Chu Jian: Digital transformation is the trend of The Times, and the only thing to discuss is where digital transformation will go in the future, and there are some things we can’t imagine. Since the advent of generative artificial intelligence (such as ChatGPT), we have recognized that AI may present us with enormous challenges and, of course, enormous opportunities. For the industrial field, it is logically clear in most cases, from the principle to the product, whether it is a physical process or a chemical process, it is scientific. But there is still a lot of knowledge that is not sound and comprehensive, it is scattered in the minds of different experts, and most of the knowledge is in books, research reports and production practices. If we can do a good job of connecting data and knowledge, it is actually an AI-based idea that we may have to challenge in the future.

Today’s industrial Internet if only said to be “industry + Internet”, may be incomplete, I think we should achieve from “industry 3.0” to “industry 4.0” through digital transformation, digital transformation includes the interconnection of equipment, data, expertise, from the acquisition of data to better solve the problems in production on the basis of data. For manufacturing enterprises, digital transformation is not the purpose, intelligence is not the purpose, the ultimate purpose is to improve the competitiveness of enterprises, first of all, to make the profitability of enterprises become stronger, product innovation ability becomes stronger, energy consumption is reduced. In the process industry, safety production is the first, environmental protection, low-carbon, human efficiency, equipment long cycle operation, etc., are the core competitiveness of enterprises. The focus is to help enterprises solve these problems through digital and intelligent means, taking into account safety, quality, cost, efficiency and low carbon.

HBR: You mentioned that the market transformation of digital intelligence is not only the improvement of management mode, but also the requirement of process industry transformation and upgrading. So what are the practices worth discussing in helping process industry transformation and upgrading?

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Chu Jian: All enterprises are aware of the necessity of digital intelligence transformation, and obviously realize that their competitiveness is facing challenges: for example, talent recruitment is very difficult. Because most chemical companies are distributed outside the city, even in very remote places, some in the raw material production area. Due to the impact of transportation costs and environmental factors, they will encounter personnel training, equipment maintenance and other aspects of the problem, which is a challenge for them. As long as we stand in the user’s perspective to look at this matter, we may be able to find a way out. Zhongcon has the advantages of ZhongCon. For example, we have designed nearly 180 5S stores covering all 643 chemical parks in China (Sales, Service, Spare parts, Specialists consulting and Solutions) to reduce the service radius. Reduce response times and become more sensitive to customer pain points. In the face of more than 30,000 customers accumulated over the years, we have heard countless demands, are there any common problems? Is there a common solution? We can’t solve it one by one, but we can abstract out common problems and develop products and solutions with common characteristics. Now, we have mastered part of the technology of artificial intelligence, based on so much data, scenarios and the original knowledge and experience accumulation, how can we make it play a better role? The bottom line is to help enterprises improve competitiveness.

HBR: From the earliest automation hardware supplier, Zhongcong has gradually transformed into a supplier integrating hardware, software and intelligent system solutions, and now it has proposed a new model of “1+2+N” smart factory. For Zhonghong, has there been a very significant change in business model or even strategy in the past 30 years? What kind of thinking is behind this change?

Chu Jian: In the past, for quite a long time, we just sold DCS companies, mainly hardware, coupled with embedded software. Some customers have told me that Zhongcong should not only be a DCS (Distributed Control System) company, nor should it only do automation. But at that time, the central control is exactly a DCS company, in the field of automation services is still incomplete, because the scope of automation is large, our ability is not enough. In the face of more and more personalized needs of customers, we feel a lot of opportunities, of course, there is pressure. Since then, we think that if the needs of customers can not be solved temporarily today, it doesn’t matter, you can first collect demand, reserve information, until we have the ability to turn the demand into a product, and then provide services, so that a virtuous circle is formed, and the service is better.

With this information accumulated, we thought: Can we innovate in the business model? Therefore, Zhonghong put forward the idea of a new one-stop intelligent service model of “5S store +S2B platform”, which is a different transformation. The S2B platform is not only to provide the products of the central control, but also to provide the products of the partners that customers need. But only this innovation is not enough, how can we work hard in product innovation? For example, we originally do compressor unit control, compressor is the heart of the process industry, compressor unit control is very complex, optimization control is more complex. If you do not understand the process and equipment, it is impossible to make it energy-saving and efficient. In addition, we have done a lot of on-site control and real-time optimization, and we must be familiar with the process and modeling. In this context, we put forward a comprehensive integration of automation technology (AT), information technology (IT), process technology (PT), operational technology (OT) and equipment technology (ET) 5T strategy. At the bottom of the concrete scenario is the basic software, which is the process simulation software, so we put a lot of effort into the process industrial Process simulation and Design platform (APEX). Then the customer asked the question, saying that the DCS has the operating parameter data, but the equipment status is not clear, do not know which equipment is normal or abnormal. If the data on the DCS is obviously inaccurate, is there a problem in the production situation, or the equipment itself? It must be traced back to the source, so we built a full-device intelligent perception platform PRIDE to help enterprises become more transparent production processes and realize digital intelligence. In order to better achieve all this, we also hope to integrate into a more open innovation ecology, so that more experts, schools, and research institutes can research and develop applications on this basis.