NTP Product Lineup

We will specifically explain the framework for infrastructure development that is essential for the successful implementation of AI. To achieve AI readiness, it is important to balance the three elements of "business, data, and organization." Specifically, it is necessary to simultaneously advance the standardization of processes (business), the integrated infrastructure using tools like Databricks (data), and the cultivation of a data utilization culture (organization). FDE strongly supports the integration of these three elements at the operational level, from infrastructure development before implementation to self-sustainability. We are currently offering a free guide for building an organization for AI utilization.

We will detail the three-tiered support solutions provided by NTP, tailored to the phase of AI utilization in companies. We offer a roadmap that includes "1.0" for foundational development, "2.0" for rapid implementation through FDE, and "3.0" for self-sustainability through the establishment of an AI Center of Excellence. By consistently supporting the transformation of business, data, and organization at each phase, we help companies move beyond just proof of concept. In an era where AI technology is becoming commoditized, the true differentiators are "implementation capability" and "reduction of time-to-value." We are also offering guidance on the "AI-Ready Assessment" to help companies understand their current position.

We will explain the strategic shift needed to break through the "barrier to AI utilization" that many Japanese companies are facing. Currently, the AI utilization efforts of many manufacturing companies are limited to the automation of routine tasks (Level 1.0). However, to build true competitive advantage, it is essential to transition to "Level 2.0" and beyond, where AI is integrated into core business activities such as decision-making and strategic planning. By steering towards proactive AI utilization, we will present specific steps to achieve sustainable business growth and revenue expansion. For more details on the maturity model of AI utilization that expands revenue, please download the materials and check them.

In a specialized trading company in the chemical industry, we will detail a successful case of how AI was utilized to reduce the workload on-site and enhance sales efficiency. In this project, we first conducted a business assessment of all employees and quantified the reality that "non-core tasks," such as creating estimates and processing orders, accounted for approximately 80% of total work hours. By replacing these bottleneck administrative tasks with an AI agent, we created an environment where sales representatives could focus on deepening customer relationships and generating business opportunities. The key to success is to prioritize digital transformation (DX) in areas with high return on investment (ROI) based on numerical evidence rather than intuition. For detailed examples of specific AI applications that significantly reduced administrative burdens, please download the materials and take a look.

Instead of waiting for long-term, large-scale system development, we will explain a strategy that starts from areas where the field can immediately experience benefits. To accelerate DX with the cooperation of the field, it is important to accumulate immediate "Quick Wins," such as automating CRM input with voice AI. For example, a system where AI automatically summarizes meeting recordings into minutes and stores them in CRM dramatically reduces the burden on the field and promotes positive changes towards DX. Additionally, by building a knowledge base that allows AI to learn from past insights, even young employees can make strategic proposals like veterans, making it a shortcut for embedding AI as a "capability enhancement tool" in organizational transformation. Check the points for initiating DX that will please the field and specific strategies for Quick Wins in the downloadable materials.

Conventional AI has primarily focused on data analysis and decision-making support, but we have now entered an era where AI directly interacts with the physical space. Physical AI (Acting AI) is a set of technologies that enables AI to understand physical environments and autonomously control and optimize equipment and robots. Unlike traditional AI that only produces digital outputs, by combining LLM, physical simulations, and robotics, it achieves flexible actions without programming and the ability to adapt to unknown environments. Please check this document to see the full picture of this "acting AI," which will shape the future of manufacturing. Download the white paper that condenses the fundamental knowledge of AI and make use of it right away.

We will explain the three core elements of how physical AI interacts with the real world and advances learning and optimization. The heart of the system lies in the cycle of "Sensing," which accurately reads dynamic environments using sensors and cameras; "Thinking," which predicts and determines the optimal next move on a digital twin; and "Acting," which directly controls robots and equipment. This loop operates in real-time, enabling direct intervention in the physical layer. By understanding advanced architecture, let's elevate your automation to the next level. Detailed materials explaining the components and implementation mechanisms of physical AI can be obtained here.

The structural challenges faced by the manufacturing industry, such as labor shortages and the disruption of skill transfer, are difficult to resolve with traditional rule-based automation alone. In today's manufacturing environments, the loss of tacit knowledge due to the retirement of skilled workers and the urgent need to adapt to frequent changeovers associated with small-lot production are pressing issues. Physical AI provides flexible judgment and control in response to the increasingly complex behaviors of these environments, dramatically promoting labor reduction. Against the backdrop of a societal labor shortage, we will clarify in detail why this technology is gaining attention now, along with its background and necessity. Please view our report summarizing the industry's challenges and the need for AI utilization, available for free right now.

One of the global trends in physical AI is the incredible speed of hardware mass production in China and the introduction of implementation examples. In China, the commercialization of humanoid robots is accelerating, with production numbers increasing exponentially from 1,000 units to 10,000 units in just a few months. The phase of skill verification has now ended, and we have moved to a stage of scalable value provision. With low-cost and mass production as their weapons, there are many lessons that Japanese manufacturing can learn from the strategies of Chinese companies that are rapidly advancing social implementation. Details of China's leading robot implementation trends are available in the downloadable materials.

We will explain the specific shipping performance and market expansion of major Chinese robotics companies that are experiencing rapid growth toward 2025. Companies like AGIBOT, which boasts a shipping share of approximately 39%, and Unitree Robotics, which has entered the market with a shocking low price of around $6,000, are making remarkable strides. Additionally, UBTech Robotics is deploying the industry-specific humanoid "Walker S2" for automotive factories and has secured large orders. Through these specific examples, you can grasp the actual business scale at which physical AI is operating. Please download the case study materials that cover specific figures such as shipping numbers and order amounts for each company.

The U.S. physical AI strategy is not just about standalone hardware, but rather focuses on building systems that combine AI platforms and digital twins. At Amazon's logistics centers, the humanoid robot "Digit" has achieved commercial viability. Not only is the introduction of standalone robots involved, but integrated and optimal control has been realized by linking with higher-level systems such as warehouse management systems (WES) to simulate the entire site in a virtual space. We will provide a detailed explanation of the essence of the U.S. platform strategy that seamlessly connects physical and virtual spaces. Let's check out examples of building an AI ecosystem that supports massive logistics in detail through free materials.

We introduce the next generation of manufacturing sites, where Japan's leading robot manufacturer FANUC and AI computing powerhouse NVIDIA are taking on new challenges. Efforts are underway to build a photorealistic virtual factory using NVIDIA Isaac Sim, efficiently generating AI training data through tens of thousands of simulations. By accurately reproducing the learning outcomes in virtual space as real robot trajectories, highly flexible and precise tasks that were previously difficult have become possible. This is an advanced use case that leads human-robot collaboration to the next stage, including operations through natural language. Details of AI utilization cases through the highest level of technical collaboration in the country can be found in this document.

We will clarify the true nature of the so-called "barrier to AI implementation," which many Japanese companies are facing, where they cannot progress beyond the PoC (proof of concept) stage. Although data is being accumulated through IoT, there are many cases where decisions vary by site, and the data is not in a format that can be handed over to AI. Additionally, the "limitations of retrofitting" where existing equipment and rules do not assume collaboration with AI is also a significant challenge. We will discuss the importance of developing a data integration platform necessary to break through these barriers and achieve "autonomous control (physical AI)" that goes beyond mere visualization. Let's obtain a free resource that provides solutions to eliminate the bottlenecks in successfully implementing AI in your company.

We would like to introduce NTP's unique support system that sublimates complex manufacturing processes into a "Sensing→Thinking→Acting" loop. The effectiveness of physical AI is ensured by "on-site completion" support from FDE (Field Deployment Engineers) who are well-versed in actual business operations. With a sense of speed that eliminates "translation loss" between strategy and implementation, we provide comprehensive support from understanding the current situation to KPI design, and the construction of a modern data infrastructure utilizing Databricks and Fivetran. We propose the value of our co-piloting consulting aimed at making the field AI-Ready. We are currently offering a free document summarizing the details of our co-piloting support services and the steps to achieve AI-Readiness.

As the first step towards AI implementation, we are pleased to offer a free program to diagnose your company's "data structure," "business flow," and "organizational structure." We will evaluate the current state of IoT implementation, identifying gaps in data and infrastructure within the physical AI loop across three axes. Through the assessment, we will define how physical actions directly contribute to ROI and productivity improvements on-site, and we will formulate specific steps towards scalable implementation. Please utilize this as the "first step" to maximize your existing assets and achieve results in the shortest distance possible. The assessment guide, which explains the process from current diagnosis to improvement, is available for download here.

This document explains the utilization of Physical AI in manufacturing sites. It includes the core architecture of Physical AI, reasons why Physical AI is needed in the field today, global trends in Physical AI, case studies from Chinese and American companies, as well as domestic use cases. Additionally, we introduce our support achievements, such as advanced failure prediction through the integration of equipment data and spatial data. We encourage you to read it. 【Contents】 ■ What is Physical AI: An overview of AI that drives manufacturing sites ■ Global Trend: Implementation of hardware in China ■ Global Megatrend: System orchestration in the United States ■ Advanced domestic use cases: Integration of precision work and virtual environments ■ NTP's solution: Making sites "AI-Ready" starting from the field *For more details, please download the PDF or feel free to contact us.

This document is a guide for transforming organizations and foundations to be AI-Ready. It includes the design of the shortest path to a "state where AI functions," practical AI development integrated into on-site operations, the construction of a modern data stack, and support achievements for industry-specific challenges. Our specialized consultants will work closely with your front line to unravel your challenges. Let's start together by organizing your information structure. Please feel free to contact us for inquiries or consultations. 【Contents】 ■ Consulting | AI-Ready Assessment and Organizational Design ■ Engineering | AI Development that Creates Business Value ■ Engineering | Building a Modern Data Infrastructure that Supports AI ■ Case Studies | Support Achievements from Strategic Planning to Implementation ■ Next Step | Taking the First Step Towards Your "AI-Ready" Together *For more details, please download the PDF or feel free to contact us.