OMRON Corporation (6645) Earnings Call Transcript & Summary

Thank you very much for waiting. We will now begin OMRON's Corporation Data Solutions Briefing. This briefing is held at site and online and is on a hybrid format. The presentation materials are available for download from your seat at the venue and from the URL posted in the chat on Zoom. And in terms of the language, we will provide simultaneous interpretation between English and Japanese. Now we would like to introduce today's participants. OMRON Corporation, Representative Director, President and CEO, Junta Tsujinaga.

Hello, nice to meet you.

Next, OMRON Corporation Managing Executive Officer, Senior General Manager of Data Solution Business Headquarter and Senior General Manager of Innovation Exploring Initiative Headquarters, Hidetaka Ishihara [indiscernible].

Hello.

JMDC, Inc. Representative Director, President and CEO, Ryo Noguchi.

Hello.

JMDC, Inc. Executive Officer in charge of Insurance Business Unit and Digital Data New business unit, Takayuki Hamada.

[Foreign Language]

I am from OMRON Corporation, Okada, who will be moderating this session. First, we will conduct the presentation thereafter, we'll move on to the Q&A. In terms of the Q&A, first, we will take questions from the investors and the analysts. Thereafter, we will take questions from the media. For those participating online or at the venue or Zoom and telephone, we can take questions in all cases, but we will prioritize those who are in the venue. Thank you for your understanding. We are scheduled to end at 6:15, but depending on the length of the Q&A, it may be a little early or after 6:15. Further, the web streaming including the Q&A session, will be posted on our website. Please also refer to this. Now we would like to start. Tsujinaga-san, please.

Good afternoon. I am Tsujinaga, President of OMRON Corporation. Thank you very much for participating in our Data Solutions Briefing today out of your busy schedules. One year has passed since JMDC joined the OMRON Group. Today, we would like to share with you our data solution creation activities that we are promoting with JMDC. I will be explaining together with Noguchi-san. This is today's agenda. First, we will explain about how we position Data Solutions in OMRON's Group. How we position Data Solutions in OMRON's Group. Next, we will introduce our activities during the past 1 year. He will explain in detail the progress of Data Solutions creation, together with our business growth potential. Finally, we will share our medium to long-term numerical targets and move on to the Q&A session. Although time is limited, we wish to respond to as many questions as possible. Thank you very much for this opportunity. First, in OMRON's long-term vision, SF2030, we set a goal to solve 3 social issues: achieve carbon neutrality, realizing a digital society, extending healthy life expectancy. Through these efforts, we aim to maximize corporate value. In order to make a substantial contribution in solving social issues, which are becoming more profound and complex, we are trying to offer not just our devices, but also our -- create various solutions, including service. The key to this endeavor is co-creation with JMDC. We are concurrently working on structural reform and at the same time, reinforcing and accelerating our co-creation with JMDC. OMRON has achieved growth by having each of our businesses spread devices globally and create a strong market position. Many customers are currently using our products through which we have collected various on-site data and health care data. However, simply collecting data does not provide value that solves customers' issues. By combining JMDC's data management capabilities and solution development capabilities, we are developing solutions not only in health care, but also in social solutions and industrial automation. OMRON has been making efforts to expand our services in health care, such as remote patient monitoring and also maintenance in industrial automation. During this 1 year, through verification of new services, we are now confident enough to say that we are making progress. In health care, in addition to the health big data business offered by JMDC, we are also working on new prevention and health promotion business. In Social Solutions, we are making steady progress towards launching the management service business that we introduced last year and in promoting the carbon neutral business. In Industrial Automation, we are clear on the direction towards which we will evolve the i-BELT business, which utilizes data and initiatives to strengthen the competitiveness of IAB are steadily progressing. Today, we wish to share with you the specific progress we have made. First, health care solution will be explained by Noguchi-san. Noguchi-san, the floor is yours.

Tsujinaga-san, thank you for the introduction. I am Noguchi from JMDC. I would like to explain about JMDC's business as well as future growth potential. JMDC is a company that handles health care big data. As you can see, our mission is to provide a healthy and rich life for all individuals. And the data that we handle, there's a wide variety of data that we handle vis-a-vis various health care players utilizing data, we offer service, and we collect various data to create a large health care data platform. And this is what we have been aiming in conducting our business. It was in 2019 that JMDC got listed on the stock market. And during the 5 years since then, we've been growing significantly. Sales has grown by 3x. CAGR is 35%. JMDC's revenue has grown by 3x and CAGR is 35%. Our business model may be a bit difficult to understand, so I would like to explain. A bit more than 20 years ago, JMDC was established as the company. And first, we embarked on, as you see in the top left corner, service to health insurance associations. And health insurance associations data we collected and we had been offering a service using that data. That is how we started out our company's business and we anonymized the data from health insurance associations so that the data can be utilized by other health care players. That was our business scheme. And as you can see on the right-hand side of the slide, the anonymized data is offered to various companies like life insurance, nonlife insurance company, academia, government, pharmaceutical companies, et cetera. And we started out data business from health insurance association-related business. But in order to respond to various needs, we are now expanding our service to medical institutions, dispensing pharmacies. We are now able to collect data from various kinds of organizations. If I may explain in a bit more detail what we are doing. This is an example of health insurance association service. A bit more than 20 years ago, health insurance associations health care data, this is a health insurance claim data that is received through medical diagnosis in the [indiscernible]. And this information was only available in paper. And we started out by digitizing this paper data, and we cleansed the data so that it could be analyzed. That is the first kind of service we started to offer. And after the data was digitized using web tools, we created a platform where data can be easily analyzed. And health insurance members, what kind of illness diseases are they solving for. We were able to collect this kind of data. And based on this data or information, we came up with various solutions. So that is how we have been handling health insurance associations data. Furthermore, we anonymized data and -- how is this anonymized data being used by various players? I want to introduce to you example of pharmaceutical company. So regionally, pharmaceutical companies, when they're utilizing our data, were using it for epidemic diseases. In the -- among the areas of medical pharmaceuticals to utilize this data and conduct that for R&D, that was most utilized. However, when you utilize this data, you're able to see many things. And recently, in various areas, it is also being utilized. For example, in the area of marketing, it is being utilized. The drug marketing at the pharmaceutical companies in the past, it was mainly in the primary care, which is to have drugs that are utilized by Board perspective. So MR would visit the pharmaceutical companies and doctors and make a promotion to the doctors. However, recently, we are shifting to specialty care, which is something where there is less patients for rare diseases we provide these drugs. So the pharmaceutical companies thinking is trying to understand the concrete details of the therapy and have to provide more objective information as we transition to this, [ in case of our ] data, as we show here, we got it a patient data. But for those who are member of the health agents insurance, what progress and what facility, what kind of treatment they have received, we are able to capture that. And with that, at what process of the patient treatment is there this unmet needs, and we are able to acknowledge that, and we need to make appropriate appeal to that. As such, we are able to utilize the service in a broad perspective. This is just one example, but through this in various health care player, our data is being utilized, and this is kind of broadened. We collect various data, and we have put that as one platform, as I mentioned earlier, but the health care in Japan, we have been collecting this quite a lot. This is what we disclosed last year, our target for collecting data and the progress. As you see on the right-hand side, the target, this is the data that you can capture in Japan is basically half of that is our target. So for example, in terms of health insurance claims data, health insurance association data, those member, they are roughly a little less than 40 million. And roughly half of that 20 million is our target. If we're able to capture that much data as a data, that value will further increase is how we think. Against that target, our progress so far is in terms of the health insurance [ association ], I think we are quite close to that. In the various other data types, we are progressing strongly and are collecting data. And the reason why we are able to capture these data, I would like to supplement. It is because data in order to appropriately capture these data, you need the personnel as well as technology. For over 20 years, we have been working in this industry and to be able to have human resources who can handle data correctly, we have sufficient personnel. The team, we have data cleansing approximately 50; and database engineer, we have 50; data scientists, we have approximately 30; and health care professionals, we have approximately 30 people; and data consultant, 50 people. So we have broad-based people. Through having this data, capable people have participated and joined our company as a result of that. And of course, these are very stringent information of health, so you require various knowledge as well as infrastructure against actual on the protection of personal information as well as data security, including cybersecurity, data anonymization, knowledge through this accumulation of data, this is why we are able to handle data and is also a subject for differentiation. As we proceed with these initiatives, JMDC among the Japanese health tech have been able to have an overwhelming presence. In the past 5 years, when you look at the growth rate, and also the margin in the other health tech competitors, we have taken a very nice balanced level. Up to now, we have touched upon our track record. But in our initiatives so far, as I said, we have collected mass data and come up with foundation. On the other hand, this data, in order to return this to a value, I think we are still halfway through. In health care, as we take on various initiatives, this data is going to be a necessary asset. But as you see on the right-hand side, our penetration rate, for example, per capita medical expenditure is approximately JPY 358,000. The people who capture data is 20 million or 30 million personnel. But the financial resources of that is approximately JPY 100 from these personnel. Our data is value in order to really return this to society and optimize the medical fee. If we are able to provide such service, I think this percentage will further increase. This is not just limited to service provided to individuals, but also include services to pharmaceuticals as well as medical institutions. Even if you look at that, our penetration has only started. The data value, we are still at the entrance of returning those values. In the 5 years' time, the potential size of the market, this is an illustrative image, our data in various areas, the person who is using most, we want others to also become close. And what would be the kind of scale? We've kind of roughly -- it's a rough number, but for example, for pharmaceutical companies as well as life and nonlife insurances, I think for industries, we're able to have opportunities of approximately JPY 40 billion to JPY 50 billion in the medical service providers as well as Medical Health Association, we have very large potential. If we add this up, the market sizing monetization is roughly JPY 250 billion of scale. For us, as we try to take these initiatives, we want to optimize this potential sizing. And further, together with OMRON as we collaborate with them, we want to see new opportunities, and I would like to share with you a couple of that. As I said, to the health insurance association, one of the service we provide, we call PHR, which is the personal health record service. Already, there are about 7 million personnel who are utilizing this service. But the receipt as well as those health check of these data, we are able to access such data. And there, vital data, which OMRON has such as blood pressure monitor, weight as well as body composition monitor, we are trying to combine that aggregating that. And we have started this OMRON Connect and Pep Up service of us. As such, we are integrating these various data. By combining this, what we were not able to see just through [ recept ] we are now able to visualize and allows a new potential of providing services. This is an example of a solution. But for example, in terms of blood pressure, the [ recept ] that we have as well as the health check data by combining that and by utilizing algorithm and analyzing that, what kind of -- even if it's the same hypertension patient, whether that's increasing or it's decreasing, depending on that, the action that person should take should be different, and we're able to make appropriate recommendation based on that. For example, we believe that we can provide that kind of services. And regarding this algorithm coming up, developing, and we have already starting these initiatives. As we are able to broaden these, this might be also returned to individuals through PHR and this may become a potential business. And in the case of OMRON devices, one of the value added of this is that to accelerate these devices. And by considering these, we would like to take on various initiatives. Further, in terms of collaboration with OMRON, we have started health and productivity management alliance. So we currently have more than 400 companies participating where we promote health at the company by using data. Through these alliances, the relationship that we have been able to gain, we want to utilize that so that our initiatives to companies have also evolved. JMDC to health insurance association not only do we provide services to them, but we also provide corporate health insurers to companies beyond that. And in terms of the numbers we've already implemented after the health and promoted management alliance, we have been able to expand that by 7 times. As I said, the PHR, the Pep Up service, we are making it to be utilized by companies and by linking the Pep Up industrial services as well as mental services are the things that we provide as we try to increase the company who implements this. As such, JMDC on a stand-alone basis, we are growing in the health area and together through the co-creation with OMRON, we try to further evolve. That is all for myself.

Noguchi-san, thank you very much. I will now explain about progress in Social Solutions. First, I will briefly introduce the Social Solutions business. OMRON is offering equipment and services to various industries that support the infrastructure in Japan, such as railway, transportation and energy. These equipments need to be maintained. And in order to respond to such needs, OMRON also offers maintenance service throughout Japan. As you can see, we are the #1 position in the device industry and in maintenance as well, we have a nationwide network, which is one of our major strengths. On the other hand, the environment surrounding maintenance business is undergoing a substantial change. With an increasing number of devices due to the trend towards IoT, there is increased burden on site to manage these devices. On the other hand, the working population is declining and labor shortage has become a serious issue at the maintenance sites. Furthermore, there is increasing need to utilize data obtained from device and equipment for future product planning, et cetera, which is a significant business opportunity for companies like us, working to evolve our maintenance service. In order to capture such business opportunities, we have started to utilize on-site data to create a new solution. As you can see on the left-hand side, for various industries, including railway, transportation, finance and retail, OMRON engineers are responsible for maintenance of both our own companies or other companies' IoT devices. This is why OMRON can gain access to a vast array of on-site data, and combining this with the strength of JMDC, it becomes possible for us to create unique solutions. One outcome of such effort is Smart M&S. It is a solution that utilizes data to mechanize operations and optimize and streamline on-site environment instead of people. We are thinking of offering this solution to the retail and distribution industry to companies with many stores nationwide. Today, we will introduce 2 examples. First is improvement of field service efficiency. Please refer to the next page. This slide summarizes on-site issues in managing equipment. Currently, when there are equipment and facility failures at the customer site, maintenance staff would have to visit on-site to fix them. However, if maintenance staff are dispatched even for minor failures, and in emergencies such as the failure of POS system, it becomes necessary to wait for the maintenance staff to arrive, which is a difficult issue for customers. Moreover, in order to maintain the store utilization rate, there is increased need to prevent failures in advance. In order to solve these issues, we created the field service efficiency improvement solution. Please refer to the left-hand side of the slide. Equipment data as such as store operation status, usage environment and customer response log, including repair records, all of this data was analyzed with JMDC to develop a new AI solution. I will explain in detail using the right-hand side of the slide. First is optimal response prediction AI. When there are customer inquiries, the optimum response is proposed by AI to operators. This proposal enables operators to solve minor failures remotely and can reduce considerably the frequency of dispatching maintenance staff. The second type of solution is failure prediction AI. AI can predict failures and issue alerts to maintenance staff so that maintenance work can be conducted in advance. This helps to prevent sudden failure. The concept of this failure response efficiency improvement solution is already developed. And we wish to offer this solution going forward to as many customers as possible. Next, we will introduce the remote on-site management support solution. Managers in the retail and distribution industry faced various challenges regarding store operation. One is increase of sales and profit per store. Customers are making efforts to improve store operations and reduce costs. They are constantly looking for new ideas. Improvement of store management efficiency is another challenge. Area managers visit each store to give advice and guidance. But as the store network expands, there is more burden and increasing performance variability among stores also needs to be resolved. In order to solve these challenges, we created the remote on-site management support solution. I will explain in detail. Please look at the right-hand side of the slide. First, images such as store layout, visiting customers, staff service are taken by a camera and collected as video data. Customer traffic, how much time customers spend per product shelf, staff movement. All of this data is accumulated and AI analysis result is fed back to customer marketing division and area managers. By utilizing this information to optimize store and product layout and to improve staff assignments, it becomes possible to increase sales and profit per store. Furthermore, area managers can communicate with employees remotely using a camera robots in each store, thereby reducing the number of store visits. This business concept of this solution -- the business concept of this solution is also completed, and we are now working on proof of concept with customers. Using this page, we wish to share with you the track record of each solution and future plans. We are currently in the proof-of-concept stage. We are discussing with customers on improvement points to generate further benefit and to get prepared for full-fledged commercialization. As you can see on the right, we are working on proof of concept with a major retail chain, but many other companies seem to be facing the same challenge not only in the retail and distribution industry, which we introduced as an example today, but we think this is a business that will prove to be helpful for other customers as well, such as [ restaurants and daily necessities ]. In other words, helpful for customers who use IoT equipment and have a nationwide network. Therefore, the potential market size is very large, and we think this is a business that will contribute to the future growth of OMRON Group. Next page, please. The potential size of the retail and distribution industry is estimated to be about JPY 500 billion for on-site service and for manager management service, approximately JPY 250 billion. This solution can also be deployed to other industries, which means there is further growth potential. We will continue our utmost effort to develop this business into a growth pillar. We want to continue our efforts so that this business grows into a growth pillar. Next, we would like to explain the progress of industrial automation. Even before the collaboration with JMDC, OMRON has been providing data-driven service, i-BELT to many clients. i-BELT uses OMRON's wide range of control devices and the control devices partner companies collect the data, enabling at the manufacturing site can be easily collect, analyze and utilize this service. This service was launched in 2017 and has been installed over 200 customer sites. i-BELT service through the 4 steps realizes to solve the clients' challenges. Please look from the left-hand side. First, we assess the situation of the customers' equipment and visualize equipment's operating conditions, analyze the problem area, and based on the analysis, provide the optimal control solutions in one stop. Up to now, we have been providing various data solutions to our customers. And from that, we would like to share with you a video, a solution that supports the -- that transcending the know-how of a proficient skilled worker. This is reproducing a proficient skill using a solution using generative AI. This is a screw-tightening process based on the movement of the operator working according to the assembly guidelines analyzed by AI in real time. And when the work is finished, the AI immediately charges the accuracy of the work and reviews. As you can see, after the work is done, the AI voluntarily asked the skilled operator for advice on improvements. The advice obtained from the skilled operator is accumulated the knowledge, and when the same operator or a different operator is noticed, can make prompt proposals for improvement. This is the following day. The content of that the advice is reflected on the operating guideline, and you can see how improvements are being made. As such, based on the operator's proficiency work guidelines are provided and thereby, we are able to reproduce the skills of the skilled operator. The data solution that we have just shared, we are increasing the adoption each year. In 2019, the adoption was 30 companies. But last year, it has exceeded 200 companies. It is being utilized by many customers. However, this i-BELT service is still halfway through. Please look at the right-hand side, the challenge that i-BELT has, that is the standardization of nonstandard data. Currently, each solution is tailored to site of each individual customers and thus, cannot be sufficiently scaled out. And the reason for this is because the data of the FA field is diverse and further the data assumption like the manufacturing items, equipment and processes defer from site to site, making standardization and analysis extremely difficult. In order to solve this IAB, together with JMDC, have started a new initiative. And that initiative is to build a digitalized knowledge base for the manufacturing site. If you look at -- please look at the bottom part of the slide. The information utilized -- our information is like process as well as yield volumes as well as the intuition track as well as tacit information that are on standard data by the proficient skills. On the other hand, the standardization data that JMDC has been able to accumulate at the medical industry and the high analysis capability can be utilized for these kind of nonstarter data and manufacturing side. Through these data management power, industry, client, product, item, from these different data that are different, you're able to come up with commonality as well as relation between the data, and through that, the field data that OMRON has can be utilized in some industry as well as in similar products as well as in processes will become a knowledge base to sophisticate the manufacturing site. By utilizing this new base, we want to be able to make i-BELT a more versatile data solution and involve that so that we are able to create new values to many, many clients. As I explained, i-BELT is still halfway through its growth. And by combining the capability of JMDC, we can expect a big involvement. We believe that this is a very important business that would increase the competitiveness of IAB, and that development will lead to the expansion of solutions business as well as expanding our customers. The digitalization of the manufacturing is a very complex theme, but together by mobilizing technology know-how of OMRON and JMDC, we will definitely strive. I hope that you had high hopes for i-BELT going forward. This concludes this part. Next, I would like to touch about the outlook of our future business performance. First, in terms of the time line in terms of the growth of the Data Solutions business. As you see, JMDC, with the existing business is expecting a very strong growth in the mid to long term. For health care business, the health version business growth as we try to strive for that -- in the area of Social Solutions, Industrial Automation, we will come up with a new solution on a phased approach and will fortify the sustainability of growth. First, I would like to explain about 2027, which was the first milestone. Please look at the left-hand side. The Data Solutions business, as of FY '27, in terms of sales exceeding JPY 100 billion. OP margin exceeding 12% is where we want to challenge. Of course, it is a high target, but the growth of JMDC as well as Solution Business where we see concrete move, it's not a target that is unreachable. On the right-hand side, we have the business image according to each businesses. For health care, including the group with JMDC sales exceeding JPY 80 billion with OP margin exceeding 18%. For the Social Solutions, as the Smart M&S business as core, we want to grow that would exceed JPY 20 billion. Lastly, for Industrial Automation, we want to set a target in the growth strategy that we are moving so that we are able to accelerate the initiatives so that we can look for an upside. That explains this part. Lastly, I would like to share my thoughts. Over the past year, OMRON and JMDC, both members held numerous discussions to put forward actions for commercialization. As explained today, we have been able to see concrete progress, and I feel strong confidence. The creation of these data solutions in the mid- to long term, it will heighten that and it's an important initiative. We do expect to face some challenges in future, but through collaboration with JMDC and with a strong talk, we want to make it a driving force, the group, and we will continue to nurture it. We look forward to reporting back with you with an even wonderful result, and we will strive as a company-wide. Thank you very much. This concludes the presentation. Thank you for your kind attendance.

First, we will respond to questions from investors and analysts. [Operator Instructions].

Goldman Sachs Securities, [ Yuichiro Isayama ] speaking. I'm very much looking forward to what you mentioned today. And in the explanation session last year you've been raising a number of keywords like synergy. And what's actually happening? You made your presentation last year and what has actually happened since then? And JMDC, you mentioned that analog data is going to be digitized, and there's no other competitor who can do this. That's what you mentioned. And you're now collaborating with OMRON and you take a look at the data. And are you facing any difficulties? Are there any challenges in data cleansing? At the same time, I also want to ask to OMRON, health care and social security, I think you're working on these measures. And you may have had some certain goals, but you may thereafter be facing some challenges. What are some challenges or bottlenecks that you may be facing as you try to standardize or conversely, if there are any particular success that you are enjoying, please share them with us as well.

Thank you for your question. This is Noguchi from JMDC. I would like to respond. Last year, we made a presentation and digitizing analog data, we're confident in that. That's what I mentioned and what is the current situation. That was your question. I explained about health care today and what we've been doing so far. There's a manufacturing on-site data of OMRON. Of course, the data we have been handling from before is not the same as the data that we are now handling under the collaboration with OMRON. But there are common objectives that we have to achieve. That is the impression we have. And as I introduced to you today, by connecting various kinds of data, including digital data, we can work on the initial phase of commercialization, and we are confident that we are making progress here. And our capability to begin with, we started out in health care business. And now we're collaborating with OMRON, and we think we can broaden, expand our business further by collaborating with OMRON. So there are both challenges as well as the positive results that we are starting to see. Any supplementary explanations?

This is Hamada. I would like to respond as well. Converting analog data to digital data, we are confident in that capability, and that is where we started out our business. And now there's increase in popularity of generative AI, and that is a tailwind, following wind for us. So recent technology, together with our intrinsic capability, I think that is combining us to able to grow. We are doing much better than we originally thought. Using our data, JMDC has significant service development capability, and we are surprised at how capable they are in that regard. For example, M&S, social system. And when we try to develop this kind of a service, 30 or so solution ideas came up all at once. And OMRON alone would not have been able to do this, and this is incredible capability that resides in JMDC. Our data, combining that with JMDC's capability will bloom. There's a lot of potential for that. And conversely, in IAD, using various capabilities, we can come up with a lot of ideas. So we want to continue to come up with ideas to achieve sustainable growth. And this is where it's taking time. And we want to create a data knowledge platform that is substantially what we want to do. And JMDC has much more capability than we thought, and we want to be able to capitalize on their capability with what OMRON as soon as possible. And I'd like to also call upon Ishihara-san to respond because he's actually responsible for the business.

Page 17, there's JMDC's digital human resources, number of people they have and about 30 or so physicians and nurses. And of course, these people are dedicated medical professionals, but there are other -- 4 other types of people. And we were expecting something like this, but much more than we expected. Regardless of domain, JMDC is strong in various kinds of domains. And of course, JMDC started out in health care business. And we thought that there would be a good synergy with OMRON's HCP, our health care business, and social systems also going on very well. Factory automation, IAB is not as easy as some other endeavors. And we were expecting this from the beginning, but we are actually trying out and this task is a bit more difficult than other types of tasks. And that is why we are expecting a bit more time for this part. And what is the difference? And for health care, I think you can easily imagine that you take a medical checkup, health checkup and various indicators come up. And there's a limited number of indicators that come out as a result of health checkup. And data that explains about the human body, there isn't so much type of such data. And then there's medical device. And medical device data is standardized globally and social systems similarly. For example, if you take a look at chain stores. A certain retail company has about 5,000 stores nationwide and all 5,000 retail stores are equipped with similar machines. Therefore, the format of the data that is extracted is the same amongst all stores. But on the other hand, factory automation. Different factories have a massive amount of different types of machines. And even in the same company, depending upon the type of manufacturing line, machines, equipment used are different, which means the format of the data that comes out is also very much different. In addition, for factory automation, production and manufacturing per se is the source of competitiveness. Therefore, the data that comes out is unique. So there are these differences by domain type of data, the amount of data as well as the format of data happen to be different in factory automation. And that is why we are trying to standardize and create a structured database to convert data into knowledge, which is more difficult than the other types of solutions, but it is a challenge that is feasible. And once we are able to overcome this challenge, I think it will become a very unique solution of OMRON. We would like to indeed achieve our goals.

But in the presentation, there was one comment that was very interesting. I think it was when Noguchi-san, Page 19, regarding monetization. So you have only been able to monetize this, which I think it has an upside potential, but this is how I'd like to ask you. By partnering with OMRON, does the target of monetization has increased? What's your sense? And to OMRON, on the other hand, in SSB, you said JPY 20 billion in terms of your sales target. I think that's amazing. But how are you going to monetize that? It's very hard to collect the data. But from Noguchi's explanation, we understand monetization is hard. So in what form? Is it that the after value is going to increase? But to Noguchi-san, I want you to explain how you're going to accelerate the monetization and to Tsujinaga-san, how you are going to monetize that in SSB?

In terms of monetization at JMDC, as we showed, originally, the various things that we are taking, we still have a lot of leeway. In addition, by collaborating with OMRON, which is the connecting of data as well as initiatives in the corporate health domain, of course, we believe that these results, we have high hopes. However, data business is one, but when through our past experience, if we start today, especially in the area of health care business, can we monetize this tomorrow? That is not the case. We do require a certain period of time to foster that. Therefore, we think that the potential is going to broaden and in the PHI initiative, we have the OMRON Connect data that has been accumulated as well as through health care alliance, we want to access to the health care data. These are definitely going to be a positive for us. But whether how much monetize we can do tomorrow, we have to take time to foster that. But it would broaden the business area domain and has a higher potential, which is going to further support our mid- to long-term growth.

First, in terms of the social system, which is the M&S business is a new business. I think that there are no other companies that do have this kind of businesses. Amidst that, we conduct POC with customers and what way is the most way in order for us to monetize or have the customer to acknowledge our solution? We are negotiating this. For example, on an annual contract, according to each company, do we target in that way or according to each maintenance and service to monetize that or the solution to management will directly connect to revenue and whether we can receive monetization from that. Various ways can be thought. According to stores and industries, I think it will be slightly different. Once that becomes more clear, I would like to share that. So I hope you have high hopes.

We would like to move on to the next question.

Mizuho Securities, Tanaka speaking. I have 2 points, profit margin and the difference compared with other companies. First, profit margin, Page 45. You have information on this page and not just selling products, but you want to offer services. And I have the impression that, that would push up the profit margin, but health care, 18% SSB and lower than current profit margin of JMDC of 20%. Is it because you need to make some upfront investment or gross margin of the business is not expected to low? And compared with product sales, why is it that you expect a lower margin from the Solutions business?

This is for fiscal year 2027. This is a new business, as we've been mentioning. And we are just starting this new business and including HR cost, we need to make initial investment to get this business up and running. 2030 and beyond, of course, we want to continue to increase profit margin. That's the first and foremost response I want to make.

In that sense, compared with product sales, there's more earning power in the Solutions business?

Yes.

And my second question is difference compared with other companies. Data is utilized to offer solutions. That is your concepts and many other companies seem to have a similar concept in Lumada and other endeavors exist. And compared with other companies, what is the difference or uniqueness of OMRON and JMDC collaboration, health care and [ SETC ], if you're able to obtain data, you're strong. Or is there any other difference that you may have compared with other companies?