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IoT-based Enterprise Resource Planning: Challenges, Open Issues, Applications, Architecture, and Future Research Directions
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In today’s highly competitive markets, organizations can create a competitive advantage through the successful implementation of Enterprise Resource Planning (ERP) systems. ERP works with different technologies, including the Internet of Things (IoT). IoT uses a unique Internet protocol to identify, control, and transfer data to individuals as well as databases. The data is collected through IoT, stored on the cloud, and extracted and managed in through ERP. In this study, we review the challenges, open issues, applications, and architecture of the IoT-based ERP. For this purpose, we review and analyze the latest IoT-related articles to present the unique features of the IoT and discuss its impact on ERP. The results show sensors and devices connected to the Internet can manage the stored data processed in the cloud through ERP without human intervention. We also discuss the challenges and opportunities in the relationship between ERP and the IoT risen by the introduction of the cloud.
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... The authors note that manufacturing environments are particularly data-intensive, with sensor networks continuously generating operational information that must be transmitted, stored, and analyzed effectively to deliver business value. This data deluge requires organizations to develop robust infrastructure strategies that balance performance requirements against cost considerations [7]. ...
... The authors note that many organizations are adopting hybrid storage approaches that combine on-premises solutions for performance-critical applications with cloud storage for longer-term retention and analysis. This balanced approach helps organizations manage both performance and cost considerations while maintaining appropriate data accessibility [7]. ...
... The authors particularly emphasize challenges related to Enterprise Resource Planning (ERP) integration, noting that these systems typically serve as the operational backbone for most organizations but often lack native IoT connectivity capabilities. Their research indicates that successful integration requires not only technical expertise but also careful planning and cross-functional collaboration to ensure that IoT-generated data enhances rather than disrupts existing business processes [7]. ...
The Internet of Things (IoT) has emerged as a transformative force in contemporary supply chain management and financial analytics, creating unprecedented opportunities for data-driven decision-making across the entire enterprise ecosystem. This article examines how IoT sensors and connected devices facilitate enhanced visibility through real-time monitoring of assets, inventory, and processes throughout the supply chain, enabling organizations to track products with precision and respond proactively to emerging issues. The article explores how IoT implementation revolutionizes inventory management, predictive maintenance, and asset optimization while simultaneously enhancing financial analytics through more accurate performance metrics, improved cash flow management, and sophisticated predictive capabilities. Despite the compelling benefits, the evaluation acknowledges significant implementation challenges related to data volume, infrastructure requirements, system integration, and security considerations. Looking forward, the article examines how the integration of artificial intelligence with IoT technologies represents the next evolutionary frontier, enabling autonomous analysis, decision-making, and adaptive optimization in response to changing operational conditions.
... Integration IoT integration with an ERP system is one of the popular trends nowadays, with special emphasis on manufacturing and supply chain-related industries. This will help immensely in the real-time monitoring and predictive maintenance, therefore changing the operational efficiency of any organization drastically [13]. Some other key benefits are: 1. Real-time data from these smart things provides unprecedented visibility into operations, inventory levels, and equipment that enables a manager to view his/her organization differently today than in the past. ...
The strategic integration of Enterprise Resource Planning systems is critical in enhancing business efficiency in the dynamic digital landscape of 2022. This paper explores how modern methodologies like cloud-based architectures, artificial intelligence, and even mobile ERP solutions drive productivity exponentially and smoothen business operations. By combining real-world data and hypothetical situations, the paper brings forward the actual benefits and challenges of ERP integrations in data security and change management. It provides cutting-edge solution models targeted at modern business environments and predicts new trends, such as blockchain and IoT connectivity to ERP systems. It concludes the strategic recommendations for any business intending to harness ERP systems to their full potential. It creates tailored integration strategies and ongoing optimization of the system that can keep the business ahead of competitors in a digital marketplace.
... The result is a systematic framework for ERP systems which consists of six different clusters. Tavana, Hajipour, and Oveisi [21] reviewed the challenges, open issues, applications, and architecture of the IoT-based cloud and discussed the challenges and opportunities in the relationship between cloud ERP and the IoT. Sunmola and Lawrence [22] conducted SLR that investigates the key success factors of blockchain and cloud ERP integration. ...
Cloud Enterprise Resource Planning (ERP) systems are known to be crucial for optimal business flow. Organizations implement and adopt, i.e. establish, cloud ERP systems to keep costs to a minimum, while pushing the productivity and competence levels towards the maximum. However, regardless of the multitude of different methodologies and approaches, establishment of cloud ERP systems within an organizational context remains difficult. In existing scientific literature, various challenges characteristic of the process of establishing a cloud ERP system have been identified. However, they are not systematized in a way that clearly indicates to everyone interested in this process in which phases of establishing a cloud ERP system these challenges may appear, as well as which roles in that process may be burdened with them. For this reason, the objective of this study is to illuminate and thus facilitate cloud ERP system establishment in organizations by describing challenges managers face while establishing cloud ERP systems. To accomplish this objective a systematic literature review method was adopted to collect the studies and thematic synthesis for their analysis. The study results include bibliographic data, description of challenges in establishing cloud ERP systems, perception of their significance from client and provider perspective, as well as phases, activities and roles affected by these challenges. The study results can facilitate managers and researchers by providing up-front knowledge about establishment challenges, thus enabling more informative decision making and research planning.
... The results indicate that cloud-integrated ERP solutions allow seamless data flow and automation, reducing the need for human intervention. However, challenges such as security, infrastructure costs, and system compatibility must be addressed for widespread adoption [13]. ...
Enterprise Resource Planning (ERP) systems have become integral in streamlining academic and administrative processes in technical education institutions. However, their impact on human resource (HR) trends, including faculty performance, student outcomes, and job placements, remains underexplored. This study leverages ERP-generated data to predict HR trends in Odisha’s technical education sector using machine learning models. The dataset comprises institutional records, faculty evaluations, student performance metrics, and employment statistics collected from ERP systems. We employ Random Forest and Gradient Boosting models to analyze key determinants influencing HR efficiency. Results indicate that faculty engagement and student ERP usage significantly correlate with improved job placement rates and academic performance. The study highlights the predictive power of machine learning in forecasting HR trends, aiding policy decisions for educational institutions. By integrating ERP analytics with AI-driven models, institutions can optimize HR strategies and enhance student career outcomes.
... Cloud computing is integrated with IoT to process IoT device's data remotely on the internet [28,29]. Regardless of efforts to improve IoT applications with cloud capabilities, IoT device computation issues remain unresolved, as IoT applications generally demand specific features such as mobility, geographical distribution, location awareness, and low latency [30,31]. Fog computing technology was introduced as a solution to enable computing at the edge of the network, offering new services for IoT devices [8,32,33]. ...
The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for dependent task scheduling in IoT-Fog computing. The proposed AFA is a modified version of the standard Firefly Algorithm (FA), considering the execution times of the submitted tasks, the impact of synchronization requirements, and the communication time between dependent tasks. As IoT-Fog computing depends mainly on distributed fog node servers that receive tasks in a dynamic manner, tackling the communications and synchronization issues between dependent tasks is becoming a challenging problem. The proposed AFA aims to address the dynamic nature of IoT-Fog computing environments. The proposed AFA mechanism considers a dynamic light absorption coefficient to control the decrease in attractiveness over iterations. The proposed AFA mechanism performance was benchmarked against the standard Firefly Algorithm (FA), Puma Optimizer (PO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) through simulations under light, typical, and heavy workload scenarios. In heavy workloads, the proposed AFA mechanism obtained the shortest average execution time, 968.98 ms compared to 970.96, 1352.87, 1247.28, and 1773.62 of FA, PO, GA, and ACO, respectively. The simulation results demonstrate the proposed AFA’s ability to rapidly converge to optimal solutions, emphasizing its adaptability and efficiency in typical and heavy workloads.
... SMEs with seasonal or variable demand benefit most from scaling. It lets them change their resources and skills without disrupting operations (Tavana et al., 2020). Cloud ERP systems boost accessibility and employees can access ERP systems and data from anywhere with an internet connection, enabling remote work and mobile workforce initiatives. ...
This study empirically investigates the impact of Cloud ERP systems on firm performance in small and medium-sized enterprises (SMEs), focusing on the mediating role of innovation capability and the moderating role of top management support. The purpose is to understand how the adoption and utilization of Cloud ERP influence financial outcomes, operational efficiency, innovation capabilities, and overall competitive advantage in SMEs. A quantitative research approach was employed, collecting data through a structured questionnaire distributed to 450 SME managers in KSA, yielding 198 valid responses. Statistical analyses, including regression, mediation, and moderation analyses using SPSS, were conducted to examine the relationships between Cloud ERP implementation, innovation capability, top management support, and firm performance. The findings indicate that Cloud ERP implementation significantly enhances firm performance in SMEs. Innovation capability partially mediates the relationship between Cloud ERP implementation and firm performance, suggesting that ERP systems foster innovation, contributing to improved organizational outcomes. Furthermore, top management support moderates these relationships, highlighting its critical role in maximizing the benefits of ERP investments. This study contributes to the literature by providing empirical evidence of the transformative effects of Cloud ERP systems on SME performance, elucidating the mechanisms through which ERP adoption drives innovation and organizational success. The study's originality lies in its comprehensive examination of both direct and indirect pathways linking Cloud ERP to firm performance. It underscores the importance of strategic management and technological integration in enhancing SME competitiveness in the digital era.
IWAPI West Java is a center for community collaboration which has an impact on increasing the creativity of residents, therefore the various programs it implements need support from all levels of society. Functionally, IWAPI activation is expected to be a means of locating the activities of women entrepreneurs in West Java Province as well as a means of providing and increasing access and mastery of technology for its users. Their role is also very potential for development in the Creative Economy sector, so these creative economy actors need to be continuously nurtured and empowered in a sustainable manner so that they can develop and progress further, considering that the female business actors who are members of IWAPI West Java still have very little knowledge, especially regarding with digital marketing and business management. Based on the description of target partner problems that has been described in the previous sub-chapter, the solutions offered by our team can be categorized into two aspects, namely the technology side and the management side regarding business management aspects and digital marketing tools which can be described as follows. First, providing workshops and assistance regarding how to manage a business effectively by implementing an integrated financial and operational management and integration system. Second, develop and implement a digital promotion strategy to promote and improve organizational operational optimization. Digital tools will be developed through providing workshops on social media marketing and other promotion tools. Third, providing a workshop on Augmented Reality Based Social Media Content. It is hoped that by creating interesting information and promotional content on social media using AR technology, the promotions created will be able to attract the attention and interest of potential consumers.
The deployment of intelligent auto-scaling solutions across the cloud environment simultaneously decreases the operational spend as well as distribute resources effectively. The research investigates the deployment of predictive auto-scaling with machine learning in Amazon Web Services (AWS) to improve system scalability as well as management efficiency and economical resource usage. The proposed system implements advanced ML algorithms to reach 92% prediction accuracy thus it minimizes scaling latency and optimizes resource utilization. Analysis reveals that ML-based approaches exceed threshold-based methods because they provide superior response times as well as reduced costs and maximum system availability. Performance evaluations with cost analysis reveal that predictive resource allocation has great future potential for cloud infrastructure management. The discovery demonstrates how ML-based auto-scaling creates a perfect solution for modern cloud challenges by uniting cost-saving measures with high scalability and efficiency benefitsIndependent Researcher
This study analyzes the challenges associated with the implementation of the Warehouse Management (WM) module in SAP ERP at PT XYZ, a leading FMCG company in Indonesia, and proposes solutions to address them. The implementation of the WM module aims to reduce manual processes and enhance warehouse efficiency, thereby supporting operational success by improving stock data accuracy and integrating business processes. However, the current implementation has not fully met the company’s operational needs. The research adopts a qualitative approach using the Soft Systems Methodology (SSM). Data were collected through interviews with six respondents and operational observations. The analysis focuses on system quality, data quality, organizational culture, and user support to identify existing gaps. The findings reveal several issues, including non-real-time information, stock inaccuracies, reliance on manual processes, and the absence of knowledge management. Recommendations include integrating IoT, developing real-time applications for picking and stock counting, automating replenishment using AI, implementing SAP Analytics Cloud (SAC), and creating a knowledge management system. This study provides practical solutions for similar companies to enhance ERP implementation success, particularly in the Warehouse Management module.
The level of global GHGs (Green House Gasses) production has increased dramatically over the past 20 years. To meet new targets for global GHG levels, it is key to monitor and model the effects of land use on GHG production. Peatland areas play a significant role in GHG modeling. Recent research in the area has been working to provide solutions to better monitor the environment through the provision of real-time data streams on key drivers of the environment. The Internet of things (IoT) technologies has found application in peatland monitoring as it provides useful tools to better monitor the environment. Current IoT systems, however, are too expensive to be deployed on a scale that would allow for refined monitoring of the biodiverse region. Also, deployment of IoT sensing devices requires expertise and specialized equipment resulting in prohibitive costs for many sites. Furthermore, there is no formalized architecture in existence for the application of ecological surveying and no complete system have been developed for data handling and analysis. This paper therefore aims to detail a system that enables cost-effective collection, curation and processing of data in peatland areas, however, the system can be generalized for any environmental monitoring case.
Data and object connectivity are the key and the critical component of industry 4.0, Connectivity of software, equipment, data ... are all massive data to be processed. Combined with cybersecurity, they become fundamental elements for creating intelligence in an industrial system that can increase the flexibility of production in addition to allocating resources more efficiently. In order to successfully transition to ecosystem 4.0, the industry must examine the new skills that are required and the need for qualified personnel. The fourth industrial revolution corresponds to the widespread use of sensors and intelligent agents to connect machines, components, processes and operators. This is generally called the digital factory or industry 4.0. It suggests a global system of effective communication between the modules of decision-making. World 4.0 will allow interaction between products, machines and even between machines (M2M). The product will also be able to communicate with machines in the production phase. This is called intelligent production, which will allow the modification and customization of products in their production phase.
In this article, we present a communication model based on Modbus and multiband communication using NB-IOT, this communication model allows data to be exchanged between different industrial equipment (PLCs, SCADA systems, etc.) via MODBUS RTU and ASCII, via RS485, or via analog and digital inputs for sensors and actuators.
Pervasive IoT applications enable us to perceive, analyze, control, and optimize the traditional physical systems. Recently, security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk. Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications. As an extension of the cloud, the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications. Although there are some research efforts in this area, edge-based security designs for IoT applications are still in its infancy. This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs. We first present an edge-centric IoT architecture. Then, we extensively review the edge-based IoT security research efforts in the context of security architecture designs, firewalls, intrusion detection systems, authentication and authorization protocols, and privacy-preserving mechanisms. Finally, we propose our insight of future research directions and open research issues.
Increasing diverse consumer demands, rapid advancements in digital technology and increasing internet access is shifting the market trend to smart, connected and customized Internet of Things (IoT) products. This forces industries to design, develop and mass produce affordable smart customized products to stay ahead of the competition. Early in the product development stage the complete product lifecycle needs to be taken into account. Here, concepts such as systems engineering, mass customization, flexible production planning and smart manufacturing with customer integration, can address new challenges and change the way products are being handled within their life cycles. The motivation of this paper is to present a systematic product development methodology for developing customizable IoT devices, which involve more processes than a mass-produced mechatronic product. Concepts such as variant management and smart service management come into picture to enable an efficient customizable smart product development. Numerous product development methodologies already exist. Therefore, in this paper, a methodology is detailed by identifying, combining and/or enhancing existing product development methodologies to assist in systematically developing smart customizable IoT.
Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely SoftAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of software-defined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and perform protocol conversions between IoT networks and radio access networks. Moreover, a cross-domain optimization framework is proposed in this extended SoftAir architecture concerning both upstream and downstream communication, where the respective sum-rates are maximized and system-level constraints are guaranteed, including (i) quality-of-service requirements of IoT transmissions, (ii) total power limit of mmWave RRHs, and (iii) fronthaul network capacities. Simulation results validate the efficacy of our solutions, where the extended SoftAir solution surpasses existing IoT schemes in spectral efficiency and achieves optimal data rates for next-generation IoT communication.
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
As Internet-of-Things (IoT) networks provide efficient ways to transfer data, they are used widely in data sensing applications. These applications can further include wireless sensor networks. One of the critical problems in sensor-equipped IoT networks is to design energy efficient data aggregation algorithms that address the issues of maximum value and distinct set query. In this paper, we propose an algorithm based on uniform sampling and Bernoulli sampling to address these issues. We have provided logical proofs to show that the proposed algorithms return accurate results with a given probability. Simulation results show that these algorithms have high performance compared with a simple distributed algorithm in terms of energy consumption.
The Internet of Things (IoT) is a fast growing and rapidly evolving field. Everyday objects from smart home appliances (e.g. toasters and refrigerators) to health monitoring products (e.g. pacemakers and blood pressure monitors), to minuscule sensors and actuators, are being redesigned to be able to transfer data over a network. The connection of billions of these new objects to the Internet will cause a dramatic increase in the amount of shared data, which will simultaneously introduce a plethora of security risks. Understanding how to secure the data being sent over IoT links is necessary for the IoT paradigm to succeed. A popular data transfer protocol connecting IoT devices is the Constrained Application Protocol (CoAP). We used an open-source implementation of CoAP along with Datagram Transport Layer Security (DTLS) to implement secure data transfer between IoT devices. We studied the impact of DTLS on CoAP in the real IoT testbed we have developed using resource-constrained IoT devices and open-source software. Our tests showed that utilizing a CoAP-DTLS implementation with a symmetric key cipher suite resulted in noticeable performance costs. A secure connection with DTLS over CoAP used approximately 10% more energy than an unsecure connection. In addition, our latency tests revealed over a 100% increase in average latency time for secure messages compared to when no encryption is used. We also highlight some of the implementation challenges encountered while developing a real IoT testbed for secure experimentation.