Ministry of Industry and Information Technology, Ministry of Finance, and Three Other Departments Release: Reference Guidelines for Typical Scenarios of Intelligent Manufacturing (2021)

Recently, the General Office of the Ministry of Industry and Information Technology, the General Office of the National Development and Reform Commission, the General Office of the Ministry of Finance, and the General Office of the State Administration for Market Regulation issued the "Notice on Conducting 2021 Pilot Demonstration Action for Intelligent Manufacturing", below is the "Reference Guidelines for Typical Scenarios of Intelligent Manufacturing (2021)".

Reference Guidelines for Typical Scenarios of Intelligent Manufacturing (2021) indicates that intelligent manufacturing scenarios refer to single or multiple links facing the entire manufacturing process, achieving applications with collaborative and autonomous characteristics, specific functions, and practical value through deep integration of new generation information technology and advanced manufacturing technology. Based on the development of intelligent manufacturing and corporate practices since the "13th Five-Year Plan" and combining technological innovation and integrated application development trends, 52 typical intelligent manufacturing scenarios across 15 links have been refined and summarized as references for building intelligent manufacturing demonstration factories.

I. Factory Design

Achieve model-based factory planning, design and delivery through three-dimensional modeling, system simulation, design optimization and model transfer, improving design efficiency and quality while reducing costs.

Digital design of workshops/factories: Apply workshop/factory three-dimensional design and simulation software, integrating factory information models, manufacturing system simulation, expert systems and AR/VR technologies for efficient factory planning, design and simulation optimization.

Digital delivery of workshops/factories: Build a digital delivery platform, integrating virtual construction, virtual debugging, big data and AR/VR technologies, achieving model-based digital delivery of factories, breaking down data barriers between factory design, construction and operation periods, providing basic common data support for factory main business systems.

II. Product Research and Development

Achieve data-driven product development and technological innovation through raw material property analysis, design modeling, simulation optimization and test verification, improving design efficiency and shortening R&D cycles.

Digital design and simulation of products: Apply computer-aided design tools (CAD, CAE, etc.) and design knowledge base, integrating three-dimensional modeling, finite element simulation, virtual testing and other technologies, applying new materials and processes, conducting model-based product design, simulation optimization and testing.

Raw material property characterization and formula development: Build property characterization systems or formula management systems, applying rapid evaluation, online preparation detection, process simulation and material testing technologies, creating raw material property databases and model libraries, optimizing raw material selection and formula design, supporting quality optimization and efficiency improvement throughout production.

III. Process Design

Achieve digital process design and process technological innovation through manufacturing mechanism analysis, process modeling and virtual manufacturing verification, improving process development efficiency and ensuring process feasibility.

Digital design of discrete processes: Apply computer-aided process planning tools (CAPP) and process knowledge base, adopting efficient machining, precision assembly and other advanced manufacturing processes, integrating three-dimensional modeling, simulation verification and other technologies, conducting model-based discrete process design.

Digital design of process-type processes: Build process technology systems and process knowledge base, combining raw material property characterization, process mechanism analysis, process modeling and process integration technologies, conducting process design and global optimization.

IV. Planning and Scheduling

Conduct order-driven planning scheduling through market order forecasting, capacity balance analysis, production plan formulation and intelligent scheduling, optimize resource allocation and improve production efficiency.

Production plan optimization: Build enterprise resource planning systems (ERP), applying theory of constraints, optimization algorithms and expert system technologies, achieving production plan optimization based on procurement lead times, safety stock and market demand.

Intelligent workshop scheduling: Apply advanced planning and scheduling systems (APS), integrating scheduling mechanism modeling, optimization algorithms and other technologies, conducting workshop scheduling optimization under multi-constraint and dynamic disturbance conditions.

Precise job assignment: Relying on manufacturing execution systems (MES), establish staff skill databases, position qualification databases, etc., conducting precise staff assignment based on person-job matching and staff performance.

V. Production Operations

Achieve intelligent production operations and refined production control through dynamic resource allocation, precise process control, intelligent processing and assembly, human-machine collaborative operations and lean production management, improving production efficiency and reducing costs.

Flexible production line configuration: Apply modular, group and production line reconfiguration technologies, build flexible reconfigurable production lines, achieving rapid adjustment of production lines to adapt to orders, working conditions and other changes.

Dynamic organization of resources: Build manufacturing execution systems (MES), integrating big data, operational optimization, expert system and other technologies, achieving dynamic configuration of manufacturing resources such as personnel, equipment, and materials.

Advanced process control: Relying on advanced process control systems (APC), integrating process mechanism analysis, real-time optimization and predictive control technologies, achieving precise, real-time and closed-loop process control.

Dynamic optimization of process flows/parameters: Build integrated management platforms for the entire production process, applying process mechanism analysis, process modeling and machine learning technologies, conducting dynamic optimization adjustments of process flows and parameters.

Human-machine collaborative operations: Integrate robots, high-end machine tools, human-machine interaction devices and other intelligent equipment, applying AR/VR, machine vision and other technologies, achieving efficient organization and operational collaboration of production.

Lean production management: Relying on manufacturing execution systems (MES), applying lean tools and methods such as Six Sigma, 6S management and fixed-position management, conducting data-driven precise control of people, machines, materials, etc., eliminating production waste.

VI. Warehousing and Distribution

Achieve refined inventory management and efficient logistics distribution through precise distribution planning, automatic entry and exit from warehouses (enterprises), automatic logistics distribution and tracking management, improving logistics efficiency and reducing inventory levels.

Smart warehousing: Integrate intelligent warehousing (storage and transportation) equipment, build warehouse management systems (WMS), apply barcode, radio frequency identification, intelligent sensing and other technologies, achieving automatic material entry (enterprise entry), inventory checking and exit (enterprise exit) based on actual production operation plans.

Precise distribution: Apply warehouse management systems (WMS) and intelligent logistics equipment, integrating visual/laser navigation, indoor positioning and machine learning technologies, achieving dynamic scheduling, automatic distribution and path optimization.

Real-time material tracking: Apply manufacturing execution systems (MES) or warehouse management systems (WMS), adopting identification sensing, positioning tracking, Internet of Things and 5G technologies, achieving full-process tracking of raw materials, work-in-progress and finished products circulation.

VII. Quality Control

Achieve refined quality control through intelligent online inspection, statistical analysis of quality data and full-process quality traceability, reducing defect rates and continuously improving product quality.

Intelligent online inspection: Apply intelligent inspection equipment, integrating defect mechanism analysis, property and composition analysis and machine vision technologies, conducting online inspection, analysis and result determination of product quality.

Precise quality traceability: Build quality management systems (QMS), integrating barcode, identification and blockchain technologies, collecting quality information of product raw materials, production processes, and customer usage, achieving precise product quality traceability.

Product quality optimization: Relying on quality management systems (QMS) and knowledge bases, integrating quality design optimization, quality mechanism analysis and other technologies, conducting identification of product quality influencing factors, defect analysis prediction and quality optimization improvement.

VIII. Equipment Management

Achieve refined equipment management and predictive maintenance through automatic patrol, maintenance management, online operation monitoring, fault prediction and operation optimization, improving equipment operation efficiency, reliability and accuracy retention.

Automatic patrol: Apply industrial robots, intelligent patrol equipment and equipment management systems, integrating fault detection, machine vision, AR/VR and 5G technologies, achieving efficient equipment patrol and abnormal alarm.

Intelligent maintenance management: Build equipment management systems, applying big data and AR/VR technologies, conducting optimization of maintenance plans, resource allocation optimization, virtual maintenance scheme validation and skill training.

Online operation monitoring and fault diagnosis: Build equipment management systems, integrating intelligent sensing, fault mechanism analysis, machine learning, Internet of Things and other technologies, achieving equipment operation status determination, performance analysis and fault warning.

Predictive maintenance and operation optimization: Build fault prediction and health management systems (PHM), integrating fault mechanism analysis, big data, deep learning and other technologies, conducting equipment failure mode judgment, predictive maintenance and operation parameter optimization.

Full lifecycle asset management: Establish enterprise asset management systems (EAM), applying Internet of Things, big data and machine learning technologies, achieving full lifecycle management of asset operation, maintenance, renovation, and disposal.

IX. Safety Control

Achieve comprehensive safety control facing all links through hazard identification, safety situation awareness, safety event decision-making and emergency linkage response, ensuring that safety risks are predictable and controllable.

Real-time monitoring and identification of safety risks: Relying on safety perception devices and safety production management systems, integrating hazard and operability analysis, machine vision and other technologies, conducting dynamic perception and precise identification of safety risks.

Intelligent decision-making and emergency linkage for safety events: Based on safety event linkage response disposal mechanisms and emergency disposal plan libraries, integrating big data, expert system and other technologies, achieving intelligent decision-making and rapid response for safety event disposal.

Intelligent control of hazardous chemicals: Build hazardous chemical management systems, applying intelligent sensing, physicochemical characteristic analysis and expert system technologies, achieving real-time monitoring, abnormal warning and full-process control of hazardous chemical inventory, location, and status.

Automation of dangerous operations: Relying on automated equipment, integrating intelligent sensing, machine vision and 5G technologies, achieving minimization and unmanned operations in dangerous operation links.

X. Energy Management

Achieve refined energy management facing the entire manufacturing process through comprehensive energy consumption monitoring, energy efficiency analysis optimization and energy balance scheduling, improving energy utilization rates and reducing energy consumption costs.

Energy consumption data monitoring: Establish energy management systems (EMS), integrating intelligent sensing, big data and other technologies, conducting full-link, full-element energy consumption data collection, measurement and visual monitoring.

Energy efficiency optimization: Relying on energy management systems (EMS), applying energy efficiency optimization mechanism analysis, big data and deep learning technologies, achieving improvement in energy utilization rates based on equipment operation parameters or process parameter optimization.

Energy balance and scheduling: Relying on energy management systems (EMS), integrating mechanism analysis, big data and other technologies, conducting energy consumption prediction, achieving comprehensive balance and optimal scheduling of energy for key equipment and key links.

XI. Environmental Protection Control

Achieve refined environmental protection control through pollution source management and environmental monitoring, emission warning and control, solid waste disposal and reuse, reducing pollutant emissions and eliminating environmental pollution risks.

Pollution source management and environmental monitoring: Build environmental protection management platforms, applying machine vision, intelligent sensing and big data technologies, conducting pollution source management, achieving collection, monitoring and alarm of full-process environmental protection data.

Emission warning and control: Relying on environmental protection management platforms, integrating machine vision, intelligent sensing and big data technologies, achieving real-time emission monitoring, analytical warning and auxiliary decision-making for emission optimization schemes.

Solid waste disposal and reuse: Build solid waste information management platforms, integrating barcode, Internet of Things and 5G technologies, conducting full-process monitoring and traceability of solid waste disposal and recycling.

Carbon asset management: Develop carbon asset management platforms, integrating intelligent sensing, big data and blockchain technologies, achieving full-process carbon emission tracking, analysis, calculation and trading.

XII. Marketing Management

Achieve demand-driven precise marketing through market trend prediction, user demand mining, customer data analysis and sales plan optimization, improving marketing efficiency and reducing marketing costs.

Rapid market analysis and prediction: Apply big data, deep learning and other technologies, achieving precise analysis, judgment and prediction of future market supply-demand trends, influencing factors and their changing patterns.

Dynamic optimization of sales plans: Relying on customer relationship management systems (CRM), applying big data, machine learning and other technologies, mining and analyzing customer information, building user profiles and demand prediction models, formulating precise sales plans.

Sales-driven business optimization: Through integration of sales management systems with design, production, logistics and other systems, applying big data, expert system and other technologies, dynamically adjusting design, procurement, production, logistics and other schemes according to customer demand changes.

XIII. After-sales Service

Achieve precise response to personalized service demands through service demand mining, proactive service push and remote product operation and maintenance services, continuously improving product experience and enhancing customer stickiness.

Proactive customer service: Build customer relationship management systems (CRM), integrating big data, knowledge graph and natural language processing technologies, achieving customer demand analysis, refined management, providing proactive customer service.

Remote product operation and maintenance: Establish remote product operation and maintenance management platforms, integrating intelligent sensing, big data and 5G technologies, achieving remote operation and maintenance of products based on operational data, predictive maintenance and continuous improvement of product design.

Data value-added services: Analyze product operational conditions, maintenance, faults and defects data, applying big data, expert system and other technologies, providing new businesses such as professional services, equipment valuation, financial leasing, and asset disposal.

XIV. Supply Chain Management

Achieve intelligent supply chain management through procurement strategy optimization, supply chain visualization, logistics monitoring optimization, risk warning and resilient control, improving supply chain efficiency, flexibility and resilience.

Procurement strategy optimization: Build supply chain management systems (SCM), integrating big data, optimization algorithms and knowledge graph technologies, achieving comprehensive supplier evaluation, precise procurement decision-making and dynamic procurement plan optimization.

Supply chain visualization: Build supply chain management systems (SCM), integrating big data and blockchain technologies, connecting upstream and downstream enterprise data, achieving supply chain visualization monitoring and comprehensive performance analysis.

Real-time logistics monitoring and optimization: Relying on transportation management systems (TMS), applying intelligent sensing, Internet of Things, real-time positioning and deep learning technologies, achieving full-process tracking and abnormal warning of transportation and distribution, optimizing loading capacity and distribution paths.

Supply chain risk warning and resilient control: Establish supply chain management systems (SCM), integrating big data, knowledge graph and remote management technologies, conducting identification, positioning, warning and efficient disposal of supply chain risk hazards.

XV. Model Innovation

Facing the entire value chain of enterprises, the entire lifecycle of products and all asset elements, promote manufacturing model and business model innovation through the integration of new generation information technology and advanced manufacturing technology, creating new value.

User-direct manufacturing: Through deep interaction between users and enterprises, provide customized product design, flexible production and personalized services that meet personalized needs, creating unique customer value.

Mass customization: Through production flexibility, agility and product modularity, provide customized products and services at low cost, high quality and high efficiency of mass production according to customers' personalized needs.

Shared manufacturing: Establish manufacturing capability trading platforms, promote supply-demand matching, output surplus manufacturing capabilities externally through various models such as rent instead of purchase, time-sharing rental, piece-rate billing, promoting optimal allocation of manufacturing resources within the industry.

Network collaborative manufacturing: Based on network collaborative platforms, promote close connections between enterprises in design, production, management, service and other aspects, achieving network-based manufacturing resource allocation and parallel collaborative production business.

Digital twin-based manufacturing: Apply modeling simulation, multi-model fusion and other technologies, build digital twin systems at different levels such as equipment, production lines, workshops, and factories, achieving real-time mapping between physical world and virtual space, promoting comprehensive improvement of perception, analysis, prediction and control capabilities.