NTP Product Lineup

The creation of business value is influenced by the organization of "metadata," which defines the meaning and context of data. When metadata is properly managed, the reliability of the data and the accuracy of its interpretation are ensured, facilitating smooth decision-making across the organization. Going beyond traditional static management, "Active Metadata Management," which dynamically updates information during the analysis process, is essential. We will explain the three classifications of metadata management that serve as the shortest path to becoming an AI-ready organization. We will publish materials on how to organize "metadata" so that AI and BI can truly function in practice.

• Other information systems

The use of an ETL tool that can flexibly respond from a small scale is the first step in building an advanced data infrastructure. It enables rapid implementation and operation by a small team without requiring specialized skills, eliminating the reliance on manual processes. By automating the processes of data extraction, transformation, and loading, it can prevent human errors and reduce operational burdens. We will discuss the benefits of storing data in a DWH, maintaining consistency, and enhancing the reliability of the analytical infrastructure. The advantages of utilizing ETL for efficient data collection and processing are condensed in the materials.

• Other information systems

To enable accurate and efficient data analysis, it is essential to design the relationships between data through "data modeling." By organizing business information and defining its structure, we can proactively eliminate data duplication and inconsistencies. When this is visualized, it serves as a clear guideline for foundational design and becomes the basis for advanced data utilization. We will explain how to create high-quality data that directly contributes to business outcomes. Please check the materials for the blueprint to build a consistent data infrastructure.

• Other information systems

In today's rapidly changing market and needs, the introduction of agile methods is required for building data infrastructure. By not deciding everything in the initial stages and repeatedly implementing and improving in short cycles, we can sequentially provide high-priority features. This method, which immediately reflects feedback, maximizes development speed and enables a direct connection to business value. We will provide a detailed introduction to the process of building a foundation that continues to evolve flexibly and quickly. You can learn agile development techniques for building a resilient data infrastructure through our materials.

• Other information systems

Data users want to "analyze easily with their favorite tools," while administrators prioritize "control and stable operations." This gap in needs leads to a common issue of data stagnation and underutilization. It is important to build an analytical environment that can be easily used by non-engineers while complying with compliance regulations. To promote data utilization across the organization, we propose breaking the "utilization stagnation" through inventorying data and restructuring management systems. We will explain in the materials how to remove organizational barriers and accelerate data utilization governance.

• Other information systems

With the diversification of business operations, the data structure has become more complex, and manual management has already reached its limits. Manual maintenance and operation not only lead to human errors but also pose significant barriers to sustainable utilization. To eliminate human errors and management burdens, automation and labor-saving through external tools are essential. We will explain the benefits of automating batch processing and updating report data, transitioning to a stable operational foundation. We propose a system of automation that reduces operational costs and achieves stable operations in our documentation.

• Other information systems

Due to the poor quality of data, there is a challenge in advancing its use on-site, and a clear improvement process is necessary. First, based on the purpose of use, we will "define" the standards, and then "measure" the current situation to determine the degree of improvement. After that, we will carry out "improvements" according to the content and apply them to operational data, cycling through the PDCA cycle. By continuing this process, we will finally obtain reliable data that can yield practical benefits for the business. For details on the improvement process that dramatically enhances data reliability, please refer to the materials.

• Other information systems

In the 2023 survey, "securing talent" in data utilization emerged as a significantly prominent issue, overwhelming other items. The sense of shortage has increased dramatically from 45.5% in 2022 to 57.5% in 2023, becoming a critical bottleneck for many companies. In response to this situation, strategies that promote the establishment of a foundation requiring advanced specialized skills "without relying on personnel" are being sought. We will present how to build an automated foundation using external tools and AI. A "de-personalization" strategy to ensure data utilization continues even in the absence of experts will be made available in the materials.

• Other information systems

The failure of data integration can be summarized mainly in two points: "lack of preparation of provided data" and "lack of established integration rules." Simply accumulating data is insufficient; a "transformation process" that optimizes the data into a usable format across systems is essential. Thoroughly understanding the current data retention situation and promoting integration design that aligns with the intended use is the shortcut to success. We will explain how to transform a mountain of non-standardized data into a valuable asset. We provide specific guidance in the materials on how to break down the barriers that hinder smooth data integration.

• Other information systems

If the field promotes DX without company-wide rules, it will lead to the proliferation of systems and data fragmentation (siloing). Data fragmentation makes collaboration between core systems difficult and can lead to security vulnerabilities and increased management costs. To continuously generate results, it is essential to have a system that ensures data integrity while maintaining the freedom of the field. The solution to this is the establishment of a common infrastructure that supports the "AI-Ready" transformation of data. You can learn about the design of a foundation that avoids disorderly systemization and achieves company-wide optimization through the materials provided.

• Other information systems

The greatest advantage of on-site-driven DX is its ability to quickly reflect the voices of business users and foster a culture of voluntary improvement. By leading from the front lines, trials become easier, and the cycle of hypothesis testing can proceed rapidly. Additionally, even when there are changes in operations, the responsible individuals can move forward with a sense of conviction, which enhances execution capability. In this way, cultivating a corporate culture that "actively engages in problem-solving" becomes the driving force behind digital transformation. We have compiled hints for promoting DX that maximizes the vitality of the front lines in a document.

• Other information systems

The utilization of AI by domestic companies is rapidly shifting from "special measures" to "standard infrastructure." In a survey conducted in the fiscal year 2023, about half of the companies have already implemented or are considering AI, making it a prerequisite for business. However, while more companies are moving from consideration to proof of concept (PoC), generating results in practice has become a common challenge. To create a true impact, establishing an "AI-Ready" environment to quickly overcome PoC is the shortest path. Please check the materials for the new domestic DX trends and the roadmap for AI utilization.

• Other information systems

We will present a current situation analysis and a realistic roadmap to take the first step towards predictive maintenance. Through a 30-minute free assessment, we will estimate specific effects, such as how much we can reduce unexpected downtime, based on the current data and infrastructure diagnosis. We will clarify practical steps on how to leverage existing assets while capitalizing on the insights of veterans. This will serve as a powerful tool to gain project approval from management. We provide detailed information on specific steps and diagnostic menus for implementation in our materials.

• Other information systems

We will redirect the investment in the "thick reports" typical of traditional consulting towards building systems that operate on-site. In many projects, excessive requirements definition and report creation consume the budget, often neglecting the crucial implementation. NTP thoroughly eliminates low-value intermediate costs by having professionals from the field directly engage in hands-on work. Even with the same budget, we enable focused investment in "operational systems" that actually generate profits on-site, maximizing the return on investment. A detailed comparison of the cost structure with traditional methods is explained in the materials.

• Other information systems

Successful predictive maintenance requires not only technical skills but also a deep understanding of the "context" in the field, along with a gritty execution capability. NTP Corporation is composed of domain experts familiar with sites like Kubota and IT professionals from IBM. Our approach is characterized by the "FDE style," which goes beyond merely creating polished strategies; we immerse ourselves in the front lines of the field to ensure implementation. We will guide projects to successful completion even from a state where there is no in-house know-how. You can find more details about our field-led, collaborative engineering approach in the materials provided here.

• Other information systems

We will solve the challenge of technology transfer that many manufacturing sites face by utilizing AI for digital asset creation. Simply analyzing sensor values (such as vibration and temperature) makes it difficult to predict true anomalies. By linking the event logs from the field that experienced professionals use to explain "why that operation was performed at that time," we can incorporate the long-developed intuition for detecting anomalies into the AI model. This enables us to elevate individual technical skills into a lasting asset for the entire organization. We provide a detailed explanation of specific methods for embedding expert skills into AI in our materials.

• Other information systems

The key challenge is how to efficiently transform the vast and diverse raw data obtained from IoT devices into value. Databricks' data lakehouse integrates low-cost storage (lake) with high-quality centralized management (warehouse) that can withstand AI learning. By eliminating the complexity of infrastructure construction, companies can focus their resources on their primary goal of "prediction and analysis." We will explain the importance of a foundation that enables the shortest route to AI implementation. The ideal configuration diagram for the data infrastructure is included in the downloadable materials.

• Other information systems

The implementation of predictive maintenance requires an extremely broad range of expertise that spans both IT and the field. Three major challenges are "network construction" for stable data collection, "secure data management" to prevent siloing, and "AI model development" using advanced algorithms. If you try to optimize these individually within your company, the system will become more complex, and costs and time will escalate endlessly. It is essential to build an efficient implementation process. The technical approaches to minimize implementation costs are detailed in the materials.

• Other information systems

The introduction of predictive maintenance faces different "barriers" at each phase, causing many companies to stagnate along the way. The main hindrances are the "lack of clarity in ROI" at the pre-implementation stage, "low data quality" during the prototyping phase, and "enormous operational costs" during the production phase. At the root of all these failures lies a structural issue: the absence of an "AI-Ready data infrastructure" that assumes the use of AI. Clearly defining measures for each phase is the shortcut to success. Please take a look at the materials for a roadmap to promote projects without failure.

• Other information systems

In the manufacturing field, unexpected line stoppages pose a fatal risk that can lead to losses on the scale of tens of millions of yen. Traditional reactive maintenance cannot prevent failures, and preventive maintenance has been challenged by increased costs due to excessive parts replacement. Predictive maintenance is a strategic investment aimed at maximizing profit margins by balancing these issues, maintaining operational efficiency while minimizing costs. We will provide a detailed explanation of the maintenance approach that is directly linked to management indicators. You can find the complete overview of the maintenance strategy that dramatically changes profit margins in this document.

• Other information systems

This document explains predictive maintenance, which directly improves profit margins. It provides detailed information on the risks and costs of maintenance strategies, the biggest barriers for many manufacturing sites, and integrated solutions. Additionally, it includes information about a 30-minute free assessment, so please take a moment to read it. 【Contents (partial)】 ■ Why predictive maintenance directly improves "profit margins" ■ Phase by phase: Three pitfalls that hinder predictive maintenance projects ■ The "three major hurdles" that impede implementation and the barrier of specialized knowledge ■ Solving the three major hurdles with Databricks ■ Digitally capitalizing on the "intuition" of veterans ■ The significance of having professionals who understand the field provide "FDE (support)" *For more details, please download the PDF or feel free to contact us.

• Document and Data Management

This document is a guide for building an AI-ready data infrastructure for the manufacturing industry. It provides a detailed explanation of the advantages and disadvantages of AI utilization, the importance of "maintenance" and "design" in determining the success of data integration, the worsening shortage of data personnel, and the shift towards "de-personalization." We also introduce methods for accelerating data infrastructure development and metadata management to unlock the true value of data utilization. [Contents] ■ Trends in generative AI utilization and challenges in data utilization ■ Why data utilization is not progressing as expected ■ Requirements for data infrastructure in the era of generative AI *For more details, please download the PDF or feel free to contact us.

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

This document explains the shift from fact-based approaches to "on-site implementation" methods. It includes detailed information on on-site implementation engineering (FDE), a roadmap to becoming AI-Ready, and case studies. This is a valuable read, so please take a look. 【Contents】 ■ Executive Summary: Competitive Advantage in the AI Era ■ Challenges: Why do DX/AI implementations stop at PoC? ■ Solutions: On-site Implementation (FDE) and the Palantir Model ■ Three Elements that Constitute AI-Ready ■ NTP's Solutions ■ Case Study: Specific Results at Yamaha Motor Co., Ltd. ■ Conclusion: A Time to Differentiate Through Implementation *For more details, please download the PDF or feel free to contact us.

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