AI CROSS List of Products and Services

This document explains three benefits that demand forecasting AI brings to inventory management in the manufacturing industry. It details typical challenges related to "inventory" in manufacturing, such as "excess inventory/stock shortages," "complex inventory management leading to resource strain," and "vulnerability to changes in the external environment," as well as the causes of these challenges. Additionally, it introduces the no-code AI demand forecasting tool "Deep Predictor," which requires no specialized knowledge, making it a useful reference for those considering implementation. [Contents] ■ Three challenges related to "inventory" in manufacturing ■ Three issues with traditional demand forecasting ■ Three benefits brought by demand forecasting AI tools ■ Conclusion *For more details, please download the PDF or feel free to contact us.

This document explains how to measure ROI (Return on Investment) in AI forecasting services, focusing on demand forecasting. It introduces the basic methods for calculating ROI and addresses the common inquiries regarding the investment effectiveness when implementing AI forecasting services, particularly in relation to demand (such as sales and sales volume) predictions. Additionally, it presents the no-code AI demand forecasting tool "Deep Predictor," which allows for accuracy verification, making it a useful resource when considering implementation. [Contents] ■ Reasons for needing ROI calculations when implementing AI forecasting services ■ Methods for calculating ROI in AI forecasting services ■ Business impact of implementing AI forecasting services ■ Reasons why ROI increases with AI forecasting services ■ Conclusion *For more details, please download the PDF or feel free to contact us.

This document explains inventory optimization through AI demand forecasting. It clearly outlines important points such as "maintaining appropriate order quantities," "managing inventory based on detailed ordering plans," and "understanding demand levels," as well as factors that can lead to inventory issues. Additionally, it introduces the no-code AI demand forecasting tool "Deep Predictor," which requires no specialized knowledge, making it a useful reference for those considering implementation. 【Contents】 ■ What is inventory optimization ■ Three important points for inventory optimization ■ Three barriers to advancing inventory optimization ■ Demand forecasting for inventory optimization ■ Summary ■ Conclusion *For more details, please download the PDF or feel free to contact us.

This book is a resource that explains the service implementation flow and design flow for those who have limited knowledge of AI services and do not know how to proceed with their considerations, as well as for those who do not want to fail in implementation. In addition to the general flow of service implementation/design, we also introduce the no-code AI demand forecasting tool "Deep Predictor," which does not require specialized knowledge. This book will serve as a useful reference when considering implementation, so please download it and take a look. [Contents] ■ Flow of implementing AI demand forecasting services ■ Flow of designing AI demand forecasting services ■ Conclusion *For more details, please download the PDF or feel free to contact us.

This document is a necessary data format and data preparation guide for demand forecasting. It provides a detailed explanation of how to collect the data required for demand forecasting, a checklist for data preprocessing, and examples of the data needed to conduct demand forecasting. Additionally, it introduces the no-code AI demand forecasting tool "Deep Predictor," which does not require specialized knowledge, making it a useful reference when considering implementation. [Contents] ■ What data is required for demand forecasting ■ Data required for demand forecasting by case ■ Guide to selecting forecasting data by purpose ■ Conclusion *For more details, please download the PDF or feel free to contact us.

This document explains the impact of market data and seasonal fluctuations on demand forecasting. It includes benefits such as the ability to identify correlations that influence demand forecasting and the opportunity to identify entry points into new markets by incorporating external data into demand forecasts. Additionally, it introduces the no-code AI demand forecasting tool "Deep Predictor," which does not require specialized knowledge, making it a useful reference for those considering implementation. [Contents] ■ Two types of external data commonly used in demand forecasting ■ How to collect and analyze external data ■ Two benefits of incorporating external data into demand forecasting ■ The challenges of demand forecasting that takes external data into account ■ How external data utilization is changing with demand forecasting AI ■ Conclusion *For more details, please download the PDF or feel free to contact us.

In companies where inventory management operations occur, accurate inventory forecasting is required. However, achieving accurate inventory forecasts is very challenging, and it is not uncommon to struggle with excess inventory or stock shortages. Additionally, the burden on the personnel responsible for inventory forecasting is also a significant issue. This article will explain the challenges of inventory forecasting operations and discuss the introduction of demand forecasting AI as a method to solve these challenges. *For more detailed information, you can view the related links. For further details, please download the PDF or feel free to contact us.*

The evolution of IT has particularly drawn attention to AI technology in recent years. This article will explain the basic knowledge of demand forecasting using AI, the social background that is attracting attention, the advantages and disadvantages of its use, specific implementation steps, and case studies. *You can view the detailed content of the blog through the related links. For more information, please download the PDF or feel free to contact us.*

For food manufacturers, the accuracy of demand forecasting is a crucial factor that influences management. If forecasts are incorrect, it can lead to food waste due to excess inventory or lost sales opportunities due to stockouts, which can pressure profits. In recent years, the response to the SDGs and intensified market competition have revealed the limitations of traditional forecasting methods that rely on intuition and experience. This is where AI-driven demand forecasting comes into focus. High-precision forecasts based on vast amounts of data can reduce food waste and maximize profits, leading to sustainable management. Particularly for food manufacturers, utilizing AI has become urgent to capture the highly variable demand. This article will explain the environment surrounding food manufacturers, the challenges of demand forecasting, the benefits of AI demand forecasting, and AI demand forecasting services that can be utilized with no-code solutions. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*