2023 Aianalysis · Panoramic Report on Hyperautomation Manufacturers｜Aianalysis Report

Key findings
The current definition of hyperautomation mainly expounds the connotation of hyperautomation from the perspective of technology combination, and it is difficult to establish a link with business value. Aianalysis makes the following new definition of hyperautomation: hyperautomation refers to the use of RPA, iPaaS, AI, low code, BPM, process mining and other automation technologies to realize end-to-end process automation of organizations and rapid orchestration of new business processes, helping organizations improve efficiency, Platform products or solutions for innovative capabilities and customer experience.
Hyperautomation has two business values: end-to-end process automation and rapid orchestration of new business processes. End-to-end process automation, that is, breaking the island of enterprise automation; rapid orchestration of new business processes, that is, building an assembleable workflow, so that Party A can quickly orchestrate new businesses across businesses/departments/systems/platforms.
Three stages of hyper-automation: task automation, process automation, and operation automation, creating an enterprise-level neural network. Task automation mainly solves single-point problems such as development, approval, and integration, and provides "neurons" for Party A's operations; process automation solves corporate linear problems through cross-business/department/system/platform, and provides "local neurons" for Party A's operations. "; Operation automation builds an assembleable workflow to help Party A realize the rapid arrangement of new businesses, and endow Party A with a "nervous system" for operations.
In 2023, the size of China's hyperautomation market will be RMB 84.83 billion, with an annual growth rate of 31.5%.
Hyperautomation manufacturers have three development paths for reference: self-developed output solutions/platform products, M&A output solutions/platform products, and specialized tools. Self-developed solutions/platform products, that is, manufacturers enter the hyperautomation market from a certain product according to their own endowments, and then expand product boundaries through self-developed methods to create hyperautomation solutions or platform products; mergers and acquisitions output solutions/platform products , common in major Internet companies or leading software manufacturers, and more common among European and American manufacturers; specialized tools, that is, through the complementary capabilities of manufacturers to form hyper-automated solutions.
The large model may drive hyperautomation into the fourth stage-autonomous operation. At this stage, AI is upgraded from a tool to a hyperautomated "brain", which is used to control the "nervous system" in the operation automation stage, and promote the transformation of enterprise operation methods.

01 Definition of research scope

Research Scope
With the rapid changes in the external market environment and the increasingly diverse needs of customers, enterprises have gradually realized that their business needs to be more agile and efficient, and have the ability to quickly iterate according to market needs. The automation of business processes can help enterprises to achieve agile and efficient business, so it has attracted more and more attention from enterprises.
The "automation arsenal" of enterprises is rich in categories, including RPA, iPaaS, AI, low-code, BPM, process mining, etc. Enterprises can use multiple automation tools, but often the result is that each automation tool is in its own "automation chimney" and can only achieve fragmented automation. For example, an enterprise's IT team may be using a low-code development platform, the finance team may be using RPA, and the call center may be using intelligent robots. Automation chimneys kill the synergy between multiple automation tools. Moreover, the use of a small number of automation tools by enterprises may lead to short-sighted behavior, making it difficult to obtain optimal automation solutions. For example, if an enterprise introduces RPA in a financial automation scenario, it will easily lead to the priority or even forced use of RPA in other subsequent scenarios, regardless of whether it is the best solution.
The above problems can be dealt with by an automated overall solution. The overall automation solution refers to the integration of various automation tools to fully release the value of synergy, and to find a way for enterprises to improve the level of automation and change the operation mode through combined innovation.
Gartner proposed the concept of hyperautomation in 2019. It mainly explained the connotation of hyperautomation from the perspective of technology combination. It is difficult to establish a link with business value, which leads to the lack of interest of Party A. Therefore, there is an urgent need to redefine hyperautomation and drive market development. Aianalysis makes the following new definition of hyperautomation: hyperautomation refers to the use of RPA, iPaaS, AI, low code, BPM, process mining and other automation technologies to realize end-to-end process automation of organizations and rapid orchestration of new business processes, helping organizations improve efficiency, Platform products or solutions for innovative capabilities and customer experience.
Aianalysis divides the hyperautomation market into platform layer and application layer from the perspective of technical architecture. The platform layer includes specific markets for a series of automation, integration, and AI-related tools, such as intelligent decision-making, RPA, BPM, etc., as well as new markets formed by the integration of these tools, such as the integration of RPA and iPaaS to form AutoPaaS. The application layer includes vertical industry scenario applications and general scenario applications, each of which includes several specific markets. The hyperautomation market segmentation is detailed in the figure below.
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This report focuses on the five markets of low-code development platform, iPaaS, process mining, RPA and process middle platform as the key research objects, and conducts research on hyperautomation.
Selection criteria for photo manufacturers:
The manufacturers selected for this report must meet the following conditions at the same time: the
products and services of the manufacturers meet the manufacturer's capability requirements of each market analysis;
the manufacturer has more than a certain number of enterprise paying customers in the past year (refer to the market analysis in Chapter 4 part);
the operating income of the manufacturer in a specific market in the past year has met the target requirements (refer to the market analysis section in Chapter 4).
(Note: "nearly one year" refers to Q2 in 2022 to Q1 in 2023)

02 Market Insights
2.1 Hyper-automation helps enterprises achieve end-to-end process automation and rapid orchestration of new business processes

Automation technology can effectively solve Party A's single-point problems, such as applying RPA to solve desktop-level task automation problems, applying iPaaS to solve integration problems, and applying BPM to solve process management problems. The application of automation technology is like giving Party A "neurons", but due to the lack of unified planning, a "nervous system" cannot be formed. Fragmented automation allows enterprises to form a large number of automation islands, which need to be integrated to establish end-to-end process automation.
RPA has been widely used in the securities industry. It is mainly used to solve links such as account reconciliation and flow query, and can realize task-level or "short-process" level automation. The end-to-end process is relatively long, spanning multiple systems and complex scenarios, making RPA difficult to handle. Taking the retail counter trading business of a securities firm as an example, the trading counter transaction operation is regarded as the beginning of the process, and the subsequent process needs to traverse the counter system, business outlet system, backstage system of the securities firm, the exchange platform, the internal settlement platform of the securities firm, and the internal analysis platform of the securities firm, etc. system.
Figure 1: Brokerage retail counter transaction business process (simplified schematic diagram)
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Realizing the automation of a certain end-to-end process is only a preliminary manifestation of the value of hyper-automated business. /Department/System/Platform, realize the rapid arrangement of new business, which is beneficial for Party A to improve efficiency, innovation ability and customer experience.
Taking the digital transformation of urban housing rental information as an example, fast, low-cost, and flexible workflow arrangement can be realized through hyper-automation. When the tenant information is uploaded to the community system, the community system uses RPA to quickly query the owner’s information, confirm the tenant and house information with the owner through smart calling, and synchronize the verified rental information to the public security system, housing construction system and community system through iPaaS . If the demand for digital transformation of urban housing rental information is realized through customized development, there will be shortcomings such as long cycle, high investment and weak flexibility, and the phenomenon of "reinventing the wheel" is unavoidable.
Figure 2: Digital transformation of urban rental information (simplified schematic diagram)
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2.2 Three stages of hyper-automation: task automation, process automation, and operation automation
Party A’s road to hyper-automation is not achieved overnight, and needs to go through three stages: task automation, process automation, and operation automation.
Figure 3: Three stages on the road to hyperautomation
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1. Phase 1: Task automation. It mainly solves single-point problems such as development, approval, and integration. Task automation endows Party A with "neurons" in its operations.
2. The second stage: process automation. Through cross-business/department/system/platform, solve the problem of enterprise line type. Process automation endows Party A with "local nerves" in its operations.
3. Phase 3: Operational automation. Construct a work flow that can be assembled, and Party A realizes the rapid orchestration of new services. Operation automation provides a complete and flexible "nervous system" for Party A's operations.
   2.3 In 2023, China's hyperautomation market will exceed RMB 80 billion

Ai Analysis estimates that the scale of China's hyperautomation market will be 84.83 billion yuan in 2023, with an annual growth rate of 31.5%.
The caliber of the hyperautomation market includes RPA, BPM, low code, iPaaS, process mining, artificial intelligence software, and related new markets derived (such as hyperautomation platforms, process middle platforms, AutoPaaS, etc.), and is mainly calculated based on vendor-side revenue data. The high growth rate of the market is mainly due to three factors: Party A’s continuous demand for automation, favorable policies, and the upsurge of large language models triggered by ChatGPT.
Figure 4: Forecast of China's hyperautomation market scale
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2.4 There are three possible development paths for hyperautomation manufacturers: self-research, mergers and acquisitions, and specialized
hyperautomation manufacturers must have the ability to supply multiple automation technologies and products. A prerequisite for the realization of hyperautomation. There are multiple development paths to enrich the technology and product supply capabilities of hyperautomation manufacturers.

1. Manufacturers output hyperautomation solutions or platform products through self-development. According to their own endowments, manufacturers enter the hyperautomation market from a certain product, and then expand product boundaries through self-development to create hyperautomation solutions or platform products. This is a relatively mainstream development path for hyperautomation manufacturers, and even develops into a group attribute. For example, RPA manufacturers are developing in the direction of "RPA+AI" as a whole, and BPM manufacturers are actively exploring ways to combine with process mining.

2. Manufacturers export hyperautomation solutions or platform products through mergers and acquisitions. It is common in major Internet companies or leading software manufacturers, and it is more common among European and American manufacturers.
   Figure 5: Mergers and acquisitions of European and American manufacturers (partial)
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3. Manufacturers specialize in hyperautomation tools, and form hyperautomation solutions through complementary capabilities among manufacturers. Hyper-automation solutions require in-depth integration of multiple automation technologies/tools/platforms, which can be obtained through self-research, mergers and acquisitions, or through cross-vendor cooperation. Manufacturers deeply need to develop super-automation tools, clarify their own capabilities and business boundaries, actively integrate into the ecology of top manufacturers, collaborate and output hyper-automation solutions. This path does not require manufacturers to make more investment in market education, and can quickly enter the commercialization stage.
   2.5 Large models may drive hyperautomation into the fourth stage—autonomous operation. The
   current large model reasoning ability is still relatively weak, especially in complex vertical scenarios, and the application ability is still in the stage of verification, but the imagination space is very broad.
   Before the emergence of large models, AI was one of the tools to solve single-point problems in hyperautomation. After the emergence of large models, the status of AI in hyperautomation has undergone major changes. Large models are not based on established rules, but on active thinking, with reasoning and decision-making capabilities. The combination of large model and hyperautomation, hyperautomation transmits data to the large model, and the large model generates analysis conclusions and decision instructions, which are realized through hyperautomation. The emergence of large models is a revolutionary upgrade beyond automation.
   The large model may drive hyperautomation into the fourth stage-autonomous operation. At this stage, AI is upgraded from a tool to a hyperautomated "brain", which is used to control the "nervous system" in the automation stage of operations. Most of the responsibilities of software engineers are performed by AI, which determines which automation tools to choose and how to assemble processes. Users directly pass new processes or even new business demands to the ultra-automated intelligent interactive robot, and the robot automatically arranges the business process quickly according to the rules obtained by reasoning, becoming an efficient assistant for the CXO. Moreover, hyper-automation can operate, manage, optimize and improve each business process by itself, without relying on manual setting of rule details. At that time, enterprises will be as agile as humans, with reform and development of operation methods and subversive improvement of human efficiency.
   Figure 6: Hyperautomation 4.0 phase - operational autonomy

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At this stage, due to factors such as large-scale model performance, laws, and ethics, Party A does not want or cannot achieve complete operation autonomy, and can adopt a more moderate human-machine collaboration method. At that time, there may be a situation similar to automatic driving classification. operating autonomy classification.

03 Manufacturer Panoramic Map

Based on the research and desktop research of Party A's enterprises and typical manufacturers, Aianalysis selects the selected manufacturers with mature solutions and implementation capabilities in the hyperautomation market.
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04 Market Analysis and Vendor Evaluation
Aianalytics defines the specific markets focused on this hyperautomation project as follows. At the same time, for some of the representative manufacturers participating in this report, Aianalytics wrote a manufacturer capability assessment.

Low-code development platform

Market definition:
The low-code development platform is based on the concept of visualization and modularization. By encapsulating business components into reusable modules and combining scripting language and other expansion capabilities, the development of rapid application development can be completed with minimal or no coding. The platform is mainly for developers with software thinking.

Party A’s end users:

IT department development engineers, product managers, operation and maintenance personnel, etc.; ITBP

Party A's core needs:

The low-code development platform can not only package the code into application components, build systems and processes visually, but also expand the scope of developers, allowing roles such as product managers to participate in system development, effectively solving the shortage of IT talents.
In recent years, with the gradual maturity of low-code development platform products, enterprises have put forward new requirements for low-code development platforms based on their own IT capabilities and business conditions. As one of the core tools of hyperautomation solutions, the low-code development platform has the capability of 1+1>2 when used in combination with other automation tools such as RPA, AI, and iPaaS. Enterprises need to combine low-code development platforms with other automation technologies to increase the level of automation. In addition, enterprises also need to develop applications with industry attributes, meet the development needs of enterprise-level complex systems, and enable low-code development with large models.
Enterprises need to combine low-code development platforms with other automation technologies to improve automation. The core purpose of enterprises purchasing low-code development platforms is to achieve agile development and shorten the cycle of application development and process construction. It needs to be combined with other automation tools such as RPA, AI, and iPaaS to improve the level of automation. For example, by integrating low-code development capabilities into the AI ​​platform, developers can package AI capabilities such as NLP and OCR into components to efficiently build enterprise AI applications; by integrating the low-code development platform with the iPaaS platform, developers can quickly design and develop And call the API interface to improve integration efficiency.

Enterprises need to use low-code development platforms to develop systems with industry business attributes. Compared with cross-industry applications such as finance and HR, low-code development platforms usually do not have standardized systems and components due to the large differences in industry systems of enterprises and the need for industry Know-how support. Taking the oil price management system in the energy industry as an example, the "oil price" component in the system is composed of fuel type, current price, date and other elements. In the process of developing the oil price management system, energy companies need to obtain this component through customized development. Enterprises bring additional costs and R&D cycles. To this end, enterprises need a low-code development platform that has the components and models required for industry system development, and supports enterprises in developing systems with industry attributes.

Enterprises need to use low-code development platforms to develop "enterprise-grade" complex applications. Central state-owned enterprises and leading enterprises in the industry are the pioneers and main user groups of low-code development platforms. The application development needs of such enterprises are not limited to lightweight applications such as forms and automated office programs, but also include complex systems such as ERP and WMS. However, the underlying architecture of this type of system is more complex, involving background functions and layouts such as micro-service architecture and high concurrent processing capabilities, and the cost of code development is high and the cycle is long. Therefore, enterprises need to use low-code development platforms to develop "enterprise-level" complex applications.

Enterprises need to combine generative AI and large model technology to enable low-code application development. The large model makes the generative AI more intelligent, thus forming a good complementary relationship with the low-code development platform and improving the experience of low-code development. For example, natural language development fills the thinking gap between low-code development and traditional code development. The "drag and drop" development method of the form of the low-code development platform has changed the development process and does not conform to the natural expression habits. The natural language interaction mode brought by generative AI is more suitable for the development thinking of such users. For another example, generative AI has the ability to execute rules. Enterprise application construction needs to complete a lot of configuration work, such as configuring coding rules, upstream and downstream push of documents, etc. Generative AI can reduce the workload required for configuration work and improve accuracy. Enterprises hope to make full use of the value of generative AI supported by large models to empower low-code application development. In addition, generative AI can also provide functions such as code generation and development suggestions for low-code development platforms. To this end, enterprises need to combine generative AI and large model technology to enable low-code application development.

Figure 7: Large models empower low-code application development

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Vendor Capability Requirements:

Enterprises' demand for low-code development platforms puts forward multiple capability requirements for low-code manufacturers, including accumulating industry capabilities through industry components and expanding ISV partners, and having complex enterprise-level system development capabilities. In addition, low-code vendors need to actively explore the integration path of large models and low-code development platforms to empower enterprise application development.
Low-code vendors need to have rich industry accumulation and accumulate industry capabilities by providing industry components and expanding ISV partners. In order to meet the needs of enterprises to develop industry applications, low-code vendors and their ISV partners need to accumulate industry experience in past projects, such as industry norms, industry logic, and industry-specific business processes. On this basis, manufacturers need to transform the industry Know-how into the characteristic system of the low-code development platform, and store it in the industry component library in the form of components for enterprises to call.

Since it is difficult for low-code manufacturers to accumulate enough components for each industry, manufacturers should actively expand ISV ecological partners in different industries. Standardized industry applications developed by ISV partners based on low-code development platforms should provide SaaS services for enterprises on cloud applications together with manufacturers' self-developed applications.

The low-code development platform needs to have the ability to develop complex business systems at the enterprise level, and reduce product learning costs through the technical service system. In order to meet the needs of enterprises to integrate low-code capabilities and develop complex business systems, the low-code development platform needs to have enterprise-level complex business system development capabilities. To this end, the low-code development platform should first have an enterprise-level PaaS base, providing technical support such as distributed architecture, process consistency, high performance, high concurrency, and observability during operation and maintenance required for enterprise-level application development. Secondly, each module of the low-code development platform should have the characteristics of low coupling, can be flexibly split, and open up with the middleware, metadata and cache of the enterprise technology base, so as to separate from the original low-code/zero-code technology platform of the enterprise. layer integration and complementary capabilities. In addition, low-code vendors should have a service team composed of professional IT personnel, or through customer development partners, to provide technical support throughout the complex system construction.

Considering that the introduction of new low-code products by enterprises will bring additional learning costs, low-code development platforms need to have a smooth technical service system to provide guarantees for enterprise developer teams to learn and adapt to new platform development. When developers put forward requirements and feedback on products, manufacturers should respond quickly and solve them quickly.

Low-code manufacturers need to explore the complementary integration path of generative AI and low-code development platforms to empower enterprise application development. In order to meet the needs of enterprises for generative AI empowered by large models, low-code manufacturers should actively explore the integration path of generative AI based on large models and low-code development platforms to empower enterprise application development. For example, manufacturers can train their natural language model design capabilities based on their own project requirements documents after self-development or access to large models, and reduce the accuracy requirements for natural language descriptions by increasing the richness of models.

Inclusion Criteria Description:

1. Comply with the vendor capability requirements of the low-code development platform market analysis;
2. The manufacturer's revenue in this market in the past year is not less than 10 million yuan;
3. In the past year, the manufacturer has no less than 10 paying customers in this market.
   Evaluate on behalf of the manufacturer:

Jinzhiwei
manufacturer introduction:

Zhuhai Jinzhiwei Information Technology Co., Ltd. (hereinafter referred to as: Jinzhiwei) is an artificial intelligence company focusing on providing enterprise-level RPA platforms. With "RPA+AI+big data" as the core technology, it creates an "RPA+X" product matrix and capability platform. Provide one-stop digital employee overall solutions for government and enterprises. Jinzhiwei takes RPA as the core technology, integrates innovative technologies such as AI, big data, low code, and cloud native, explores and realizes end-to-end hyper-automation, and is committed to promoting the digital transformation of enterprises with technological innovation.

Product service introduction:

Qingsong K-Pine is Jinzhiwei's new generation of enterprise-level low-code development platform, which was originally developed to solve the business needs of Jinzhi RPA and operation and maintenance automation. Qingsong makes software development more efficient by abstracting the programming model and encapsulating functional components, usually serving technical personnel and reducing the workload of programmers. Qingsong is suitable for relatively complex various application systems, and is widely used in finance, government affairs and public utilities, manufacturing, e-commerce and retail, communication operations, real estate, construction and other industries.

Vendor Evaluation:

Jinzhiwei Qingsong low-code development platform has advantages in the diversity of landing scenarios and the development of enterprise-level complex systems. In terms of the diversity of landing scenarios, as a practitioner of the concept of hyper-automation, the self-developed Qingsong low-code development platform of Jinzhiwei deeply integrates the functions of Jinzhiwei's RPA and AI products, and has the ability to develop automated process engines and quickly implement intelligent projects. In terms of system development, Qingsong low-code integrates enterprise-level complex system development capabilities, and realizes lightweight development with the help of low-code drag-and-drop development methods. In addition, Jinzhiwei has rich project experience in the financial field, and has accumulated the characteristic components of the financial industry as Qingsong low-code development platform, which can effectively improve the application development efficiency of financial enterprises.
Qingsong low-code development platform integrates JW RPA and AI product functions, and has the ability to develop automated process engines and quickly implement intelligent projects. As a practitioner of the concept of hyper-automation, Jin Zhiwei is good at combining various automation tools to produce the effect of "1+1>2". Thanks to Jinzhiwei's mature RPA technology and AI product matrix, Qingsong low-code development platform can not only extend the RPA field, develop an automated process engine, but also continuously integrate various AI capabilities, realize the rapid implementation of intelligent projects, and empower enterprise business and Production.

Figure 8: Qingsong low-code development platform integrates RPA and AI capabilities

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RPA and AI capabilities enrich the landing scenarios of Qingsong low-code development platform. For example, Qingsong low-code development platform can serve the intelligent transformation of enterprise IT, help enterprises quickly build or transform industrial applications with intelligent capabilities, realize business upgrades, and meet scenarios such as industrial quality inspection, intelligent inspection, production management, and capacity analysis. demand. In addition, the Qingsong low-code development platform can also develop SaaS applications with AI features, such as RPA robots such as intelligent form filling and automatic process approval, and deeply integrate with data middle platform, AI middle platform and microservice products to jointly build an enterprise digital center Platform, to help enterprises realize application development/management middle platform and information management center.
Taking the development of low-code intelligent review system as an example, low-code development can quickly expand the automatic review rules and review indicators of various businesses. RPA technology helps enterprises realize 7*24 hours automatic review of remote authentication services such as account opening and centralized operation. With the blessing of AI capabilities, through continuous machine learning sample training and parameter adjustment, the company can greatly improve the recognition accuracy of face, OCR, video and voice recognition modules. The single recognition accuracy rate reaches 95%, and the overall recognition rate exceeds 90%. .
Qingsong low-code development platform can support lightweight development of enterprise-level complex applications. The development of enterprise-level complex applications puts high demands on the underlying architecture of manufacturers, and tests the technical background and comprehensive strength of manufacturers. For more than ten years, Jinzhiwei has insisted on self-research and continuous polishing of the underlying architecture. It has the ability to face enterprise-level applications such as micro-service architecture, and can guarantee complex enterprise-level application requirements such as high concurrency, system security, and interface response speed. In terms of high concurrency, the application developed by Qingsong supports more than 500 users online at the same time and more than 200 processes running at the same time. For enterprise core business applications with a large number of users and high concurrency, Qingsong can support load balancing at the deployment level, doubling the system's processing capacity. In terms of system security, Qingsong provides complete guarantees at the three levels of code, data and applications, and also has multiple security assurance methods such as authority management and intranet deployment; in terms of response speed, Qingsong's process release synchronization and process response can be completed within 2 seconds , the page average response time is less than 3 seconds.

Figure 9: Product functions of Qingsong low-code development platform
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On this basis, Jinzhiwei integrates enterprise-level complex system development capabilities into Qingsong low-code development platform through programming model abstraction and functional component packaging. The development method enables developers to realize lightweight development. Qingsong low-code development platform has the following four technical advantages in lightweight development capabilities:

1. Full-stack end-to-end visualization capability: the development of the program can be completed by clicking, dragging and configuring operations, one-time development and multi-terminal adaptation;
2. Full lifecycle management capability: implement systematic monitoring and management in all aspects of application design, development, testing, and deployment;
3. Excellent scalability: rapid development by creating or importing functional components;
4. Efficient reuse capability: Functional components or applications that have been developed and implemented can be migrated to new development requirements at any time, and existing capabilities can be quickly reorganized to serve new needs.
   Based on the above enterprise-level complex system development capabilities and lightweight development advantages, Qingsong low-code development platform is suitable for the development of various relatively complex systems, such as intelligent management systems, customer order systems, OA, CRM, etc.
   Qingsong low-code development platform has rich financial industry characteristic components, which can effectively improve the application development efficiency of financial enterprises. Jinzhiwei has been deeply involved in the financial field for more than ten years, and has a deep understanding of the business needs of banking, securities, funds, futures and other fields, and has accumulated industry Know-how into industry components suitable for financial enterprises, such as multi-dimensional log query.

In the actual research and development of enterprises, these financial industry components can effectively solve the business problems of financial enterprises. Taking the development of the configuration management system CMDB as an example, in order to solve the problems of IT operation and maintenance managers in the financial industry who "do their own thing" and the complex and abstract relationship between maintenance and management objects, Qingsong low-code development platform integrates all components of the enterprise IT operation and maintenance architecture Digitization, multi-dimensional display of its attributes, relationships and change tracks. During this process, Qingsong provides a large number of configuration models, supports developers to flexibly add configuration items, attributes and relationships, and can customize multi-configuration associated queries online. Qingsong also provides a management interface that supports multi-dimensional data change history query to meet data compliance requirements. Up to now, many securities firms such as GF Securities and Cinda Securities have used the CMDB system developed based on Qingsong, which has effectively improved the work efficiency of maintenance personnel and reduced operating costs.

Typical clients:
China Construction Bank Hunan Changsha Branch, Industrial and Commercial Bank of China Guangxi Branch, Postal Savings Bank Tianjin Branch, Haitong Securities, Essence Securities

Definition of iPaaS
market:
The iPaaS platform (integration platform as a service), also referred to as iPaaS for short, is a set of solutions that support the integration of applications, data, processes and services, covering development, execution and governance.

Party A’s end users:

IT Department, Business Unit ITBP

Party A's core needs:
With the rapid changes in the external market environment, enterprises need to develop corresponding business systems and introduce cloud applications according to market demands. These systems and procedures create data, system, and process isolation between corporate departments, hindering cross-departmental collaboration. In addition, the previous ESB system based on the SOA architecture was weak in handling high-concurrency scenarios, and it gradually became difficult to meet the requirements of the enterprise's north-south traffic opening. iPaaS has high concurrent processing capabilities, and can open enterprise data and business capabilities to the upstream and downstream industry chains through API gateways or open portals. Therefore, enterprises are paying more and more attention to iPaaS, a new type of business automation technology, which aims to solve the problems of development, execution and governance through the integration of applications, data, processes and services.
iPaaS is an important part of hyper-automation, and it is committed to realizing the goal of connecting business and data within the enterprise. The needs of enterprises for the iPaaS platform are reflected in four aspects: breaking down system islands, improving integration efficiency, formulating integration management standards, and monitoring integration processes. Enterprises need to quickly open up system islands, realize information sharing between business systems and cloud applications, shorten system integration time, and improve system integration efficiency. In terms of integration management, enterprises need to formulate system integration management standards, establish an authority management system, and grasp the operation status of the integration system to detect and solve integration problems in a timely manner.
Figure 10: Enterprises' core requirements for iPaaS platforms
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Enterprises need to quickly open up system islands and realize information sharing between business systems and cloud applications. In the process of enterprise informatization construction, multiple business systems will be built and a large number of cloud applications will be introduced. While these systems and procedures solve the business needs of each department, they also create data isolation between departments and hinder cross-departmental collaboration. In the face of more and more systems and applications, traditional point-to-point integration is gradually becoming difficult due to reasons such as heavy development workload and integration flexibility. Enterprises need new integration solutions to quickly open up system islands, realize information sharing, and improve inter-departmental collaboration.

Enterprises need to have business agility and improve system integration efficiency. As the external market environment changes rapidly, enterprises need to adjust their business processes in a timely manner according to market demand. Business process adjustment requires a new system integration design, but due to the lack of system and application integration frameworks in enterprises, IT engineers need to write codes from scratch, resulting in long system integration cycles and reduced business agility. In addition, the all-code development method is difficult to cope with data integration of complex logic, and cannot give full play to the value of enterprise data. Therefore, enterprises need to reduce the amount of code development, shorten the system integration cycle, and improve system integration efficiency on the basis of ensuring complex integration development capabilities.

Enterprises need to formulate system integration management standards and establish authority management systems. The design, creation, and governance systems of traditional integrated applications are not unified, and enterprises need to use multiple systems to complete the integration work, resulting in complex integration processes and low efficiency. In addition, because enterprises lack integrated system management standards and authority management systems, data security is difficult to guarantee. Therefore, enterprises need to formulate system integration management standards to reduce system integration complexity, improve integration efficiency, and strengthen integrated system authority management to ensure data security.

Enterprises need to master the operation status of the integrated system, discover and solve integration problems in time. After an enterprise integrates systems and applications, it needs to master the operation of the integration process. However, traditional integrated analysis relies on researchers to manually collect integrated operation data, and then convert it into data that can be recognized by the analysis system, and then conduct unified analysis. This process is relatively complicated, difficult to implement, and takes a long period, making it difficult for enterprises to find process operation problems in a timely manner. Therefore, enterprises need to automatically collect and analyze process data, grasp the operation status of the integrated system, and find and solve integration problems in time.

Manufacturer's capability requirements:
Party A's demand for the iPaaS platform puts forward multiple capability requirements for the manufacturer. In order to break through system islands, the iPaaS platform needs to have a complete product ecosystem, support custom API development, and manage enterprise API assets in a unified manner; to improve integration efficiency, manufacturers need to provide low-code or no-code integration tools; in terms of integration management, the iPaaS platform needs It has the ability to manage the entire life cycle of integration and is equipped with an authority management system; in terms of integration process monitoring, the iPaaS platform requires manufacturers to automatically collect log data related to integration operations, monitor the operation of the integration process in real time, and detect integration problems in a timely manner.
The iPaaS platform needs to have a complete product ecosystem, support custom API development, and manage enterprise API assets in a unified manner. In order to shorten the system integration project cycle and quickly connect the original system of the enterprise with the new system, manufacturers need to package HTTP, FTP, XML and other communication protocols into connectors and build them into the iPaaS platform. In order to meet the needs of enterprises for the integration of different systems, on the one hand, the iPaaS platform should have a complete product ecosystem, and the connectors must cover mainstream third-party software systems. On the other hand, the iPaaS platform also needs to support enterprise custom API development, and integrate APIs scattered in various business systems and cloud applications into the iPaaS platform to achieve unified API management.

The iPaaS platform needs to have integrated lifecycle management capabilities and be equipped with a rights management system. The iPaaS platform needs to have the ability to manage the entire life cycle of integrated design, creation, governance, and destruction. A single system undertakes all integration work. By reusing past integration examples, it helps enterprises quickly build system integration models, and realize inter-system resource integration, data arrangement and integration. Business connection. In the process of integrated management, the iPaaS platform also needs to implement authority management, assign independent accounts to each user, and set operation authority for each account separately.

The iPaaS platform requires manufacturers to automatically collect log data related to integration operations, monitor the operation of the integration process in real time, and discover integration problems in a timely manner. In order to meet the needs of enterprises to analyze and monitor the integration process, the iPaaS platform needs to deposit the log data related to the operation of the integration process on the platform, monitor the operation of the integration process in real time, and give timely warning to relevant personnel when problems occur. On this basis, the platform also needs to have visualization components to convert integration process data into visualization charts to help professionals analyze integration operations and solve integration problems.

Inclusion Criteria Description:

1. Meet the vendor capability requirements for iPaaS market analysis;
2. The manufacturer's revenue in the market in the past year is not less than 5 million yuan;
3. In the past year, the manufacturer has no less than 3 paying customers in this market.
   Evaluate on behalf of the manufacturer:

Guyun Technology
Manufacturer introduction:
Guyun Technology (Guangzhou) Co., Ltd. (Guangzhou) Co., Ltd. (referred to as Guyun Technology) was established in 2017. It is an enterprise-level integration platform (iPaaS) software product provider, focusing on service integration, data integration, and message integration. research and software development. Guyun Technology's RestCloud iPaaS products are widely used in industries such as retail, manufacturing, finance, education and government agencies.

Product service introduction:
Guyun Technology RestCloud iPaaS integration platform is composed of API low-code development platform, enterprise-level API gateway, API visual orchestration platform, ETL data integration platform, application linker and other parts, focusing on system integration, data fusion, SaaS integration, MQ message integration and API lifecycle management. The RestCloud iPaaS integration platform can solve complex integration scenarios of enterprise systems and data, covering the entire product line of API development, testing, orchestration, and management. It is an integration platform centered on enterprise APIs and can quickly integrate enterprise ERP, MES, WMS, OA, and BPM , HR and other business systems.
Figure 11: Architecture diagram of Guyun Technology RestCloud iPaaS integrated platform
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Vendor evaluation:
Guyun Technology RestCloud iPaaS platform has excellent concurrency performance, the maximum single-user management capacity is nearly 30,000 APIs, and the concurrency exceeds 50,000 QPS per second. The combination of iPaaS product functions and industry and project experience has created multi-industry best practices for Guyun Technology, accumulated multi-faceted consulting capabilities and unique project promotion methodologies, and promoted the successful implementation of projects. In the process of project implementation, Guyun Technology attaches great importance to improving user experience, is committed to creating a product matrix that is close to user needs, and continuously enriches the iPaaS product ecology. In addition, Guyun Technology insists on self-developed products, adapts to the Xinchuang environment, and has rich experience in domestic alternative projects.
Guyun Technology has deeply cultivated the network layer and data transmission algorithm technology, and the RestCloud iPaaS platform has both complex protocol processing capabilities and high concurrency performance. Enterprises often leave many business systems behind, which leads to very complicated conversion between different protocols, such as Web Socket transparent transmission, TCP/IP link multiplexing, cross-regional network delay and other problems, which put forward high requirements on the technical capabilities of the network layer of manufacturers. The average working experience of Guyun Technology R&D personnel is more than 10 years, and they are deeply involved in network layer technology. The technical capabilities of R&D personnel are deposited in the RestCloud iPaaS platform, so that the product can cover many common problems in protocol conversion.

In addition, after all internal and external system APIs of the enterprise are connected to the iPaaS platform, they will also face high concurrency. Once the number of requests surges in a short period of time, it is very easy to cause the iPaas platform to fail, and all exchanges between business systems will also stop. Guyun Technology adopts a micro-service architecture to solve technical problems such as middleware, underlying Java framework, and TCP connection multiplexing brought about by high concurrency requirements in a horizontal expansion method, and optimizes the micro-service architecture through custom annotations. The maximum management volume of a single user is nearly 30,000 APIs, with over 50,000 QPS concurrently per second, further guaranteeing high concurrent performance.

Guyun Technology has profound industry and project experience, with multi-industry best practices, multi-faceted consulting services and unique project promotion methodology to ensure the successful implementation of the project. Guyun Technology has rich experience in industries such as manufacturing, retail, education, government, and finance, and has accumulated a large number of benchmark cases. It can issue API governance analysis reports for Party A according to the specific situation of Party A, helping enterprises locate their own industry development stages, so as to Accurately locate benchmarking companies and reuse proven system integration solutions from past projects. Taking the manufacturing industry as an example, Guyun Technology once assisted a large-scale manufacturing Party A to locate a benchmarking enterprise, referred to the API quality optimization experience in the past project experience, and rectified the API of the order management system according to the common needs of the manufacturing Party A.

Due to the lack of experience in system integration, Party A still needs consulting services from iPaaS vendors. Guyun Technology has a lot of experience in project implementation, from which it has accumulated effective system integration methodologies, which can be applied to consulting projects to help Party A sort out enterprise API assets, including existing API capabilities, number of API interfaces, API problem rate, security wait. On this basis, Guyun Technology can establish an API plan for Party A, such as the number of APIs to be developed, API development specifications, saving specifications, management systems, etc.

During the landing process of the iPaaS platform, it will face multiple difficulties from inside and outside the enterprise, such as integrating Party A's original ESB system and existing microservice API gateway, etc. At the same time, it also needs to assist Party A to promote third-party manufacturers to open their system APIs. For the whole process of implementing the iPaaS platform in Party A's enterprise, Guyun Technology has summarized a set of business system integration promotion plan and project management methodology, detailed planning of the implementation steps of the iPaaS platform, and jointly formulated project planning with Party A, including the online process and system switching strategy etc. to effectively promote business system integration.

Guyun Technology has a product matrix and product ecology that are close to user needs, and can flexibly meet the needs of enterprises with different IT capabilities. Guyun Technology comprehensively considers the IT capabilities of Party A, and designs the low-code integration platform and no-code integration platform SaaS software for privatization deployment in a targeted manner. For Party A with strong IT capabilities and large volume, Guyun Technology can provide low-code integration platform products, support 60% of API code-free release and complex API development, and independently deploy the developed API based on the Spring Cloud architecture Run as a microservice. For small and medium-sized enterprises with low system complexity and weak IT capabilities, Guyun Technology has created a no-code integration platform under the condition of ensuring the number of integrations to empower IT capabilities for Party A's business personnel.

In addition, Guyun Technology has built a complete product ecosystem. Up to now, Guyun Technology has created more than 200 connectors, including financial systems such as Kingdee and UFIDA, as well as common office software such as DingTalk, Enterprise WeChat, and Feishu, which can automatically connect with mainstream systems and effectively speed up the delivery of iPaaS projects.

Guyun Technology's iPaaS platform adapts to the Xinchuang environment, and has rich experience in domestic alternative projects. Since its establishment, Guyun Technology has positioned itself as a global enterprise, aiming at the domestic and foreign enterprise system integration market. In order to make products internationally competitive, Guyun Technology does not use open source frameworks, insists on 100% self-developed underlying frameworks, and creates differentiated integrated platform products with independent and controllable technologies. In addition, RestCloud iPaaS has been adapted to domestic CPUs such as Kunpeng and Loongson, domestic databases such as NTU General and Renda Jincang, and domestic middleware such as Dongfangtong and Zhongchuang, so as to adapt to the domestic mainstream Xinchuang operating environment. The independent and controllable underlying framework and the adaptability of Xinchuang's operating environment provide Guyun Technology with an entry ticket for localized alternative projects. Experience.

Typical customers:
Yili, Infinitus, Changan Automobile, EVE Lithium Energy, Shengyi Technology

Process mining
Market definition:
Process mining collects, transforms and analyzes enterprise business data, visually restores the actual business process of the enterprise, and then evaluates the process operation status, diagnoses process operation problems, finds process improvement opportunities, and realizes continuous process optimization and monitoring. Party
A’s end users:
corporate management; sales and other business departments; financial, human, risk management and other functional departments . Business automation adjustments to improve business agility. Process mining can obtain process quantitative data from business data. Compared with traditional process analysis methods, it is scientific, accurate, and convenient. In recent years, enterprises have gradually realized the value that process mining brings to enterprise processes, and embraced this new technology instead. Enterprises' demand for process mining is gradual. In the initial stage of using the process mining solution, the core requirement of the enterprise is process transparency, that is, to present the business process in a visual way and to maximize the value of business data. Process transparency is the basis of process mining. Enterprises also need to obtain information on the execution of standard processes, discover and stop process violations in a timely manner, and realize process standardization and standardized execution. On this basis, enterprises need to improve the efficiency of process execution, and break through process blockages from two aspects of process design and manual node optimization. In addition, some enterprises are actively exploring the possibility of process mining to improve business quality and efficiency, hoping to improve the effect of process execution and achieve business goals. Enterprises need to break the process "black box" and achieve process transparency. Process opacity is a problem that has plagued enterprises for a long time. Most enterprises lack suitable process analysis tools. From the enterprise managers to the front-line employees of the department, they are not clear about the operation of the enterprise process, which leads to the appearance of the process "black box". At the same time, a large amount of business data will be generated during the operation of the enterprise, which contains information related to the enterprise process and has important analysis value. However, enterprises lack the ability to use data assets to analyze processes, so that business data is hoarded in the database and lacks a place to be used. Therefore, enterprises need to use process mining technology to analyze business data, give full play to the value of data assets, and realize process transparency.

Enterprises need to standardize process execution and correct non-compliant operations in a timely manner. A single process mining can only achieve process transparency, but the actual business process is more complex than the version shown by the visual flow chart, and there are many process variants. Taking the common procurement process of an enterprise as an example, there are hundreds of process variations in sourcing, bidding, acceptance and payment, such as emergencies and market price fluctuations. In addition, some key process nodes rely on manual operations, and improper operations will also generate new process variants, such as operators breaking the frozen payment process in violation of regulations, increasing business transaction risks. Such non-compliance operations may cause damage to the interests of enterprises, but it is difficult to detect and stop them. Therefore, enterprises need to ensure the standardization of process execution and correct illegal operations in a timely manner.

Enterprises need to break through process blockages and improve process execution efficiency. The above process transparency and process monitoring cannot solve the problems existing in the process itself, such as long process throughput time and high error rate of key nodes. Such process problems are usually caused by improper process design and manual operation of process nodes, which will prolong the process operation cycle and reduce process efficiency. Taking the procurement process of the manufacturing industry as an example, the execution error rate of the secondary procurement node of the enterprise is high, and usually requires 2-3 times of repeated work. The high rework rate of the process is one of the main reasons for the low procurement efficiency. To this end, enterprises need to use process mining to break through process blockages and improve process execution efficiency.

Enterprises need to improve the effectiveness of process execution to meet business needs. Breaking through the process blockage can only improve the efficiency of process execution, but the process effect may not meet the requirements. Compared with process efficiency, enterprises pay more attention to whether the process can achieve business goals. Taking bank personal loans as an example, the customer conversion rate is the core indicator of this business. Existing customer journeys are efficient, but may not convert as well. To this end, enterprises need to use process mining to improve the effectiveness of process execution and achieve business goals.

Vendor capability requirements:
The gradual demands of enterprises for process mining put forward layered capability requirements for process mining vendors. First of all, the process mining system must have large-scale data processing capabilities and visualize enterprise data. Secondly, in order to meet the requirements of process standardization, the process mining system needs to have the ability of rapid data update and early warning of illegal operations. Thirdly, the process mining solution should have process simulation technology to break through process blockage points without affecting business operations. Finally, process mining vendors need to have rich business experience, deposit it into the front-end function of the process mining system, and use best practices to help enterprises judge how to improve the quality and efficiency of process mining solutions.
The process mining system needs to have large-scale data processing capabilities and visualize the enterprise process. To meet the needs of enterprise process visualization, the process mining system needs to convert business data into visual flow charts. Considering that the business data scale of enterprises with process mining needs is generally large, the process mining system needs to have large-scale data processing capabilities. In the project, the process mining system needs to clean and model the original business data of the enterprise, then convert the processed data into a process model, extract the three elements of people, time and events, and finally use the charts and time of process mining Sequence and other front-end functions are displayed visually.

The process mining system needs to have the ability of rapid data update and automatic early warning, timely present the latest running status of the process, and prompt illegal operations. In order to meet the needs of enterprise process standardization, the process mining system needs to present the latest running status of the process in a timely manner. In this process, the data cleaning and conversion link takes the longest time, which is the key factor restricting the speed of process mining. To this end, the process mining system needs to have the ability to quickly update business data. By improving data processing efficiency, business data can be transformed into a visualized process in a timely manner, so that enterprises can obtain the latest operating status of the process.

In addition, the process mining system needs to have automatic early warning capabilities. Once there is an illegal operation in the process of enterprise process monitoring, the process mining system should send reminders to relevant personnel in a timely manner, so that the enterprise can take timely actions and reduce the profit loss caused by non-compliant operations.

The process mining system needs to find the blocking points of the enterprise process and get rid of the blocking points with the help of process simulation technology. In order to break through the process blockage points, enterprises can use the process mining system to analyze the execution time of each stage of the process, so as to determine the location of the process blockage points. After finding the process blocking point, the enterprise can design a new process on the process mining system to replace the original process. Some process mining solutions also offer process simulation capabilities, allowing companies to simulate new process executions. Enterprises can compare simulated data with real-world process-related data to deepen their understanding of the process itself, provide data support for the optimization of new processes, and discover possible problems in new processes in advance. When the process simulation results meet the requirements of the enterprise, the enterprise then replaces the original process with a new process to reduce the impact on business operations caused by unblocking process congestion points.

Vendors need to have rich experience in implementing process mining projects, and work with enterprises to optimize processes. In order to improve the quality and efficiency of the process for the enterprise, process mining vendors need to have a deep understanding of the enterprise process and analyze the deviation between the existing process and the enterprise's needs. This requires manufacturers to accumulate cases and experience from past projects, judge the value that process mining can bring to enterprises, and communicate with enterprises through best practices. In addition, manufacturers should also accumulate and transform the experience of process mining projects into the front-end functions of the process mining system to make it more suitable for the needs of enterprises. For example, in response to the needs of banks to improve the customer conversion rate of personal loan business, a front-end function of the process mining system can display the customer churn rate at each stage of the customer journey, helping enterprises to judge and optimize the intermediate steps of the process.

Inclusion Criteria Description:

1. Meet the vendor capability requirements for process mining market analysis;
2. In the past year, the manufacturer has no less than one paying customer in this market.
   Evaluate on behalf of the manufacturer:

Entropy Review Technology
Manufacturer introduction:
Shanghai Entropy Review Technology Co., Ltd. (referred to as Entropy Review Technology) was established in 2021 and is headquartered in Shanghai. A scientific and technological innovation enterprise of the global enterprise intelligent process management platform (PROXVERSE Studio). Entropy Review Technology has teams in China and Europe. The core members have experience in well-known companies such as Deloitte, Ali, Microsoft, and Huawei. The team has successful delivery experience in hundreds of projects around the world. Many leading companies in logistics and other industries provide process mining services.
Product service introduction:
PROXVERSE STUDIO enterprise real-time process intelligence platform of Entropy Review Technology is a comprehensive platform covering the entire life cycle of process intelligent management, including data fusion module, process design and simulation module, process analysis core module, and execution optimization module and rights management module. Among them, the data fusion module is responsible for data import, conversion and calculation, and builds a process mining data model; the process design and simulation module provides process planning and simulation services, which is convenient for users to plan business processes and simulate their results; the core process analysis module includes process exploration and automated processes Core process analysis functions such as bottleneck analysis, consistency check, and custom secondary development; the execution optimization module provides connection services with external tools; the authority management module manages user authority at the data model level.
Vendor Evaluation:
Entropy Review Technology has advantages in project experience and product performance. In terms of project experience, Entropy Review Technology not only has rich experience in process mining project implementation, but also can provide direction and technical support for enterprises throughout the project, and has global delivery capabilities, which can serve Chinese enterprises going overseas, foreign-funded enterprises and multinational enterprises in localization. In terms of product performance, PROXVERSE STUDIO has big data processing capabilities, has a self-developed process analysis engine that supports data analysis capabilities of more than 10 billion rows, supports hybrid process analysis language HPQL (supports SQL/PQL mixed writing and UDF), and is based on years of experience The process mining modeling experience has created an industry-leading process modeling framework, which has full-stack advantages in the entire data processing pipeline. In addition, PROXVERSE STUDIO is a platform product covering process design, simulation, mining and forecasting, which can manage enterprise process assets in one stop. The front desk of the product is rich in functions, and has a low-code platform, which supports customer-defined secondary development.
Figure 12: Low-code functional module architecture diagram of Entropy Review Technology PROXVERSE STUDIO
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Entropy Review Technology has rich experience in implementing process mining projects, and can provide business consulting and technical support for enterprises throughout the project. Entropy Review Technology is good at serving leading enterprises in the industry. Such enterprises have a relatively complete data and IT foundation, and are more willing to embrace new technologies and new ideas. At present, the domestic process mining market is in its infancy, and most enterprises and manufacturers have relatively limited knowledge of process mining, and do not understand how process mining can be combined with business. Although Entropy Review Technology is a start-up company, it has rich experience in project implementation. In the past seven years, its product team members have served nearly 50 companies and government organizations in industries such as automobiles, medicine, industrial manufacturing, logistics, and food processing around the world. Hundreds of process mining projects have provided consulting and technical implementation services. Entropy Review Technology summarizes the members' past project experience into a process mining project implementation methodology, which is used to guide the implementation of domestic enterprise process mining projects. On the one hand, Entropy Review Technology can help enterprises sort out business processes, find the connection point between process mining and enterprise business, and then determine the key points of process analysis and the use of process mining. On the other hand, Entropy Review Technology can help enterprises solve problems in the process of process mining. For example, in the process of data extraction, cleaning and modeling, Entropy Review Technology has experience in dealing with problems such as low data quality and missing node data. Can give full play to the value of enterprise data.

Take a central enterprise as an example. Faced with the pain point of data redundancy, the enterprise hopes to establish a data sharing center to maximize the value of data. However, the enterprise lacked experience in data governance and did not know where to start. Referring to the common pain points and error-prone links of different types of customers in past projects, as well as the issues that the sharing center needs to pay attention to, Entropy Review Technology created a sharing center model for the company and completed a preliminary process analysis. During this process, Entropy Review Technology not only combines process mining with enterprise business, but also helps enterprises establish the cognition of finding problems from data, which has been recognized by enterprise managers.

The front desk of PROXVERSE STUDIO is rich in functions, and supports customers to carry out custom secondary development based on the low-code platform. As an emerging application of data mining, the development time of process mining is relatively short, and the front-end functions of products are relatively lacking. Manufacturers often face the scenario of "customers have needs, but products have no solution". In order to better solve the customized requirements of customers, the entropy review technology system summarizes the demands of the enterprise process mining project when it is implemented, extracts the common parts, and transforms them into the front-end functions of PROXVERSE STUDIO, such as the one that can visually display the team efficiency and process standards. charts etc. These original front-end functions closely meet the needs of enterprises and effectively improve the usability of the product.

In addition, considering that the preset front-end functions may not be able to cover all the individual needs of enterprises, PROXVERSE STUDIO has also created a low-code development platform to provide enterprises with custom secondary development capabilities. Taking the custom page function as an example, users can independently add various process analysis and KPI related components by dragging and dropping. The details of the components are flexible and configurable, helping users to quickly create process analysis reports that meet customized requirements.

PROXVERSE STUDIO has big data processing capabilities. The core data technology team comes from Ali, Microsoft, and Kyligence. It has developed a process analysis engine, a hybrid process analysis language HPQL, and a process mining modeling technology framework. It has a full-stack in the entire data processing pipeline. Advantage. The increasing amount of enterprise data puts forward high requirements on the capacity of the data model for process mining. PROXVERSE STUDIO is based on years of accumulated big data processing technology and self-developed process mining analysis engine, which can support horizontal expansion of data volume and support single data The number of data rows of the model reaches more than 10 billion rows, and it has dynamic capacity management capabilities. At the same time, large enterprises often have high requirements for business compliance, and need to shorten the process mining cycle and increase the frequency of process monitoring. However, every time an enterprise uses process mining, it needs to update the model. In the past, the method of full data update required a large amount of data conversion work, and it was difficult to meet the enterprise's requirements for process mining time. To this end, Entropy Review Technology self-developed a process mining technical framework, defined the steps of data cleaning with modular ideas, and used configurable parameters to make complex process logic flexible and controllable, and facilitate later maintenance and modification. Entropy Review Technology also built different calculation logic and algorithms into the framework to deal with the process model update of incremental data and real-time data, which can realize near-real-time data update, and enterprises can set the process detection frequency according to their own needs.

PROXVERSE STUDIO will cover process design, simulation, mining and forecasting platform products, and can manage enterprise process assets in one stop. As enterprises move towards in-depth digital transformation, the demand for process mining has also begun to extend upstream and downstream. It is hoped that applications such as process design, simulation, mining, and forecasting can be implemented on the same platform, and based on process mining, deployment process automation. PROXVERSE STUDIO covers the upstream and downstream functions of process mining, and can complete the whole process from process design to forecasting in one stop. All links can collaborate with each other on the same platform, reducing data and application communication costs and improving process management efficiency. For example, the process design and simulation module is compatible with the BPMN2.0 standard, which can help users quickly plan realistic business processes and simulate results. It can also cooperate with the process analysis core module to help users find the difference between process design and actual processes, and advance Issues that may arise during the judgment process.

Figure 13: One-stop management of enterprise process assets
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Entropy Review Technology has global delivery capabilities and can serve Chinese companies going overseas and overseas companies. With the gradual saturation of the domestic market and the increasing pressure of competition, more and more Chinese enterprises choose to expand overseas business. The differences between overseas and domestic markets put forward different requirements for process mining products. The main members of the Entropy Review technology product and delivery team have worked in the Deloitte Global Process Bionics Center, and have provided process mining consulting and technology implementation services for nearly 50 companies and government organizations in the automotive, pharmaceutical, industrial manufacturing and other industries around the world, with global delivery capabilities Experience and understanding of the key points of process mining in international markets such as Europe, the Middle East, and Southeast Asia, in line with the needs of overseas companies. Entropy Review also plans itself as an international process mining manufacturer, actively expands overseas markets, and has landed several POC or POV projects for overseas customers.

Typical customers:
a leading comprehensive financial group in China, a top-tier city commercial bank, a top-tier automotive electronics multinational company, and a global pharmaceutical group
RPA
market definition:
RPA stands for Robotic Process Automation, which is an interactive process that simulates humans and software systems. Realize the technical application of automatic execution of workflow by software robots. RPA software includes three basic components: design platform, robot, and control platform. Combined with other functional components, it can realize the automatic execution of enterprise processes and improve the efficiency of enterprise operations.
Party A's end users:
IT department, business department
Party A's core requirements:
In order to support business development and internal collaboration needs, the enterprise needs to open up the data islands of internal and external systems. However, it is usually difficult to solve heterogeneous problems at the code level, and RPA provides new options for enterprises. When enterprises deployed RPA in the early stage, their needs mainly focused on opening up data islands and saving manpower. They focused on the functions and cost performance of RPA, and hoped to use RPA to solve a single point of business. In the past two years, enterprises have gradually begun to think about the RPA layout from a global perspective. From the dimension of enterprise-level system implementation, new requirements have been put forward for RPA, such as stronger compatibility, data security and compliance, integration of industry components, and AI and other automation technologies for customized development.
Figure 14: Changes in enterprise demand for RPA
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In the future, RPA will be combined with more automation technologies to form an overall enterprise-level automation solution. On Party A's side, the RPA robot has a higher level of intelligence and a wider range of application scenarios is the main appeal of the enterprise. For example, the combination of RPA and AI can expand the capability boundary of RPA and complete more tasks that would otherwise need to be performed manually. The AIGC empowered by the large model has also brought a new development direction for RPA. The next generation of RPA robots combined with AI capabilities may be able to communicate with humans through natural language, and possess higher intelligence, actively discover problems in the work and automatically solve them. Adjust workflow.
Enterprises need RPA to be compatible with systems of different architectures and versions. Some enterprise IT systems, such as ERP, OA, etc., were built earlier, because of the limitations of system functions, it is difficult to modify them according to changes in business requirements. Due to the high cost of data migration, many businesses choose to continue using such systems for the short term. However, the secondary development of the legacy system is difficult, and it is difficult to realize the data connection with the new system from the code level. To this end, enterprises need to use RPA to bridge the data silos between legacy systems and new systems.

Enterprises need RPA to have the ability to run on Xinchuang system. In the context of the accelerated development of localization substitution, Xinchuang requirements have gradually spread from central state-owned enterprises and finance to other industries. In order to meet the requirements of operating in the domestic Xinchuang environment, RPA must adapt to domestic mainstream Xinchuang middleware, servers and other Xinchuang systems.

Enterprises hope to reduce RPA operation and maintenance costs on the premise of improving RPA performance. One of the core driving factors for enterprises to deploy RPA is to reduce costs. Enterprises hope to go further on this road to achieve the goal of continuous cost reduction. At present, enterprises hope to reduce costs in terms of RPA performance and operation and maintenance. On the one hand, enterprises have higher requirements for RPA performance. For example, they hope that the RPA control platform can have process orchestration capabilities and use fewer RPA robots to complete more work. On the other hand, enterprises hope that RPA operation and maintenance will be simple and convenient, such as rapid update and longer running time, so as to reduce the number of internal IT personnel and technical requirements, and reduce operation and maintenance costs.

In the main application fields of RPA such as finance and government affairs, enterprises attach great importance to the safety of RPA robots. In the main application fields of RPA such as finance and government affairs, security is the core issue that enterprises pay attention to. For example, banks, securities and insurance companies in the financial field have clear regulations on business compliance, including traceable behavior and auditable business. For this reason, enterprises in these fields attach great importance to the security of RPA robots and hope that RPA can meet enterprise compliance requirements.

Enterprises need RPA vendors to have mature business components and allow enterprises to use the underlying development platform for customized development. Businesses in various industries and fields are quite different, and the requirements for RPA are also different. For example, financial audit requires RPA to have document identification components, which can automatically identify information on documents such as tickets and itineraries; business department login systems require QR code identification, and RPA robots are required to automatically identify QR code information. Different application scenarios require special RPA components. If the manufacturer develops it from scratch, it will seriously affect the progress of the project implementation. To this end, enterprises need RPA vendors to have mature business components.

In addition, enterprises may choose to promote RPA robots in more scenarios in the future, or due to business changes, the usage scenarios and capability requirements for RPA robots will change, requiring the integration of automation capabilities such as AI and low-code, and the development of new business components. However, enterprises do not want to be bound by third-party manufacturers, and hope to use the underlying development platform for customized development.

Manufacturer's capability requirements:
Enterprises' demand for RPA puts forward multiple capability requirements for manufacturers, including building a well-compatible RPA robot that can run in different systems and environments including Xinchuang environment; focus on process orchestration and underlying architecture research and development , so that RPA has both high performance and low operation and maintenance costs; through authority management, communication encryption, etc., to ensure the safe operation of RPA robots; and to transform project experience into components, and allow enterprises to use the RPA underlying platform to develop their own.
RPA manufacturers need to create RPA robots with good compatibility, which can run in different systems and environments. In order to meet the needs of enterprises to connect new and old system data, RPA robots need to have good compatibility. The specific requirements are as follows:

Figure 15: RPA robot compatibility requirements
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RPA manufacturers need to create RPA robots that can run in the Xinchuang environment. In order to meet the compatibility requirements of the enterprise Xinchuang system, the RPA robot should have the ability to operate in the Xinchuang environment:

Figure 16: Xinchuang environment where RPA runs
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RPA manufacturers should focus on process arrangement and underlying architecture research and development, so that RPA has both high performance and low operation and maintenance costs. In order to give full play to the value of RPA robots, manufacturers should combine hyperautomated process orchestration capabilities with RPA products, and use limited RPA robots to complete more work.

Figure 17: RPA process orchestration steps
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At the same time, RPA manufacturers should pay attention to the development of the underlying architecture such as designers and executors, including robot script engines, RPA core architecture, recorders and plug-ins, and reduce RPA operation and maintenance in the following aspects Cost:
Figure 18: Methods for reducing RPA operation and maintenance costs (partial)
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RPA manufacturers need to ensure the safe operation of RPA robots through authority management and communication encryption. RPA has continuous access requirements for enterprise-critical business systems, and its access permissions are usually encoded in scripts. In order to meet enterprise security and compliance considerations, RPA manufacturers need to ensure the safe operation of RPA robots in the following ways:

1. User permissions: With a complete permission management system, it provides users with the minimum permissions required for their job duties in accordance with the principle of least privilege (PoLP) to protect high-value data and assets;

2. Access permission: delete the access permission of the key system from the script and store it in a centralized encrypted manner;

3. Operation logs: RPA administrators protect the security of console access, ensuring that managers can monitor and track all activities of RPA robots, and use real-time error reports for troubleshooting if the tracking process fails;

4. Others: user name and password storage security, communication process encryption, etc.

RPA vendors need to convert project experience into components and allow enterprises to use the RPA underlying platform for self-development. In order to meet the needs of rapid implementation of RPA projects, manufacturers need to fully accumulate project experience in past projects, and convert the common or common needs of enterprises into RPA components. When RPA application scenarios are limited, manufacturers should introduce AI capabilities such as NLP and OCR in a timely manner to expand the application range of RPA robots.

At the same time, in order to meet the self-development needs of enterprises, manufacturers should open the RPA underlying development platform interface, simplify the development process, and reduce RPA development costs. In addition, manufacturers should predict the future demand changes of the enterprise, and improve the scalability of the RPA design platform accordingly.

Inclusion Criteria Description:

1. Comply with the vendor capability requirements of RPA market analysis;
2. The manufacturer's revenue in the market in the past year is not less than 5 million yuan;
3. In the past year, the manufacturer has no less than 3 paying customers in this market.
   Evaluate on behalf of the manufacturer:

Jinzhiwei
Manufacturer introduction:
Zhuhai Jinzhiwei Information Technology Co., Ltd. (hereinafter referred to as Jinzhiwei) is an artificial intelligence company focusing on providing enterprise-level RPA platforms. With "RPA+AI+big data" as the core technology, it creates "RPA+X" product matrix and The capability platform provides government and enterprises with a one-stop digital employee overall solution. Jinzhiwei takes RPA as the core technology, integrates AI, big data, low-code, cloud native and other technologies, explores and realizes end-to-end hyper-automation, and is committed to promoting the digital transformation of enterprises with technological innovation.
Product service introduction:
With RPA+AI as the core, Jinzhiwei creates a three-layer product matrix of COE planning and consulting, RPA+B solutions, and RPA+X product matrix and capability platform.
Figure 19: Jinzhiwei product matrix
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In terms of basic product services, Jinzhiwei mainly uses robotic process automation (RPA), AI and other digital technologies to develop the "RPA+X" product matrix and capability platform. At present, Jinzhiwei's "RPA+X" product matrix and capability platform have been widely used in various industries such as finance and government affairs, helping government and enterprises realize end-to-end digital process reconstruction, break through inter-departmental business barriers and process breakpoints, and achieve cross-cutting Flexible customization and orchestration of roles and cross-sequence services, creating a business processing model with seamless links, real-time information interaction, and efficient resource collaboration to better support the rapid development of digital services.
In the field of industrial technology integration, Jinzhiwei deeply cultivates customer business development and innovation, uses digital technology to create various automatic operation platforms, and launches "RPA+B" solutions. That is, combined with the application and management characteristics of each business scenario, a complete business process automation solution has been created in multiple business scenarios, and benchmark scenarios with advanced technology, remarkable results, and strong reproducibility have been continuously introduced to help government and enterprise customers quickly land , efficient management, flexible innovation, flexible service.
At the top-level planning level, Jinzhiwei has also set up an industry research institute and Wanli Learning Institute to help Party A complete comprehensive services such as digital planning, process optimization, scene mining, talent training, and achievement promotion.
Vendor Evaluation:
Jinzhiwei is deeply involved in RPA bottom-level research and development and data security, and provides bottom-level completely self-developed solutions, forming technical barriers in robot performance, stability, security, cross-platform compatibility, and ease of maintenance. It can also provide industry-leading solutions in terms of security, scalability, etc., focusing on the product image of "safe, stable and easy to expand" and the implementation services of consulting and coaching level.
While maintaining the leading technical parameters of RPA tools, as a practitioner of the concept of hyper-automation, Jinzhiwei combines RPA with low-code development platforms, AI, process mining and other automation technologies to create a digital base for enterprises and empower enterprises to digitally transform Jinzhiwei
. RPA bottom-level self-research and data security, good at "enterprise-level" RPA projects. When enterprises deploy "enterprise-level" RPA projects, they pay more attention to aspects such as compatibility, stability, and data security. Jinzhiwei has been focusing on automated operation and maintenance for a long time, has a deep understanding and accumulation of security, stability and maintainability in the field of automation, and has deeply cultivated the underlying research and development, and has a completely self-developed underlying architecture.

The K-RPA software management system also has a sound security mechanism to meet enterprise data security and compliance requirements. K-RPA has a robot security isolation control mechanism, which can avoid the impact of daily business operations on existing systems or machines, and realizes deployment isolation, operation isolation, management isolation, and monitoring isolation. In addition, K-RPA can also provide multiple means such as execution screenshots, execution videos, and execution logs, providing accurate process audits for all execution processes of the robot.

Jinzhiwei builds a product matrix with RPA as the core, realizes the linkage of automated development tools, and empowers the digital transformation of enterprises. Jinzhiwei actively integrates low-code, AI, digital twins, digital humans, process mining and other tools to provide 1+1>2 innovation capabilities. Taking the collaboration between RPA and low-code development platforms as an example, the collaboration between the two has achieved mutual cooperation in human-machine collaboration, business management, and office experience optimization.

1. First of all, when RPA software robots are actually applied to business processes, they often need to perform human-computer interaction, and automate the operation of different process branches based on the operator's choice. In this process, the low-code development platform can customize the development of user dialog boxes for related processes to improve the efficiency of human-computer interaction.

2. Secondly, the low-code development platform can optimize the business management of the RPA automation process. For example, RPA for bank reconciliation can only export financial system data as an Excel table, which is not conducive to data sharing, query and authority control. The low-code development platform can develop result query risk applications, support RPA to write financial data directly into the database in the reconciliation scenario, and allow financial personnel to query, retrieve and count at any time.

3. Thirdly, the low-code development platform can optimize the office experience of business personnel in RPA operation scenarios. RPA operation involves multiple processes and component information, but business personnel are more concerned about product information, such as product quantity, status, operation progress, etc. The low-code development platform can package the automation perspective of RPA into a product perspective, and present the process and scheduling tasks to business personnel with business information such as the number of accounts and the average daily balance.

Jinzhiwei actively embraces AIGC and introduces an innovative engine for in-depth services. Jin Zhiwei has a keen insight into the changes that AIGC, which is supported by large models, may bring to RPA, and is cooperating with a number of leading companies to set up COE departments to develop a new generation of AI-powered RPA digital employees. The combination of RPA and AIGC can endow digital employees with natural human dialogue capabilities, interface reading comprehension, recognition and automatic operation capabilities.

Based on the financial industry, Jinzhiwei radiates to government and enterprises, and can provide customers with full-process services for RPA projects. With the penetration of RPA into subdivided industries, enterprises pay more attention to the project experience of RPA vendors in related industries. Jinzhiwei focuses on "enterprise-level" RPA projects, and has accumulated a large number of benchmark customers and rich landing experience in finance, government affairs, manufacturing, real estate, medical care, transportation, communications, energy, tobacco and other industries, including but not limited to the six major Headquarters of state-owned banks, top 20 securities companies, China Airlines, Anta, Deyang Municipal Government, Country Garden, Yutong Bus, Yuexiu Group, C&D Group, etc.

On this basis, Jinzhiwei has accumulated project experience into a business component library. If the manufacturer needs the corresponding RPA business component, it can be used out of the box, effectively shortening the project implementation cycle and cost. In addition, Jinzhiwei has polished a number of benchmark cases in related industries, and can provide planning and consulting services for enterprises, from RPA project planning to implementation, to provide enterprises with full-process support.

Typical customers:
the six major state-owned banks, CITIC Securities, Deyang Municipal Government, C&D Group, and China Aviation
Process middle platform
Market definition:
Process middle platform refers to the integration of BPA, BPM, iPaaS, process mining and other tools, with full process lifecycle management An enterprise-level process capability sharing and service platform for capabilities.
Party A's end users:
business departments such as process management department, IT department, risk control and compliance.
Party A's core needs:
In order to adapt to the ever-changing market environment, enterprises need to adjust their business in a timely manner and develop new processes. According to specific business needs, enterprises will use specialized development and management tools at each stage of the process life cycle, but the lack of synergy between process tools leads to low efficiency of enterprise process management. In addition, when different process tools are used independently, there will be a low reuse rate of process capabilities and a waste of process development capabilities. Therefore, enterprises are gradually turning to the process middle platform, hoping to realize the reuse of process capabilities and improve the full life cycle management capabilities of processes by integrating process management and automation tools. The process platform is an important support for enterprises to achieve hyperautomation.
The demand of enterprises for the process middle platform is reflected in the two aspects of process lifecycle management capability and process capability reuse. On the one hand, the process platform integrates all process services of the enterprise into the capability reuse platform for unified management. Enterprises need to manage enterprise processes from five aspects: process governance, development, automation, integration and optimization, and use the process platform to coordinate the processes required at each stage tool to provide one-stop service for the whole process life cycle. On the other hand, the process middle platform needs to encapsulate common process components, RPA, etc., allowing enterprise users to quickly build or adjust processes in a drag-and-drop manner to achieve process capability reuse.
Enterprises need to scientifically plan and design enterprise processes and build a process standardization system. With the advancement of enterprise digital transformation, the internal processes of enterprises are gradually slow and inefficient, and it is difficult to meet the needs of enterprise operation. It is necessary to re-plan, design and verify enterprise processes. Specifically, enterprises first need to plan business processes hierarchically and hierarchically, give priority to planning core processes, and then plan supporting processes and operating processes in turn. Secondly, the enterprise needs to design the enterprise process according to the process planning, clarify the input and output of the process, clearly define the intermediate links, and stipulate the scope of responsibility of the relevant responsible persons. Finally, the enterprise needs to conduct a small-scale verification test on the process before it is put into operation to ensure the quality of the process. To this end, enterprises need to rely on the process platform to establish a process standardization system, and scientifically design and plan the enterprise process according to the enterprise scale, development status, business goals, etc.

Figure 20: Requirements of Party A in the whole process life cycle
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Enterprises need to improve the efficiency of process development and execution, and provide process support for enterprise operations. In order to adapt to the rapidly changing market environment, enterprises need to adjust their business and develop new processes in a timely manner. In the traditional development mode, the business department first communicates the requirements to the IT department, and the IT personnel develop the process, and then the business personnel test and give feedback. After several rounds of iterations, it is officially launched. A cumbersome development model will delay the process launch time, resulting in reduced business agility and even missed market opportunities. Therefore, enterprises need to speed up process development and provide process support for business adjustments in a timely manner.

In addition, there are many nodes in the traditional business process of the enterprise that need to be processed manually, such as the node of signing and stamping and contract creation in the financial management process, and the node of answering common questions in the sales process. However, most manual nodes have the characteristics of high operating frequency and strong repetition, requiring a lot of repetitive manual labor, resulting in a high error rate in process execution, making process nodes more likely to become "process blocking points" and affecting process operation efficiency. Therefore, enterprises need to reduce the artificial nodes in the process and break through the process blockages.

Enterprises need to quickly break through process islands and use process data to diagnose and optimize business processes. During the operation phase, the business process will be affected by many unpredictable factors and deviate from the expectations of the process design. Enterprises need to compare the preset process with the actual operation of the process to make targeted process optimization. However, enterprise business data is scattered in various systems, and it is difficult to convert into process data. The invisible and unusable process data has become a pain point for enterprise process optimization. In order to obtain process data, enterprises need to open up various business systems, but the traditional system opening method is that IT personnel complete the secondary development of the system API interface point-to-point, with heavy workload and poor flexibility. Therefore, enterprises need to use the process platform to quickly break through process islands, give full play to the value of process data, and provide services for process optimization design.

Enterprises need to realize process lifecycle management and improve process efficiency. Enterprises will use specialized development and management tools at each stage of the process life cycle according to business needs, such as process design software, development software, RPA, process mining, etc. Although demand-driven process management can solve specific business needs, the lack of synergy between the above-mentioned process tools leads to inefficiency in process lifecycle management. For example, after using the process optimization information obtained by the process mining tool, the enterprise needs to manually use the process design system to redesign the process, and then manually add the new design to the process development software. Therefore, enterprises need various process tools to be able to collaborate efficiently based on the entire life cycle of the process, and comprehensively improve the efficiency of each stage of the process.

Vendor capability requirements:
Enterprises’ demand for process mid-platform puts forward multiple capability requirements for process mid-platform products, including providing process management systems, packaging process components and RPA tools, and having API asset management capabilities and process mining capabilities. In addition, the middle office of the process needs to coordinate the process tools and provide one-stop service for the whole life cycle of the process.
The process platform must have a complete process management system to support enterprise process planning, design and verification. In order to build a standardized process system, the process center needs to have a process management system to manage the process structure, process view and process elements, and provide enterprises with the resources required for process planning. In the process design stage, the process management system needs to have a visual design interface, which allows business personnel to design the process in a drag-and-drop manner, and clearly defines the responsible person and the scope of power and responsibility. In the process verification stage, the process management system needs to have business process simulation capabilities and support simulation tests or small-scale process tests for key users.

The process middle platform needs to encapsulate rich process components and support reuse to realize rapid process development and ensure enterprise agility. In order to shorten the process development time, the process middle platform needs to have the ability to reuse process components, and encapsulate common process components, such as user authentication and authorization, rule judgment, etc. When business users need to develop new processes, they can quickly complete the process building.

The process platform needs to integrate iPaaS and process mining capabilities, obtain process data, and extract process optimization solutions from it. In order to break through enterprise process islands and obtain process data such as process quantity, operation status, operation efficiency, and personnel input, the process platform needs to have iPaaS capabilities, and support local to cloud, private cloud to public cloud, and public cloud to public cloud integration. The platform in the process needs to uniformly manage the API interface for the enterprise, and provide out-of-the-box connectors, covering all mainstream protocols, enterprise applications and SaaS services. In addition, the platform in the process also needs to provide API interface monitoring call and viewing services to assist enterprises in managing enterprise API assets.

After the process island is opened up, in order to maximize the value of process data, the process middle platform needs to integrate process mining capabilities, deposit enterprise business process data into the process platform, automatically monitor process operation, or convert it into a visual flow chart through process mining algorithms, It is used for process diagnosis and suggestions for process optimization.

The process middle station needs to be able to coordinate process management tools and provide one-stop service for the entire process life cycle. In order to solve the problem of collaboration between process tools and realize the full life cycle management of processes, manufacturers need to open up the full link of process development and management tools, and integrate the capabilities required for all stages of process design, development, operation, and maintenance into the process platform , to create a process management platform integrating process modeling, development testing, implementation, operation and maintenance management, and statistical analysis. For example, after an enterprise user completes the process design using the process platform, they can import the finished process design software into the development software with one click to automatically generate a flow chart.

Inclusion Criteria Description:

1. Comply with the vendor capability requirements of Taiwan market analysis in the process;
2. The manufacturer's revenue in the market in the past year is not less than 5 million yuan;
3. In the past year, the manufacturer has no less than 3 paying customers in this market.
   Evaluate on behalf of the manufacturer:

Weihong Technology
Manufacturer introduction:
Hangzhou Weihong Technology Co., Ltd. (referred to as Weihong Technology) was established in 2012, focusing on the development and solutions of the new generation of BPA business process management and automation software. Up to now, Weihong Technology has provided more than 1,000 domestic and foreign large and medium-sized enterprises and governments with full-lifecycle process software product solutions including process planning and design, process operation, process automation, process integration, and process mining. Many industries such as manufacturing, finance, electrical and electronic, pharmaceutical, service industry, high technology and government.
Product service introduction:
Weihong Technology's full-process product system includes BPA process planning and design platform, BPMA process management and automation platform, BPI process analysis and mining platform and other platform-level products. The BPA process planning and design platform can design, manage, and display the process structure and process documents, and integrate the process and system, performance appraisal, risk, etc. in an all-round way; the BPMA process management and automation platform can implement the process through zero-code implementation, and open up process islands , improve the automation and intelligence level of the process; the BPI process analysis and mining platform can use process data to assist business decision-making and continuously optimize the process. The three core products together form the process platform of Weihong Technology, providing process full life cycle management services.
Vendor evaluation:
Weihong Technology’s process middle platform has a complete product line for the entire process life cycle, which can provide Party A with automation and one-stop services, and cooperate with Party A’s data middle platform and business middle platform to build the internal IT ecosystem of the enterprise and empower the enterprise Digital transformation of processes. In addition, Weihong Technology has advantages in Xinchuang's domestic substitution.
Figure 21: Micro-macro technology process platform architecture diagram
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Micro-macro technology process platform has the ability to manage the entire life cycle of the process. The process platform of Weihong Technology is composed of products in the whole process life cycle, including core products such as BPA process planning and design platform, BPMA process management and automation platform, and BPI process analysis and mining platform, which can provide Party A with automated and one-stop process services. Different from using products at each stage of the process life cycle alone, Micro Macro Technology's process middle platform has the ability of "integration and mutual collaboration", providing Party A with a full range of services and realizing the functional synergy of process products. in particular:

1. Weihong Technology's process center has a dedicated flow chart online sorting and drawing tool, and can centrally manage process design and planning documents, empowering the management department to plan and design processes;

2. The process design can be imported into the BPMA platform of the platform in the process with one click, and it can be launched in a low-code drag-and-drop manner;

3. The iPaaS platform integrated on the BPMA platform can solve the system docking requirements during process operation;

4. The Alpha Bot process robot platform integrated on the BPMA platform can provide process node automation services;

5. After the process and business data are deposited on the process platform, the BPI process analysis and mining platform equipped on it can automatically realize process analysis and process optimization.

In addition, the product system development and product iterative upgrade of Weihong Technology are driven by the needs of Party A's users, and the products meet the needs of users. For example, when the BPM product was launched, Weihong Technology found that Party A’s enterprise had a rigid need for data connection with heterogeneous systems, but the traditional point-to-point interface secondary development method was time-consuming and labor-intensive, and difficult to monitor. To this end, the technical team of Weihong Technology has created a lightweight iPaaS platform dedicated to the BPM engine, on which it completes the standard API full life cycle management with heterogeneous systems, and realizes data connection through configuration .

Micromacro's process middle platform is suitable for making up for the shortcomings of process management and resource utilization for large enterprises, and improving business agility. The process platform mainly serves large-scale enterprises with high business complexity and mature IT capabilities. The complexity of business leads to difficulties in system integration, low efficiency of data resource utilization, and poor internal response speed and external business agility. Micromacro's process middle platform can make up for the shortcomings of process management and resource utilization for large enterprises, cooperate with Party A's data middle platform and business middle platform, and jointly build the internal IT ecosystem of the enterprise, thereby improving business agility. For example, after the salesperson of Party A uploads the customer's business card, it is hoped that OCR will automatically recognize the business card information. After the business center has designed the business card information identification function, the process center can incorporate this node into the process of the sales department to build customer portraits in a timely manner; if Party A has built a data center to manage business system data in a unified manner, the process center can provide data The middle platform provides process data, fully exerts the capabilities of the data middle platform, and realizes multi-dimensional process analysis.

Figure 22: The process center, the data center, and the business center jointly build the enterprise's internal IT ecosystem
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Weihong Technology has a variety of service methods in the process center, which can flexibly help enterprises realize process digital reform. Micromacro Technology Process Center can flexibly promote the digital reform of enterprise processes and create digital processes for Party A. On the one hand, large enterprises can use Weihong Technology's process platform as a group-level management and control tool, and rely on the process platform to sort out the processes of the group's subsidiaries. Take the conglomerate management and control needs of a large group as an example. The subsidiaries of the group usually have complex heterogeneous systems, and the conglomerate management and control needs to build a new system on top of it. Micro-macro Technology's process center has the ability to support architecture and services. Its high-performance and high-concurrency scene processing capabilities can support staged and complex IT environments, and provide process center service capabilities for the entire group. The group can rely on the process platform to formulate plans, continuously access the processes and systems of each subsidiary on the process platform, and gradually realize the digital transformation of internal processes.

On the other hand, Weihong Technology's process middle platform can also be mounted on Party A's existing business system as an invisible support to provide process services for system operation. Taking the banking system as an example, the bank's financial system is directly connected to the user. The platform in the process of Weihong Technology can independently collect financial process data, analyze and optimize the process blockage, and serve as the "process engine" behind the business system to output the process engine for the financial system. service and improve the process capability of the original system.

Weihong Technology meets the requirements of Xinchuang and has rich project experience in the field of government affairs. In recent years, domestic manufacturers have successively emerged at the CPU, operating system and other levels to provide operating environment support for localized systems. However, the system running in the domestic Xinchuang environment must be adapted to the domestic mainstream Xinchuang operating environment, and the software and products must not have any foreign third-party plug-ins and controls. The product structure and specific application of the platform in the process of Weihong Technology can adapt to the domestic mainstream Xinchuang environment, and the product code and core engine of Weihong Technology are originally developed, which can meet the technical investment requirements of Party A in the domestic alternative project.

In addition, Weihong Technology has launched domestic substitution cooperation with more than 100 government agencies. Government departments have a large demand in the field of domestic substitution, but the business systems of various government departments are complex, and domestic substitution projects are more difficult. On the basis of systematically sorting out business systems, Weihong Technology builds a cross-departmental process middle platform, integrates internal processes and cross-departmental collaborative process systems, and provides a full-stop service around the process life cycle. Taking the whole career cycle of civil servants as an example, Weihong Technology merged multiple sub-processes such as transfer, open recruitment, retirement, and resignation in the process of Weihong Technology, and opened up business systems such as medical insurance, social insurance, provident fund, citizen card, and office logistics support, and used RPA Robots replace repetitive and cumbersome manual nodes in the process, creating an end-to-end "one thing process" for government departments.

Typical customers:
China Merchants Port Group, Chambroad Holdings Group, Bank of Hangzhou

05 Selected manufacturers list
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