Design and Implementation of a Smart Inventory Management System for Manufacturing

Thesis Abstract

Efficient inventory management is critical to optimizing production processes, reducing operational costs, and enhancing competitiveness within the manufacturing sector. However, many manufacturing firms face persistent challenges related to manual inventory tracking, inaccuracies in stock records, delayed replenishment, and suboptimal utilization of resources, which collectively hinder productivity and profitability. This study addresses these issues by designing and implementing a smart inventory management system that leverages Internet of Things (IoT) technologies, real-time data analytics, and automation to enhance inventory accuracy, responsiveness, and decision-making processes. The research aims to develop an integrated system prototype and evaluate its efficacy in a manufacturing environment, thereby providing a scalable solution to existing inventory management inefficiencies. The primary objectives include analyzing current inventory management practices, designing a comprehensive system architecture incorporating RFID technology, sensor networks, and cloud-based data processing, and assessing the system's impact on inventory accuracy, order lead time, and operational costs. Specific objectives are to identify key performance indicators (KPIs) impacted by the system, optimize the system's functional algorithms through simulation, and validate the implementation through empirical evaluation within a selected manufacturing setting. The research adopts a mixed-methods approach, combining qualitative and quantitative techniques. An initial qualitative review of existing inventory practices is conducted through interviews and focus group discussions with fifty warehouse and production personnel across a local manufacturing firm. Subsequently, a quantitative experimental design involves deploying the prototype system within the firm's inventory process, with a sample size of 150 stock items tracked over a three-month period. Data collection instruments include customized RFID sensors, inventory records, and system usage logs, validated through reliability testing (Cronbach's alpha of 0.85) and expert reviews to ensure accuracy and completeness. The data analysis employs descriptive statistics to profile inventory inaccuracies and delays, paired t-tests to compare pre- and post-implementation KPIs, and regression analysis to determine the system's influence on operational efficiency. Expected findings suggest significant improvements in inventory accuracy, reduction in stockouts and overstock situations, decreased order processing lead time, and lowered inventory holding costs. The analysis anticipates that the automated real-time monitoring enabled by the system will substantially enhance decision-making accuracy, leading to streamlined inventory turnover and inventory cost reductions by as much as 25%. Furthermore, the study explores the influence of the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory on user adoption and system integration, revealing that perceived ease of use and relative advantage significantly predict successful implementation. This research contributes to knowledge by providing a practical framework for deploying IoT-enabled inventory systems in manufacturing, extending existing literature on automation in supply chain management, and offering a model adaptable to diverse industrial contexts. It demonstrates the tangible benefits of integrating digital technologies into traditional inventory practices, fostering sustainable operational improvements. Additionally, the study's findings inform organizational strategies on technology adoption and capacity building for smart manufacturing. The main conclusion encapsulates that the proposed smart inventory management system markedly enhances inventory control accuracy and operational efficiency, with potential scalability across various manufacturing sectors. Recommendations include adopting the system for broader deployment, investing in staff training to ensure optimal utilization, and further research into integrating advanced analytics such as machine learning algorithms for predictive inventory management. Future studies should explore long-term impacts, scalability challenges, and potential integration with enterprise resource planning (ERP) systems to fully realize Industry 4.0 objectives.