A Study of Data-driven Methods for Inventory Optimization
It addresses inventory optimization for supermarket managers, but it is incremental as it compares existing methods without introducing new techniques.
This paper analyzes the effectiveness of Time Series, Random Forest, and Deep Reinforcement Learning algorithms across three inventory models (Lost Sales, Dual-Sourcing, Multi-Echelon) in a supermarket context, evaluating them based on forecast accuracy, adaptability, and impact on inventory costs and customer satisfaction.
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.