Artificial Intelligence in ERP: Unlocking New Horizons in Supply Chain Forecasting and Resource Optimization

Abstract

Purpose: This article aims to evaluate the potential of Artificial intelligence (Ai) in ERP systems to enhance Forecasting in supply chain and resource optimization. It explores how AI can improve forecast accuracy, automate routine tasks, and optimize resource allocation within ERP systems. By analyzing the benefits and challenges of AI integration, the document provides insights for organizations seeking to leverage AI for enhanced supply chain management.

Methodology: The methodology employed in the document involves both qualitative and quantitative research techniques. Quantitative data, such as forecasting accuracy, inventory turnover, and cost, is gathered from real-time ERP system data logs to measure ERP performance before and after AI implementation. Qualitative data is collected through interviews with ERP system administrators and supply chain managers to gain insights on user experience and challenges faced in Supply chain function and during AI integration. The research also discusses the selection of suitable machine learning models and their implementation methodology, including data preprocessing, training, and testing phases. Performance metrics, such as Mean Absolute Percentage Error (MAPE), are used to assess the improvements achieved through AI integration.

Findings: The study found that AI integration in ERP systems significantly improved forecasting accuracy by 20%. This was attributed to AI's ability to analyze vast amounts of data and identify patterns that traditional ERP systems cannot do without significant work. Inventory turnover ratio increased by 33%, indicating faster movement of stock and reduced holding costs. This was due to AI's improved demand forecasting and real-time inventory adjustments. Operational costs were reduced by 15% due to automation of routine tasks, optimized resource allocation, and minimized waste in production and logistics.

Unique Contribution to Theory, Practice and Policy: The research supports existing literature and case studies, confirming AI's potential to revolutionize ERP systems and supply chain management. The findings support existing literature on the potential of AI in supply chain management, specifically in forecasting and resource optimization. The research demonstrates the tangible benefits of AI integration, such as improved forecasting accuracy, optimized resource allocation, and reduced operational costs. The discussion on potential challenges, such as data security and algorithmic bias, helps organizations anticipate and address these issues proactively. The findings can inform government policies and industry regulations related to AI adoption in ERP systems and supply chain management. The emphasis on addressing algorithmic bias and data security concerns encourages responsible and ethical AI implementation. The research highlights the transformative potential of AI, encouraging businesses and policymakers to invest in AI-driven solutions for enhanced supply chain resilience and competitiveness.