Industry intelligence Research Report

2020年1月，谷歌公司首席执行官桑达尔·皮查伊在达沃斯世界经济论坛上接受采访时说：“人工智能是我们作为人类正在研究的最重要的技术之一。它对人类文明的影响将比火或电更深刻。”从历史来看，人工智能却不如我们想象那般发展迅速，因为商业应用的失败，曾经历两次“寒冬”，如今正处在“第三次繁荣”。与前两次技术发展推动不同，此次繁荣是基于商业应用。人工智能在某些领域的应用已经验证了商业价值，工业便是其中之一。“人工智能+工业”也创造出一个固有名词——“工业智能”。与通用人工智能不同，工业智能基于“问题导向”，是围绕工业系统的痛点寻求人工智能技术的解决方案。

In January 2020, Google CEO Sundar Pichai said in an interview at the World Economic Forum in Davos, "AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire." Historically, AI has not developed as rapidly as we thought, because of the failure of commercial applications, has experienced two "winter", and is now in the "third boom". Unlike the previous two booms, this one is based on commercial applications. One of the areas where AI has proven its commercial value is industry. "AI+industry" has also coined a term - "industrial intelligence". Unlike general AI, industrial intelligence is based on "problem orientation" and seeks solutions by AI technologies around the pain points of industrial systems.

对于大多数工业企业而言，数字化转型迫在眉睫，而工业智能是解决方案之一。一是消费端发生改变，2020年消费者的数字化率高达44%，对于定制化、个性化的产品愈加偏好，“围绕着用户需求”的生产模式对制造的各个环节提出更高的要求。而工业智能通过对生产和消费者数据的分析，可以预测客户的喜好，并基于此制定生产计划，是打造“柔性供应链”必不可少的一部分。二是制造业的劳动力成本大大提高，据中国国家统计局的数据，2019年制造业平均工资增长30.7%，从事制造业的人口却逐年减少。工业智能能够有效帮助降低人员参与生产的程度，削减人力成本。从这两个角度说，工业智能是企业数字化转型的重要抓手，市场前景看好。

For most industrial companies, digital transformation is imminent, and industrial intelligence is one of the solutions. First, the consumer needs have changed, with consumers going digital at a rate of 44% in 2020. Consumers' preference for customized and personalized products is increasing. The production model of "around the user's needs" puts higher demands on all aspects of manufacturing. Industrial intelligence, through the analysis of production and consumer data, can predict customer preferences and develop production plans based on this, which is an essential part of creating a "flexible supply chain". Second, labor costs in manufacturing are greatly increased, and according to China's National Bureau of Statistics, the average manufacturing wage increased by 30.7% in 2019, while the population engaged in manufacturing is decreasing year by year. Industrial intelligence can effectively help reduce the level of personnel involved in production and cut labor costs. From these two perspectives, industrial intelligence is an important grip for the digital transformation of enterprises, and the market outlook is promising.

工业智能的内涵是什么？具体能解决什么工业问题？在生产环节中的应用场景有哪些？市场中工业智能服务商的市场格局是怎么样的？应该从哪些维度衡量企业工业智能综合实力？短期或长期工业智能领域的投资机遇在哪里？目前工业智能发展面临怎样的挑战？凯盛研究团队依托案头研究和行业专家资源对整体行业以及重点可关注企业进行了全面的分析和调研，并在此报告中为您逐一解答。

What is the meaning of industrial intelligence? What specific industrial problems can be solved by AI? What is the market pattern of industrial intelligence service providers? What dimensions should be used to measure the comprehensive strength of industrial intelligence? Where are the investment opportunities in industrial intelligence? What are the challenges facing industrial intelligence? In this report, based on desk-top research and interviews with industry experts, Capvision research team has conducted a comprehensive analysis and research on the overall industry and representative companies to address these questions.

凯盛工业组2021年12月出版《“中国智造”系列一：工业智能行业研究报告》是该系列的第一个部分，后续我们会就“中国智能制造”中的其他细分市场进行拓展研究和分析。本报告内容主要分为四部分——工业智能内涵、工业智能市场分析、应用场景和服务商分析以及趋势和挑战。第一部分，通过大量的案头研究和专家访谈划定“工业智能”定义的边界，搭建基础架构，从技术、应用场景和服务商三个角度回答“工业智能是什么”的问题；第二部分，通过与业内资深专家进行深度交流，论述工业智能近些年发展的驱动因素，并分析市场投融资情况，对市场规模进行预测；第三部分，结合大量一手调研和案头研究，对工业智能的典型应用场景和头部服务商进行分析，阐述不同类型服务商的优缺点；第四部分，基于整体研究，输出工业智能行业洞察，包含投资机遇、未来发展趋势和挑战三个部分。正文内容约60页，适合需要了解及工业智能行业现状和未来展望的客户参考。

In December 2021, Capvision Research Group will publish 2021 Industry intelligence Research Report which is the first part of a series on Smart Manufacturing in China. We will expand our research and analysis on other market segments in "Smart Manufacturing in China" later. This report consists of four main parts - industrial intelligence connotation, industrial intelligence market analysis, application scenario and service provider analysis, and trends and challenges. In the first part, through extensive desk research and expert interviews, we define the boundary of the definition of "industrial intelligence", and answer the question of "what is industrial intelligence" from three perspectives: technology, application scenarios and service providers. In the second part, we summarize the driving factors for the development of industrial intelligence in recent years and analyze the market investment and financing situation to forecast the market size through in-depth communication with senior industry experts. In the third part, combining a lot of first-hand research and desk research, we analyze the typical application scenarios and head service providers of industrial intelligence, and explain the advantages and disadvantages of different types of service providers. The fourth part, based on the overall research of industrial intelligence, outputs industry insights, containing three parts: investment opportunities, future development trends and challenges. The main report is about 60 pages, suitable for clients who need to understand the current situation and future outlook of the industrial intelligence industry.

◆??报告目录?Contents

·???????1.??工业智能内涵 Industrial Intelligence Connotation

??1.1?工业智能概念界定?Definition of Industrial Intelligence

??1.2?工业互联网、智能制造、工业智能概念辨析?Industrial Internet, smart manufacturing, industrial intelligence concept identification

??1.3?通用人工智能和工业智能概念辨析?General artificial intelligence and industrial intelligence concept identification

??1.4?工业智能发展历史?History of industrial intelligence development

??1.5?工业智能基础架构?Industrial Intelligence Infrastructure

??1.6?适用于工业的人工智能技术?Artificial intelligence technologies for industry

??1.7?工业智能技术要素?Elements of Industrial Intelligence Technology

??1.8?工业智能生产场景应用情况?Application of industrial intelligence in production scenarios

??1.9?产业图谱?Industry Mapping

2.?工业智能市场分析?Industrial Intelligence Market Analysis

??2.1?行业驱动因素一：个性化需求倒闭企业转型?Personalized demand drives business transformation

??2.2?行业驱动因素二：劳动力成本提高?Higher labor costs

??2.3?行业驱动因素三：政策影响?Policy Impact

??2.4?行业驱动因素四：工业领域有一定的数据积累?Industrial field has data accumulation

??2.5?行业驱动因素五：智能算力发展?Intelligent computing power development

??2.6?市场投融资情况?Market Investment and Financing

??2.7?市场规模测算?Market size estimation

3.?应用场景和服务商分析?Application scenarios and service provider analysis

??3.1?生产场景分析——工业质检?Production scenario analysis - industrial quality control

??3.2?生产场景分析——物料运输?Production scenario analysis - material transportation

??3.3?生产场景分析——预测性维护?Production Scenario Analysis - Predictive Maintenance

??3.4?头部服务商——ABB分析?Head Service Provider - ABB Analysis

??3.5?头部服务商——创新奇智分析?Head Service Provider - Innovation Qiqi Analysis

??3.6?头部服务商——第四范式分析?Head Service Provider - Fourth Paradigm Analysis

??3.7?头部工业智能服务商比较?Comparison of head industrial intelligence service providers

4.?结论、趋势和挑战?Conclusions, Trends and Challenges

??4.1?结论一：工业智能仍然处在发展初期?Industrial intelligence is still in the early stages of development

??4.2?结论二：场景重于技术?Scenario is more important than technology

??4.3?结论三：短期投资看芯片和硬件?Short-term investment opportunities are smart chips and devices

??4.4?结论四：长期投资看集成Long-term investment opportunities are integrators

??4.5?趋势一：场景中心模式vs技术平台模式?Scene Center Model vs. Technology Platform Model

??4.6?趋势二：小数据工业智能可能爆发?Small data industrial intelligence may explode

??4.7?挑战一：技术不确定性vs工业需求确定性?Technological uncertainty vs. industrial demand certainty

??4.8?挑战二：解决方案不可复制性差?Poor solution replicability

??4.9?挑战三：工业智能人才缺口大，企业意识不足?Large shortage of industrial intelligence talent and lack of corporate awareness