The changes in management performance through AI technology

Jae-Cheol Lee / AI WG Team Leader

In recent years, applying artificial intelligence (AI) to the analysis of Enterprise Resource Planning (ERP) has received great attention as a means to improve business performance. ERP solutions are used to manage various aspects of company operations, such as sales, accounting, production, finance, cost, and human resources, and to generate large amounts of data that can be analyzed to gain insights into business performance. However, manually analyzing such a large amount of data is time-consuming and error-prone. By applying AI technology, these limitations can be overcome, and more accurate and timely insights into business performance can be provided. In this article, we will discuss the changes in management performance when AI technology is applied to ERP solutions, focusing on key areas such as sales, accounting, production, finance, cost, and human resources.

Firstly, using AI technology in sales management can provide insights into customer behavior and preferences, which can have a significant impact on sales performance. AI algorithms can analyze customer data to identify patterns and trends, enabling companies to provide more personalized products and services. This can lead to improved customer satisfaction, loyalty, and ultimately, increased sales. Additionally, AI can be used to optimize pricing and promotions, enabling companies to offer the right products to the right customers at the right prices in a timely manner.

If AI technology is applied in accounting management, many tasks can be automated. This reduces the risk of errors in routine accounting tasks such as data entry and report generation, allowing employees to focus on more strategic tasks. AI can also be used to detect fraudulent activities such as billing fraud, vendor fraud, and employee fraud, reducing the risk of financial loss and reputational damage. Additionally, AI algorithms can provide insights into financial performance, including cash flow, profitability, and investment returns, by analyzing financial data.

AI can improve production efficiency and quality. It optimizes production planning and schedules, reduces waste, and predicts equipment failures. AI algorithms analyze production data to identify bottlenecks and other inefficiencies, enabling companies to make informed decisions about production improvements. Additionally, using AI to monitor equipment performance and predict maintenance needs can reduce downtime and increase productivity.

If AI is applied in the financial management area, AI algorithms can analyze financial data, such as cash flow, profitability, and investment returns, to identify trends and patterns. This can help companies make informed decisions about investments, acquisitions, and divestitures based on the information provided by AI analysis. Furthermore, by using AI to optimize financial planning and forecasting, companies can predict financial performance and take measures to improve it.

In the cost management module, AI can optimize supply chain operations and reduce costs. This includes demand forecasting, inventory management, and logistics. AI algorithms analyze supply chain data to identify inefficiencies and improvement opportunities, allowing companies to reduce costs and shorten delivery times. Additionally, AI can be used to monitor and analyze operational data to identify cost-saving opportunities such as energy usage, maintenance, and labor costs.

In the area of human resource management, AI can be used to optimize personnel management by analyzing employee data to identify trends and patterns in employee behavior and performance. AI algorithms can be used to identify outstanding employees, enabling companies to develop and retain the best talent. AI can also be used to optimize workforce planning and scheduling, reducing labor costs and improving productivity.

We have examined the process of change when applying AI to the main modules of ERP. While there are significant benefits to applying AI analytics capabilities to ERP data analysis, there are also several limitations and challenges to consider. Data quality is essential to the accuracy of AI-generated insights, and implementing AI technology can require significant investment in hardware, software, and personnel. Finding and retaining personnel skilled in data science, machine learning, and artificial intelligence can also be difficult. Additionally, using AI in business operations is subject to regulatory compliance considerations, including data privacy, security, and ethical considerations.