We have compiled a list of manufacturers, distributors, product information, reference prices, and rankings for Predictive tools.
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Predictive tools Product List and Ranking from 4 Manufacturers, Suppliers and Companies

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

Predictive tools Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

  1. テラバイト Tokyo//software
  2. ウェーブフロント 本社 Kanagawa//software
  3. AI CROSS Tokyo//Information and Communications
  4. 4 パトコア Tokyo//IT/Telecommunications
  5. 5 null/null
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Predictive tools Product ranking

Last Updated： Aggregation Period:Nov 19, 2025～Dec 16, 2025
This ranking is based on the number of page views on our site.

  1. Processing Hardening Prediction Tool 'HYCRASH' テラバイト
  2. Failure Rate Calculation and Reliability Prediction Tool "Prediction" ウェーブフロント 本社
  3. [Data] Three Benefits of Demand Forecasting AI for Inventory Management - Manufacturing Industry Edition AI CROSS
  4. 4 hERG Inhibition Prediction Tool 'hERG Predictor' パトコア
  5. 5 Risk Prediction AI Tool "Logging Analysis"

Predictive tools Product List

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Processing Hardening Prediction Tool 'HYCRASH'

Processing Hardening Prediction Tool 'HYCRASH'

Fully automated generation of collision model data considering processing analysis!

HYCRASH is a tool that allows for the incorporation of strain hardening through the use of a collision model, without the need to create data for processing analysis by using a one-step method. By utilizing the collision model itself, it calculates the thickness and plastic strain of the pressed material and automatically generates initial conditions for Ansys LS-DYNA. ■ Input is only the model for collision analysis ■ Configuration is limited to specifying the part of the pressed material ■ Automatically executes forming analysis on the specified part ■ Automatically sets the effects of pressing as initial conditions ■ Collision data considering strain hardening is completed

  • Structural Analysis

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[Data] Three Benefits of Demand Forecasting AI for Inventory Management - Manufacturing Industry Edition

[Data] Three Benefits of Demand Forecasting AI for Inventory Management - Manufacturing Industry Edition

What is different from traditional demand forecasting? An explanation of demand forecasting using AI.

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.

  • Other operation management software

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Failure Rate Calculation and Reliability Prediction Tool "Prediction"

Failure Rate Calculation and Reliability Prediction Tool "Prediction"

Support from failure rate calculation to report creation in a seamless manner.

The Reliability Workbench is a tool based on reliability engineering used for evaluating and analyzing reliability and safety in accordance with functional safety. The Prediction module assists in calculating electronic component failure rates based on various handbooks.

  • simulator
  • Other analyses
  • ISO-related consultant

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hERG Inhibition Prediction Tool 'hERG Predictor'

hERG Inhibition Prediction Tool 'hERG Predictor'

A highly reliable hERG inhibition prediction tool that eliminates cardiotoxicity risks early in drug development!

Optimization of pharmacokinetic and toxicity parameters is an important goal throughout the drug discovery project. The "ADMET Group" applies machine learning techniques to curated datasets to create reliable predictive models. By predicting hERG inhibition using trained models, we can assess the cardiotoxicity risk of candidate compounds. 【Features】 ■ Application of machine learning techniques to curated datasets ■ Creation of reliable predictive models ■ Prediction of hERG inhibition using trained models ■ Ability to assess cardiotoxicity risk of candidate compounds *For more details, please refer to the PDF materials or feel free to contact us.

  • Software (middle, driver, security, etc.)

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