research paper on enterprise resource planning

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• Published: 04 August 2023

Mechanisms for successful management of enterprise resource planning from user information processing and system quality perspective

• Hyeon Jo   ORCID: orcid.org/0000-0001-7442-4736 1 &
• Do-Hyung Park 2  

Scientific Reports volume  13 , Article number:  12678 ( 2023 ) Cite this article

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• Mathematics and computing

Enterprise resource planning (ERP) systems are now ubiquitous in modern organizations. A number of previous studies have focused only on system factors and perceptions, there is a noticeable shortfall in research that concurrently addresses technological factors and human roles in explaining user satisfaction. This study aimed to identify these variables from the perspectives of information systems, technology, and human participation, thereby addressing this knowledge gap. The focus of the study was a large shipbuilding and marine company utilizing an ERP system. The participants, a sample of 234 ERP users, were carefully selected by the company’s executives and practitioners, and data was collected through online questionnaires. They were selected through purposive sampling from among employees who use ERP systems in large-scale shipbuilding and marine engineering companies. The study aimed to clarify the relationships between user satisfaction and perceived ease of use, perceived usefulness, system quality, service quality, participation, and information quality. A partial least squares structural equation modeling (PLS-SEM) was used to analyze the collected data. The results indicated that perceived ease of use, system quality, service quality, and participation positively influenced user satisfaction, whereas perceived usefulness did not have a significant impact. Interestingly, participation was found to lessen the effects of perceived usefulness on satisfaction. The findings of this study suggest that to enhance ERP user satisfaction, managers should strive to make the ERP system easy-to-use and stable, encourage employee participation in the decision-making process, and bolster the role of the support team. It should be noted, however, that the study has limitations as it did not consider all possible factors, such as training and support. Future research should take a broader view of the variables involved in the operation of an enterprise-wide information system.

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Introduction.

Information technology (IT) offers numerous benefits, such as cost-saving 1 , customer satisfaction 2 , and production flexibility 3 to firms for managing processes 4 . Particularly, most large and medium organizations have adopted enterprise resource planning (ERP) systems to increase organizational effectiveness 5 . ERP systems represent comprehensive software packages designed to integrate all corporate operations and processes, offering a complete view from a single IT architecture 6 . They support key corporate operations including manufacturing, supply chain management, and human resources, among others 7 . Most companies operate an ERP system because it allows managers to access real-time statistics on sales, profitability, and inventory levels 8 . An increasing number of organizations have begun to agree that they need a modern ERP system to keep up with their competitors 9 . With a market value of $35.81 billion in 2018 and an expected $78.4 billion in 2026, ERP system utilization is still increasing 10 .

User satisfaction is a critical determinant of ERP system success 11 . A satisfied user is likely to accept and use the system more effectively, thereby improving organizational efficiency and productivity 12 . Over the years, numerous studies have explored the factors influencing ERP user satisfaction, focusing on aspects such as system quality, information quality, and service quality 13 . Despite the extensive research on ERP user satisfaction, there are notable gaps in the literature. Many studies have treated user satisfaction as a unidimensional construct, neglecting the reality that satisfaction is multifaceted and influenced by a combination of technological and human factors 14 . Furthermore, the influence of context-specific factors such as organizational culture or industry type on ERP user satisfaction has been largely overlooked in previous research 15 . Given these gaps in the literature, there is a clear need for a more comprehensive exploration of the factors influencing ERP user satisfaction. Research is needed to elucidate the relationships between user satisfaction and understudied variables like technological factors, perception elements, and human engagement.

The quality of an ERP system is a determinant of its usability and reliability 16 . High system quality ensures that the ERP system functions smoothly, with minimal errors or breakdowns, enabling users to complete their tasks efficiently 17 . Conversely, poor system quality can lead to user frustration and dissatisfaction, as it impedes their ability to perform their work effectively 18 . The quality of the information provided by the ERP system is vital for decision-making processes within an organization 19 . Information that is accurate, timely, relevant, and complete enhances user satisfaction as it allows them to make informed decisions and perform their tasks more effectively 20 . Conversely, poor information quality can lead to incorrect decisions and inefficiencies, resulting in user dissatisfaction. The quality of the services provided to ERP system users, such as technical support and training, can greatly influence user satisfaction. High service quality ensures that users receive the necessary support to use the system effectively, which in turn enhances their satisfaction and acceptance of the system 21 . On the other hand, poor service quality can lead to user frustration and dissatisfaction. Thus, understanding the effects of system, information, and service quality on user satisfaction is key to improving service practices and user support in ERP system implementations.

Perceived ease of use and perceived usefulness are critical to user satisfaction with ERP systems. If a system is perceived as easy to use, it reduces the cognitive load on the user, increasing satisfaction 22 . Conversely, a system perceived as difficult to use can lead to dissatisfaction. If users perceive the system as useful and believe it can improve their job performance, they are more likely to be satisfied with the system 23 . Understanding the relationship between these factors and user satisfaction can inform strategies to enhance user satisfaction.

User participation in the development and implementation of a system is crucial for several reasons. Firstly, users who participate in system development and implementation are more likely to have a better understanding of the system’s functionalities and are thus better equipped to use the system effectively 24 . Secondly, participation can enhance users’ sense of ownership and commitment to the system, leading to higher satisfaction 25 . Lastly, user participation can ensure that the system is designed to meet users’ needs, thus enhancing its perceived usefulness and ease of use 26 .

Moreover, the role of user participation can extend beyond a direct influence on satisfaction; it may also moderate the effects of perceived ease of use, perceived usefulness, and service quality on satisfaction. For instance, when users participate in system development, they may perceive the system as easier to use because they are familiar with its design and functionalities, thereby enhancing their satisfaction 27 . Similarly, user participation may enhance perceived usefulness, as users can ensure that the system is tailored to their needs, thus increasing their satisfaction. Furthermore, user participation may also influence the relationship between service quality and satisfaction. When users are actively involved in the implementation process, they may have more direct communication with service providers, leading to a better perception of service quality and, consequently, higher satisfaction 28 . Given the potential moderating role of user participation, it is crucial to consider this factor in studies on ERP user satisfaction. Such considerations can provide insights into how to enhance user satisfaction by fostering user participation.

The study is underpinned by several fundamental theories related to user satisfaction and technology adoption. The technology acceptance model (TAM), developed by Davis 12 , is one of the most influential models for understanding user acceptance and usage behavior of information systems (ISs). It posits that perceived usefulness and perceived ease of use are the two primary determinants of user acceptance of a system. In the context of this study, TAM helps to explain how these factors can influence ERP user satisfaction. Moreover, a part of this study is based on the DeLone and McLean (D&M) ISs success model 11 . This model suggests that ISs success can be evaluated based on six interrelated dimensions: system quality, information quality, service quality, use, user satisfaction, and net benefits. This study incorporates the concepts of system quality, information quality, and service quality from this model and explores their relationship with user satisfaction in the context of ERP systems. Finally, this paper borrows core logics from the user participation theory 25 . This theory posits that user participation in system development and implementation can lead to higher user satisfaction 25 . It provides the basis for considering user participation as a determinant of ERP user satisfaction and as a potential moderator in the relationships between other variables and satisfaction. By integrating these theories, this study offers a comprehensive theoretical framework for understanding ERP user satisfaction, considering a range of technological and human factors.

Despite the extensive body of research on ERP systems, there remain several gaps in our understanding of the factors that influence user satisfaction, leading to knowledge lags in this area. Primarily, while individual studies have examined factors such as system quality, information quality, service quality, perceived ease of use, perceived usefulness, and user participation separately, few have considered these factors collectively within a single study. This means we have a limited understanding of the interrelationships between these variables and their collective impact on user satisfaction. Secondly, although user participation is widely recognized as important, its potential moderating effect on the relationships between other variables and satisfaction has not been thoroughly explored. The role of user participation, particularly as a moderator, is thus still not well-understood. Lastly, there has been a strong focus on the adoption of ERP systems in previous research, with less attention given to user satisfaction, especially in the post-implementation stage. This has resulted in a knowledge lag regarding the factors that can enhance user satisfaction after ERP systems have been implemented. Considering these knowledge lags, the objective of this study is threefold:

To provide an integrative understanding of the determinants of ERP user satisfaction by examining system quality, information quality, service quality, perceived ease of use, perceived usefulness, and user participation collectively.

To explore the potential moderating role of user participation in the relationships between perceived ease of use, perceived usefulness, service quality, and user satisfaction.

To focus on user satisfaction in the post-implementation stage of ERP systems, providing insights that can help organizations enhance the effectiveness of their ERP systems after implementation.

To achieve these objectives, this study suggests a conceptual framework as displayed in Fig.  1 . The study posits that perceived ease of use affects satisfaction directly and indirectly via perceived usefulness. This paper also postulates that system quality and information quality influence perceived usefulness and satisfaction. Moreover, it proposes that service quality and user participation affect satisfaction. Finally, the current study hypothesizes that participation moderates the effects of perceived ease of use, perceived usefulness, and service quality on satisfaction.

Research model.

This paper is organized in a structured manner. It begins with a literature review in “ Literature review and hypotheses formulation ” section, focusing on ERP, and introducing the hypotheses. The research methodology is explained in “ Research methodology ” section. The results of the data analysis are presented in “ Empirical results ” section. In “ Discussion ” section, the findings are discussed. “ Results ” section outlines the theoretical and practical implications of the study, along with a discussion on the limitations of the research and future directions for further investigation.

Literature review and hypotheses formulation

The extant literature on user satisfaction with ERP systems is expansive and diverse. A significant portion of this literature has utilized quantitative research methodologies to examine the relationships between various constructs and ERP user satisfaction. While our study follows this quantitative tradition, we acknowledge the insightful critiques and perspectives provided by qualitative research in this area. To facilitate a more comprehensive understanding of our study’s relevance and its theoretical contributions, the study begins by reviewing the primary models and theories relevant to this investigation, followed by a thorough examination of empirical studies pertaining to ERP. Ultimately, this process guides the establishment of hypotheses.

The D&M IS success model was first presented in 1992 by DeLone and McLean to describe the success of ISs 29 . According to the model, system quality and information quality of ISs determine use and user satisfaction, and affect individual and organizational impact. Later, the authors updated the framework by adding service quality to the model and replacing the impacts with net benefits 11 . Researchers have extensively applied and validated the D&M model in multiple IS contexts 30 , 31 , 32 , 33 , 34 . In early research, much of the focus was on the technical aspects of ERP systems, examining factors such as system quality, system integration, and customization capabilities 35 , 36 , 37 . For example, Li and Zhu 38 unveiled that quality of e-learning system and information quality influence user satisfaction. Koksalmis and Damar 39 discovered the significant impacts of consultant support perceived ease of use in the case of ERP. Cheng et al. 40 confirmed that system quality and information quality are significant factors that influence perceived usefulness and satisfaction. Andreas and Natariasari 41 argued that system quality and service quality are important factors that contribute to the satisfaction of ERP users. Ghani et al. 33 demonstrated that system quality and service quality have a positive impact on the performance of employees using ERP.

TAM explains user behaviors in the process of technology acceptance 12 . According to the model, external factors determine attitude through perceived usefulness and perceived ease of use. In turn, attitude determines the use of the system through the intention to use it. IS literature has introduced TAM to explicate the behaviors of ERP users 42 , 43 , 44 . For instance, Uddin et al. 45 showed that effort expectancy (similar concept to perceived ease of use) and performance expectancy (similar concept to perceived usefulness) affects actual use of ERP via intention to use. AlBar and Hoque 46 revealed that information and communication technology (ICT) skills and ICT infrastructure determine the intention to adopt ERP. Koksalmis and Damar 39 uncovered that perceived ease of use affects behavioral intention to use ERP indirectly through perceived usefulness. Cheng 47 found that as users perceive ERP to be more useful, their satisfaction increases. Furthermore, Cheng 48 confirmed that perceived ease of use indirectly influences satisfaction through perceived usefulness. This body of work provided valuable insights into the technical determinants of ERP user satisfaction and formed the foundation for our understanding of ERP success. However, this stream of research largely employed quantitative methodologies and often overlooked the nuanced human and organizational factors that influence ERP satisfaction.

Building on these qualitative insights, recent research has begun to take a more integrative approach, combining both technical and human-organizational factors in the study of ERP user satisfaction 49 , 50 . This line of research recognizes that while technical factors are crucial, they do not operate in a vacuum. Instead, they interact with a host of human and organizational factors to influence user satisfaction. User participation in the development and implementation of IS has been a central topic of discussion in IS research for decades, and a significant body of literature exists on this topic 51 , 52 , 53 , 54 . User participation in IS development is generally regarded as beneficial, with numerous studies finding a positive relationship between user participation and various measures of IS success 55 , 56 , 57 , 58 . User participation can improve the quality of the system design, enhance system acceptance, increase user satisfaction, and ultimately lead to more successful system usage 20 , 26 , 59 , 60 . For example, Boudreau and Robey 61 used a qualitative case study approach to explore the role of emotions in ERP implementation. Their work shed light on the dynamic, emotional, and often contentious process of ERP implementation, highlighting the importance of considering the human side of ERP systems. Wu and Wang 35 employed a mixed-methods approach, integrating qualitative and quantitative data to measure ERP success from the viewpoint of key users. Their work emphasized the importance of user perspectives in understanding the overall success of an ERP system. Matende and Ogao 54 shed light on the importance of user participation in ERP implementation, emphasizing the need for active involvement to ensure successful outcomes. AlBar and Hoque 46 validated the significance of top management support in influencing ERP adoption intention, highlighting the crucial role of human participation. Vargas and Comuzzi 62 compiled a list of critical success factors for ERP implementation and included end user involvement as one of the key factors.

In summary, our study is situated within this integrative research tradition. Drawing from both the D&M IS success model and the TAM, we examine a range of both technical and human-organizational factors that may influence ERP user satisfaction. Furthermore, in response to calls for more process-oriented research on ERP systems 63 , we also consider the role of user participation—a process factor—in shaping user satisfaction. By integrating insights from these diverse research streams, we aim to provide a more holistic and nuanced understanding of the determinants of ERP user satisfaction. This approach not only allows us to address gaps in the existing literature but also positions our study at the forefront of current empirical work in this area.

Perceived ease of use

Perceived ease of use is defined as the degree to which a user anticipates that using a particular technology will be effort-free 12 . It has been demonstrated in multiple studies that perceived ease of use can significantly enhance satisfaction with ERP systems 43 , 64 . This concept is intrinsically tied to the user experience, with systems that are easy to navigate and understand typically resulting in higher user satisfaction. The influence of perceived ease of use extends to perceived usefulness, with several studies demonstrating a positive correlation between these two factors 42 , 65 , 66 , 67 , 68 . This relationship is particularly significant in the context of ERP systems. ERP systems, due to their extensive functionalities, are generally more complex than other individual unit-of-work ISs. Hence, to facilitate user understanding and navigation, it is crucial that these systems are designed with an easy-to-use structure 8 , 69 , 70 . The easier an ERP system is to use, the more likely users are to be satisfied with it. Furthermore, users are also likely to perceive the ERP system as more useful when it is easy to navigate and understand. In summary, perceived ease of use plays a critical role in shaping user satisfaction and perceived usefulness, particularly in the context of complex ERP systems. As such, this study proposes the following hypotheses:

Hypothesis 1a

Perceived ease of use significantly influences satisfaction.

Hypothesis 1b

Perceived ease of use significantly influences perceived usefulness.

Perceived usefulness

Perceived usefulness is defined as the degree of conviction a user has in the potential benefits that a specific technology can offer 12 . This factor has been shown to play a significant role in enhancing user satisfaction 71 , 72 , 73 , and has been identified as a critical determinant of satisfaction among ERP users 13 , 74 . The influence of perceived usefulness extends beyond mere user satisfaction. Research indicates that perceived usefulness can shape behavioral intention through arousing interest, especially in the context of ERP 69 . Zviran et al. 23 further underscored that perceived usefulness boosts employer satisfaction with ERP usage, suggesting a broader organizational impact. Klaus and Changchit 8 highlighted that perceived usefulness influences satisfaction related to task completion via attitudes toward ERP usage, reinforcing the centrality of this construct in the ERP context. Considering the above, the importance of perceived usefulness in shaping user satisfaction and other behavioral outcomes cannot be overstated. As such, this study proposes the following hypothesis:

Hypothesis 2

Perceived usefulness significantly influences satisfaction.

System quality

System quality is a widely used measure to assess the effectiveness of ISs and is primarily evaluated based on functionality, stability, and usability 11 , 75 . The influence of system quality on user satisfaction is substantial and has been consistently confirmed across various studies 76 , 77 , 78 , 79 . Chaveesuk and Hongsuwan 4 highlighted that system quality is a crucial determinant of user satisfaction. Further, the role of system quality in shaping perceived usefulness has been underscored in multiple IS contexts 80 , 81 , 82 . When an ERP system delivers high quality—being stable, functional, and user-friendly—users are likely to express higher satisfaction. Moreover, the more reliable and stable the ERP system, the more users perceive it to be useful. In consideration of these findings, this study puts forth the following hypotheses:

Hypothesis 3a

System quality significantly influences satisfaction.

Hypothesis 3b

System quality significantly influences perceived usefulness.

Information quality

Information quality is a critical aspect of assessing the effectiveness of ISs in achieving their intended goals 71 . It encompasses attributes such as accuracy, clarity, and adequacy 29 . A number of studies have provided evidence that information quality directly influences user satisfaction in the context of ISs 11 , 77 , 78 . Additionally, information quality has been shown to contribute to perceived usefulness, particularly in the context of ERP systems 64 . When users perceive the information provided by an ERP system to be accurate, clear, and sufficient, they are likely to find the system more satisfying to use. This enhanced satisfaction is also likely to enhance their perception of the system’s usefulness. Hence, the following hypotheses are proposed:

Hypothesis 4a

Information quality significantly influences satisfaction.

Hypothesis 4b

Information quality significantly influences perceived usefulness.

Service quality

Service quality in the context of IS, such as ERP, pertains to the support provided by the service provider, which could include technical support, training, and user documentation 11 . Service quality has been identified as a key determinant of user satisfaction in various IS studies 83 , 84 , 85 , 86 . In an ERP system, service quality refers to the extent to which the system can meet users’ needs and provide a satisfactory user experience, and this can be influenced by factors such as system reliability, responsiveness of the service provider, and the adequacy of system documentation and training. High service quality implies that the system is reliable, the service provider is responsive to users’ needs, and users are provided with adequate documentation and training to use the system effectively. A study conducted by Hsu et al. 7 on the determinants of user satisfaction in the context of ERP systems found that service quality has a significant influence on user satisfaction. The author argued that users’ perception of service quality influences their satisfaction with the ERP system, and this is consistent with the findings of previous studies 30 , 31 . Similarly, a study by Lu et al. 87 on the success factors of ERP systems identified service quality as one of the key determinants of user satisfaction. Furthermore, a study by Hsu et al. 88 on the impact of service quality on user satisfaction in the context of ERP systems found that service quality has a positive and significant influence on user satisfaction. The authors argued that the quality of service provided by the ERP system influences users’ perception of the system’s usefulness and ease of use, which in turn affects their satisfaction with the system. Based on these findings, service quality plays a critical role in determining user satisfaction in the context of ERP systems. Therefore, the following hypothesis is proposed:

Hypothesis 5

Service quality significantly influences satisfaction.

Participation

User participation represents the actions and behaviors in which users participate during the IS establishment phase 52 . Research has been conducted on how to best define and measure user participation. For instance, Barki and Hartwick 25 proposed that user participation should be measured along three dimensions: the degree to which users are involved in the decision-making processes related to IS development, the extent of user responsibility in the development process, and the frequency of user-developer interaction. User participation is a proximal condition for the successful implementation of IT/IS 25 , 53 , 89 , 90 . More recent studies have focused on the role of user participation in Agile IS development methods. These methods, such as Scrum and Extreme Programming, emphasize frequent interaction between developers and users, and they view user participation as vital for successful system development 91 , 92 . Empirical studies on Agile methods have generally found positive effects of user participation on project outcomes, further reinforcing the importance of user participation in IS development. This multi-dimensional view of user participation provides a more nuanced understanding of how users can contribute to IS development and implementation. Users become to sense ownership when they engage in system development 93 . ERP is a system for efficiently managing tasks across the enterprise. The participation and involvement of end-users would be essential from the initial stage to the operation stage. Users with a higher level of participation would be satisfied with ERP more. Thus, this study suggests that:

Hypothesis 6

Participation significantly influences satisfaction.

User participation in system development and implementation has been identified as a significant determinant of IS success 59 , 94 . The presents study investigates the moderating effects of user participation on the relationship among variables. The role of user participation as a moderator in the relationship between perceived ease of use, perceived usefulness, service quality, and satisfaction has its roots in several theoretical and empirical works. The underlying principle is that involving users in the development process fosters a sense of ownership and familiarity with the system, which can lead to higher satisfaction and improved system use 95 . In the context of perceived ease of use, user participation can enhance users’ familiarity with the system and its functionalities, thereby making the system easier to use 27 . When users are involved in the system design and implementation, they are more likely to understand how to use the system effectively, which reduces the perceived complexity and increases the perceived ease of use, leading to higher satisfaction 56 . Regarding perceived usefulness, user participation can ensure that the system is tailored to meet users’ needs, thus enhancing its perceived usefulness 26 . When users are directly involved in system development, they can provide valuable input on what features and functionalities would be most useful for their tasks, thereby improving the perceived usefulness and, consequently, user satisfaction 96 . In terms of service quality, user participation may enhance users’ perception of the quality of services provided. When users are actively involved in the implementation process, they may have more direct communication with service providers. This direct interaction can lead to a better understanding of the system and the services provided, resulting in a higher perception of service quality and, consequently, higher satisfaction 28 . User participation can therefore moderate the relationships between perceived ease of use, perceived usefulness, service quality, and satisfaction. By involving users in the development and implementation of the IS, organizations can enhance these key determinants of satisfaction, leading to more successful IS outcomes.

Hypothesis 7a

Participation significantly moderates the effects of perceived ease of use on satisfaction.

Hypothesis 7b

Participation significantly moderates the effects of perceived usefulness on satisfaction.

Hypothesis 7c

Participation significantly moderates the effects of service quality on satisfaction.

Research methodology

This research was performed in accordance with the Declaration of Helsinki.

Instrument development

A quantitative analytical survey was employed in this study. This paper uses quantitative methodologies to test research models and hypotheses. Indicators were sourced from the existing literature to measure constructs. The measurement items were adjusted to fit the ERP framework. Specifically, this study replaced the subject of survey items in source studies with ERP. For example, it replaced “The information provided by our employee portal is understandable.” in Urbach et al. 97 with “The information provided by ERP is understandable”. The same amendments were made to the other indicators. A seven-point Likert scale (1 = strongly disagree, 7 = strongly agree) was applied to evaluate each item. The questionnaire was first designed in English by the authors, the official business language. An expert linguist translated the original document from English to Korean. Subsequently, a meticulous review of this translated text was performed to confirm accurate conveyance of the original content. This involved a side-by-side comparison of the original and translated texts, with adjustments made as necessary. To verify the translation’s quality, the Korean questionnaire was then translated back into English by a professional translator. In the final step, the authors and the translator scrutinized the translated document to certify smooth readability and the absence of grammatical errors or awkward expressions. Furthermore, a panel of experts in IS and quantitative analysis reviewed the questionnaire for relevance, clarity, and comprehensiveness. Before distributing the questionnaire, we conducted a pretest with a small sample of ERP users to ensure clarity, relevance, and comprehensibility of the items. Based on the feedback received, we made necessary modifications to the wording and order of the items. The pretest also allowed us to check the reliability of the scales, which was confirmed as all Cronbach’s alpha values were above the commonly accepted threshold of 0.7 98 . Table S1 provides a list of the indicators and sources.

The analytical model was validated by analyzing data collected from an online survey. While this research primarily employs a quantitative approach, elements of grounded theory have informed the interpretative aspects of the study, particularly in understanding the latent social patterns and structures that govern ERP user satisfaction. Grounded theory, as articulated by Strauss and Corbin 99 , is a research methodology that enables the development of theory through the systematic gathering and analysis of data. It is particularly suited for exploring complex phenomena where existing theories may not sufficiently capture the intricacies of the studied context. In this research, we leveraged the principles of grounded theory in two ways: First, during the development of our research model, we drew on existing theories (TAM and IS Success Model), but we allowed the model to be informed and adjusted based on the patterns emerging from the preliminary analysis of the collected data. This iterative process aligns with the constant comparative method of grounded theory, which involves continual refinement of the conceptual framework based on the observed data 100 . Second, grounded theory’s emphasis on understanding the actors’ perspectives was reflected in our analysis of the survey responses. We didn’t merely quantify the responses but sought to understand the underlying reasons for users’ satisfaction or dissatisfaction with the ERP system. This process helped us identify unexpected moderating factors, such as the negative moderation of participation on the perceived usefulness–satisfaction relationship.

The sampling procedure primarily relied on convenience and access to a large-scale manufacturing company, referred to as company “A”, which extensively uses an ERP system in the shipbuilding and marine engineering industries. The decision to focus on this specific company aligns with the rationale provided by Neuman 101 , who suggests that convenience sampling is appropriate when access to a specific population is limited or when other sampling methods are impractical. The selection of this company was motivated by its substantial use of ERP and the diverse user roles involved, which allowed for a comprehensive understanding of user satisfaction. To ensure representativeness within the organization, participants were recruited from various departments and roles, following the principles of stratified sampling as outlined by Creswell and Creswell 102 . This approach involved dividing the population into distinct groups or strata and selecting samples from each stratum to ensure representation. The selected company has a long history of implementing and operating ERP systems since the early 2000s, resulting in a workforce that is familiar with using such systems to streamline operations, manage resources efficiently, and enhance productivity. The survey was distributed randomly to employees across different departments and hierarchical levels, aiming to obtain a diverse cross-section of ERP users within the organization and enhance the validity and reliability of the findings.

We explained the purpose and importance of this study to the company’s executives. Executives agreed to the survey. The in-house enterprise system staffs selected 400 employees using ERP within the company. In order to increase the efficiency of data collection, they produced online questionnaires through groupware and distributed questionnaire links to 400 target employees. A total of 237 data were collected, yielding a response rate of 59.25%. The authors and the staffs conducted a preliminary survey of the collected data to look for any missing information, insincere responses, or outliers. Responses with a high percentage of consistently answering with one answer were removed. A total of 234 replies were deemed to be reliable enough for further examination. The sample size of 234 participants in this study was determined based on the guidelines and recommendations for structural equation modeling (SEM) studies. SEM studies typically require a sufficient sample size to ensure statistical power and reliable estimation of the model parameters 103 . According to Hair et al. 103 , the minimum recommended sample size for SEM studies is 200 participants. This recommendation is based on the need to achieve an acceptable balance between statistical power and the complexity of the model. In studies with a larger number of latent variables and observed variables, a larger sample size is generally required to ensure adequate statistical power 104 . In this study, there are 7 latent variables and 21 observed variables, indicating a moderate level of complexity in the measurement model. While a larger sample size would have been desirable, the sample of 234 participants meets the minimum recommendation for SEM studies and allows for a reasonable estimation of the model parameters 103 . Therefore, considering the guidelines for SEM studies and the demonstrated feasibility of obtaining reliable results with similar sample sizes, the sample of 234 participants in this study is justified.

Table 1 presents the demographic information of the 234 participants in the study. Many participants were male (89.3%), while 10.7% were female. In terms of age distribution, 1.7% of participants were in their 20 s, 41.9% in their 30 s, 41.9% in their 40 s, and 14.5% were in their 50 s. The working period of participants varied, with 9.4% of participants having worked for less than 6 months, 23.9% between 6 months to 1 year, 15.8% between 1 and 2 years, 22.2% between 2 and 5 years, and 28.6% of participants having worked for over 5 years. Regarding the ERP modules used by the participants, 12.0% used Operation Management, 13.7% used Design, 4.7% used Sales/Business Management, 15.0% used Materials/Procurement, 13.7% used Quality Management, 12.8% used Management/Human Resource, 16.7% used Finance Accounting, and 11.5% used other modules. Overall, the sample represents a diverse group of individuals with varying demographic characteristics and differing experiences with various ERP modules.

Ethical approval

This study was approved by an institutional review board of RealSecu.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

Consent to participate was obtained from all individual participants included in the study.

Empirical results

The partial least squares SEM (PLS-SEM) method was employed to estimate the measurement model and the structural model. PLS is the one of types of SEM which is a sophisticated multivariate method 105 . These days, SEM has dominated the research landscape 106 . It has been extensively employed in the organization and IS fields. The hypotheses were tested using SmartPLS 3.0 107 .

This study derived the empirical results through the following analysis steps. It controlled and evaluated the common method bias. Next, this research verified the measurement model and structural model. In the first step, this paper checked and managed the content validity and manage issues related to the common method bias. In the second step, the current study confirmed the reliability and validity of the measurement model. Reliability was confirmed by Cronbach Alpha and composite reliability (CR). This work demonstrated the validity by dividing it into convergence validity and discriminant validity. The convergence validity was confirmed by average variance extracted (AVE), and the discriminant validity was verified by the Fornell and Larcker 108 criterion. In the second step, the structural model was verified. This study applied a bootstrapping method that generates 5000 resamples. Based on this, the path coefficient and R 2 values were calculated.

Common method bias (CMB)

In this study, we took several precautions to mitigate CMB, which can occur when both predictor and criterion variables are obtained from the same source at the same time, potentially inflating the observed relationships 109 . As the data for this study was collected via self-reported surveys, there was a potential risk of CMB. Firstly, we adopted procedural remedies to minimize the chances of CMB. These included assuring the respondents of the confidentiality of their responses and emphasizing that there were no right or wrong answers to the survey questions 109 . This helped reduce evaluation apprehension and encouraged respondents to answer honestly. Secondly, we took steps to separate the measurement of different constructs both in terms of the order of questions and by explicitly signaling to respondents when questions related to different constructs began 109 . This helped reduce the likelihood of participants’ responses to one set of questions influencing their responses to subsequent questions. Finally, we used statistical controls to test for the presence of CMB. Specifically, we conducted a Harman’s single-factor test 110 , where all items are loaded onto a single factor in an exploratory factor analysis. If a single factor emerges or one factor accounts for most of the covariance among the measures, then a substantial amount of CMB is present. In our case, multiple factors emerged and no single factor accounted for most of the covariance, suggesting that CMB was not a major issue in our study. By combining these procedural and statistical controls, we aimed to minimize the risk of common method bias in our study.

Measurement model

Reliability and validity evaluations were carried out to assess the measurement model. The factor loadings from 0.778 to 0.970 (p = 0.001), indicating strong reliability 111 . Based on Cronbach’s alpha and the composite reliability (CR) value, internal consistency reliability was evaluated. Table 2 shows that all of the constructs have Cronbach’s alpha and CR estimates are more than the recommended minimum value of 0.7 108 , which suggests that adequate reliability exists. By looking at the AVE of the indicators, convergent validity was demonstrated. AVE scores were from 0.723 to 0.929, which are above the cut-off point of 0.5 108 . Finally, to confirm discriminant validity, the root square of AVE values of the each factor were compared to the off-diagonal entries 108 . All of the diagonal entries were over any other corresponding rows or column values, presenting an adequate discriminant validity. Table 3 describes the results of discriminant validity.

Structural model

A structural model was analyzed to verify the hypotheses within the model. A bootstrap resampling technique iterated 5000 resamples to calculate the path coefficients and R 2 . The results of structural model are shown in Fig.  2 .

Structural model assessment.

Consistent with our expectations, perceived ease of use has a significant impact on both satisfaction and perceived usefulness, providing support for H1a and H1b. However, perceived usefulness does not have a significant effect on satisfaction, resulting in the non-support of H2. System quality has a significant influence on satisfaction but does not affect perceived usefulness, thus accepting H3a and rejecting H3b. Information quality does not directly impact satisfaction, but it strongly influences perceived usefulness, leading to the non-adoption of H4a and the adoption of H4b. Service quality significantly affects satisfaction, offering empirical evidence for H5. As predicted, participation has a strong influence on satisfaction, leading to the acceptance of H6. However, participation does not moderate the relationship between perceived ease of use and satisfaction (H7a), indicating non-support. On the other hand, participation negatively moderates the relationship between perceived usefulness and satisfaction (H7b), supporting this hypothesis. Participation does not moderate the impact of service quality on satisfaction (H7c), failing to accept this hypothesis. The research framework accounts for 75.7% of the variance in satisfaction and 67.7% of the variance in perceived usefulness, as depicted in Table 4 , which presents the results structural model assessment.

The current study explored the predictors of user satisfaction with ERP. It incorporated factors from the IS success model and components from TAM, while also considering participation as an additional variable.

The findings from this study that perceived ease of use influences both satisfaction and perceived usefulness corroborate existing literature within the realm of ERP systems and TAM. The result aligns with the fundamental tenets of TAM, which posits that the perceived ease of use of a technology can affect its perceived usefulness and subsequently the user satisfaction 12 . As well, previous research have validated that perceived ease of use influence satisfaction 64 , 81 , 112 and perceived usefulness 43 , 113 . The impact of perceived ease of use on satisfaction can be comprehended through the lens of user experience. When users find a system effortless to use, it enhances their interaction with the technology, making it more enjoyable and satisfactory. This outcome resonates with prior research that has established a positive relationship between perceived ease of use and user satisfaction 114 , 115 . In the context of perceived usefulness, the finding suggests that when users perceive an ERP system to be easy to use, they are more likely to recognize its utility. This is because an easy-to-use system lowers cognitive burden and allows users to focus more on the task at hand rather than on the operation of the system itself, thus enhancing its perceived usefulness 22 .

The finding that perceived usefulness does not significantly affect satisfaction in the context of ERP systems in this study is somewhat surprising, considering the postulates of the TAM and prior research findings 12 . Several previous studies have indicated a positive relationship between perceived usefulness and user satisfaction 80 , 116 , 117 . However, this study deviates from those findings, raising several considerations. Firstly, it might be that in the specific context of the study—A big size manufacturing company, a high-level technology environment—other factors could overshadow perceived usefulness in terms of impacting satisfaction. It is plausible that in such a technologically advanced setting, users may take the usefulness of systems for granted, causing other factors to become more influential in determining satisfaction. Secondly, the non-significant relationship might be due to the ERP system’s characteristics or the specific tasks for which it is used. If the tasks are routine or do not require the full functionality of the ERP system, users might not perceive its full usefulness, which could affect their satisfaction. Lastly, there could be measurement or sample-specific factors that led to this unexpected result. It’s possible that the items used to measure perceived usefulness did not capture the construct effectively in this context or for these specific users.

The analysis unveiled that system quality significantly influences satisfaction while it does not affect perceived usefulness. The significant effects of system quality on satisfaction 78 , 79 , 118 and perceived usefulness 80 , 81 have been demonstrated in the past research. These findings could be explained by the reason that workers are more satisfied when the ERP system is better structured and provides appropriate functions. The reliability, speed, and understandability of ERP enhance satisfaction, while they can’t guarantee a high level of perceived usefulness.

Although many authors have argued that information quality influences satisfaction 73 , 79 , 119 , the empirical findings found that information quality does not impact satisfaction. Several reasons might explain this discrepancy. First, the quality of the information provided by ERP may not be as critical to users as other factors such as system functionality or ease of use. Second, users may have developed strategies to cope with poor information quality, reducing its impact on overall satisfaction. Alternatively, the users’ perceptions of information quality may be influenced by other factors not accounted for in this study. For example, organizational culture, individual user experience, or training may affect how users perceive the quality of information provided by ERP. The analysis showed that information quality strongly affects perceived usefulness, which is consistent with the conclusions of previous studies 64 , 83 , 118 . These observations also further support the previous works, which demonstrated the significant relationships between DM success factors and perception components of TAM 8 , 46 , 64 . Particularly, information quality is more significant than other factors to let users perceive ERP as useful. This may be because when the information provided by ERP is more understandable and reliable, users perceive it more useful. Since ERP is a company-wide IS, it needs to provide various information according to the needs of users. Because workers use ERP to save time of searching or processing, a higher level of information quality forms a greater degree of perceived usefulness.

The study analysis verified the significance of service quality on satisfaction. This observation further supports the previous research, in which service quality improve satisfaction 79 , 118 , 120 . The association between service quality and satisfaction indicates that when the IT support department better solves problems related to ERP, user satisfaction is improved.

The results of this study indicate that user participation significantly impacts satisfaction in the context of ERP. This finding supports the notion that user involvement in IS-related activities, such as system design, implementation, and usage, is crucial in shaping their perceptions and attitudes toward the system 56 , 59 , 121 . User participation can manifest in various forms—from providing feedback on system design to active involvement in decision-making processes regarding system implementation and use. This participatory approach can lead to a better understanding of the system’s capabilities, improved alignment with user needs and expectations, and a sense of ownership and commitment to the system, thereby enhancing user satisfaction 26 , 122 . In the context of ERP, user participation becomes particularly critical given the complexity and organization-wide nature of these systems. ERP systems often require substantial changes in business processes and workflows, which can cause disruption and resistance among users. Active user participation can help mitigate these challenges by fostering better understanding, reducing resistance to change, and enhancing system acceptance and satisfaction 123 , 124 .

The results of this study suggest that user participation negatively moderates the effects of perceived usefulness on satisfaction in the context of an ERP system. This finding is somewhat unexpected, as prevailing literature generally suggests that user participation has a positive impact on satisfaction 26 , 59 , 121 . However, in the case of our study, it seems that increased participation may lessen the impact of perceived usefulness on satisfaction. This surprising result can be understood in several ways. First, it might be that user participation in the context of ERP system use involves a certain degree of cognitive and time investment. The more users participate in system-related activities, the more time and cognitive resources they might need to dedicate to the system. If users perceive the system as useful but find the participation process burdensome or overwhelming, this could negatively affect their satisfaction levels. Second, it’s possible that the quality, rather than the quantity, of user participation is what truly matters. If user participation involves tasks that users find uninteresting, irrelevant, or overly complex, this could lead to frustration or disengagement, thus negatively impacting satisfaction despite high perceptions of usefulness. Last, user participation could create higher expectations for the system’s usefulness. The more users are involved, the more they might expect from the ERP system in terms of its performance and utility. If these heightened expectations are not met, users may become dissatisfied, even if they perceive the system as relatively useful.

The results of this study indicated that user participation did not moderate the effects of perceived ease of use and service quality on satisfaction in the context of an ERP system. This finding might initially appear counterintuitive, given the substantial body of research that suggests user participation plays a pivotal role in enhancing system use and satisfaction 25 , 26 , 59 . However, upon closer inspection, this outcome can be interpreted in several ways. First, while participation is often correlated with positive outcomes, its effectiveness may depend on the nature of the system being used. In the case of ERP systems, their complex and integrated nature might mean that simply participating in the system’s use may not be sufficient to improve satisfaction. The user might require a deeper level of engagement or understanding of the system to see improvements in satisfaction. Second, the nature of the tasks performed in an ERP system might play a role. If users are completing complex tasks that require a high level of knowledge and expertise, then merely participating might not lead to increased satisfaction. In such scenarios, the quality of participation, rather than the quantity, might be more critical. Finally, the results might reflect the characteristics of the users themselves. If users have a high level of self-efficacy or prior experience with similar systems, then their satisfaction might be less dependent on their level of participation.

Theoretical implications

The current study makes several significant theoretical contributions to the existing body of knowledge, specifically in the realm of ERP systems and user satisfaction. Firstly, by incorporating variables from both the IS success model and the TAM, this research offers a more comprehensive framework for understanding user satisfaction. Although both models have been widely used and validated in numerous studies 12 , 29 , their combined application in the context of ERP systems remains relatively unexplored. The study’s results shed light on the unique and combined effects of constructs from both models on user satisfaction, thereby enhancing our understanding of the complex dynamics at play. For example, while previous studies have emphasized the role of perceived usefulness and ease of use in user satisfaction 12 , 115 , our findings underscore the significance of system quality and service quality, which are derived from the IS success model. This suggests that user satisfaction in ERP systems may be influenced by a combination of factors from both models, an aspect that has previously received limited in-depth exploration.

Secondly, this study highlights the role of user participation as a significant predictor of user satisfaction in the context of ERP systems. Despite the extensive research on user participation in IS success 26 , 59 , its impact in the specific context of ERP systems has received less thorough examination. By demonstrating that user participation significantly influences satisfaction, our study paves the way for more comprehensive models that integrate user participation into these established frameworks. Additionally, our research underscores the complex nature of the relationships among factors influencing ERP satisfaction. For instance, we found that user participation negatively moderates the relationship between perceived usefulness and satisfaction, but not the relationships of perceived ease of use and service quality with satisfaction. This finding signals the need for more intricate models that account for potential moderating or mediating effects among different factors. Furthermore, the contrast between our findings and those from previous studies invites scholars to revisit and refine the measurement of constructs such as perceived usefulness, especially in specific contexts like high-tech manufacturing. Our study suggests that traditional measures of perceived usefulness might not fully capture the construct in this setting, pointing to the need for more context-sensitive measures.

Thirdly, our research challenges the conventional wisdom that perceived usefulness always positively influences satisfaction. While numerous studies have established a positive relationship between perceived usefulness and user satisfaction 12 , 13 , 74 , 115 , our findings reveal that this relationship may not hold true in the context of ERP systems within high-technology environments. This unexpected finding opens new avenues for researchers to explore the conditions under which perceived usefulness may or may not impact satisfaction. It suggests that the impact of perceived usefulness on satisfaction may be contingent upon various factors, such as the nature of the tasks, specific system features, or user characteristics, thereby calling for a more nuanced understanding of the relationship between perceived usefulness and satisfaction.

Finally, the unique features of our study sample provide additional theoretical contributions. Specifically, our study was conducted within a large-scale manufacturing company operating in a high-technology environment. This context has its characteristics that add new insights to the existing body of knowledge on ERP satisfaction. Previous research has largely focused on more general or diverse settings, failing to examine how the dynamics within high-tech, manufacturing environments might impact user satisfaction with ERP systems 62 , 125 , 126 . In our study, the finding that perceived usefulness does not significantly affect satisfaction is particularly noteworthy, as it contradicts the typical assumptions of the TAM and previous empirical findings. This unexpected result suggests that in high-tech environments, where the utility of systems is often taken for granted, other factors might become more critical in determining satisfaction. This could be the case in large-scale manufacturing companies where technology application is widespread and advanced, and users might have a higher baseline expectation for system usefulness. Additionally, our study revealed that user participation has varying effects on satisfaction and its predictors, which further extends the understanding of the user participation role in the IS success literature. Particularly, in a high-tech manufacturing setting, users might be more specialized and experienced 127 , leading to different dynamics between participation, perceived usefulness, and satisfaction compared to more general settings. These findings offer a more nuanced view of ERP satisfaction determinants, particularly in high-tech manufacturing settings. They suggest the need for further research to examine how specific organizational contexts, such as industry type, technology maturity, or user characteristics, might impact the relationships among factors influencing ERP satisfaction. This context-specific approach can yield more granular insights, enhancing the theoretical understanding of ERP satisfaction and providing more precise guidance for practice.

Practical implications

The results of this study offer several practical implications for organizations implementing ERP systems. The first significant implication is the importance of system quality and service quality in enhancing user satisfaction. Given that our findings show a strong positive correlation between these constructs and user satisfaction, organizations must pay careful attention to the design and ongoing maintenance of their ERP systems. For instance, they need to ensure that the system is reliable, efficient, and user-friendly to enhance its perceived quality. Likewise, the quality of IT support services is crucial. Organizations need to establish efficient helpdesk services, regular system checks, and prompt troubleshooting mechanisms to resolve user issues swiftly. Training programs aimed at enhancing users’ skills in using the system can also help boost service quality.

Secondly, our findings underscore the role of user participation in shaping satisfaction with ERP systems. This suggests that organizations need to actively involve end-users in the process of ERP system design, implementation, and use. For example, organizations could include representatives from different user groups in the system design and selection process to ensure that the chosen system meets users’ needs and preferences. Similarly, encouraging user involvement in decision-making related to the system’s use, such as customizing system features or defining process workflows, can enhance their sense of ownership and commitment to the system, thereby boosting satisfaction. Organizations can also establish feedback mechanisms to solicit users’ opinions and suggestions about the system, thereby enabling continuous improvement based on user input.

Thirdly, the fact that perceived usefulness does not significantly affect satisfaction in our study implies that organizations should not solely rely on promoting the usefulness of ERP systems to enhance user satisfaction. While demonstrating the system’s utility is undoubtedly important, it might not be sufficient in high-tech environments where users might take the system’s usefulness for granted. Instead, organizations should focus on other aspects, such as providing a seamless user experience, ensuring system reliability, and offering efficient user support services. For instance, in a manufacturing context, ensuring that the ERP system integrates well with production workflows and provides real-time, accurate information could be more crucial than simply showcasing the system’s functionality.

Fourthly, our study’s results suggest that user participation does not necessarily amplify the effects of perceived ease of use and service quality on satisfaction. This means that organizations should not merely focus on increasing user participation in the hope of improving satisfaction. Instead, they need to consider the nature of the tasks performed in the ERP system and the characteristics of the users. For example, in situations where users perform complex tasks requiring specialized knowledge, organizations might need to invest in more intensive training or provide additional support to enhance satisfaction. Similarly, if users have high self-efficacy or prior experience with similar systems, their satisfaction might be more influenced by factors such as system performance, information quality, or the responsiveness of support services.

Lastly, our findings highlight the importance of information quality in influencing perceived usefulness, although it does not directly impact satisfaction. This implies that organizations should focus on improving the quality of information provided by ERP systems to enhance users’ perceived usefulness, even if it may not directly boost satisfaction. This can be achieved by ensuring data accuracy, timeliness, completeness, and relevancy. For instance, organizations could implement robust data governance practices, automate data input processes to reduce errors, and regularly audit the system data to ensure its quality. By enhancing the perceived usefulness of the system through improved information quality, organizations can indirectly contribute to overall user satisfaction.

Limitations and future research

The current paper has several limitations. Firstly, the data collected for this study was limited to South Korea, which may limit the generalizability of the findings to other countries with different business environments and industries. Future research should consider surveying multiple countries to enhance the external validity of the study. Secondly, this paper only considered participation as a moderating variable, neglecting other potential variables such as commitment to learning, peer impact, and top manager engagement. Including these variables in future studies would provide a more comprehensive understanding of the research topic. Additionally, future research could introduce additional control variables to enhance the explanatory power of the proposed research model. Thirdly, this study focused exclusively on ERP systems, and it would be beneficial to apply the theoretical framework to other enterprise information systems like supply chain management and customer relationship management for comparative analysis. By doing so, the reliability of the research model can be further established. Moreover, factors such as training and support, as well as financial outcomes and operational efficiency, were not comprehensively included as exogenous and final variables, respectively. Future research should strive for a balanced inclusion of various variables to generalize the results more effectively. Lastly, the study is limited by its small sample size, which may impact the generalizability and robustness of the conclusions. Future research should aim to include a larger and more diverse sample to enhance the generalizability and strengthen the validity of the study’s findings.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review

• Zainab Nadhim Jawad   ORCID: orcid.org/0000-0001-6154-2151 1 &
• Villányi Balázs 1  

Beni-Suef University Journal of Basic and Applied Sciences volume  13 , Article number:  4 ( 2024 ) Cite this article

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In the dynamic and changing realm of technology and business operations, staying abreast of recent trends is paramount. This review evaluates the progress in the development of the integration of machine learning (ML) with enterprise resource planning (ERP) systems, revealing the impact of these trends on the ERP optimization. In recent years, there has been a significant advancement in the integration of ML technology within ERP environments. ML algorithms characterized by their ability to extract intricate patterns from vast datasets are being harnessed to enable ERP systems to make more accurate predictions and data-driven decisions. Therefore, ML enables ERP systems to adapt dynamically based on real-time insights, resulting in enhanced efficiency and adaptability. Furthermore, organizations are increasingly looking for artificial intelligence (AI) solutions as they actually try to make ML models within ERP clear and comprehensible for stakeholders. These solutions enable ERP systems to process and act on data as it flows in, due to the utilization of ML models, which enables enterprises to react effectively to changing circumstances. The rapid insights and useful intelligence offered by this trend have had a significant impact across industries. IoT (Internet of Things) and ML integration with ERP are continuously gaining significance. These algorithms allow for the creation of adaptable strategies supported by ongoing learning and data-driven optimization, which has a number of benefits for ERP system optimization. In addition, the Industrial Internet of Things (IIoT) was investigated in this review to provide the state-of-the-art and emerging challenges due to ML integration. This review provides a comprehensive analysis of the integration of machine learning algorithms across several ERP applications by conducting an extensive literature assessment of recent publications. By synthesizing the latest research findings, this comprehensive review provides an in-depth analysis of the cutting-edge techniques and recent advancements in the context of machine learning (ML)-driven optimization of enterprise resource planning (ERP) systems. It not only provides an insight into the methodology and impact of the state-of-the-art but also offers valuable insights into where the future of ML in ERP may lead, propelling ERP systems into a new era of intelligence, efficiency, and innovation.

1 Background

The use of machine learning (ML) in enterprise resource planning (ERP) systems is a top priority of technical advancement in today's data-driven corporate environment. The primary concern of this in-depth investigation is to investigate extensively the complex world of ML-driven ERP improvement. This review has been divided into several sections, each tackling key aspects of this integration in order to offer clarity for future development. ERP systems are multidimensional software solutions made up of modules or applications, each of which caters to a different company function. Accounting, human resources, supply chain management, manufacturing, sales, and customer relationship management (CRM) are among the most often utilized modules, according to recent reviews. These systems are made up of a number of crucial elements that cooperate to promote effective and data-driven management. Additionally, ERP systems come with reporting and analytics capabilities that let users create personalized reports, view data, and discover how well their businesses are performing.

Traditional ERP [ 1 ] systems have been critical in simplifying corporate operations in the modern global economy; nonetheless, they are not without faults and have more obvious drawbacks as the business environment changes; however, research has repeatedly demonstrated that these limitations could be explained regarding:

The process requires costly software licensing, hardware infrastructure, customization, training, and continuous maintenance.

ERP systems may need substantial customization to fit with a company's particular business procedures. Longer implementation periods and greater costs may be the results of this complexity.

ERP, in contrast, has both strategic and tactical effects [ 1 ]. Strategical effects will have an impact on the company's strategic decisions and on its future business. On a management and operational level, tactical effects will have an impact on how the company conducts its internal business. However, modern ERP systems demand integration with other systems including those of suppliers, customers, and third-party applications. This relationship improves accessibility and data exchange.

Since the beginning, ERP systems have encountered significant changes and have influenced contemporary corporate processes [ 1 ]. Material requirements planning (MRP) systems, which first appeared in the 1960s, are where the origins of ERP systems could be located. These early systems were primarily concerned with meeting the production and inventory requirements of manufacturing firms. By maximizing material needs, MRP systems assisted firms in planning and managing their manufacturing processes more effectively.

MRP II systems introduced a new generation in the 1980s, with expanded features such as finance management, production scheduling, and capacity planning. The goal of MRP II systems was to bring together multiple organizational areas for more comprehensive planning and decision-making. In addition to these key historical eras, the phrase "enterprise resource planning" (ERP) became well-known in the 1990s, when ERP systems went beyond manufacturing to include all essential business operations, including finance, human resources, procurement, and more. ERP systems sought to offer a single, integrated platform that would unify data and expedite procedures inside a business.

ERP II solutions [ 1 ] were developed in the 2000s to support e-business and e-commerce activities. These systems placed a strong emphasis on connectivity with suppliers and customers as well as cooperation and real-time data exchange. ERP II systems gave businesses the tools for online transactions, supply chain visibility, and customer relationship management that they needed to adapt to the digital age.

Until the Cloud-Based ERP [ 2 ] and Mobile Accessibility (2010–2020s) that offered scalability, flexibility, and lower IT infrastructure costs, as a common feature, mobile accessibility now enables clients to access ERP data and features via smartphones and tablets, improving real-time decision-making.

According to the analysis of the efficiency measurement results in [ 3 ], the decision-making unit became more efficient after the deployment of ERP. Nonetheless, the incorporation of ML into ERP systems is a reaction to the growing business environment, which is characterized by big data, IoT, and the need for informed decision support. Enterprise resource planning (ERP) systems serve as the foundation of organizational operations in today's corporate environment by simplifying procedures and maximizing resources. These systems' seamless technological integration has increased their effectiveness while also opening up a wide range of new opportunities. Among these, machine learning (ML) has become a ground-breaking paradigm that is transforming how we view and use ERP systems. ML approaches have the ability to convert ERPs into intelligent, adaptable, and decision-supportive platforms by utilizing the strength of data-driven insights and predictive analytics. For instance, paper [ 4 ] goes into further detail on how ERP supports auditing procedures, including the advantages and disadvantages of using ERP for auditing. Furthermore, future research would focus on the audit process's overall influence on growing technology innovations.

However, using machine learning in ERP systems can be challenging for organizations. There are different problems they may encounter, like complicated algorithms and issues with the accuracy of the data. To fully utilize the synergy between ML and ERP, it is essential to comprehend the intricacies of these issues and investigate strategies to address them. This study starts with an investigation of machine learning and ERP interaction and attempts to find new methods to overcome the problems businesses face. This review aims to offer a comprehensive overview of the environment by closely examining the methodology used and the challenges encountered. Additionally, it highlights the substantial advantages that businesses adopting ML-driven ERP optimization may have, such as improved functionality and the ability to make data-driven decisions. By taking into account factors like equipment availability, raw material availability, and production prices, ML models improve production schedules. They reduce resource waste and production interruptions. The workforce engineering tries to reduce the expense and time required to complete a task.

In [ 5 ] an assistance system is provided to assist sales engineers in making suggestions on new goods and services that may be marketed to their clients. We've reviewed the latest research papers that involve state-of-the-art integration of ML and ERP systems throughout this deep review investigation. A step toward a more intelligent and adaptable organizational structure is represented in multiple aspects one of them the predictive analytics. This work explores current developments and new trends as well, foreseeing the future directions of this dynamic scientific field.

The combination of machine learning with enterprise resource planning systems offers not just efficiency but also a fundamental in how firms operate, strategize, and prosper in the digital age. Although there exists considerable research regarding the use of ML algorithms in ERP, the state-of-the-art investigation in this work provides an overview that contributes to the knowledge base of information systems and the entire enterprise management systems.

The aim of this review is to conduct a comprehensive investigation of the body of knowledge about the integration of machine learning (ML) techniques into enterprise resource planning (ERP) systems. To leverage answering the specific research question of how machine learning techniques can effectively be harnessed to optimize enterprise resource planning (ERP) systems, and what are the associated challenges, benefits, and emerging trends? The methodology of finding pertinent research studies, journals, and articles in this area constitutes a component of this topic. Reputable sources, including academic databases and journal databases, are used to compile pertinent information. Thus, the chosen information covers both historical and contemporary trends, guaranteeing a thorough comprehension of the topic. ML applications inside ERP were carefully divided into categories based on functionality and sectors. Consequently, this review provides a clear picture of the heterogeneous ML-driven ERP improvement environment.

This review started with background and context section, where we establish the context, starting with exploring how data-driven decision-making has become pivotal for enterprises in responding to market changes and making informed choices. Subsequently, we delve into machine learning, offering an overview of its core concepts and methodologies. Moving forward, we explore how ML seamlessly integrates with ERP systems, ushering in unprecedented levels of efficiency and productivity. Within this work, we examine various ML-enhanced features such as inventory management, production scheduling, quality control, and predictive maintenance. Lastly, we explore adaptive process automation and its transformative impact on workflow optimization. By structuring our review, we aim to provide a comprehensive and coherent examination of the landscape of ML-driven ERP optimization, offering insights into recent advancements and emerging trends that hold the potential to reshape the future of enterprise technology.

2 Main text

2.1 methodology and scope.

In this section, we outline the scope and methodology, setting the stage for a comprehensive exploration of the integration of machine learning (ML) with enterprise resource planning (ERP) systems. Using a strict approach, this review sought to give academics and businesses a thorough, up-to-date and informative examination of integration of the machine learning techniques into enterprise resource planning systems.

To highlight the methodology, and state-of-the-art, research scope and define the challenges, a comprehensive analysis approach was used. Consequently, the relevance of the papers chosen took a vital role, focusing on ML applications in ERP systems, although it is one of the selection criteria. while, the second is the conventional method of publication, where peer-reviewed journals, conference proceedings, and scholarly articles were prioritized to ensure the accuracy of the data. Recent papers were given preference. The review concluded with the identification of emerging trends, challenges and prospects in the integration of ML in ERP systems. This review's scope covers a wide range of topics related to ML-driven ERP innovation. It encompasses the following areas, without being restricted to them:

Problems and difficulties with ML integration in ERP systems.

Industries and use cases where ML-driven ERP optimization has been implemented, including manufacturing, inventory and energy.

The potential advantages of ML in terms of improved decision-making, cost savings, operational efficiency, and sustainability.

Emerging patterns and potential avenues for ML-ERP integration.

Nevertheless, integrating machine learning algorithms into ERP systems provides decision-makers with valuable insights that help enterprises make better decisions.

The contribution of this research work lies in its systematic literature review methodology. This review extends our knowledge by employing rigorous review techniques, it offers a comprehensive and well-structured analysis of the integration of machine learning (ML) in enterprise resource planning (ERP) systems. The methodology ensures the reliability and depth of the analysis, providing readers with a clear understanding of the current state-of-the-art in this dynamic field.

Additionally, through the systematic literature review, we contribute to a deeper understanding of how ML techniques are harnessed within ERP systems. The study was conducted in the form of a review, with data being gathered by exploring the wide range of publications, methodologies, challenges, and benefits, shedding light on the multifaceted relationship between ML and ERP.

As a result, readers gain insights into this integration and its implications for modern businesses. Also, the identification of emerging trends within the ML-driven optimization of ERP systems considered as a significant contribution. In addition, this review serves as a valuable resource for researchers, practitioners, and decision-makers seeking to explore the possibilities of ML within ERP systems. By presenting an overview of applications across various ERP modules, such as inventory management, customer relationship management (CRM), and supplier relationship management (SRM). This guidance can catalyze further advancements in the field, ultimately benefiting industries and businesses.

2.2 Machine learning in enterprise technology

This subsection delves into the applications of ML in various aspects of enterprise technology, from data-driven decision-making to enhancing the functionalities of ERP systems. Machine learning (ML) is a subset of artificial intelligence that enables computers to learn from data without having to be explicitly programmed. ML algorithms have the ability to quickly and precisely evaluate enormous volumes of data when connected with an ERP system. This implies that based on current statistics, decision-makers are able to make well-informed decisions. Further, key performance indicators (KPIs) will be developed utilizing data mining and machine learning and will be predictive in nature. Hence, software may learn from data and make predictions or decisions using machine learning algorithms without having to be explicitly programmed.

According to [ 6 ], the implemented methods of analysis will enhance machine learning capabilities to provide recommendations on new products for users based on service support systems to effectively monitor various KPIs related to business and aid in their decisions. The system only provides an end-to-end recommendation based on KPIs from customer relationship management. Traditionally, ERP systems are software applications that help organizations manage and supervise their diverse activities. Tasks like inventory management, accounting, human resources, and customer relationship management are made easier with the use of these technologies. Enterprise information systems (EISs) are able to sense, detect, analyze, or recognize more and more, even going beyond the range of human cognition, and then react based on that understanding [ 7 ]. ERP usage in businesses saves time and costs by eliminating the need for several software packages or manual data input by combining all of these activities into a single system. Using ERP systems, it is possible to manage vital resources like cash and human resources. Cost reductions are produced by ERP resource optimization [ 8 ].

Furthermore, the integration of these two technologies results in more effective and efficient collaborative operations. For instance, real-time analysis of massive volumes of data using ML algorithms alongside ERP platforms is possible. Having more informed decisions on issues like inventory management or customer service [ 9 ] enhances data-driven process creation in order to generate an evidence-based decision support tool for business process management. Thus, AI and ML usage serve a more significant part in novel endeavors as the analytical techniques and decision intelligence further include modeling enhancement and decision-making process. Besides, this integration attempts to facilitate digital transformation and the conversion of production into adaptable manufacturing systems, thereby improving business agility.

The management function is based on planning in general and planning in enterprises in particular. In order to economically justify the outcomes obtained, the authors of [ 8 ] automated the production planning process based on the ERP system. Implementation of the SAPERP system's automated "production" process planning unit results in a decrease in the amount of time needed to maintain the production planning process, improved production process management, lower costs, and an increase in overall enterprise productivity and investment appeal. According to the difficult issues that many firms have faced in recent years, the study presented in the paper [ 8 ] was carried out utilizing a comparative analysis of the primary enterprise architecture frameworks, highlighting the advantages and disadvantages of each. While the majority of the conclusions concern what and how things should be done, article [ 2 ] explored four of the most well-known frameworks in order to provide further insight into the various business architectural elements. It emphasizes the thorough measurement strategy to evaluate the total value contribution of some of the primary enterprise architecture frameworks.

2.2.1 Data-driven decision-making in responding to market changes

ERP systems' significance in corporate operations arises from their ability to minimize business procedures, and encourage cross-functional cooperation, all of which boost productivity, cut down on effort duplication and enhance decision-making. Correspondingly, data-driven decision-making involves making decisions that are well-informed and based on data analysis. In order to comprehend market trends, consumer, behavior, and other elements that have an impact on corporate operations, this strategy involves gathering and assessing pertinent data. Businesses may better adapt to market changes by utilizing data to guide their actions.

Likewise, domain knowledge improvement for service engineers [ 5 ] through the product and services recommender system provides an IIoT analytic system that proposes new prospective sales, up-sales, and cross-sales of products and/or services. This method also promotes the exchange of business expertise among salespeople and increases the creation of sales possibilities in their customer portfolio by enhancing the cooperation of their subject knowledge.

The system does not make decisions instead of the user; rather, it equips them with the tools they need to make better informed and data-driven decisions about the products they are presenting to clients.

Alternatively, relying on optimizing processes and resources, ERP systems help organizations reduce operational costs, minimize waste, and improve resource allocation. Businesses that leverage ERP systems gain a competitive edge by responding more swiftly to market changes, customer demands, and emerging opportunities.

The consequences of integrating machine learning algorithms into an ERP system provide decision-makers with valuable insights that help them make better decisions. In [ 10 ] the automation of maintenance decision processes for Industry 4.0 integrated manufacturing applications was the main focus. Data-driven decision-making is a process of making informed choices based on the analysis of data, which involves collecting and analyzing relevant information to understand market trends, customer behavior, and other factors that affect business operations. By using data to inform decisions, businesses can respond more effectively to changes in the market.

An organization-prospective ERP system with integrated customer relationship management (CRM) and supplier relationship management (SRM) modules enables organizations to better manage their relationships with customers and suppliers. Many ERP providers, including Oracle, have been progressively integrating ML and AI technologies into their ERP systems to enhance various functionalities. These technologies are used for predictive analytics, automation, data analysis, and decision support within ERP processes. This approach came as a factor which took a role in the increase of the overall revenue in addition to the illustrated increment in the workforce. The integration of ML and advanced technologies into SAP's ERP systems, as well, contributed to the company's competitiveness and customer satisfaction, although it’s just one element of SAP's overall business strategy [ 11 ].

In view of all that has been mentioned so far, one may suppose that the major benefits of an ERP system are fewer effective products, process integration, better forecasting and planning, formalization of the business operations of the organization, and protection against operational mistakes [ 8 ], while the drawbacks include opposition and the owners of the firms' mistrust of high-tech solutions [ 8 ]. On the other hand, the innovative Graduation Intelligent Manufacturing System (GiMS) with synchronization-oriented manufacturing planning and control (MPC) for Industry 4.0 manufacturing is then examined in [ 12 ] paper, which summarizes the development of MPC systems with enabling technologies and the altering business climate at that time. Examined cutting-edge MPC designs and methods for manufacturing with Industry 4.0. A method for developing and utilizing a predictive maintenance platform in automobile manufacturing has been described in [ 13 ]. Its methodology is not specific to the automobile sector; rather, it can be applied to other industries and to both new and existing machinery.

In addition, a comprehensive approach to assess the identified challenges for adopting the BDA into IIoT systems in the context of industry 4.0 was developed in [ 14 ]. The obtained results confirmed the efficiency, stability, and reliability of the proposed method.

2.2.2 Leveraging data for informed decisions

Data-driven decision-making is the cornerstone of ML in the enterprise landscape. Here, we examine how ML algorithms enhance decision-making by responding to dynamic market changes. Modern ERP systems now have dominant analytics, AI, and machine learning capabilities, enabling businesses to gather useful insights, forecast trends, and encourage innovation. ERP systems are anticipated to progressively adapt as technology develops and plays a crucial part in the digital transformation of enterprises. ERP systems are used in the industrial sector to increase productivity [ 15 ] by tracking supply, requests, scheduling, finished inventory products, and other essential information required for management, ERP systems are capable of regulating every aspect of enterprise. Numerous issues may be solved by ML integration in ERP.

ERP system solution, taking into account the issues with AI and ML integration into ERP, is attempting to develop solutions by outlining the challenges, identifying potential risks, and offering predictive maintenance. Furthermore, machine learning paired with an ERP system technology will deliver highly comprehensive forecasting insights. The selection of an ML technique for enterprise resource planning (ERP) system optimization relies on the particular use case that has to be optimized; Fig.  1 summarizes several of these algorithms and their applications in ERP optimization for the selected use cases. As well as some of their latest innovative applications are compared in [ 16 ]. The existing body of research pertaining to review methodology has extensively explored the potential benefits and challenges associated with its integration. Table 1 presents the machine learning approaches used and the corresponding outcomes in the selected use case.

Selected ML algorithms along with the functionalities used in ERP applications

Despite the presence of challenges related to data accuracy, algorithmic complexity, and setup complexities, the integration of Industrial Internet of Things (IIoT) and machine learning (ML) within the enterprise resource planning (ERP) environment has yielded remarkable outcomes in terms of increased productivity, cost savings, and improved decision-making.

These outcomes underscore the immense value of incorporating IIoT and ML within the ERP framework. The results of this study provide a foundation for a prospective scenario whereby intelligent and data-oriented enterprise resource planning (ERP) systems become indispensable assets in the fiercely competitive global market, as organizations persist in their efforts to adapt to the digital age.

To minimize delay and reduce maintenance costs, machine learning algorithms examine sensor data from equipment to forecast whenever repair is expected, which directly affects the lengthening of equipment lifespan, lowering maintenance costs, and boosting the equipment uptime. In addition, [ 17 ] paper's goal was to provide a general overview of ML-enabled predictive maintenance (PdM) in automotive applications to readers from a wide range of backgrounds. It underwent a thorough review of the literature that included 62 publications on ML application cases. It suggested future studies, according to the author which may focus on how generic PdM advancements apply to use cases in the automobile industry, whereas businesses can keep ahead of the curve by regularly evaluating and adjusting their strategies in response to economic developments by using a data-driven approach. They get essential knowledge that helps them make wise decisions and eventually propels them to success in the competitive market of today.

2.3 The integration of machine learning with ERP

This subsection provides an overview of the integration of ML with ERP, highlighting its transformative impact on enterprise systems. Machine learning (ML) applications within enterprise resource planning (ERP) systems have ushered in a transformative era for businesses. One of the key applications lies in predictive analytics, where ML algorithms analyze historical data to forecast trends and demand patterns. This capability enables businesses to optimize inventory levels, anticipate customer needs, and streamline supply chain operations.

Additionally, ML-driven anomaly detection enhances cybersecurity measures by identifying irregular patterns in data, safeguarding sensitive information from potential threats. Moreover, the integration of machine learning with ERP systems is a multidimensional process. Further this will enhance data analysis, enabling businesses to gain actionable insights from massive datasets. By processing vast amounts of information in real time, ML-powered ERPs facilitate dynamic decision-making, empowering organizations to respond swiftly to market changes.

Furthermore, machine learning algorithms enhance personalization within ERP interfaces, tailoring user experiences based on historical interactions and preferences. This level of customization not only boosts user satisfaction but also augments overall system efficiency. Ultimately, the synergy between ML and ERP redefines traditional business processes, creating intelligent, adaptive systems that pave the way for a more efficient, responsive, and innovative organizational future.

ERP systems are capable of automating time-consuming, repetitive operations using some sort of machine learning algorithm. In the long run, this would save time and assist in reducing human error. Some of the industrial advancements that integrate machine learning into ERP systems enable businesses to make use of data's potential for more intelligent, data-driven decisions, improved operational efficiency, and a competitive edge in the dynamic business environment.

As a result, Industry 4.0 concentrates on how technology is changing industries. Automation, data interchange, and smart manufacturing goals are all in accord with the aims of ML integration in ERP. For instance, predictive maintenance is essential for avoiding costly breakdowns and simplifying maintenance cycles in a variety of sectors. An overview of intelligent manufacturing techniques is provided in [ 25 ], which also expands on research on the shift from discrete to intelligent manufacturing.

According to [ 26 ] enterprise data collected through a survey that examined the relationships of an enterprise's strategy orientation, digital transformation capability, and operational performance, digital transformation capability will enable enterprises to integrate their business processes and routines through digital technology to gain a competitive advantage.

The goal of [ 27 ] was to execute digital strategies and transformation needs, and the findings revealed that, although technical competencies come largely from on-the-job training, epistemological competencies are insufficient and require more substantial consideration and training. Also, [ 28 ] study enhanced the IoT sensor networks with a proof-of-concept heating, ventilation, and air conditioning plant using a preventative maintenance technique. The findings demonstrated the applicability of the developed building structures preventive maintenance technique and the combined IoT and BIM dashboard system.

A data-driven approach to sourcing and inventory management for SMEs was proposed in [ 29 ]. It improved the use of artificial intelligence (AI) and fuzzy inference systems (FIS) instead of conventional inventory models. The results show how well the suggested approach performs in giving SMEs decision support solutions in the face of uncertainty. Hence, enhanced product quality, reduced defects, and minimized rework [ 30 ]. Supplier risk management, ML-based image recognition and sensor data analysis can identify defects and anomalies in real time during the manufacturing process. A considerable amount of literature involved the investigation of ML integration and application in ERP, as selected and illustrated in Table  1 .

2.4 Functionalities enhanced by ML in ERP

We explore the specific functionalities within ERP that are enriched through the integration of ML, setting the stage for in-depth analysis. ERP systems would use a vital ML algorithm to automate repetitive and time-consuming tasks. This might include quality control, inventory management, and production recommendations through the use of machine learning algorithms within an ERP system. Companies may save time, eliminate mistakes, and enhance the process by automating these sorts of processes using machine learning algorithms incorporated into an ERP system. The enhanced functions via ML and ERP connection are listed in Table  2 .

In view of this, [ 31 ] explored artificial intelligence strategies for solving challenges in communication networks. The suggested deep learning algorithm for IoT prediction was built at edge computing, it has done IoT traffic prediction techniques based on deep learning, and the prediction accuracy was reviewed and tested. However, firms may avoid making instantaneous decisions based on unreliable data by using data-driven decision-making. When reacting to market developments, businesses may use insights from a variety of sources, including consumer feedback and social media analytics, rather than just intuition or prior experiences. By evaluating historical data and patterns to make predictions or automate tasks, state-of-the-art machine learning algorithms and models are being implemented into ERP systems to improve decision-making processes.

2.4.1 Inventory management and demand forecasting enhancement by ML

Within ERP, ML enhances inventory management, streamlines demand forecasting, and even augments human resources functions. We delve into these enhancements; in order to estimate demand accurately, machine learning algorithms examine external variables and past sales data. This causes lower carrying costs and optimizes inventory levels.

On the other hand, we optimize inventory management and control in the ERP environment and propose specific solutions and corresponding development measures; [ 32 ] analyzes the issues and causes of inventory management and human resource management in the ERP environment. This analysis was done to help businesses achieve reasonable inventory control and boost their competitiveness in the market. The processing effectiveness is better after system optimization, and the inventory may be adjusted to encourage resource conservation. Additionally, in [ 29 ] utilization of hybrid GN-ANN approach is used in the development of a DSS for sourcing and inventory management in SMEs that have limited resources and knowledge.

In this review paper, we present an overview of the usage of machine learning algorithms in various IoT use cases, which is explicitly given in Table  3 that illustrates the state-of-the-art methodologies along with its pros and cons, while the functionality of ERP is shown in Fig.  1 and Table  2 , with supporting use case scenarios. The present gaps in machine learning and IoT integration were examined to identify difficulties and future prospects.

2.4.2 Production scheduling and optimization

ML-driven production scheduling and optimization is vital for operational excellence. This section delves into the use of ML in these domains. This subsection's thorough examination has highlighted how ML algorithms enable industrial processes to adjust to real-time data and act quickly in response to changes in demand and supply. The end result not only reduces downtime and resource waste, but also has the potential to increase competitiveness in a market that is becoming more dynamic. Design is more sustainable, robust production planning and control (PPC) systems by integrating management approaches, along with their specific tools and procedures, with I4.0 technologies, such as the IIoT and the DT. We integrate data analytics and machine learning into PPC I4.0 to handle difficult issues automatically and autonomously, and create novel optimization, simulation, and artificial intelligence models and algorithms to assist PPC systems [ 33 ].

A significant field in the context of the machine learning (ML) integration with enterprise resource planning (ERP) systems is the scheduling and optimization of production. This goes into the core of the manufacturing process, where complex equipment, resources, and timetables must work together for productive and economic output.

It is a real paradigm shift for companies, and the transition to Industry 4.0 impacts their entire operation, namely the production chain, the organization of work, the economy of the company, the management and business logistics, business strategy, and consumer habits [ 34 ]. All the sensors and data management via the cloud make it possible to follow the product in its life cycle; This challenges the current product/service divide in product tracking.

Production scheduling, which has long been problematic in various industries, from food processing to the production of vehicles, is frequently used to solve difficult challenges. However, machine learning (ML) techniques in this domain have ushered in a new era characterized by enhanced precision and adaptability. A survey [ 35 ] was conducted to assess the extent to which the deterministic maximum principle may be used in the context of production scheduling, supply chain management, and Industry 4.0 technologies.

2.4.3 Quality control and predictive maintenance

Quality control and predictive maintenance are critical for seamless operations. We discuss how ML is revolutionizing these aspects within ERP. In the enhancement of quality control and production optimization, authors of [ 36 ] proposed a platform that enables a visualizing framer work and maintains track of every alteration. IIoT sensors collect accurate data during the manufacturing process. ML algorithms examine these data to discover errors, identify quality disparities, and enhance production settings. Integration with ERP allows for instant quality control input, enabling for quick changes to meet product quality criteria. As a consequence, operations are more efficient, there is less waste, and customers are more satisfied.

Authors in [ 37 ] introduce topics in the realm of energy-efficient industrial IoT-based big data administration and analysis in cloud settings. ML algorithms incorporated into ERP systems evaluate IIoT data to intelligently automate repetitive chores and procedures. The proposed future aim was to include security and privacy concerns into this framework to achieve energy-efficient and safe cloud-based administration.

Consequently, IIoT sensors and devices are easily connected with ERP systems, allowing massive amounts of real-time data to be collected from multiple sources such as manufacturing equipment, supply chain components, and operational activities. ML algorithms process these data in real time, giving ERP modules rapid insights. This real-time connectivity enables ERP systems to make data-driven choices in real time, assuring maximum performance across all company functions.

2.5 Predictive maintenance and asset optimization and adaptive process automation

This part examines the manner in which machine learning (ML) techniques optimize assets and expedite maintenance operations. In order to accurately assess maintenance requirements, machine learning algorithms analyze data obtained from industrial Internet of Things (IIoT) linked machinery and equipment. Predictive maintenance models aim to optimize maintenance schedules, minimize downtime, and extend the lifetime of assets via the identification and analysis of trends and irregularities.

The use of this proactive technique ensures that enterprise resource planning (ERP) systems are able to effectively oversee and allocate resources, hence minimizing disruptions and enhancing production efficiency. Furthermore, adaptive process automation is the future of ERP. We examine how ML-driven automation is making ERP systems more agile and responsive, while ERP systems acquire the capacity to assess consumer behavior, preferences, and market trends by combining IIoT data with ML algorithms. In [ 38 ] paper a comprehensive literature review was conducted that focused on primary research articles pertaining to predictive maintenance, industry 4.0, and data science. Consequently, a range of essential procedures performed by a data scientist in the context of predictive maintenance were successfully identified. Yet, predictive analytics forecasts future patterns using past data and statistical algorithms, whereas prescriptive analytics suggests steps to improve outcomes. Predictive analytics in ERP may be used to estimate sales or demand, and prescriptive analytics can recommend appropriate pricing strategies or supply chain changes. Predictive analytics models reliably estimate demand in demand forecasting and consumer Insights domains, helping firms to align production and inventory levels with consumer demands. This connectivity guarantees that ERP systems can adapt to market demands in real time, improving customer happiness and boosting sales tactics.

In the same context, paper [ 42 ] investigated the effects of the IoT big data analytics paradigm (IoT BDA) on the installation and utilization of IoT-based technologies in healthcare services. Cloud ERP combined with IoT is a contemporary sector that promises better administration and customer service. By evaluating sensor data or photos, ML discovers flaws in real time. Predictive maintenance algorithms anticipate when equipment may break, saving downtime and maintenance costs.

2.5.1 Supply chain management and logistics

In the era of global supply chains, ML plays a pivotal role. We investigate how it's redefining supply chain management and logistics. By more correctly forecasting demand, ML approaches have changed inventory management. As a consequence, extra inventory expenses and inventory shortage are decreased. ML aids in talent recruiting, workforce planning, and employee retention in HR management. IIoT-enabled RFID, Radio-frequency identification, tags and sensors allow real-time visibility into inventory levels in the field of Intelligent Inventory Management. These data are processed by ML algorithms, which forecast demand patterns and optimize inventory level. The [ 43 ] growing field of prognostics, which leverages big data analytics, ML, and IoT, can enhance maintenance cycle and spare parts demand forecasting." The suggested maintenance, repair, and operation (MRO) categorization for all components and accompanying strategies was designed to address inventory management solutions from the standpoint of Industry 4.0 technologies.

ERP systems use ultimate insights to automate inventory replenishment, avoid stockouts, and reduce surplus inventory, resulting in considerable cost savings and enhanced supply chain efficiency. Supply chain management using predictive analytics [ 44 ] logistics predictive analytics employs both quantitative and qualitative methodologies to forecast past and future material flow and storage behavior, as well as associated costs and service levels. Predictive analytics may aid the supply chain managers obtain a competitive edge by forecasting consumer behaviors, minimizing risks, discovering new consumers, optimizing operations, and increasing customer happiness and loyalty in real time.

In [ 45 ] a scoping assessment of the relevant literature on supply chain resilience, SC management, and I4.0 presented and its findings highlighted and summarized the impact of I4.0 integration into SCs to SC resilience enhancement.

By forecasting demand, managing inventory, and dynamically routing shipments, machine learning optimizes supply chain operations. It adjusts to changes in real time, such as traffic conditions.

In [ 46 ], the authors examined and clarified the potential of artificial intelligence (AI) for supply chain and logistics management. Their conclusion was that AI technologies offer a predictive element to help decision-makers. However, in [ 47 ] the authors conducted an analytical review of the literature to determine which ML practices might assist the various stages of the "CRM" life cycle and extract current methods and applications for "CRM" life cycle management. From another perspective, paper [ 40 ] assessed major supply chain operations and investigated RFID technology acceptance and deployment to optimize supply chains. Other ML-ERP integration use cases from previous work are mentioned in Table  1 .

3 Reviewing state-of-the-art techniques and advancements

This section critically reviews the latest developments in ML-driven ERP optimization, evaluating recent techniques and advancements. It offers valuable insights into the current trajectory of this field. Consequently, the state of the art in IIoT and ML integration within the ERP framework predicts an era of intelligent, data-driven decision-making. Businesses may achieve unprecedented levels of productivity, adaptability, and competitiveness in the contemporary digital world by harnessing the power of real-time data, predictive analytics, and adaptive automation.

Machine learning (ML) plays a pivotal role in enabling ERP systems to process and act on data as it flows in, ensuring that businesses can respond swiftly to changing conditions. This trend has had particularly impactful across industries, providing instant insights and actionable intelligence. The fusion of IoT and Edge Computing is gaining prominence. Edge computing, which processes data closer to the source, enhances ERP systems by enabling faster decision-making. Whether in manufacturing, logistics, or other sectors, this trend facilitates more agile and efficient operations.

The most recent studies, illustrated in Table  3 , summarize the state-of-the-art use cases in the IIoT field. The table provides information on the ML algorithms used, along with a description of the pros and cons of each case.

However, the results of a first evaluation of distributed ledgers that could be used in the Internet of Things (IoT) in [ 51 ] showed that while they can process thousands of transactions per second, their performance usually does not scale to tens of devices because it falls off significantly as the number of devices increases. The study proposed that future research should concentrate on assessing distributed ledgers. Looking ahead, the future of ML-driven ERP optimization offers even greater advancements, such as more complex AI integration and larger applications.

According to studies [ 52 ], IoT technology has discovered more and more uses in a variety of contexts and aspects of daily life. It looked at a number of IoT protocols, technologies, and applications in day-to-day life. It also included a glossary of related words and the most recent, cutting-edge IoT architecture across a range of industries.

The authors of [ 53 ] set upward a framework for assessing the economic, social, technological, functional, and non-functional aspects of SMS performance. The development of SMS services has also advanced significantly, with the aim of enhancing the effectiveness of business requirements. Pairwise comparison is used to create a predictive analytics solution that examines SMS services and corporate requests.

4 Conclusion

This comprehensive investigation has unveiled a fertile landscape for integrating machine learning (ML) into enterprise resource planning (ERP) systems. Through an extensive exploration of literature and research, diverse applications, methodologies, challenges, and potential benefits have been uncovered, showcasing the multifaceted ways ML can optimize ERP functionality, efficiency, and decision-making. A summary of the key functionalities is provided in Table  2 .

ML has emerged as a catalyst, significantly enhancing ERP performance across various domains. This enhancement is exemplified in areas such as inventory management, demand forecasting, production scheduling, quality control, predictive maintenance, adaptive process automation, and supply chain management. The transformative potential of ML in ERP is underscored by promising outcomes, including increased efficiency, reduced costs, and improved decision-making capabilities, as detailed in various use cases outlined in Table  1 .

Moreover, our review delves into the state-of-the-art techniques, recent advancements, and emerging trends in this rapidly evolving field, offering valuable insights for both researchers and practitioners. The integration of Industrial Internet of Things (IIoT) and ML in the context of ERP has resulted in ground-breaking breakthroughs, as illustrated in Table  3 . This review contributes to the knowledge base by describing the state-of-the-art improvements in ERP and showcasing the most advanced research works in this domain, encouraging a deeper understanding of the synergy between ML and ERP. It serves as an initial step toward novel advancements and applications within the realm of business technology.

In summary, the incorporation of Industrial Internet of Things (IIoT) and machine learning (ML) technology into ERP systems has brought about a significant transformation in conventional business operations. Enterprises now have the potential to enhance decision-making capabilities, optimize resource allocation, and improve customer satisfaction by effectively leveraging real-time data and predictive analytics.

5 Future work

This comprehensive review article scrutinizes the integration of Industrial Internet of Things (IIoT) and machine learning (ML) technologies in the context of enterprise resource planning (ERP) systems. In essence, our analysis illuminates the significant potential for the integration of machine learning (ML) into ERP systems, showcasing notable improvements in operational efficiency, cost-effectiveness, and decision-making across diverse ERP functions.

As we envision the future, promising prospects for further study and development emerge. Enhancing enterprise resource planning (ERP) systems with machine learning (ML) holds the promise of significant progress. Notably, there is a need for research in scalability studies focused on large organizations, along with an examination of the long-term impact of machine learning on company performance. These areas bear significant importance for future investigations.

By acknowledging and embracing these challenges and opportunities, both academics and practitioners possess the potential to drive the advancement of machine learning-driven ERP systems. This commitment ensures the sustained relevance and capacity for innovation within the dynamic realm of business technology. In summary, the incorporation of IIoT and ML into ERP systems has ushered in a significant transformation of traditional business operations. This study serves as a pivotal step toward novel advancements and applications within the realm of business technology. By acknowledging and embracing both opportunities and challenges, academics and practitioners have the potential to propel the advancement of ML-driven ERP systems, ensuring their sustained significance and capacity for innovation within the dynamic realm of business technology.

Availability of data and materials

Not applicable.

Abbreviations

• Enterprise resource planning

Industrial Internet of Things

Internet of Things

Industry four

• Machine learning

Key performance indicators

System analysis program development

Material requirements planning

Customer relationship management

Supplier relationship management

Graduation Intelligent Manufacturing System

Manufacturing planning and control

Big data analytics

Predictive maintenance

Digital transformation

Long short-term memory

Support vector machine

Production planning and control

Radio-frequency identification

Maintenance, repair, and operation

Supply chain

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Department of Electronics Technology, Budapest University of Technology and Economics (BME), Budapest, 1111, Hungary

Zainab Nadhim Jawad & Villányi Balázs

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ZNJ and BV contributed equally to this work. ZNJ conducted the research, data collection, and initial drafting of the manuscript, with significant contributions to the introduction and methodology sections. BV provided valuable guidance and expertise, particularly in the analysis, results, and discussion sections, contributing critical insights to the interpretation of findings. Both authors reviewed and approved the final manuscript.

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Correspondence to Zainab Nadhim Jawad .

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Jawad, Z.N., Balázs, V. Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review. Beni-Suef Univ J Basic Appl Sci 13 , 4 (2024). https://doi.org/10.1186/s43088-023-00460-y

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Received : 14 October 2023

Accepted : 13 December 2023

Published : 04 January 2024

DOI : https://doi.org/10.1186/s43088-023-00460-y

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