Introduction
The large model rushed to the industrial field, staged a "fast and furious", and joined forces to form CP!
In the industrial field, a large number of collaborative processes are interrelated, from procurement, R&D, production, quality control to sales, after-sales and other processes, each link will generate a large amount of data information. How to analyze?It's always been a sad problem!
The big model said, "It's up to me how I answer this question!"
The 14th Five-Year Plan for the Development of the Digital Economy has proposed to strengthen the innovation and application of key digital technologies, accelerate the promotion of digital industrialization, and promote the digital transformation of industries.
The 14th Five-Year Plan for the Deep Integration of Informatization and Industrialization proposes new models and new forms of business to be widely popularized. The digital penetration rate of enterprise operation and management has reached 80%, and the transformation of enterprise form to flattening, platformization and ecology has accelerated. The penetration rate of digital R&D and design tools has reached 85%, and platform-based design has been promoted on a large scale. The numerical control rate of key processes is 68%
From the perspective of policy orientation, the digital transformation of the industrial field tends to be strong!
According to the relevant institutions**, the percentage of revenue from digital services in the industrial sector will increase significantly in the next two years.
Data**: KPMG 2023 – The Future of Smart Industry in the Industrial Sector: The Race for Digital Services).
In 2025, 37% of revenue will come from digital services, and its digital transformation will bring to industrial manufacturing enterprises"Positive Transformation".At a minimum, the following aspects are included:
Reduce service costs: Based on tools and insights such as artificial intelligence (AI) and machine learning (ML), AR, and VR, it empowers various service processes and efficiency improvements
Create new productsIncrease customer value through marketing and the provision of new value-added services, which in turn will provide new revenue** for manufacturers, ultimately reducing the total cost of ownership for customers
Improve service efficiency: Reduce service response or resolution time and maximize equipment performance by using connected product data to resolve service needs.
Increase customer lifetime value: Monetize the value they provide to customers by selling more services over the life of the product, increasing the profit margins of those services, and increasing customer loyalty for service updates and further product sales.
Challenges and Opportunities: In the Era of Large ModelsIndustrial development game.
The data mining ability is weak, and it is difficult to release the value of data. Data is the "blood" of the industrial field, including sensor data, production data, engineering data, equipment data, maintenance data, process systems, management manuals, etc., which exist in a structured and unstructured way.
The AI construction is weak, and the insight analysis is not comprehensive. Limited by the current status of technology platform construction, the introduction of big data analysis, AI**, machine learning and other capabilities is slow or not deep enough, which cannot fully understand and describe the status quo of production and operation, and cannot strongly support business insight analysis. At the service nodes that need to be improved, the ability to change is weak.
Be brave in innovation and breakthroughs and embrace digital transformation. AI technology, based on large-scale language generation models, has pushed the world into a "new round of technological revolution", and with the successive release of relevant national policies, regulations and guidance, industrial digitalization has risen to the national strategic level. Actively embracing new digital technology, the industrial field has ushered in new changes in intelligent maintenance, first-chain management, process data management, production and operation optimization, and process quality control.
Smart industrial solutions
Based on the large model technology of Wenyin Internet, a solution with digital base as the underlying technology support is built. Through the four major engineering designs of corpus engineering, industrial parameter market, AI task, and OPS design, the rapid expansion of scenarios and the comprehensive empowerment of services are realized. This scheme can be applied to:Industrial maintenance scheduling, process quality control, equipment and material management, production and operation, human resource management, after-sales pre-sales services and other businessesto promote the digital transformation and innovative development of the industrial sector.
In the industrial field, a large number of collaborative processes are interrelated, from procurement, R&D, production, quality control to sales, after-sales and other processes, each link will generate a large amount of data information. If this data can be structured (especially sensor data) and processed by systems with specialized knowledge, it can lead to unexpected efficiency gains in the industrial sector.
The typical digital innovation canvas in the manufacturing industry is as follows。On this canvas, we can see a myriad of data points that are inextricably linked. Through the use of cutting-edge technologies such as large models and big data analysis, we can deeply mine and analyze these data points to discover the internal rules and connections between them, so as to provide strong support for the digital transformation and innovative development of the manufacturing industry. Examples of application scenarios include but are not limited to:
In the manufacturing industry, digital transformation has become an inevitable trend. Through digital transformation, enterprises can better grasp the production process, improve product quality, reduce costs, and improve efficiency, so as to gain more business opportunities and competitive advantages. Large model technology is an important part of digital transformation, which can help enterprisesMake better use of your data, optimize processes, improve decision-making efficiency, and promote digital innovation and development in the manufacturing industry.
Powerful tool for digital transformation: Cutting-edge AI technology with large models as the mainstream
Based on the capabilities of large model dialogue engine, writing engine, extraction engine, etc., a variety of shared AI scenarios are built, subverting the previous AI technology and scenario construction path.
The model is built in a unified manner: Construct a unified industrial model to avoid decentralized model construction.
The application scenarios can be expanded: It can support different tasks such as content generation, content extraction, and search, and is widely used in different scenarios.
Efficient Scenario Services: Prompt engineering allows you to quickly establish scenario services.
Business experience is availableAccumulation: You can quickly build applications in similar scenarios through task orchestration and reuse existing business experience.
The difficulty of business operation is reduced: The operation work is mainly completed through prompt engineering, without the need for frequent model training, and the complexity of model operation and personnel skill requirements are significantly reduced.
Unified maintenance of the model: Maintain a unified financial model for management, and maintain it in the later stage.
Essence: Use advanced AI technology to optimize or solve enterprise management problems
Business management is a complex process that involves six core elements: strategic management, organizational management, marketing, production operations (manufacturing), financial management, and human resource management. These elements are the foundation of a business's operations, and they are influenced by the company's internal systems, culture, and standard operating procedures (SOPs). In general, whether a business is robust or not depends on itThe level of technical constructionHigh and low to decide;How far an enterprise can go depends on the inheritance of corporate cultureKnowledgeDegree of precipitation and sharing. The former is for the enterpriseBones, the latter for the enterprise"Blood".Based on these two, we can consolidate the foundation of business activities such as the pursuit of profits, the design of business models, and the implementation of strategies.
Artificial intelligence (AI) technology is the key means to build the "skeleton" of enterprises, and it has roughly gone through two stages: the first is "decision-making AI", which mainly focuses on decision-making based on data and algorithms;Then came "generative AI", typified by large-scale language generation models.
The big model itself is a "large" evolvable cognitive intelligence engine, which has:"Generalist".foundation, also available"Professionals".Skill. It can continuously strengthen the "bones" and "blood" of the enterprise, and bring higher operational efficiency and stronger competitiveness to the enterprise.
For example, it can be used in the following scenarios:
Scenario 1:Build a new generation of enterprise knowledge base based on large modelsIn the industrial sector, there is a huge amount of structured and unstructured data. Historically, it has been difficult to turn data into knowledge, let alone knowledgeKnowledge sharing and value release。The powerful knowledge question answering, reasoning, and generation capabilities of large models make all this easier. Based on the transformation of data to knowledge, it can ultimately assist high-level decision-making.
Of course, from the perspective of application boundaries, it can be roughly divided into internal knowledge base and external knowledge base (both of which are embodied forms of cognitive intelligence), the latter is more empowered by customer service, marketing, after-sales and other links, which makes the service process more "anthropomorphic". In this process, cognitive intelligence can be used to maintain the merger of the server and the clientSet incentives reasonably to promote the closed loop of business.
Scenario 2:Smart MarketingThe industrial model empowers front-end user interaction scenarios, including customer service, marketing, etc. In the front-end service link, the large model can be used as:Intelligent customer service, marketing assistant, shopping guide robotand other "assistants". Through service touchpoints, obtain user behavior data (including preferences, preferences, **sensitivity, etc.), business data (transaction frequency, transaction amount, complaints, etc.) and other relevant data.
Combined with data analysis and text mining and other technologies, users and the market can be deeply "portrayed", such as performing relevant NLP tasks through large models, including customer group classification, customer complaint label classification, marketing analysis, market trends**, etc.
Finally, based on the analysis results, various action plans are reformulated and the process is improved to improve service efficiency. In the process of improvement, there must be the addition and update of knowledge (the process also belongs to knowledge), which can continuously strengthen the "cognition" of the large model. Cognitive enhancement leads to better service, thus creating a virtuous circle
Scenario 3:Ticket dispatch and processingIn the industrial field, ticket dispatch and processing is a very important scenario, which can be:Complaints about work orders, maintenance work orders, which can also beProduct optimization work orders based on market feedback (VOCs).Wait. For example, if a user submits a ticket through a service touchpoint, the large model can classify and extract the content of the ticket, and call relevant plug-ins to send the ticket to efficiently improve processing efficiency. In addition, it can also take the initiative to collect market public opinion information, and feed back product design, process design and other aspects based on the analysis results.
Take, for example, the automotive industryBased on the large model, an agent is designed to automatically obtain relevant information from external public channels, and then conduct multi-dimensional combination analysis based on VOC analysis topics, including users' sensitivity to different models**, driving function preferences, spatial tendencies, and acceptance of new functions of competing products.
Based on the analysis and mining results, work orders are dispatched by product, marketing, service and other business lines, so as to improve product planning, channel strategy, market strategy, etc. A feedback mechanism will be established in the process to evaluate the overall effect to form a virtuous circle.
Large model + smart industry, through the construction of a parameter market in the industrial field, combined with the generalization ability of the large modelDig deeper into the value of your internal data。With the implementation of high-quality knowledge engineering, high-quality corpus is output, and then through text processing technology, it helps industrial text processing. In this process, the large-scale model provides full-link support services to quickly build industrial domain models.
Wenyin Internet is a familyAI knowledge management solutionsBased on large model technology, combined with NLP, prompt learning, knowledge graph and other technologies, and through years of industry practice accumulation, the service provider realizes document analysis, intelligent information extraction, intelligent content generation, deep semantic understanding and association analysis of business texts, and is committed to the in-depth mining and orderly inheritance of enterprise knowledge, so as to help enterprises improve work efficiency and precipitate knowledge engineering.
Since its establishment, it has served various subdivided scenarios in finance, construction, medical care, aviation, communications and other fields, and has landed hundreds of projects and obtainedIDC FinTech50, CB Insights FinTech50, KPMG FinTech50and other authoritative certifications.