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"Deep Predictor" is an AI forecasting and decision support service that anyone can easily use without specialized knowledge. We provide an environment where you can always check sales forecast results with high accuracy and solid reasoning. [Examples of Issues We Can Solve] - Inventory optimization - Improvement of lead times - Production planning optimization - Reduction of food waste - Optimization of personnel allocation, etc. [Features] ■ Enables anyone to easily perform high-precision demand forecasting ■ Supports optimization based on demand forecast results ■ Visualizes the reasoning and logic behind forecast values *For more details, please refer to the PDF materials 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.*

Sales forecasting refers to predicting a company's future sales revenue, and accurately forecasting sales can lead to optimizing inventory and personnel allocation. Among those in retail and manufacturing, there may be individuals who wish to improve the accuracy of their sales forecasts. In this article, we will explain the importance of sales forecasting, how to calculate it, and methods to enhance forecasting accuracy. *For more detailed information, you can view the related links. For further details, please download the PDF or feel free to contact us.*

Demand forecasting systems support the optimization of inventory management, production planning, and marketing strategies by predicting future demand based on past data. In particular, those utilizing AI are becoming essential tools for many companies due to their high accuracy and rapid analysis capabilities. This article will provide a detailed explanation of the fundamentals of demand forecasting systems, specific use cases, the benefits of implementation, and recommended systems. *For more detailed content of the blog, please refer to the related links. For more information, you can download the PDF or feel free to contact us.*

Demand forecasting refers to predicting various "quantities of demand," such as product sales and the number of items needed for procurement. This article will clearly explain the basics of demand forecasting, specific methods, and approaches that utilize the latest technologies. *For detailed content of the blog, you can view it through the related links. For more information, 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.*

Demand forecasting refers to the process of predicting the sales and customer numbers of a company's products and services. Companies utilize this forecast to aid in inventory management, production planning, and the formulation of marketing strategies. Since demand fluctuates based on market and consumer trends, accurate forecasting is key to enhancing a company's competitiveness. This article will provide a detailed explanation of the fundamental concepts of demand forecasting, the challenges faced and their solutions, as well as points for improving accuracy and trends in the latest technologies. *For more details, you can view the related links. For further information, please download the PDF or feel free to contact us.*

"Sales forecasting" is a key factor that influences business success. By understanding its importance and using appropriate calculation methods, companies can achieve more efficient inventory management and optimize their resources. However, accurately forecasting sales is not easy. This article will provide a detailed explanation of the basic concepts of sales forecasting, how to choose calculation methods, and specific ways to improve accuracy. *For more details, you can view the related links. For further information, please download the PDF or feel free to contact us.*

In today's market environment, the accuracy of demand forecasting significantly influences a company's performance. However, accurately capturing complex consumer trends and external factors is not easy. This is where AI-driven demand forecasting comes into focus. This article clarifies the mechanisms and advantages of AI-driven demand forecasting, as well as the algorithms used, and explains its effectiveness through actual implementation examples. *For more detailed information, please refer to the related links. You can download the PDF for more details or feel free to contact us.*

In the manufacturing industry, demand planning is essential for achieving stable supply and efficient production. Proper demand planning can prevent excess inventory and stockouts, optimizing production efficiency in factories and the entire supply chain. However, in recent years, it has become increasingly difficult to ensure sufficient accuracy with traditional methods due to intensified demand fluctuations and the complexity of supply chains. This is where AI-driven demand forecasting has gained attention. By implementing AI, manufacturing sites can conduct their own forecasts, supporting quick and flexible decision-making. This article will clearly explain the basics of demand planning in manufacturing, how to create it, challenges and solutions, as well as the latest trends and the use of AI tools. *For more detailed information, please refer to the related links. For further details, feel free to download the PDF or 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.*

AI-driven demand forecasting is a noteworthy system that realizes the efficiency of various operations such as sales, inventory, production, and procurement. In recent years, we have entered an era where future demand can be predicted with high accuracy using data and AI, without relying on experience or intuition. This article will clearly explain the mechanisms, methods, benefits, implementation steps, and case studies of "demand forecasting AI." *For more detailed information, you can view it through the related links. For further details, please download the PDF or feel free to contact us.

In PSI management, it is important to effectively connect production, sales, and inventory. However, there are many constraints when managing PSI with Excel. PSI management using Excel faces issues such as the burden of data updates, the risk of human error, and a lack of flexibility. Therefore, to streamline PSI management, it is necessary to implement a system for centralized management and real-time data visualization. This article will provide a detailed explanation of the basics of PSI management, the limitations of Excel, and the implementation of systems for efficient PSI management. *For more details, you can view the blog through the related links. For further information, please download the PDF or feel free to contact us.*

In business activities, accurately predicting future sales is the key to success. However, many people may struggle with how to calculate sales forecasts and how to utilize Excel effectively. By leveraging Excel, it is possible to quickly create sales forecasts while keeping costs down. However, analyzing external factors that cannot be captured by Excel alone is also an important challenge. This article thoroughly explains the basics of sales forecasting, from fundamental concepts to calculation methods using Excel, and introduces practical points to enhance the accuracy of sales forecasts. *For detailed content of the blog, you can view it through the related links. For more information, please download the PDF or feel free to contact us.*

In modern corporate activities, AI forecasting is gaining attention as a means to maximize sales. The challenge many companies face is how to effectively utilize AI forecasting to improve sales. This article will help you find the best approach to AI forecasting for your business operations by explaining the mechanisms of AI forecasting, providing specific case studies, and comparing AI tools. By leveraging AI forecasting, it becomes possible to improve prediction accuracy, reduce labor hours, and optimize inventory and personnel, ultimately leading to an expected increase in sales. In particular, we introduce specific applications of AI forecasting across various industries, including retail, food service, manufacturing, and services. *For more detailed content of the blog, you can view it through the related links. For more information, please download the PDF or feel free to contact us.*

The PSI (Production, Sales, Inventory) management system is an essential system for integratively managing sales, production, and inventory in an increasingly complex supply chain. In today's world, where demand fluctuations are intense, an appropriate PSI management system is indispensable. This article will provide a detailed explanation of the basic mechanisms and benefits of PSI management, as well as the management systems that should be implemented. Without the introduction of an appropriate PSI management system, issues such as excess inventory and stockouts can arise, leading to a reliance on specific individuals and a decrease in planning accuracy. By implementing a PSI management system, we can prevent surplus inventory and aim for cost reduction and maximization of sales opportunities. *For more detailed information, please refer to the related links. For further details, you can download the PDF or feel free to contact us.*

The PSI management table is an essential tool for efficiently managing a company's production, sales, and inventory, allowing for centralized management of production (Production), sales (Sales), and inventory (Inventory) through the PSI management table. By utilizing the PSI management table, challenges such as excess inventory and stockouts can be addressed, promoting operational efficiency. In particular, as supply chains become more complex, the importance of the PSI management table is increasing. This article provides a detailed explanation of the basic structure and utilization methods of the PSI management table, as well as practical operational points for its implementation. It also touches on specific steps for creating a PSI management table using Excel and how AI can be utilized for demand forecasting in PSI management. Please read to the end. *For more detailed information, you can view it through the related links. For more details, please download the PDF or feel free to contact us.*

In the retail industry, responding to demand fluctuations and optimizing inventory are crucial. In particular, it is essential to minimize the risks of food waste and unsold goods while maximizing profits. Deep Predictor supports the resolution of these challenges through high-precision demand forecasting and the calculation of optimal order quantities. 【Use Cases】 - Store inventory management - Sales forecasting for seasonal products - Calculation of optimal order quantities 【Benefits of Implementation】 - Inventory optimization - Reduction of food waste - Decrease in ordering workload

In the food service industry, demand forecasting for ingredients is essential for cost reduction and improving customer satisfaction. In particular, accurate forecasts are required to address the fluctuating demand caused by daily menus, events, and seasonal factors. Low accuracy in demand forecasting can lead to food waste due to excessive ingredient orders or decreased customer satisfaction due to stockouts. Deep Predictor was developed to solve these challenges. 【Usage Scenarios】 - Ingredient ordering operations - Demand forecasting by menu - Inventory management 【Benefits of Implementation】 - Reduction of food waste - Optimization of ingredient costs - Reduction of ordering workload

In the wholesale industry, variations in order quantities by client and demand fluctuations due to seasonal factors pose significant challenges in maintaining optimal inventory levels. Inadequate demand forecasting can lead to lost sales opportunities due to stockouts, as well as increased storage costs and waste losses from excess inventory. AI predictive analysis Deep Predictor conducts highly accurate demand forecasting based on past sales data and transaction trends, supporting the optimization of inventory, ordering, and sales planning in the wholesale sector. 【Use Cases】 - Demand forecasting by product and client - Calculation of optimal inventory and order quantities - Improvement of accuracy in sales and purchasing plans 【Benefits of Implementation】 - Reduction of stockouts and excess inventory - Improvement of inventory turnover rates - Enhancement of profit margins and cash flow

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is "inventory optimization." AI analyzes a large amount of historical data to provide accurate demand forecasts that take into account market fluctuations and seasonal factors. Furthermore, it identifies order quantities that minimize the risks of stock shortages and excess inventory using optimization algorithms. By optimizing demand forecasts and order quantities, we can minimize stock shortages and excess inventory, achieving cost reductions in the range of tens of millions of yen annually and improving profit margins. 【Required Data】 ■ Historical shipment data ■ Calendar information, weather information, macroeconomic indicators, etc., based on on-site knowledge *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the "identification and prevention of churned customers." We predict the churn rate and LTV for each customer, listing those with high opportunity loss. We forecast the ROI of churn prevention measures and identify suitable actions. The identification and prevention of churned customers using AI has become an essential element in modern business strategy, significantly contributing to corporate success. 【Required Data】 ■ Customer Behavior Data - Customer behavior data such as purchase history, website browsing history, and app usage history. ■ Customer Attribute Data - Basic information about customers (gender, age, geographic location, etc.) and attribute information from purchase history. ■ Interaction Data - Communication history with customers, complaint data, feedback, etc. *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the "forecasting of customer foot traffic." By utilizing AI to collect data on past customer visits, weather information, holidays, and other external factors, we model historical patterns. This allows us to make accurate predictions of customer numbers that account for the complexity and variability of the data. Using AI to predict customer foot traffic enhances competitiveness in business and contributes to improved efficiency and customer satisfaction. 【Required Data】 ■ External Factors - Information on how external factors such as weather data, holiday calendars, and local event schedules impact customer numbers. ■ Competitor Information - Sales data and event information from nearby competitors. *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the "improvement of production planning accuracy." By utilizing past performance and external data, we build AI to forecast demand and production lead times, and implement improvements in production planning. Through AI-driven forecasting and optimization of production planning, we achieve reductions in production costs due to shorter lead times and increased profitability through inventory optimization. [Required Data] ■ Past Sales Data - Past sales data helps to understand demand trends and seasonal fluctuations. ■ External Factor Data - It is also important to consider external factor data such as weather, economic indicators, and policy changes. *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is "energy cost optimization." AI learns the patterns of energy consumption from past operational data. It identifies the conditions for equipment settings to minimize energy consumption. Additionally, by analyzing complex patterns and codifying knowledge, it helps in the accumulation and inheritance of veteran know-how. 【Required Data】 ■ Weather Data: Temperature, humidity, wind speed, etc. ■ Equipment Data: Operating status and historical settings of devices and systems ■ Economic Indicator Data: Analyze economic indicators and market trend data as needed, considering them as factors of variation. *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the "sales forecast for new store openings." By taking into account many factors such as past property data, market area data, seasonality, and the number of surrounding competitors, it enables more data-driven predictions. Additionally, through AI analysis, it can uncover relationships between sales and demographics, helping to identify factors for successful store openings and discover new hypotheses for store opening strategies. 【Required Data】 ■ Property Data ■ Market Area Data ■ Seasonality ■ Competitor Data *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the extraction of "high-value customers." By learning customer characteristics and purchasing patterns from past customer data, we can predict future high-value customers. This allows us to identify patterns and complex relationships that may be overlooked by human analysis, enabling the discovery of new high-value customers. 【Required Data】 ■ Customer purchase history ■ Behavioral data ■ Customer demographic information (gender, age, region, etc.) ■ Access history ■ Social media and website activity *For more details, please download the PDF or feel free to contact us.

One of the challenges that our no-code AI predictive analytics service "Deep Predictor" addresses is the "optimization of promotional distribution." By utilizing the predictive and optimization technology of the embedded AI, we analyze accumulated past data to forecast future user behavior. This allows us to obtain a list for delivering content to each user at the appropriate timing and through the right channels. [Required Data] ■ Customer attributes ■ Past promotional performance data *For more details, please download the PDF or feel free to contact us.