Contributing to resilient operation and worksites through connected factories using Toshiba's highly evolved manufacturing IoT solutions

This voice was created by using Toshiba’s speech synthesis middleware, ToSpeak.

Climate change, natural disasters, growing trade friction and geopolitical risks, the COVID-19 pandemic, and other factors are driving a rising tide of uncertainty. One of the major challenges being faced by the manufacturing industry is how to quickly and flexibly respond to these difficult to predict environmental changes. The industry is being called on to create environments capable of rapid decision-making, both on the management side and in the field. The use of digital technologies plays an important part in this. It is vital that manufacturers have systems for collecting, storing, visualizing, and using various types of manufacturing-related data.
Let's look at the factory IoT platform, a manufacturing IoT solution from Toshiba Digital Solutions that helps transform manufacturers, making them more resilient by connecting worksites, management, and supply chains.

DX takes a multi-stage approach to management and worksite operations vital during the age of uncertainty

The rising levels of uncertainty are causing the environment surrounding the manufacturing industry to change dramatically, at unprecedented speed. In this era of unpredictability, companies must become capable of responding flexibly to all kinds of environmental changes. It is important that they prepare their business environments from the two perspectives of management and field operations. For example, they must be able to assess overall manufacturing conditions in real time and make speedy management decisions. They must also be able to visualize production information and immediately respond in the field to issues which could affect production.

To achieve the transformation being demanded of manufacturing industry companies, eyes are turning to the use of digital technologies, such as using AI to analyze data. In conjunction with this, companies are working to use the tremendous amount of data that is being generated to identify customer needs and worksite conditions and then to transform their own services and business models, becoming stronger companies. They are carrying out digital transformation (DX).

However, according to the "DX Report 2 (Interim Report)" released in December 2020 by the Ministry of Economy, Trade and Industry, the reality is that DX has made little progress. The report states that "the message of the preceding DX Report has not been communicated accurately. For example, some companies see DX as simply 'replacing legacy systems.' Others believe that if they currently enjoy positions of competitive superiority, they have no need for further DX. Interpretations of DX do not grasp its essential nature." Furthermore, it states that "companies have not yet reached the stage of sharing a company-wide sense of crisis or promoting a change in awareness."

Toshiba Digital Solutions does not see DX as something that can be achieved in a single bound, but instead as something that is accomplished in stages.

First, digital technologies are used to improve the efficiency of value chains and enhance existing manufacturing. We call this "digital evolution (DE)." DE itself is also performed in stages.

DX is accomplished using the digital environments that were created and the resources that were obtained through DE.

By carrying out DE and DX in stages, companies transform themselves, becoming highly adaptive to change while producing steady results.

Meeting the challenges of manufacturing worksites by shifting from an isolated to an integrated manufacturing process approach

To implement DE, it is vital that data is collected from sources such as manufacturing worksite facilities, and that the scope of data usage is expanded from individual equipment and processes to encompass entire production lines, factories, groups of factories, and eventually entire supply chains.

Many manufacturing sites in Japan have been at the forefront of kaizen, or improvement, activities, and they maintain high levels of productivity and quality at individual worksites. In other words, they have achieved optimization on a site-by-site basis.

Lately, however, worksites indicate issues related to the overall manufacturing processes: "When components and materials do not arrive on schedule or have quality problems, we check and adjust them with the help of workers," "We cannot immediately identify unexpected events that occur in the previous process, so check them through individual efforts," "Even if the productivity in the factory is improved and shipped to the factory responsible for the next process, the inventory in that factory increases and does not lead to overall productivity improvement (overall optimization is not achieved)," etc.

We have read their voices and have found that there is a need to strengthen two main types of integration: the integration of information between different systems within the same factory and the integration of information between different factories. We support overall optimization by coordinating and centralizing these two types of information.

The functions of the factory IoT platform of manufacturing IoT solutions have been significantly enhanced with the aim not only of making manufacturing worksite improvements but also of helping strengthen the management of entire processes.

The factory IoT platform, the crystallization of the Toshiba group's expertise

The factory IoT platform is a platform solution for collecting, storing, and utilizing various types of manufacturing-related data. It replicates what happened and what is currently happening at manufacturing sites in cyberspace, in great detail, and uses sophisticated digital technologies to analyze and visualize this data. This solution is being used by numerous manufacturing industry customers, such as automakers, electrical component manufacturers, and semiconductor manufacturers, primarily those involved in the assembly and processing of products involving many processes or components.

It contains an integrated data model for the manufacturing industry, which we call the manufacturing data platform.

This data model was developed using the expertise of the Toshiba Group, which has extensive experience as a member of the manufacturing industry. For years, the Toshiba Group has engaged in diverse, wide-ranging manufacturing activities. We have a deep understanding of the different manufacturing patterns (production technologies) that are used. Based on that understanding, the data model is standardized as much as possible, and makes it possible to store many different kinds of data. It also collects and performs integrated management not only of IoT data, but also data from siloed systems. It was also designed with an eye towards expanding the scope of data usage, so there's no need to alter the data model when the scope of utilization is expanded in stages to include facilities, processes, or between factories.

The data model's database is "GridDB*," which was developed in-house for use with big data and IoT data. Every millisecond, manufacturing sites produce prodigious amounts of data of all types. GridDB stands out for its exceptional processing power, which enables it to handle this data at high speeds.

* GridDB is a registered trademark of Toshiba Digital Solutions Corporation in Japan.

The data stored in the integrated data model can be rapidly and flexibly applied to various use cases by using the manufacturing IoT data utilization application, a solution for leveraging manufacturing IoT data. It also offers functions, such as standard screens and dashboard groups, for visualizing data based on how it will be applied.

We have now developed functions that further enhance the integration of systems within factories, and integration between factories, led by the manufacturing data platform. These enhancements are offered in the form of a new version of the factory IoT platform. Let's look at how it works and the key improvements that have been made to it.

The two integration enhancements of the new version

First, let's look at the enhancements that have been made to integration between systems within the same factory.

Manufacturing worksites are supported by various core systems such as Enterprise Resource Planning (ERP) systems, which are used to create product manufacturing plans, and Manufacturing Execution Systems (MES), which assist with those production activities. Each individual system handles different types of data, and works with different levels of data granularity, depending on what the systems are used for. For example, while ERP is used to create production plans in one-day units, MES is used to manage production results in units of seconds.

In order to integrate data from systems with different data units (granularity), the data must be converted and made up for the differences in granularity. On the other hand, to use results data from MES in monthly plans, the data, which is in units of seconds, must be converted into monthly data units. In other words, the approach differs depending on purpose.

In addition, there are various types of MESs, and in some cases, different MES are utilized for different products, processes, etc. The types and granularities of the data often do not match, making data integration between systems difficult.

To accommodate these data differences between systems, the factory IoT platform offers enhanced functions essential for data integration. For example, acquired time-series data in millisecond units can be stored in the form of time period data in units of days, months, or the like. This makes it possible to smoothly integrate data from various systems found in manufacturing sites, such as production plan data, component and material receiving plans and inventory data, and product shipping volume data.

This integration enables real-time, on-site assessment of deviations between plans and actual performance, production conditions, and the like.

Next, let's look at the enhancements that have been made to integration between factories.
Products are made up of numerous parts, and the parts that go into a product are often manufactured by different factories and companies. One of the challenges faced by the manufacturing industry is how to assess production conditions at each production site, raise productivity, and leverage this information to benefit their businesses.
To accomplish this, we have developed the manufacturing data platform connector and offer it in the new version of its factory IoT platform. The manufacturing data platform connector collects production-related data online from different production sites. It then combines that data, enabling integrated management across multiple factories (Fig. 1)

With "connected factories," even if parts are being manufactured in multiple factories and by different companies, assembly plants can confirm production plans and production results for each part, in real time, and rapidly take necessary actions.

By combining the information collected from each production site with the new dashboards, users can select the factories whose information they wish to check and the information they wish to see. For example, they can confirm the production plans of the factory in charge of a preliminary process, check how far preparations have come, and see the production progress status (Fig. 2).

Knowing when parts and materials are expected to arrive from the preceding process would eliminate the need to keep a large inventory of parts and materials on hand. Furthermore, critical situations could be detected in advance, based on the information for the preceding process, and steps could be taken to address them.

The dashboards can be used to confirm information, eliminating the need to assess situations and make adjustments by hand, which takes a great deal of both time and effort. This reduces workloads and contributes to more effective personnel utilization. This value is created by enhancing integration between systems and factories, bringing together data for entire manufacturing processes, not only IoT data, but also data for various core systems and data scattered between production sites.

Of course, it also holds great promise from a management perspective, as well, making it possible to create holistically optimized manufacturing plans that reflect the real time conditions of physically distant production sites. What's more, the MES data in multiple plants can be used to integrate product manufacturing information, which helps ensure product traceability.

The factory IoT platform is a solution for assessing conditions across all manufacturing processes, in real time, without needing to worry about the specific systems used in individual processes or plants.

Using the Toshiba group's expertise and know-how to evolve even further

The factory IoT platform, which supports rapid decision-making by management and worksites, is a highly versatile solution. In recent years, we have received a growing number of inquiries about using it not only for industrial products, but also for processing field applications such as food production. One of its draws is that with the factory IoT platform, you can start small and then gradually scale up, making both initial deployment and later expansion easy.

Toshiba group is working to refine its own manufacturing processes, and has positioned the factory IoT platform as a standard IoT tool. The factory IoT platform will continue to evolve by applying even further expertise and know-how.

In order to deal with the uncertainty which has become so pervasive, the factory IoT platform is integrating systems and factories to contribute to speedier decision-making by manufacturing management and worksites, driving the advancement of smart manufacturing. Toshiba Digital Solutions is constantly strengthening its solutions, which are helping take manufacturing to the next level, and will continue to contribute to manufacturing industry DX in the future.

  • The corporate names, organization names, job titles and other names and titles appearing in this article are those as of December  2022.