Using IoT Data to Develop Next-Generation Smart Factories

Co-creation with Lumada! Making factories smarter by introducing IoT infrastructure

Connecting OT and IT with data to create next-generation smart factories

To achieve their goals of improving quality and productivity, reducing costs, compensating for worker shortages, and developing human resources, manufacturers are striving to utilize data to make factories smarter.

Although the data is available, some manufacturers are unable to use it effectively to improve production

Factories perform spot checks to ensure product quality. If a defective sample is found, the extent of the impact is identified based on the sample’s manufacturing history and from data such as the results of inspections of the defective sample and other samples.

Product defects can be the result of issues across multiple processes. If the cause of the defect does not lie in the process in which the defect was detected, the scope of the investigation has to be expanded to the entire factory. For example, the investigation needs to extract and analyze data on the state of the equipment in use, the product quality, and production plans.

In the past, such investigations faced a key problem: equipment operational data is usually stored separately for each process, and the data for independently built operational systems is collected at different times and in different formats. Identifying the scope of impact from clues present in such data requires a high level of expertise to prevent slight changes in the data from being overlooked. In the past, only a select few individuals were capable of performing this type of work. It also took time to narrow down the products that need to be investigated and to identify what caused the defect to occur.

The various types of data collected from a factory are utilized across many groups

Collecting, formatting, and correlating a broad range of disparate data from each factory process makes it possible to find differences between normal samples and defective samples. Identifying which products were produced under the same conditions as the defective sample allows users to pinpoint the scope and targets of the quality inspection, thereby improving the efficiency of the inspection process. These measures deliver the following benefits:

• Maintaining and improving reliability
  Manufacturers can avoid shipping defective products and can quickly respond to product recalls and inquiries.
• Eliminating the need to depend on specific individuals
  The scope of adverse impact from the defects is identifiable from points of variation in the data, and does not require special expertise.

Further, the ability to trace data back from the point where a variation occurred enables manufacturers to identify the processes where a defect occurred and to understand whether there was a problem in the materials, equipment, or work methods that were used. In this way, manufacturers can use the data to analyze the causes of defects and to take countermeasures.

Virtual production lines aid optimization across the entire production process

The introduction of digital twin technology^*1 to manufacturing sites allows manufacturers to use real-world data to create virtual reproductions of production lines. This helps identify the causes of any problems that arise, and enables the manufacturers to review and conduct trials of potential operational improvements. By visualizing a production line across processes, manufacturers can confirm how an event occurring in a certain process affects other processes. This makes it easier to identify potential areas of improvement and to deliver optimization across the entire production process.

To reproduce production lines in cyberspace, Hitachi’s digital twin solution for production sites aggregates OT and IT data collected and maintained by individual processes and correlates this real-world data with a unique production operating model built with a focus on operations and 4M^*2. This approach supports data acquisition, utilization, and analysis from the perspective of total optimization. This solution can collect and maintain data without requiring users to have expertise in the various production operations or data.

*1

This technology uses real-world data to reproduce an exact virtual replica of real-world operations in cyberspace. The following customer case scenario and solutions highlight the use of digital twin technology to optimize the entire supply chain.

• Reproducing the Entire Supply Chain in Cyberspace to Optimize Product Inventory and Distribution Sites

*2

The term 4M is derived from the following words: huMan, Machine, Material, and Method. This is a key concept in manufacturing and other industries and is vital for identifying issues, solving such issues, and implementing operational improvements on the production floor.

Connecting OT and IT data existing in multiple sites

Hitachi’s digital twin solution for production sites aids optimization of the entire production business

Data on equipment operational status and quality information (OT data) and production plans and inventory management information (IT data) is often located across multiple production sites. Hitachi’s digital twin solution for production sites correlates such data as digital data, facilitating its use in optimizing the entire production business.

• Hitachi’s digital twin solution for production sites (Japanese)

Optimizing the entire production business through managing the data suitable for production sites

Using a unique approach, Hitachi’s digital twin solution for production sites provides integrated management of the connections between data existing in different production processes and the OT and IT data generated from the operations. With a focus on the worksite, Hitachi’s digital twin solution for production sites helps to improve and optimize the entirety of production.

• Aggregation and visualization
  Hitachi’s digital twin solution for production sites facilitates data use by aggregating data from different factory sources, and correlating related information pertaining to equipment inspection and maintenance and to worker additions and changes. Visual displays and filtering tools appropriate to production processes allow users to search and retrieve relevant data, regardless of their level of IT proficiency.
• Cross-sectional management
  Hitachi’s digital twin solution for production sites provides cross-sectional management of aggregated data from the perspective of operations and 4M. This improves operations that span processes, and reduces the time required to search through production histories. Such management also allows users to retrieve, in a batch, the data related to a production site and to correlate data sets under different management perspectives (for example, correlating data based on work records, materials, semi-finished goods, or other sources of information).
• API-based application integration
  Hitachi’s digital twin solution for production sites supports standard APIs, which allows for integration with a wide range of applications. Users can retrieve data aggregated and maintained by the solution for use in other applications simply by accessing the data according to general API rules. In addition, standard API support streamlines application development and improves reusability.

Example of applying the solution

The following section showcases an example of a factory that achieved a high level of traceability, streamlined the use of human resources, and enhanced management through the aggregation and visualization of OT and IT data.

A manufacturer was considering ways to further data utilization in order to establish its facilities as a next-generation smart factory. Hitachi’s digital twin solution for production sites was introduced to a highly automated production line to reduce production issues, and to improve production efficiency and competitiveness. The solution aggregated and visualized a broad range of data spanning the equipment, devices, and IT systems in use across the entire factory. As a result, the following three benefits were achieved:

• The manufacturer could pursue product safety and security.
  Manufacturing histories and quality information are correlated and maintained together for each product, which enables prompt responses to consumer inquiries. If an equipment or device issue occurs, the manufacturing and inspection histories are traced to identify the scope of adverse impact from the issue and to confirm whether there are any issues with product quality.
• The manufacturer could use human resources more efficiently.
  The automated retrieval and processing of necessary data at the necessary times drives efficiency improvements in daily operations, such as in preparing reports and responding to inquiries. This has eliminated the need to rely on specific individuals for tasks within the factory, facilitating a shift to remote work while also improving operational efficiency.
• Through digitalization, Hitachi’s digital twin solution for production sites increased the level of sophistication of factory management.
  Data analysis is based on the perspectives of those in the field, and data required for overall operational optimization can be extracted, correlated, and reused. Accelerated PDCA (Plan Do Check Act) cycles and the clarification of new findings help drive advancements in factory management.

In addition, rolling out the IoT infrastructure to a second or third factory provides greater scope for data connectivity. The aggregation and use of data from product distribution and the supply chain, in addition to data from the factory floor, facilitates operational improvements over a broader area.

For details on our solutions, see the following webpages.