Developing Predictive Models for Inventory Optimization

EasyChair Preprint 13396

Abstract

Inventory optimization is critical for businesses to maintain profitability and customer satisfaction. Traditional inventory management methods often struggle to handle fluctuating demand and complex supply chains effectively. Predictive modeling offers a powerful solution by leveraging historical data and advanced algorithms to forecast future demand and optimize inventory levels. This abstract outlines the key steps in developing predictive models for inventory optimization, focusing on data collection, feature engineering, model selection, and implementation.

The process begins with identifying relevant data sources, cleaning and preparing the data, and exploring patterns and trends. Feature engineering involves selecting and creating relevant features for the predictive model. Appropriate modeling techniques, such as time series forecasting, regression, or machine learning algorithms, are chosen and trained on the data. The model's performance is evaluated and optimized through hyperparameter tuning and ensemble methods.

Finally, the predictive model is integrated into the inventory management system, allowing for informed decisions regarding inventory levels, order quantities, and safety stock. Continuous monitoring and iterative refinement ensure the model's effectiveness and adapt to changing market dynamics. By leveraging predictive modeling, businesses can achieve significant improvements in inventory efficiency, cost reduction, and customer service levels.

Keyphrases: Alignment with Business Objectives, Data Quality and Integration, Demand Forecasting, Inventory Optimization, Multi-Echelon Approach, predictive modeling

@booklet{EasyChair:13396,
  author    = {Godwin Olaoye},
  title     = {Developing Predictive Models for Inventory Optimization},
  howpublished = {EasyChair Preprint 13396},
  year      = {EasyChair, 2024}}