Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature

Smart manufacturing processes and systems have received great attention through the latest innovations, ongoing efforts, and best practices in the Industry 4 [...]

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Smart Manufacturing and Industry 4.0

A Study of the Aging Properties of the Common Synthetic Resin Adhesives for Paper Conservation Purposes

Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automobile manufacturing to ensure efficient manufacturing. Generally, the main manufacturer makes the annual inventory management plan, and contacts with suppliers when some material is approaching critical inventory level according to the actual production schedule, which increases the difficulty of inventory management. In recent years, many researchers have focused on using reinforcement learning method to study inventory management problems. Current approaches were mainly designed for the supply chain with single-node multi-material or multi-node single-material mode, which are not suitable to the civil aircraft manufacturing supply chain with multi-node multi-material mode. To deal with this problem, we formulated the problem as a partially observable Markov decision process (POMDP) model and proposed a multi-agent reinforcement learning method for supply chain inventory management, in which the dual-policy and information transmission mechanism was designed to help the supply chain participant improve the global information utilization efficiency of the supply chain and the coordination efficiency with other participants. The experiment results show that our method has about 45% performance improvement on efficiency compared with current reinforcement learning-based methods.

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A Supply Chain Inventory Management Method for Civil Aircraft Manufacturing Based on Multi-Agent Reinforcement Learning

An overview on the use of AI/ML in Manufacturing MSMEs: solved issues, limits, and challenges

Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models predict historical market demand, predict future demand, and enable systematic inventory management. However, in most small and medium enterprise (SMEs), there is no systematic management of data, and because of the lack of data and the volatility of random data, it is difficult for prediction models to work well. Since the predictive model is a core function derived from the management of the enterprise’s inventory data, the poor performance of the model causes the company’s inventory data-management system to be degraded. Companies that have poor inventory data because of this vicious cycle will continue to have difficulty introducing data-management systems. In this paper, we propose a framework that can reliably predict the inventory data of a firm by modeling the volatility of a firm stochastically. The framework makes the prediction using the point prediction model by means of LSTM(Long Short Term Memory), the 2D kernel density function, and the prediction result reflecting inventory-management cost. Through various experiments, the necessity of interval prediction in demand prediction and the validity of the cost-effective prediction model through the readjustment function were shown.

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Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing

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Study on the Physical Properties and Characteristics of Adhesives for Woodcraft

The BIC (Bureau International des Containers et du Transport Intermodal) Code is the identification code for ocean shipping containers and is crucial for logistics, transportation, and security. Accurate recognition of container BIC Code is essential for efficient import and export processes, authorities' ability to intercept illegal goods and safe transportation. Nevertheless, the current practice of employees recognizing and manually entering container BIC codes is inefficient and prone to error. Although automated recognition efforts have been made, challenges remain due to the aging of containers, manufacturing differences between companies, and the mixing of letters and numbers in the 11-digit combination. In this paper, we propose the design and implementation of a BIC Code recognition system using an open source-based OCR engine, deep learning object detection algorithm, and text detector model. In the logistics industry, various attempts are being made to seamlessly link the data required at each stage of transportation between these systems. If we can secure the stability and consistency of BIC Code recognition that can be used in the field through our research, it will contribute to overcoming the instability caused by false positives.

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Chae Hoon Lee

Papers

Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing

Study on the Physical Properties and Characteristics of Adhesives for Woodcraft

Design and Implementation for BIC Code Recognition System of Containers using OCR and CRAFT in Smart Logistics

Two Phases Anomaly Detection Based on Clustering and Visualization for Plastic Injection Molding Data