Optimizing Inventory Management Through Predictive Analytics

Project Cost:Rs 2500 (Project Report) Rs. 3000 (Synopsis + Project)

Can Be used in: Operations Management

Project Report Pages: 60-70 (Soft Copy Word format)

Delivery time: Within 12 hours for readymade project and 3 days for new project

Short Description: Optimizing Inventory Management Through Predictive Analytics

Description: Please refer to the Sample Project. Each project has unique content based on its topic. The above PDF sample is for an operation management project.

Introduction

The project titled “Optimizing Inventory Management through Predictive Analytics” explains how companies can use data and technology to manage inventory more effectively. Inventory plays an important role in balancing supply and demand, but poor inventory control can lead to high costs, excess stock, shortages, and unhappy customers. Predictive analytics helps organizations forecast demand accurately by analyzing past data and current trends. This allows businesses to maintain the right stock levels and reduce waste. The study also explains how technologies like artificial intelligence, machine learning, and the internet of things improve inventory decisions. Overall, predictive analytics helps organizations increase efficiency, reduce risks, and improve customer satisfaction.

AIM

The main aim of this study is to understand how predictive analytics can be used in optimizing inventory management and overall business performance. The specific objectives are:

To understand how predictive analytics helps in managing inventory and making better business decisions.
To examine how predictive models reduce costs related to storage, ordering, and stock shortages.
To study how analytics improves operational efficiency, reliability, and customer satisfaction.
To identify the role of new technologies such as artificial intelligence, machine learning, and the internet of things in modern inventory management.
To suggest simple and practical ways to use predictive analytics for improving supply chain accuracy, flexibility, and performance.

Research Methodology

This study uses a quantitative research approach to examine the role of predictive analytics in inventory management. The researcher collects numerical data to identify patterns, trends, and relationships related to inventory decisions and predictive tools. A structured questionnaire serves as the main tool for data collection, helping to maintain uniformity and accuracy in responses. The questionnaire contains closed-ended questions that focus on respondents’ experiences, usage, and views on predictive analytics and inventory practices. The researcher collects data from 50 respondents working in the manufacturing, retail, logistics, and distribution sectors. This method ensures that the data is measurable, easy to compare, and suitable for statistical analysis.

Data Collection

Primary Data: The researcher collects primary data through a closed-ended structured questionnaire distributed to 50 professionals involved in inventory, logistics, and supply chain activities.
Secondary Data: The researcher gathers secondary data from books, journals, research articles, and government reports published between 2023 and 2025 to support the study.