BALANCING DEMAND WITH USE CASE-DRIVEN PREDICTIVE ANALYTICS

Abstract

The 'Smart Inventory Management' system leverages advanced algorithms and predictive analytics to optimize supply chains, reduce waste, and improve customer satisfaction. By employing models such as ARIMA (Auto-Regressive Integrated Moving Average) and LSTM (Long Short-Term Memory), the system accurately forecasts demand, enabling dynamic stock adjustments that align with market trends. Additionally, clustering algorithms like K-Means and association algorithms help enhance inventory allocation and uncover product relationships, facilitating more efficient operations. The challenge in resource allocation lies in balancing multiple, often conflicting objectives, such as minimizing costs, maximizing output, and maintaining quality standards. Multi-Objective Particle Swarm Optimization (MOPSO) addresses these complexities by advancing traditional Particle Swarm Optimization to identify a set of optimal solutions, rather than just a single solution. The algorithm assesses these configurations based on multiple objectives, ultimately identifying non-dominated solutions that represent the best possible trade-offs among competing goals. Product monitoring plays a critical role in this system, offering real-time tracking of stock levels, shelf life, and product conditions, ensuring timely replenishments and preventing stock outs or overstocking. This, combined with staff management capabilities, allows businesses to effectively allocate human resources for tasks such as stock audits, warehouse organization, and order fulfillment, optimizing workforce efficiency and reducing operational bottlenecks. This data-driven approach, coupled with real-time monitoring, adaptive logistics, and intelligent staff allocation, leads to smarter resource utilization, faster delivery times, and higher profitability. The system is designed to scale seamlessly with business growth, offering an innovative solution to the complexities of modern inventory management.