Chapter 1

Introduction

1.1 Background of the Study

Inventory management is a critical component of supply chain operations for businesses of all sizes. Traditional methods of tracking inventory often rely on manual data entry and periodic stock checks, which are prone to human error and inefficiencies. In recent years, the integration of Artificial Intelligence (AI) has revolutionized this domain. By leveraging machine learning algorithms and real-time data analytics, companies can now optimize stock levels with unprecedented accuracy.

1.2 Problem Statement

Despite the availability of various ERP solutions, small and medium enterprises (SMEs) continue to face challenges such as stockouts, overstocking, and inaccurate demand forecasting. These issues lead to significant financial losses and decreased customer satisfaction. The core problem lies in the inability of static rule-based systems to adapt to dynamic market conditions.

1.3 Objectives

The primary objective of this project is to develop an AI-powered inventory management system capable of predicting future demand using historical sales data. Secondary objectives include creating a responsive dashboard for store managers and integrating automated restock alerts.