NTP Product Lineup

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.

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.

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.

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.

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.

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.

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.

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.

This is a project that achieved the visualization of equipment status and failure prediction using IoT data in the manufacturing industry. We implemented Azure Databricks (Datalakehouse) and built a foundation for processing and analyzing vast amounts of sensor data in real time. As a result, it has generated direct business impacts such as diversification of after-sales services and improvement of customer service. This is an example that supports advanced data utilization with a multi-layered structure from raw data to the Gold layer. We have published a document detailing the entire construction of a data lakehouse that transforms manufacturing site data into profit.

We will explain our (NTP) achievements in building an advanced failure prediction model that combines equipment data with spatial data. In addition to IoT sensor data such as equipment vibration and pressure, we integrated surrounding environmental data, such as pedestrian flow and material placement obtained from LiDAR and cameras, along a time axis. As a result, we successfully visualized the "impact of environmental changes on equipment," which had previously been difficult to capture, significantly improving the accuracy of abnormal signal detection. The case of digital twin implementation through a near-real-time dashboard directly contributes to stable operations on-site. Please download our support achievement document summarizing the actual implementation scheme and results for free right now.

"Although we have sensor data, it cannot be used on-site," and "We tried a PoC but couldn't achieve accuracy, and management approval has not been granted" — equipment maintenance often comes to a standstill at this "one step away." This case study reveals the steps taken to overcome three challenges hindering equipment maintenance in aging plants: 1. The risk of unexpected shutdowns due to aging, 2. The retirement of skilled maintenance personnel and the loss of tacit knowledge, and 3. The barrier of lacking a data infrastructure that halts PoC efforts. We will outline a four-step process centered around the Databricks Data Lakehouse (digitizing records → data integration → assetizing "intuition" → automating maintenance planning). [Recommended for those who:] - Want to implement equipment maintenance but are struggling with data infrastructure development - Have not achieved accuracy in their PoC and have not received management approval - Face challenges in inheriting tacit knowledge due to the retirement of skilled maintenance personnel - Have sensor data but are not fully utilizing it.