Inventory Management System

Description:

Introduction

An inventory management system plays an important role in modern supply chains by helping businesses control stock and meet customer demand on time. Companies use predictive analytics to forecast demand, plan purchases, and avoid overstocking or shortages. This approach improves operational efficiency, reduces storage and ordering costs, and supports smooth production. Businesses also respond faster to market changes and supply disruptions with data-based planning. Proper inventory control improves product availability, builds customer trust, and strengthens competitiveness. Although challenges like demand uncertainty, global supply networks, and technology gaps exist, predictive tools help organizations manage risks, improve accuracy, and achieve better overall supply chain performance.

Objectives of the Study

1. To study how predictive analytics improves inventory management efficiency.

2. To identify the main predictive techniques used in inventory planning and control.

3. To examine the role of predictive analytics in demand forecasting and inventory optimization.

4. To analyze the challenges faced by organizations in using predictive analytics.

5. To suggest practical ways to improve inventory management using predictive analytics.

Research Methodology

This study follows a quantitative research approach to understand how predictive analytics supports inventory management. The research focuses on collecting numerical data to identify trends, patterns, and relationships. The researcher designed a structured questionnaire as the main tool for primary data collection. The questionnaire contained close-ended questions to ensure clear, unbiased, and consistent responses. The study collected data from 50 respondents working in manufacturing, retail, logistics, e-commerce, and pharmaceutical sectors. This method helped in gathering measurable and comparable information for proper analysis.

Data Collection

Primary Data:
The researcher collected primary data through a structured questionnaire from professionals involved in inventory, logistics, and supply chain activities.

Secondary Data:
The researcher gathered secondary data from books, journals, research articles, and government reports to support the study.

Sampling Method

The study used purposive sampling to select respondents who have practical knowledge or experience in predictive analytics and inventory management.

Sample Size

The total sample size consisted of 50 respondents from different industries.

Tools for Data Analysis

The researcher analyzed the data using percentage methods and tables. The study also used bar charts and pie charts for easy understanding. Each response was carefully interpreted to draw useful managerial insights.