The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies ^†

Abstract

Recently, machine learning-based task automation framework have been gaining attention in human resource management of Multi-National Companies (MNCs). Task automation framework helps MNCs to automate repetitive HR tasks, analyse data quickly and accurately, forecast workforce, and recognize employees. MNCs are now beginning to use ML algorithms in combination with Artificial Intelligence (AI) to streamline the HR processes. Most MNCs have large-scale operations and decentralized organization structures which put additional pressure on HR teams to carry out intricate and tedious manual processes. To ease the process, ML-based task automation framework facilitates HR teams to leverage the power of AI and perform HR management tasks in a more effective and efficient manner. The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data.

1. Introduction

Human Resource Management (HRM) is essential to any business, but especially to MNCs (Multi-National Corporations) as they need to manage a diverse range of people working in different countries and from different cultural backgrounds [1]. Through recruiting the right people, HRM facilitates cross-cultural understanding and helps create a work environment where diverse cultures, perspectives, and ideas can be embraced and promoted [2]. The proper HRM helps to ensure the welfare and protection of workers in MNCs. HRM ensures that employees are treated fairly, their workplace is safe, and they are complying with labour laws in each of the countries they operate in. The human resource management is crucial for MNCs to gain a competitive advantage and ensure a safe and productive working environment [3]. HRM helps to make sure that the right people are in place with the right skills and those employees and suppliers have their needs met so that the organization can optimize the resources available to them. The emergence of global companies with multiple international branches has greatly expanded the scope and complexity of task management today [4]. These cloud-based tools also allow MNCs to better track and measure global project progress in real-time, helping to prevent costly delays and unforeseen conflicts. Additionally, cloud-based solutions make it easier for MNCs to scale their task management processes as their businesses grow without expending additional resources [5]. Unlike manual human oversight, AI-driven solutions are able to anticipate potential issues before they arise and take preventive action in a fraction of the time. The implementation of distributed resource planning, cloud computing solutions and intelligent AI systems all help MNCs to tackle challenges harder and keep ahead of the competition [6]. The main contribution of this research has the following:

• Improved Efficiency: Automated HR management processes can help streamline employee data and tasks, eliminating manual processes and reducing the burden on HR staff. This can lead to improved efficiencies of HR tasks such as recruitment, on boarding, payroll, and maintaining employee records.
• Improved Accuracy: Automation eliminates manual data entry errors, increasing data accuracy.
• Improved Data Quality: By automating and improving the quality of the data, organizations can design and implement better HR metrics and data-driven strategies [7,8,9].

2. Materials and Methods

Task management in multi-national companies presents a unique set of challenges that must be addressed to ensure successful operations. Multi-national companies typically have employees in multiple countries and cultures, which can make it difficult to communicate expectations and assign tasks [10]. These values and norms should be developed to provide a general framework for task management that can be applied across cultures. Without a common set of values and norms, there is a risk of miscommunication, mismanagement, and duplication of effort between different departments and subsidiaries [11]. Without clear role definitions and responsibilities, tasks could easily be misunderstood or duplicate efforts resulting in wasted time and money. Furthermore, cross-cultural communication and collaboration is essential for successful task management in multi-national companies. Communication should occur on all levels to ensure everyone understands their tasks, roles, expectations, resources, and responsibilities [12,13]. Overall, task management in multinational companies requires carefully planning and collaboration. It is important to develop a common set of values and norms that can be applied across cultures, create an understanding of roles and responsibilities, and foster cross-cultural communication and collaboration [14]. The challenges of human resource management are abundant, and they are often much more complicated than those found in domestic companies. On the one hand, MNCs are faced with the task of managing their large workforce of employees spread over multiple countries [15]. In terms of management, MNCs must ensure that all employees are educated on the company’s mission and values, and ensure policies and standards are understood and adhered to by all employees regardless of geographic location [16]. Moreover, employees must be adequately guarded against discriminatory behaviours. MNCs must also establish policies for accurate tracking of performance and manage the salaries of their international staff [17]. In addition, MNCs should also ensure that employees are aware of their respective rights in terms of labour laws and regulations. In terms of corporate culture, MNCs must create a strong unified corporate culture that leads to a cohesive work environment [18]. Managing the complexities of a multinational company can be incredibly difficult and challenging. However, if implemented well, these difficulties can be overcome and MNCs can achieve powerful results [19]. A successful human resource management strategy must be proactive and flexible, ensuring the maintenance of clear communication lines in a diverse and globally scattered workforce [20]. The machine learning-based task automation framework for human resource management in MNCs presents a great novelty because it streamlines the HR processes and automates mundane tasks. It helps in better decision-making, improved operations, easy access to data, increased operational efficiency and cost savings, improved accuracy and transparency, improved quality of data, automated workflow, improved resource planning and management, and improved employee experience. This framework is also expected to evolve with the changing needs of the organization and its HR teams.

Proposed Model

Data Preprocessing: It is the process of transforming raw data into a form suitable for use in a machine learning algorithm. This involves cleaning and organizing data, transforming variables, removing outliers, and scaling numerical attributes.

Feature Selection: It is the process of selecting the best subset of features from a pool of features for any particular machine learning problem. It seeks to identify the most important features that can maximize the performance of a predictive model. It not only reduces the complexity of the model but also drives the accuracy of the model.

Data Quality Considerations: In order to effectively automate and enhance HR processes, it is important to consider data quality considerations such as correctness, accuracy, integrity, completeness, reliability, and timeliness. Data quality should also be vetted using data validation techniques such as checking for correct data types, examining for null values, and ensuring that data are consistent across multiple sources. Data security and privacy should also be taken into account to ensure the confidentiality of employee data.

The proposed framework includes features such as advanced analytics for employee profiles and real-time analytics based on role-based access to ensure data security.

$$\frac{dm}{dn}=\frac{d}{dn}(e^m\ast \sin ij)$$

(1)

The ML-based task automation framework for human resource management in MNCs also provides an AI-powered assistant to provide personalized HR assistance.

S = i∗j

(2)

This helps them to make informed decisions that will help them enhance employee motivation and loyalty. Moreover, this system helps reduce operational costs as well as the risk of data security breach due to its user-friendly dashboard and role-based access.

$$\frac{dn}{dm}=\left(i\ast \frac{dj}{dm}\right)+\left(j\ast \frac{di}{dm}\right)$$

(3)

This framework aims to replaced manual and repetitive HR tasks with AI-based solutions. The framework helps HR departments to maximize efficiency, accuracy, and cost-effectiveness, while managing and deploying employees. Machine learning-driven task automation is transforming how we manage resource and operations.

$$\frac{dn}{dm}=\left(e^m\ast \frac{d}{dm}\sin in\right)+\left(\sin in\ast \frac{d}{de}(e^m)\right)$$

(4)

The purpose of this paper is to discuss the potential of machine learning-based task automation framework for human resource management. Such an AI-powered agent can intelligently identify qualified individuals from a larger pool of applications.

$$\frac{dn}{dm}=\left(I\ast e^m\cos in\right)+\left(e^m\sin in\right)$$

(5)

It can be connected to an online job board or a recruitment database to source the most suitable candidate for a specific job opening in an MNC.

$$n″=\underset{m\to 0}{\lim }\left(\frac{o(m+n)-o(m)}{n}\right)$$

(6)

Such systems automatically calculate incentives and compensation packages offered by the organization, based on the experience and qualifications of an individual.

$$n″=\underset{m\to 0}{\lim }\left(\frac{o^{m+n}-o^m}{n}\right)$$

(7)

The proposed framework can provide a comprehensive solution for task automation for human resource management that is automated, cost-effective, and efficient. The operating principle is to utilize automated technologies to streamline and improve the processes, including recruiting, payroll and attendance management, employee performance tracking and reporting, training and mentoring, and compensation. The functional block diagram has shown in the following Figure 1. With the use of machine learning, the system is able to detect patterns, recognize trends, and other insights in order to enhance the decision-making process.

$$n″=\underset{m\to 0}{\lim }\left(\frac{(o^m\ast o^n)-o^m}{n}\right)$$

(8)

Furthermore, the system can also be programmed to automatically update the data in real time and keep accurate records of all the activities related to the HR management process. The advancement of machine learning (ML) has become a major impact on how human resource (HR) departments are managing the workforce globally.

$$n″=\underset{m\to 0}{\lim }\left(\frac{o^m\ast (o^n-1)}{n}\right)$$

(9)

For example, ML can help with HR task automation frameworks by automating many processes such as recruiting, on boarding, performance evaluations, and feedback surveys. The operational flow diagram has shown in the following Figure 2.

• Efficiency: This metric evaluates the amount of time saved through the implementation of the automated HR tasks. It should take into account how long it would take to execute the process manually, the amount of time each automated process takes to complete, and the number of processes completed by the automation framework.
• Accuracy: This metric assesses the accuracy of the automation model by tracking the number of tasks correctly identified and the number of tasks incorrectly identified. It should consider the rate of success in performing the tasks as well as the percentage of tasks that are completed correctly.
• Usability: This metric measures the user’s comfort and ease of use when interacting with the automation system. It should include measures such as the number of user errors, user experience, and response time.

Onboarding processes can be automated by designing ML-powered Chabot services that interact with candidates and answer their questions. ML can also be applied to analyse employee feedback, assess team performance, and detect problems in tasks.

$$n″=o^m\ast \underset{m\to 0}{\lim }\left(\frac{(o^n-1)}{n}\right)$$

(10)

Furthermore, ML is able to create personalized training plans for employees and enable the automation of scheduling and planning. ML can also help facilitate the management of MNC employees.

1. Privacy and Discrimination: There are privacy implications when using AI and ML in HR management. When collecting employee data, companies must ensure that they are following privacy regulations to avoid any legal ramifications. Additionally, AI and ML tools can often lead to potential discrimination and bias, as they are created in a way that can lead to the exclusion of certain groups, such as racial minorities or those with disabilities.

2. Employee Rights: AI and ML used in HR management can strip away certain employee rights such as the right to privacy or the right to be in control of their own work. It is important to consider the rights of any employee before implementing such technology.

3. Unintended Consequences: As AI and ML tools are often difficult to reverse and predict, they can lead to unintended consequences. Sometimes AI models can be gamed to a certain degree, meaning that potentially malicious actors can try to manipulate the results for their personal benefit. Additionally, AI is continuously learning, and therefore can lead to unforeseen results in certain situations.

4. Data Quality: AI and ML tools rely on data accuracy and quality. Poor data quality can lead to issues such as inaccurate or biased decisions. Companies should consider effective ways to ensure that the data collected is accurate and up-to-date.

5. Increasing Complexity: When introducing AI and ML into HR management, there is an added level of complexity. As these tools are new and everchanging, they require support and expertise to run smoothly. Companies must be aware of the associated costs and be prepared to adjust their systems as needed.

3. Results and Discussion

The proposed Machine Learning-Based Task Automation Framework (MLTAF) AI Capability Framework (AICF), Artificial Intelligence Machine Learning Models (AIMLM) and personalized human resource management (PHRM). Here, the python tool is used to execute the results.

The false negative rate measures what percent of the predicted labels are incorrect. It is calculated by taking the number of incorrect labels divided by the total number of test samples.

Figure 3 shows the comparison of false negative rate. In a computation tip the proposed MLTAF reached 94.21% false negative rate. The existing AICF obtained 48.65%, AIMLM reached 89.50%, and PHRM obtained 61.64% false negative rate.

False Positive Rate (FPR) is the ratio of false positives to the total number of positive results. The false positive rate of the machine learning based task automation framework is the ratio of incorrect predictions of positive outcomes to the total number of positive predictions. Figure 4 shows the comparison of false positive rate. In a computation tip, the proposed MLTAF reached 89.62% false positive rate. The existing AICF obtained 48.39%, AIMLM reached 63.67%, and PHRM obtained 55.11% false positive rate. Automation bots within the framework can provide a highly intuitive user experience that is compared to a user’s experience with digital assistant. These bots can interact with users through voice commands, enabling users to query and provide the necessary information quickly and with minimal effort. Most automation bots within the framework are designed to be user-friendly and require minimal user training or adaptation.

False Discovery Rate (FDR) is the fraction of incorrect decisions or false discoveries among all the decisions made. In the context of Machine Learning (ML)-based task automation framework for Human Resource (HR) management in multi-national companies.

Figure 5 shows the comparison of false discovery rate. In a computation tip the proposed MLTAF reached 96.41% false discovery rate. The existing AICF obtained 50.14%, AIMLM reached 91.47%, and PHRM obtained 64.06% false discovery rate.

• Predictive Analytics: Predictive analytics can be used to help HR determine the best applicants to hire, which employees are most likely to stay with the organization and which may be prone to leaving, and patterns of performance and productivity. Information from employee surveys can be used to create predictive models that forecast an employee’s potential for success, by taking into account things like job performance, job satisfaction, and team member interactions.
• Machine Learning: Machine learning can be used to automatically detect potential problems in the recruiting process, such as bias in candidate selection, and to provide recommendations on how to improve the process. Automated tools can also be used to assist with employee onboarding, using data from employee performance reviews and surveys to help make decisions about potential hires, as well as provide insights about employee satisfaction levels and workforce trends.

There are a few potential challenges and limitations associated with automating and enhancing HR processes. First, it can be difficult to determine which processes are more suitable for automation and which processes should remain manual. Companies may spend a considerable amount of time evaluating their current HR processes and the potential time savings associated with automation.

1. Complexity: ML-based task automation frameworks can be complex to set up and maintain. They require extensive knowledge of machine learning concepts and programming expertise.

2. Cost: ML-based task automation frameworks can be quite expensive to create and maintain, since they involve the use of expensive hardware and software components.

3. Accuracy: While ML-based task automation frameworks may be accurate in certain uses cases, there is still a risk of errors, especially when the initial data used for training is incorrect or incomplete.

4. Security: ML-based task automation frameworks can be vulnerable to security threats, since they use artificial intelligence algorithms to process sensitive information.

Additionally, inadequate resources may be an impediment to updating their HR systems or properly training their staff in the use of new HR processes. Finally, automating and modernizing HR processes can be costly. This will require an initial investment in software and hardware as well as additional IT resources to implement the system and train personnel in its use. Companies may find these expenses difficult to justify, particularly if the benefits of automating their HR processes are not immediately apparent.

4. Conclusions

A machine learning-based task automation framework for human resource management in MNC companies is a system whereby various tasks associated with human resource management can be automated by leveraging the power of machine learning. This framework can help in streamlining and improving the efficiency of HR processes such as recruitment, onboarding, payroll, training, and offboarding. It can help reduce human resources costs and improve performance. By leveraging ML algorithms, HR-related tasks can quickly and accurately be identified and managed. Additionally, the framework can detect and predict workforce trends and provide more personalized services to employees and managers. It can help create a better and streamlined HR environment that is optimized for optimal performance. In the HR field, automation has begun to disrupt traditional, manual processes and help organizations streamline processes from recruiting to onboarding. ML is an essential component of automated HR tasks, as it provides the structured format to hold key information desired by an organization.