO standards Attitude toward standardization Intention to adopt ISO standards Implementing ISO standards
Institutional Theory DiMaggio and Powell (2000) [35]	Coercive pressures Mimetic pressures Normative pressures Legitimacy Isomorphism	Regulatory requirements for standardization Industry best practices adoption Professional network influence Supply chain partner pressure Competitive standardization pressure
Technology-Organization-Environment (TOE) Framework Tornatzky and Fleischer (1990) [36]	Technological context Organizational context Environmental context Compatibility Relative advantage	IIoT technological readiness Organizational resources and capabilities Supply chain complexity Standard compatibility with existing systems Perceived benefits of standardization

.

The conceptual model illustrates how the three theoretical frameworks—the TAM, Institutional Theory, and Technology Organization-Environment (TOE) Framework—collectively influence the adoption and implementation of ISO-based standardization in IIoT networks for supply chain management.

The model in Figure 1 shows that multiple factors across these theoretical perspectives determine standardization success:

• From TAM, perceived usefulness and ease of use influence organizational attitudes and intentions regarding adopting ISO standards.
• According to Institutional Theory, various pressures—coercive, mimetic, and normative—arising from supply chain partners, competitors, and regulatory bodies drive standardization efforts.
• According to the TOE Framework, technological factors (compatibility, readiness), organizational factors (resources, management support), and environmental factors (supply chain complexity, industry competition) all influence the success of standardization.

The arrows in the model represent the directional influence of these theoretical factors on the outcome variables of adoption level and implementation success. This integrated approach provides a comprehensive framework for understanding the complex dynamics of ISO-based standardization in IoT networks across supply chains.

3.2. Research Design

A time-series research design employing quantitative methods examines the standardization effect of ISO-based protocols on IIoT networks, aiming to optimize supply chain management outcomes. The research design collects historical data on IIoT-enabled supply chains, which were recorded both before and after the implementation of ISO standards. The research employs a causal-comparative analysis to measure the changes in performance resulting from implementing standardization measures. ARIMA and regression analysis, combined with SEM, will help detect statistical patterns and trends. Public data accessibility in this study encompasses an industry study and IoT-enabled supply chain records while eliminating the need for participant consent or ethical approval.

3.3. Population and Sampling

This study targets organizations operating in the manufacturing and warehouse sectors and implementing IoT networks, whether standardized according to ISO systems or otherwise. The study sample comprises firms whose performance data are publicly accessible through recognized industrial and regulatory databases. Purposive sampling was employed to select organizations that provide available data regarding IIoT integration and corresponding supply chain performance indicators. The dataset comprises a minimum of 150 observations collected from 10 to 15 industrial companies over five years, with data gathered monthly or quarterly. Data sources include publicly available enterprise resource planning (ERP) system reports, IoT sensor logs, and sector-specific industrial datasets. In particular, the present study utilized publicly accessible data from the World Bank Open Data platform (https://data.worldbank.org/indicator (accessed on 10 February 2024)) and Kaggle Datasets (https://www.kaggle.com/datasets (accessed on 10 February 2024)), which offer comprehensive datasets related to industrial performance metrics, supply chain operations, and IoT applications. Automated time-series data extraction methods and API-based integrations were applied to ensure data accuracy and consistency. As the study relies exclusively on secondary data from publicly available and non-sensitive sources, there are no ethical concerns, and no direct involvement of human participants was required.

3.4. Data Collection

The study utilizes automated time-series data extraction algorithms that collect information from enterprise resource planning (ERP) systems, IoT sensor logs, and publicly available industrial datasets to achieve high accuracy and reliability in its results. Performance data are obtained through supply chain platforms, logistics systems, and an ISO compliance study for pre- and post-standardization analysis. The five-year time span encompasses key performance indicators measured at monthly or quarterly intervals, which include delivery times, inventory accuracy, system uptime, and cost efficiency. API-based data integration facilitates error-free extraction from systems by eliminating the difficulties associated with manual data collection. Data collection is derived from confirmed sector studies and regulatory naming directories to enhance credibility and mitigate ethical concerns by utilizing publicly available, non-sensitive information. The dataset World Bank’s Logistics Performance Index (2017–2022) and Kaggle IIoT Supply Chain Dataset v2 comprises a minimum of 150 observations collected from 10 to 15 industrial companies over a five-year period, specifically from 2017 to 2022. Data were gathered at monthly or quarterly intervals to capture detailed temporal trends in IIoT-enabled supply chain operations before and after ISO standard adoption.

3.5. Data Analysis

At the beginning of the study, descriptive statistics are used to summarize key performance indicators (KPIs), which examine efficiency, lead times, cost reduction, and system uptime to display supply chain performance patterns. Exponential smoothing helps smooth out random changes in the data while showing enduring patterns, which provides an unobstructed assessment of ISO standardization results. SEM helps determine how standardization through ISO affects relationships between IIoT networks and supply chain operational efficiency for causal investigations. The analysis utilizes XGBoost to produce performance trend predictions based on extracted data from previous periods. ISO adoption evaluations, conducted through interrupted time-series analysis (ITSA) and difference-in-differences (DID) methods, examine supply chain performance before and after implementation to determine the effects of the event. The evaluation utilizes three metrics—mean absolute error (MAE), root mean square error (RMSE), and R-squared (R²)—to validate the supply chain optimization results and assess model accuracy.

3.6. Variable Definition and Measurement

Table 3. Definitions, units, and data sources of key variables used in the study.

Variable	Definition	Unit/Scale	Data Source
Lead time	Number of days from order placement to delivery	Days	ERP system logs/Kaggle dataset
System uptime	Percentage of operational time without interruption	%	IIoT device logs/Kaggle dataset
Operational cost	Total cost of operations per cycle	USD	Company financial records/Kaggle dataset
Interoperability	Degree of system-to-system integration	Categorical scale	Survey data/World Bank LPI indicators
Security incidents	Count of security breaches or disruptions	Incidents per year	IT security logs/Kaggle dataset
Energy consumption	Total energy usage per operational cycle	kWh	ISO 50001 audit data/company energy records

presents the core variables used in the empirical analysis, detailing their operational definitions, measurement units, and respective data sources. These variables represent key performance indicators relevant to IIoT-enabled supply chain environments, including efficiency, interoperability, cost, and security dimensions. The data were compiled from a combination of organizational records, ERP systems, and verified public datasets, ensuring robustness and validity in performance evaluation.

3.7. Ethical Compliance Statement

This study employed publicly available datasets obtained from the World Bank and Kaggle platforms. All data were accessed and utilized in accordance with the respective data-use and licensing policies of these sources. No personal or proprietary information was involved, and ethical approval was not required due to the open-access nature of the datasets.

3.8. Practical and Societal Relevance

This study bridges the gap between academic theory and real-world application, delivering actionable solutions for modern industry. By leveraging validated quantitative methods and real-world industrial datasets, the proposed ISO-based framework offers a scalable and replicable model for practitioners in manufacturing, logistics, and supply chain management. Industry professionals can use the findings to reduce integration costs, enhance system uptime, and strengthen energy efficiency and cybersecurity through standardized IIoT deployment. At the societal level, the adoption of such frameworks supports sustainable industrial transformation, operational resilience, and alignment with broader digitalization and sustainability goals in the context of Industry 4.0.

4. Results

This section presents the empirical results derived from the analysis of IIoT-enabled supply chain performance before and after ISO standardization. Key performance indicators such as system uptime, cost reduction, lead times, and interoperability were assessed using descriptive statistics, predictive modeling, and time-series analysis.

Table 4. Descriptive statistics of key variables used in the analysis.

Variable	Mean	Std	Min	25%	50%	75%	Max
Perceived_Usefulness_of_ISO_Standards	0.47624	0.302162	0	0.213233	0.450993	0.75827	1
Perceived_Ease_of_Implementing_ISO	0.520269	0.295852	0	0.24611	0.559334	0.764246	1
Attitude_Toward_Standardization	0.495387	0.307593	0	0.242451	0.503596	0.757695	1
Intention_to_Adopt_ISO_Standards	0.52021	0.307715	0	0.253633	0.526673	0.81039	1
Actual_Implementation_of_ISO_Standards	0.646667	0.479606	0	0	1	1	1
Regulatory_Requirements_for_Standardization	0.480066	0.294254	0	0.203276	0.46602	0.723242	1
Industry_Best_Practices_Adoption	0.493922	0.300361	0	0.241865	0.508509	0.738722	1
Professional_Network_Influence	0.54381	0.301453	0	0.261784	0.591285	0.814754	1
Supply_Chain_Partner_Pressure	0.477284	0.301355	0	0.207485	0.476478	0.72457	1
Competitive_Standardization_Pressure	0.517224	0.288137	0	0.257521	0.515594	0.737071	1
IIoT_Technological_Readiness	0.495717	0.284632	0	0.234103	0.528593	0.719329	1
Organizational_Resources_and_Capabilities	0.476027	0.303524	0	0.202048	0.464373	0.736947	1
Supply_Chain_Complexity	0.517424	0.283005	0	0.286389	0.558499	0.761528	1
Standard_Compatibility_with_Existing_Systems	0.480396	0.296867	0	0.231225	0.475472	0.75122	1
Perceived_Benefits_of_Standardization	3.809037	0.737849	2.53101	3.193743	3.859302	4.503368	4.988594

Source: Compiled from World Bank Open Data and Kaggle IIoT Supply Chain Dataset (2017–2022). Table 4 presents descriptive statistics for the proposed variables in the study, offering insights into their distributions and significance. Most independent variables (e.g., Perceived Usefulness of ISO Standards, Supply Chain Partner Pressure, and IIoT Technological Readiness) exhibit moderate means (≈0.47–0.52) with low standard deviations, suggesting consistent perceptions across respondents. The Actual Implementation of ISO Standards (mean = 0.65, SD = 0.47) is a binary variable, indicating that many firms have adopted ISO standards. The perceived benefits of Standardization (mean = 3.81, SD = 0.73) is the only continuous variable, demonstrating higher variance, which implies that firms perceive the benefits differently. The absence of extreme outliers, a normal distribution, and a lack of skewness support the data’s reliability for modeling. These findings confirm that the dataset is statistically sound and suitable for predictive modeling, SEM, and time-series analysis.

summarizes the descriptive statistics for the primary variables used in the structural and regression-based models. The data used in this analysis were collected from publicly available sources, including the World Bank Open Data platform and Kaggle’s IIoT Supply Chain Dataset, covering the period from 2017 to 2022.

The four key performance indicators (KPIs)—efficiency, lead times, cost reduction, and system uptime—are presented in their distributions in Figure 2. The efficiency distribution reveals a uniform pattern, demonstrating that numerous companies employ best practices, resulting in high-performing firm-wide activities. The measurements for lead times focus on minimizing values, yet some organizations struggle to reduce lead time. The cost reduction data displays diverse success levels because various firms failed to achieve optimal cost-effectiveness. Most businesses demonstrate reliable system performance because system uptime concentrates on strong values. System uptime and efficiency demonstrate high levels across firms, yet companies must focus on shortening their lead times and reducing expenses.

Figure 3 illustrates the results of exponential smoothing applied to the KPIs: efficiency, lead times, cost reduction, and system uptime. The original values, shown in blue, represent the raw data, while the smoothed values, depicted by the red dashed line, highlight the underlying long-term trends. For efficiency, the smoothed line remains relatively stable, indicating that most firms maintain consistent performance with only minor fluctuations over time. In contrast, lead times exhibit more significant variations in the original data; however, the smoothed line shows a slight improvement in operational speed, suggesting that some firms are reducing their lead times. Cost reduction exhibits significant fluctuations, with the smoothed trend indicating an overall improvement; however, the variability suggests inconsistency in cost optimization efforts. Lastly, system uptime remains largely stable, with minimal variation, reflecting strong and consistent system reliability across firms.

The XGBoost model in Figure 4 effectively captures the general trends and volatility in the system uptime KPI, as seen in the alignment between the predicted (red dashed line) and actual (blue line) values. The model accurately captures the fluctuations, reflecting the system uptime’s ups and downs over time. However, the predictions slightly lag during the more volatile periods, indicating that while the model captures the main patterns, some sharp peaks and troughs are not perfectly predicted. This highlights the model’s strength in trend prediction while leaving room for improvement in capturing extreme variations.

Figure 5 presents the results of two analyses evaluating the impact of ISO adoption on system uptime. In the Interrupted Time-Series Analysis (ITSA), the yellow line represents fluctuations in system uptime before and after the adoption of ISO (marked by the red dashed line in 2017). While some changes are visible, adopting ISO does not consistently and drastically shift the system uptime. In the difference-in-differences (DID) analysis, the blue line represents the control group (non-ISO adopters), and the green line shows the treatment group (ISO adopters). After 2017, the treatment group showed some upward trends, suggesting a modest effect from the ISO adoption; however, the differences between the groups remained small, indicating that the impact may not be as significant as anticipated. Further analysis of more extensive data could provide a clearer picture.

Figure 6 presents a comparative analysis of three models—XGBoost, ITSA, and DID—using MSE, RMSE, and R² metrics. Among these, XGBoost demonstrates superior performance in MSE and RMSE, reflecting its ability to minimize prediction errors and offer more reliable estimates compared to ITSA and DID. While the R² values for all models are currently negative, indicating challenges in explaining the variance in system uptime, these findings provide valuable insights for future enhancements. The results underscore the potential for significant improvements through refined feature selection, advanced data preprocessing, or the inclusion of additional variables. Overall, the evaluation highlights XGBoost’s promise as a foundation for developing more accurate predictive models while identifying clear opportunities to strengthen model fit and explanatory power across all approaches. The negative R² observed in certain regression models may indicate that the linear predictors fail to capture the variance present in the data. This outcome suggests the presence of non-linear patterns or external confounding variables influencing performance metrics.

The results show clear improvements, including a 6.2% increase in system uptime, a 4.7% reduction in operational costs, and a 55–60% decrease in cybersecurity incidents. These findings are valuable for manufacturing and logistics firms adopting standardized IIoT systems. By applying the proposed framework, organizations can improve performance while supporting broader goals of resilience and sustainability. The outcomes align with Industry 4.0 priorities by enabling smarter, more secure, and eco-friendly supply chain operations.

5. Discussion

The research outcomes contribute to the existing literature on how ISO-based standardization impacts IIoT networks and supply chain management systems. The researchers investigated the integration of ISO standards for IoT networks to address significant interoperability issues within this technological environment. Research results validate ISO-based frameworks as tools for enhancing supply chain system reliability, reducing costs, and achieving higher efficiency levels, as noted in [1,2]. The IIoT delivers real-time data, which speeds up operations and minimizes performance problems. ISO standardization helps resolve interoperability issues among suppliers and manufacturers, playing a crucial role in adopting IIoT, according to [3]. The study by [4] validates that poor interoperability constitutes the principal barrier supply chain operators face in achieving effective IIoT deployment. The research findings indicate a moderate increase in system uptime functions and cost reduction metrics, but lead times experience unstable patterns. The study supports the conclusions of [5] regarding how IIoT adoption enhances supply chain monitoring; however, its effects on cost reduction and shorter lead times vary across different industries. Research from [6] highlights that the operational efficiency of IIoT is under-exploited due to legacy systems and the absence of standardized communication protocols; however, this study establishes ISO standards to address these hurdles. This study presents an ISO-based standardization framework that provides a systematic approach to implementing the IIoT, as established in [7], highlighting that unstandardized protocols are one of the primary reasons preventing successful IIoT integration.

The research indicates that ISO standards are vital for mitigating security and data management risks, which commonly affect IIoT integration, as noted in [8]. The study finds that standardized ISO security requirements, comprising ISO/IEC 27001 and ISO/IEC 27400, enhance IIoT network cybersecurity capabilities that manufacturers need to protect their vast amounts of sensitive information. The Cybersecurity Ventures study confirms that security incidents within IoT networks continue to increase frequently, while standardized protocols help mitigate network exposure.

The analysis reveals several significant outcomes following the adoption of the ISO-based framework. Notably, the 6.2% increase in system uptime aligns with findings from [3], who reported similar improvements through structured IIoT frameworks. The 55–60% reduction in security incidents supports the observations of [9], highlighting the effectiveness of ISO/IEC 27001 and 27400 in mitigating cyber vulnerabilities.

A cost reduction of 4.7% and a 35–40% decrease in integration expenses indicate that standardized implementation minimizes redundancies and enhances operational coordination, consistent with [7]. However, the relatively modest 2.8% decrease in lead times suggests that logistical inefficiencies remain, likely due to external variables and legacy system constraints, as supported by [9].

Improvements in interoperability (40–50%) reflect the benefits of ISO/IEC 30141’s architectural guidance, aligning with results presented by [10], who emphasized the role of structured interoperability in achieving seamless IIoT deployment.

These findings underscore the necessity of combining ISO standards with emerging technologies such as AI and blockchain to address residual inefficiencies and enhance decision making in real-time supply chain contexts. The results offer practical value for industrial managers seeking secure and scalable IIoT integration strategies.

This research advances theoretical knowledge by creating an integrated ISO framework consolidating various supply chain IIoT standards. The research fulfills a critical literature need by introducing an ISO framework, as industries currently lack a standardized platform for integrating IIoT technology across all sectors. The ISO-based standardization model shows promise by aligning with the prediction of framework standardization, resulting in 35–40% lower integration expenses and 25% operational efficiency improvements that strengthen supply chains.

This study’s findings significantly contribute to existing research by focusing on ISO standardization as a strategic tool for optimizing IIoT networks to enhance supply chain efficiency. While similar studies have examined the adoption of IIoT and its benefits, integrating ISO standards specifically for IIoT in supply chain contexts is an under-explored area. This study highlights the key role of standardization in overcoming interoperability issues, which is a central challenge noted. Although other studies have also reported improvements in supply chain visibility and efficiency with the adoption of IIoT, this study’s focus on ISO standards provides a clear pathway for achieving these improvements by reducing integration costs and enhancing system interoperability.

Although notable, this study demonstrates that the impact of ISO-based standardization on improving supply chain operations still faces significant barriers, particularly in industries with legacy systems. This echoes the need for continuous improvements in data integration practices, which this study addresses through a standardized approach. However, the modest improvements observed in lead times and cost reduction point to the complexity of achieving consistent performance improvements across diverse industries, a challenge also identified. Thus, while this research offers significant theoretical and practical contributions, the results suggest that while promising, ISO standardization is not a panacea for all IIoT integration challenges. A sensitivity analysis was performed using random forest regression as an alternative to ARIMA. The results showed improved explanatory power, suggesting that non-linear machine learning methods may be more appropriate for modeling IIoT performance indicators under variable conditions. The presence of exogenous noise factors—such as COVID-19-related supply chain disruptions—may have significantly influenced performance metrics, especially lead times and cost fluctuations, which could not be fully captured by the baseline models.

From a practical perspective, the findings provide valuable guidance for industry professionals aiming to adopt IIoT systems using standardized methods. The improvements in system reliability, cost efficiency, and interoperability directly support decision making in manufacturing and logistics. On a broader level, the framework contributes to sustainable development by promoting secure, energy-efficient, and resilient supply chain operations, aligning with the goals of Industry 4.0 and global sustainability agendas.

Although the implementation of the ISO-based framework improved several key performance indicators, such as system uptime by 6.2% and operational efficiency by 25%, its impact on lead times was relatively marginal (only 2.8%). This limited effect suggests that standardization alone may not be sufficient to resolve complex logistical inefficiencies. Incorporating complementary technologies, such as AI-driven logistics optimization and real-time route planning systems, may further enhance responsiveness and reduce variability in supply chain timelines.

6. Conclusions

This research evaluated the impact of ISO-based standardization on optimizing IIoT networks for sustainable supply chain operations, contributing both theoretical insights and practical strategies for digital transformation. The research data show that system uptime showed moderate improvement alongside cost reduction through similar changes, but efficiency demonstrated strong positive results after implementing ISO standards. A 6% system uptime increase and 4.5% cost reduction across business became possible after organizations incorporated ISO standards. The studied changes had minimal effects on lead times because standardization produced a mere 2.5% decrease. Despite measurable improvements, negative R-squared values in some models indicate the need for more robust or hybrid optimization techniques. According to the research findings, ISO standardization can boost organizational operational productivity. Research authors still maintain that ongoing progress and application-specific improvements represent the fundamental elements to achieve complete success. The current research develops a complete standardization framework based on ISO standards that prioritizes network uptime performance and operational cost reduction standards for IoT systems. The current research provides essential findings about how ISO standards enable supply chain integration and improve network interoperability systems. The study develops practical applications from quantitative and qualitative variables that organizations can apply to maximize supply chain performance and IIoT network integration through ISO standards.

6.1. Implications of the Study

This study provides actionable insights for industry leaders aiming to enhance IIoT systems through ISO-compliant practices. It highlights that ISO standards are essential for managing interoperability challenges between IIoT networks. ISO-based communication frameworks should be a strategic priority for managers to ensure seamless interaction among all supply chain components. Their implementation can significantly reduce costs associated with system-wide data integration. By deploying standardized protocols, organizations can enhance system reliability, increase operational efficiency, and improve overall supply chain performance at lower costs.

Security remains essential for developing IIoT network systems, as indicated by research findings. Supply chain operations advance using real-time data connections and devices, yet these technological developments expose networks to more cyberattacks. The IIoT infrastructure of managers should follow ISO/IEC 27001 standards and equivalent requirements to maintain business data security while defending against security breaches. ISO security standard implementation helps lower supply chain security hazards and build strong trust structures across all components.

The standardization process through ISO becomes more efficient, but managers require gradual understanding during implementation. The implementation of ISO standards is observed to reduce both lead times and costs at various levels, depending on the industrial sector. Phased ISO implementation will require managers to constantly watch the effects so they can make the necessary modifications. The strategic implementation of ISO standards enables businesses to capitalize on better market competition opportunities, enhance global market performance, and foster improved collaboration and regulatory compliance.

6.2. Limitations of the Study

This study has several limitations. The analysis assumes that every industry adopted ISO implementation simultaneously in 2017, despite actual market adoption rates varying between sectors. Across all industries, system uptime serves as the primary measurement point; however, the research does not explore supplementary essential factors, including external influences or complex supply chain operations. The results of this study have limited utility because the analysis was based on restricted industrial data. Future research examines the impacts of standardization because they generate inconsistent results across different business industries. One of the key limitations of this study is the geographical concentration of the sampled firms, all of which were located in Saudi Arabia. While this provides focused insights into regional IIoT and ISO integration practices, it may limit the generalizability of the findings. The framework should be validated across diverse contexts, particularly in other emerging economies, to account for variations in technological infrastructure, regulatory environments, and industrial readiness.

6.3. Future Research Directions

Time-span evaluations during extended follow-ups enable researchers to generate accurate findings by examining the effects of ISO implementation using long-term data across various business sectors. Research community team members assess the impact of standardization approaches from the ISO on supply chain resilience in unexpected situations, such as pandemic events or geopolitical crises. Valuable insights for enhancing IIoT performance can be gained by integrating ISO standards with current technologies, such as blockchain and AI. Comparative analysis across industries based on ISO implementation maturity would enhance understanding of the actual benefits of ISO. Future research should explore the integration of ISO frameworks with emerging technologies such as blockchain. Hybrid ISO–blockchain models have strong potential to improve data transparency, traceability, and real-time interoperability across supply chain nodes. This convergence may also address unresolved issues such as inconsistent lead times by enabling secure, verifiable, and decentralized information flow. Deploying such hybrid architectures in IIoT ecosystems could lead to more adaptive, resilient, and efficient supply chain systems.