A Study of Data-driven Methods for Inventory Optimization

TL;DR

This paper compares three data-driven algorithms—Time Series, Random Forest, and Deep Reinforcement Learning—for inventory optimization across various models in the supermarket sector, evaluating their effectiveness and challenges.

Contribution

It provides a comprehensive analysis of the performance of three algorithms in different inventory models, highlighting their potential and limitations in data-driven inventory management.

Findings

RF shows high forecast accuracy in dual-sourcing models.

Deep Reinforcement Learning adapts well to market changes.

Time Series methods are effective for short-term inventory predictions.

Abstract

This paper shows a comprehensive analysis of three algorithms (Time Series, Random Forest (RF) and Deep Reinforcement Learning) into three inventory models (the Lost Sales, Dual-Sourcing and Multi-Echelon Inventory Model). These methodologies are applied in the supermarket context. The main purpose is to analyse efficient methods for the data-driven. Their possibility, potential and current challenges are taken into consideration in this report. By comparing the results in each model, the effectiveness of each algorithm is evaluated based on several key performance indicators, including forecast accuracy, adaptability to market changes, and overall impact on inventory costs and customer satisfaction levels. The data visualization tools and statistical metrics are the indicators for the comparisons and show some obvious trends and patterns that can guide decision-making in inventory management. These tools enable managers to not only track the performance of different algorithms in real-time but also to drill down into specific data points to understand the underlying causes of inventory fluctuations. This level of detail is crucial for pinpointing inefficiencies and areas for improvement within the supply chain.