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Industry Overview

When enterprises evaluate or initiate digital transformation and smart manufacturing projects, they face numerous technologies and solution options. To define a clear roadmap—including short-, mid-, and long-term strategies, phases, timelines, and execution—expert consultation is essential to align with the company’s operational direction.

Among diverse technologies such as IT (Information Technology), CT (Communication Technology), and OT (Operation Technology), the choice of products and solutions varies depending on each enterprise’s digital ecosystem, production model, process capability, management culture, and operational needs.

Therefore, when planning and executing digital transformation, it is critical to balance investment vs. benefit, evaluate system coverage, data necessity, and ensure ease of use. Under these complex challenges, selecting an experienced consulting partner is key to successfully achieving a holistic and effective transformation strategy.

NTT DATA’s consulting team supports clients by adopting a guided approach during the blueprint phase. Based on the enterprise’s industry type, consultants conduct structured workshops to collect and analyze business requirements. From experience, many companies struggle to consolidate detailed requirements within a short time.

To address this, NTT DATA first provides solution awareness and training, helping key users understand system functions and potential operational improvements. Guided discussions then help each department refine and confirm functional and process requirements, reducing ambiguity during planning and identifying opportunities for process, operational, and management optimization.

In addition, during the blueprint stage, NTT DATA consultants leverage industry-proven management templates to accelerate system scenario confirmation and improve applicability. These templates, derived from common operational needs such as production scheduling, process data collection, material and quality management, recipe control, and dashboard visualization, offer flexible front-end interfaces including mobile app templates. This approach minimizes customization, shortens implementation time, and ensures relevance across industries.

NTT DATA’s solutions adopt a microservice and container-based architecture, combined with industry-specific templates tailored to sectors such as metalworking, textiles, chemicals, assembly, injection molding, automotive manufacturing, and more. These templates encapsulate typical production and management characteristics, enabling faster deployment and higher adaptability during system implementation.

Key Considerations for Digital Transformation Planning

Enterprises planning digital transformation and smart manufacturing are advised to implement in phases, based on their current level of digital maturity. Each stage can be adjusted flexibly depending on business needs, priorities, and budget. The recommended implementation roadmap is as follows:

(1) Foundational Digital Platform

Building a digital factory is the foundation for digital transformation and smart manufacturing.
The goal is to migrate lean management practices onto a digital platform that delivers real-time, data-driven, and transparent operational efficiency.
Typical foundational applications include:

  • Virtual factory setup
  • Electronic and real-time quality management
  • Integrated equipment connectivity
  • Management reporting platform
  • Real-time operation dashboard
  • ERP integration

(2) Optimization after Digital Transformation

After the foundational platform is established, employees begin adapting to data-driven operations and decision-making.
During this stage, optimization needs become clearer and more actionable.
The goal is to enhance baseline applications and deepen specialized functionalities for each management unit.
Typical applications include:

  • Smart scheduling (APS )
  • Smart warehouse management (WMS, ASRS)
  • Preventive maintenance management
  • Unmanned transport applications (AGV/AMR, AMHS, MCS )
  • Data platform and big data applications (Big Data / Data Platform)

(3) Data Value-Added Applications

With a stable foundation and optimized processes, enterprises can transition to data-driven management.
Collected manufacturing data—from machines, materials, and operations—can be analyzed to generate insights and predictive intelligence.
Whereas past decision-making often relied on experience or senior expertise, digital transformation enables knowledge to be codified and continuously improved through data analytics.

  • Digital Twin implementations
  • AI-driven manufacturing insights
  • Human–machine collaboration
  • Fully unmanned factory operations

NTT DATA Solutions

NTT DATA offers comprehensive services for digital transformation and smart manufacturing, with extensive experience across multiple industries.
Our service scope includes:

  • MES implementation
  • AGV/AMR automation planning
  • EAP/IIoT integration
  • System integration (IT/OT connectivity)
  • Dashboard and control room development
  • Data platform and AI-driven analytics
  • Project management with Jira

Benefits Analysis

The primary goal of digital transformation and smart manufacturing is to enhance competitiveness through digital tools and platforms.
With the guidance of professional consultants, enterprises can develop tailored digital strategies that fit their operational characteristics and management culture.

By aligning digital platforms with business goals, companies can realize tangible benefits in productivity, quality, and decision-making efficiency.
NTT DATA serves as your dedicated partner in this journey—turning digital transformation into measurable, sustainable business value.

Ready to start your digital transformation journey?
Let NTT DATA help you build a roadmap for smart manufacturing — from planning to implementation.
Contact Us

Whether it’s been nearly a decade of Industry 4.0, or the recent years of big data, machine learning, and AIoT smart factories, the essential prerequisite behind their operations is data collection through the Internet of Things (IoT). Since IoT is so crucial, how should companies progressively move forward to ultimately achieve an AIoT smart factory? The following diagram illustrates the five steps of factory digital transformation

1st step : IIoT (Machine Networking)

Organizing the existing machine networking capabilities is the first and most time-consuming step, often the one most easily abandoned. As the saying goes, “The hardest part is to get started,” which is quite fitting. We recommend categorizing machines into the following five types as a starting point:

  1. Machines that are impossible to retrofit and have limited functionality, such as machines with only start and stop buttons and no display of production results.
  2. Machines that, while unable to directly connect to the network, consistently store production-related data in fixed directories.
  3. Machines that don’t store data in fixed directories but can capture production data through modifications.
  4. Machines with connectivity, but communication is unidirectional. In other words, the machine can send data to the system, and the system can place production parameters in a fixed directory on the machine for the machine to read and execute.
  5. Machines that, in addition to the capabilities of the fourth category, can also provide real-time production status in response to system queries, making them the ideal candidates for IoT connectivity.

2nd step: Data Storage

Before the advent of the big data trend, even though machines had data that could be stored, many factories, considering the cost of storage space, did not store machine data in databases. Instead, they typically retained files for several months to several years before deletion. In recent years, the emergence of big data databases has made it economically viable to store these vast volumes of machine production data. Storing this data is essential to enable subsequent data mining and AI applications.

3rd step: Real-time Dashboard

A real-time dashboard can be created even before storing big data. The data source relies on data input from production or equipment personnel, and the inability to provide real-time data is the biggest drawback. This also affects the accuracy of subsequent OEE (Overall Equipment Effectiveness) reports, resulting in discrepancies between actual output and machine specification output. With the implementation of IIoT, not only can machine conditions be reflected in real-time on the real-time dashboard, but it also ensures that subsequent reports are based on accurate data for calculation.

4th step: Data Mining

The other purpose of IIoT is to indentify the relationships among the data in the big data. For example, if the machine continuously sends out the specific alert messages within 5 minutes, there is a 90% probability that the breakdown will occur in 10 minutes. In such cases, we can provide early warnings to equipment engineers for preemptive action, reducing the frequency and downtime of machine failures, and improving production efficiency.

Learn about data platform. By integrating data across the systems, various enterprise applications such as AI, BI, apps, or internal systems can be satisfied. This resolves the issue of data redundancy and reduces data processing costs when planning enterprise services.

5th step: AIoT to Smart Factory

With the logical insights derived from big data analysis and real-time machine connectivity, the objectives of a smart factory encompass various capabilities. Whether it’s AI automating dispatching assignments for maximizing production capacity, adjusting production parameters in real-time based on current conditions, or performing rapid quality inspections using real-time image, they all fall within the purview of a smart factory. The 4th step of data mining and the 5th step of the smart factory are quite similar, both aimed at improving factory production efficiency based on big data. The difference lies in data mining, which uncovers data logic using existing data, whereas AI utilizes real-time data for future predictions.

Learn more about Equipment automation.

Using the example of a specific warning message continuously appearing on a machine within 5 minutes, with a 90% probability of a shutdown within 10 minutes, this logic was discovered during data mining. We can incorporate this logic into the program to monitor and send notifications in real-time, which is the result of data mining. However, if we do not continue data mining to discover new logics, the monitoring logic remains static, making monitoring a passive execution method.

With the implementation of AI, in addition to the logics discovered through existing data mining, AI continues to monitor real-time data and identify data correlation logic. This transition from passive monitoring to proactive monitoring allows for the early detection of issues, providing more time for resolution.

NTT DATA Taiwan not only assists many enterprises in implementing systems such as ERP, MES, WMS, APS, but also provide the services of IIoT and setting up real-time dashboards, leadning the clients to build an AIoT smart factory in the end. We have comprehensive practical experience to speed up the digital transformation process for enterprises and reduce the period of trial and error.

What is a Quality Management System?

A Quality Management System (QMS) is designed to ensure that products or services meet customer requirements and expectations while complying with applicable laws, regulations, and standards. It typically consists of a set of interconnected policies, procedures, processes, documents, roles, responsibilities, and resources that guide an organization in achieving a management system focused on continuous improvement.

The purpose of a Quality Management System is to coordinate, manage, standardize, and optimize various aspects of an organization to improve the quality of products or services, enhance operational efficiency, reduce errors and waste, meet customer needs, and improve work processes and organizational culture. By implementing a QMS, an organization can increase customer satisfaction, enhance brand value, and be prepared for future opportunities and challenges.

What is Quality Control?

Quality Control (QC) refers to the use of operational procedures and measurement techniques to monitor the manufacturing process of products or services. It involves measuring and analyzing the quality of products or services to ensure they adhere to predetermined quality standards. It is an important component of a Quality Management System and is used to validate whether products or services meet specific standards and customer expectations.

Quality Control typically involves the following steps:
1. Measurement and analysis of the processes and characteristics of products or services.
2. Establishing inspection plans to monitor specific products or services.
3. Using statistical techniques to check the suitability and consistency of products or services.
4. Analyzing the collected data to ensure products or services meet specific standards and customer expectations.
5. Tracking and addressing any defects or issues discovered.

Under Quality Control module framework of EXC-MES, the collected data can generate control charts and process capability reports, providing real-time control and subsequent analysis for management. Why choose EXC-MES?

Why is Quality Control necessary? What are the benefits of Quality Control?

  1. Ensuring that products or services meet specific quality standards and customer expectations.
  2. Monitoring the manufacturing process of products or services and identifying and resolving defects in manufacturing or operations.
  3. Complying with legal, regulatory, and industry standard requirements.
  4. Improving the quality of products or services, enhancing customer confidence and satisfaction.
  5. Reducing manufacturing and service costs, improving production efficiency and effectiveness.
  6. Enhancing product or service innovation and competitiveness.

In addition to MES, incorporating IoT for data collection and adjustment of equipment parameters allows for a more comprehensive approach to quality control, reducing defective output and improving production efficiency. Benefits of integrating MES with IoT in the automotive industry

In Summary

In conclusion, adopting a Quality Management System (QMS) is an effective way for businesses to enhance their competitiveness, enabling organizations to better understand their strengths and weaknesses and strive for an advantage in the market competition. Quality Control (QC) is a crucial means to ensure that products or services meet specific standards and customer expectations. It helps organizations improve the quality of their products or services, achieve continuous improvement, and meet customer and market demands.