随着云计算、大数据、物联网等新兴科技技术的飞速发展，世界再一次将目光聚焦于智能制造。各国都提出不同的战略规划来支持与发展智能制造，比如美国的“再工业化”计划、日本的“新机器人战略”计划、德国的“工业4.0”计划以及中国的“中国制造2025”等。
一、什么是智能制造？
智能制造（Intelligent Manufacturing，IM）是一种由智能机器和人类专家共同组成的人机一体化智能系统，它在制造过程中能进行智能活动，诸如分析、推理、判断、构思和决策等。它包含智能制造技术和智能制造系统，智能制造系统不仅能够在实践中不断地充实知识库，而且还具有自学习功能，还有搜集与理解环境信息和自身的信息，并进行分析判断和规划自身行为的能力。智能制造通过人与智能机器的合作共事，扩大、延伸、部分取代人类专家在制造过程中的脑力劳动，把制造自动化的概念更新，扩展到柔性化、智能化和高度集成化。 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
二、智能制造未来发展趋势
1. 设备数字化率稳步提升
设备的自动化和数字化是企业实现智能制造的基础，根据平台数据结果显示，设备数字化率达 57.98%，24.04%的企业具备自动化物流设备，22.06%的企业在关键工序实现质量在线检测。设备数字化程度提升，有利于企业提升生产制造效率。
2. 设备互联互通能力持续加强
车间是生产制造信息的重要载体，包含设备、工艺、质量、作业等相关基础资源，只有通过设备、质量、生产等环节信息采集与追溯，才能真正意义上实现车间各环节的数据互通。根据平台数据分析结果显示，企业实现设备联网和设备数据采集达28.78%，实现生产数据自动采集达40.18%，实现质量全流程追溯的仅有16.97%。 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
3. 生产作业可视化程度有待进一步提高
生产过程的标准化、可视化和智能化是企业智能化改造和智能车间建设的重要目标，也是影响智能车间投资效果的关键内容。根据平台数据分析结果显示，28.43%的企业实现了生产过程可视化，30%的企业实现标准化作业文件的自动下发，10.42%的企业应用了高级排产系统。
4. 智能仓储应用场景逐渐普及
面向原料、半成品、成品仓储管控环节，依托仓储物流管理系统或平台等解决方案，借助于条形码、二维码、无线射频等标识技术，能够实现自动出入库、自动运输、配送过程监控，可有效提高配送效率、降低库存量。根据平台数据分析结果显示，28.43%的企业应用了基于标识技术的物料管理方式，仓储管理系统应用率达30%，10.42%的企业实现了基于生产需求的精准配送。
5. 数字化研发设计能力稳步提升
面向产品研发设计环节，依托计算机辅助设计、试验仿真系统、协同研发系统或平台，应用基于模型的定义、知识工程等技术，能够实现产品快速设计、缩短研发周期、降低研发成本，提高研发的效率和质量。目前数字化研发工具已在企业得到了普遍应用，由2020年的73% 提高至89%，30%的企业应用了数字化设计建模仿真技术，55%的企业实现基于三维模型的设计，32%的企业建立了典型组件和设计知识库并有效应用。 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
6. 系统集成与数据互联仍是提升关键点
系统集成和数据互联是企业迈向成熟度三级的关键特征。根据平台数据分析结果显示，20.77%的企业制定了完整的系统集成架构和规范，仅有12.77%的企业能够实现设计、生产、物流、销售和服务全业务的集成。企业集成需求旺盛，普遍存在技术水平低、人员能力弱、资金投入大等问题，难以实现互联互通，或制约企业向高成熟度阶段迈进。 数字化转型网（www.szhzxw.cn）
目前已有 75% 的企业实现了部门内的数据共享，但在数据分析利用率方面仍处于起步阶段，14%的企业采用了大数据平台，12%的企业基于模型开展数据分析及应用，驱动生产环节的业务优化，仅有5%的企业实现了智能决策。当前阶段，制造业实现基于数据驱动的精准决策仍面临巨大挑战。
7. 企业逐渐关注工业知识积累和沉淀
构建企业知识库是经验萃取的过程，是对知识进行有效管理并合理利用的重要手段，通过知识的积累和增值，企业才能够不断进行企业管理、产品研发、市场拓展和客户服务的创新，持续提升企业核心竞争力。根据平台数据分析结果显示，31%的企业注重智能制造领域的技术创新和管理创新，14%的企业已经建立了企业知识库以及知识管理平台，对知识进行系统性管理；11%的企业开始积累沉淀专家知识和经验并将其进行数字化和软件化，应用到业务活动中，以期减少经验流失和重复劳作，帮助企业解决经营管理中的复杂问题。 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
8. 开始逐步实现绿色低碳制造
我国制造企业早期发展追求迅速扩张生产规模，管理模式较粗放，导致碳排放失控。一方面是企业生产环境复杂，能耗设备分散，对设备的过载、空载状况无法进行实时监控，由于设备管理不到位导致能源损耗大。另一方面是由于不合理的工艺流程会造成工序能耗高，从而导致产生不必要的碳排放。根据平台数据分析结果显示，26%的企业已应用了能源管理平台，23%的企业实现碳排放统计，10%的企业实现了碳资产闭环管理。下一步企业将综合利用能效数据，优化设备运行参数、对传统工艺进行技术改造、优化生产管理过程，推动低碳生产工艺的创新与应用。
9. 产业链供应链数据的集成和管理
企业基于生产、库存、销售数据集成，可进行动态安全仓储分析，精准预测库存并实施采购决策以满足生产及销售的需要，同时降低库存成本，提高生产资源配置效率，缩短交付周期。根据平台数据分析结果显示，13%的企业实现供应商信息协同，12%的企业自建或使用了供应商协同平台，6%的企业逐步打造智慧供应链。 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
实现智能制造道阻且长，这意味着智能制造是一项长期的系统工程。
翻译：
What is intelligent manufacturing?
With the rapid development of emerging technologies such as cloud computing, big data, and the Internet of Things, the world has once again focused on intelligent manufacturing. Countries have proposed different strategic plans to support and develop intelligent manufacturing, such as the United States’ “re-industrialization” plan, Japan’s “New robot strategy” plan, Germany’s “Industry 4.0” plan and China’s “Made in China 2025” and so on. 数字化转型网（www.szhzxw.cn）
First, what is intelligent manufacturing?
Intelligent Manufacturing (IM) is a human-machine integrated intelligent system composed of intelligent machines and human experts, which can carry out intelligent activities in the manufacturing process, such as analysis, reasoning, judgment, conception and decision-making. It includes intelligent manufacturing technology and intelligent manufacturing system, intelligent manufacturing system can not only continuously enrich the knowledge base in practice, but also has self-learning function, as well as the ability to collect and understand environmental information and their own information, and analyze and judge and plan their own behavior. Intelligent manufacturing through the cooperation between people and intelligent machines, expand, extend, and partially replace the mental labor of human experts in the manufacturing process, update the concept of manufacturing automation, and expand to flexibility, intelligence and highly integrated. 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
Second, future development trend of intelligent manufacturing
1. The digitization rate of equipment has been steadily improved
The automation and digitalization of equipment is the basis for enterprises to achieve intelligent manufacturing. According to the platform data results, the digitalization rate of equipment reaches 57.98%, 24.04% of enterprises have automated logistics equipment, and 22.06% of enterprises realize online quality detection in key processes. The improvement of the digitization degree of equipment is conducive to improving the production efficiency of enterprises.
2. Device connectivity has been continuously enhanced
Workshop is an important carrier of production and manufacturing information, including equipment, process, quality, operation and other related basic resources, only through equipment, quality, production and other links of information collection and traceability, in a real sense to achieve the workshop of all links of data interoperability. According to the data analysis results of the platform, 28.78% of enterprises realized equipment networking and equipment data collection, 40.18% realized automatic production data collection, and only 16.97% realized full quality process traceability.
3. The visualization degree of production operations needs to be further improved
The standardization, visualization and intelligence of the production process are the important goals of the intelligent transformation of enterprises and the construction of intelligent workshops, and also the key contents affecting the investment effect of intelligent workshops. According to the data analysis results of the platform, 28.43% of enterprises have realized the visualization of the production process, 30% of enterprises have realized the automatic issuance of standardized operation files, and 10.42% of enterprises have applied an advanced production scheduling system.
4. Intelligent storage application scenarios are gradually popularized
For raw materials, semi-finished products, finished products warehousing control link, relying on warehousing logistics management system or platform solutions, with the help of barcode, two-dimensional code, radio frequency and other identification technology, can achieve automatic warehousing, automatic transportation, distribution process monitoring, can effectively improve distribution efficiency, reduce inventory. According to the data analysis results of the platform, 28.43% of enterprises have applied the material management method based on identification technology, the application rate of warehouse management system has reached 30%, and 10.42% of enterprises have realized accurate distribution based on production demand.
5. Digital R & D design capability has been steadily improved
For product development and design, relying on computer-aided design, experimental simulation system, collaborative research and development system or platform, the application of model-based definition, knowledge engineering and other technologies can realize rapid product design, shorten the research and development cycle, reduce the research and development cost, and improve the efficiency and quality of research and development. At present, digital R & D tools have been widely used in enterprises, increasing from 73% in 2020 to 89%, 30% of enterprises have applied digital design modeling and simulation technology, 55% of enterprises have realized design based on three-dimensional model, 32% of enterprises have established typical components and design knowledge base and effective application.
6. System integration and data interconnection are still the key points for improvement
System integration and data connectivity are key characteristics of enterprises moving towards the level of maturity. According to the data analysis results of the platform, 20.77% of enterprises have developed a complete system integration architecture and specifications, and only 12.77% of enterprises can realize the integration of design, production, logistics, sales and service. The demand for enterprise integration is strong, and there are widespread problems such as low technology level, weak personnel capacity, and large capital investment, which make it difficult to achieve interconnection, or restrict enterprises to move towards a high maturity stage. 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
At present, 75% of enterprises have realized data sharing within the department, but the utilization rate of data analysis is still in its infancy, 14% of enterprises have adopted big data platforms, 12% of enterprises have carried out data analysis and application based on models, driving the business optimization of production links, and only 5% of enterprises have realized intelligent decision-making. At the current stage, the manufacturing industry still faces great challenges to achieve data-driven, accurate decision-making. 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
7. Enterprises gradually pay attention to the accumulation and precipitation of industrial knowledge
Building an enterprise knowledge base is a process of extracting experience and an important means to effectively manage and rationally utilize knowledge. Only by accumulating and adding value to knowledge can an enterprise continuously carry out innovation in enterprise management, product research and development, market expansion and customer service, and continuously improve its core competitiveness. According to the data analysis results of the platform, 31% of enterprises pay attention to technological innovation and management innovation in the field of intelligent manufacturing, 14% of enterprises have established an enterprise knowledge base and knowledge management platform to systematically manage knowledge. 11% of enterprises are beginning to accumulate and digitize and software expertise and experience into their business activities, in order to reduce experience loss and duplication of efforts to help solve complex problems in business management.
8. Start to gradually realize green and low-carbon manufacturing
The early development of China’s manufacturing enterprises pursued rapid expansion of production scale, and the management mode was extensive, resulting in uncontrolled carbon emissions. On the one hand, the production environment of the enterprise is complex, the energy consumption equipment is scattered, the overload and no-load status of the equipment can not be monitored in real time, and the energy loss is large because the equipment management is not in place. On the other hand, unreasonable process will cause high process energy consumption, resulting in unnecessary carbon emissions. According to the data analysis results of the platform, 26% of enterprises have applied the energy management platform, 23% of enterprises have realized carbon emission statistics, and 10% of enterprises have realized closed-loop carbon asset management. In the next step, enterprises will comprehensively utilize energy efficiency data, optimize equipment operation parameters, carry out technical transformation of traditional processes, optimize production management processes, and promote the innovation and application of low-carbon production processes.
9. Integration and management of industrial chain and supply chain data
Based on the integration of production, inventory and sales data, enterprises can conduct dynamic and safe warehousing analysis, accurately predict inventory and implement procurement decisions to meet the needs of production and sales, while reducing inventory costs, improving the efficiency of production resource allocation, and shortening the delivery cycle. According to the data analysis results of the platform, 13% of enterprises have realized supplier information collaboration, 12% of enterprises have built or used supplier collaboration platforms, and 6% of enterprises have gradually built smart supply chains. 数字化转型网（www.szhzxw.cn）数字化转型网灯塔智造专题
The road to achieve intelligent manufacturing is long and difficult, which means that intelligent manufacturing is a long-term system engineering.
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