The Impact of Supply Chain Delays on Inventory Levels and Sale Demand Fulfillment: Analyzing the Effects of Lead Times and In-Transit Quantities
A Quantitative Exploration of Logistics Efficiency and Inventory Optimization
Received: 14 May 2024 | Revised: 30 May 2024, 7 June 2024, and 10 June 2024 | Accepted: 13 June 2024 | Online: 19 June 2024
Corresponding author: Vipul Ladva
Abstract
Efficient inventory management is essential for maintaining a balance between supply and demand in various industries. This research study aims to quantitatively examine the impact of supply chain delays, with a specific emphasis on lead times and in-transit amounts, inventory levels, and the ability to meet sales demands. Mathematical modeling and statistical analysis are utilized to create prediction models that assess the impact of variations in lead time and quantities in transit on inventory stability and fulfillment rates. The study used regression analysis to ascertain the relationships between the indicated parameters and inventory outcomes. Also, machine learning algorithms like Random Forest and Linear Regression are applied to predict possible disruptions and optimize inventory levels. The methodology followed focuses on the Tri-Model Fusion Stacking approach, which combines various models to improve the predicted accuracy and offer a more comprehensive analysis. The main goal of this research is to provide practical insights that help organizations optimize their inventory management techniques, resulting in cost reduction and enhanced service levels. The findings aim to simplify the modification of inventory management techniques in light of up-to-date supply chain information, providing a notable improvement in the resources available to supply chain experts.
Keywords:
inventory management, supply chain delays, predictive analytics, machine learning, sales forecastingDownloads
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