Asia is being automated, intelligent tide impact. The ILO study found, Vietnam, Cambodia, unemployed workers, the highest risk of the Philippines and Indonesia, it is estimated that about 50% of workers work in this area may be several automated replaced in the next 20 years. Asia, especially China, as an important manufacturing area, in the face of manufacturing to automation, intelligence, digital transformation, the ability to continue to remain competitive?
into the fast growing Chinese Intelligent Manufacturing is based on a new generation of information technology, manufacturing activities throughout the part of the design, production, management and service, with information from depth perception, wisdom, self-optimizing decision-making, self-executing precise control functions such as advanced manufacturing general term for processes, systems and modes. In short, intelligent manufacturing is supported by a system of things smart, smart manufacturing and smart service. Intelligent Manufacturing has become a global value chain restructuring and the international division of labor adjustment is important to select the next national background. Developed countries have increased efforts to reflux manufacturing CONTROL ENGINEERING China Copyright , improve the strategic position of the manufacturing sector in the national economy. There is no doubt that in the tide of Asia are actively seeking a breakthrough. Artificial intelligence, for example, governments strongly support AI, promoting innovation and technology companies, startups and academia. 2017, the Korean government announced a $ 1 billion fund artificial intelligence; artificial intelligence in Japan to encourage start-up companies and venture capital; National Research Foundation of Singapore’s government declared a state of artificial intelligence program (AI.SG), the next five years plans to invest 150 million Singapore dollars (about US $ 107 million) development of artificial intelligence.
▲ 4.0 industrial development path
In addition to government support, Asian companies are more actively to break trade barriers to accelerate the development of new products. Europe is different from similar enterprises, China’s leading uncommon cooperation between enterprises, some well-known examples include: Baidu millet and develop more application scenarios in networking and artificial intelligence; cooperation with Tencent Jingdong layout of e-commerce ecosystem; the Indian system integrators AI composition Alliance (such as OpenAI). This gives them an amazing impact, but also means that they have the technical strength and capital base can be used to drive rapid innovation. China is an important force in Asia intelligent transformation. Government to strengthen top-level design and manufacture of intelligent, pilot and demonstration standard system; enterprises plusFast digital transformation, the ability to enhance system solutions. China Intelligent Manufacturing achieved significant results, enter the high-speed growth. China Intelligent Manufacturing into the growth stage is mainly reflected in three aspects: First, China’s industrial enterprises to enhance the quality of digital capabilities, laying the foundation for future analysis and forecasting and adaptive manufacturing systems. Second, the effectiveness of financial, intelligent manufacturing enterprise profit contribution rate has improved significantly. Third, typical applications, China has become the first industrial robot consumer, the strong growth in demand. Six stages of enterprise digital business intelligence competency is reflected in its ability to use data to guide the production system and self-optimizing. We learn from internationally recognized industry 4.0 development path, business intelligence maturity is divided into six stages: computerization, connectivity, visualization, transparent, predictable and adaptive. Computerized business process efficiency through computerization repetitive work, and to achieve high-precision, low-cost manufacturing. But different information technology systems to operate independently within the enterprise, many devices do not have a digital interface. Substituted interconnected links connected IT fragmented. Each part of the operation technology (OT) systems to achieve connectivity and interoperability, but still failed to reach the level of a fully integrated IT and OT levels. Visualization understand what is happening, via the fieldbus and sensor networking technology, enterprise capture large amounts of data in real time, and establish enterprise “digital twin” to change the previous decision-making based on artificial experience, into digital-based decision-making. Clear understanding of the causes of the incident, and to generate knowledge through root cause analysis. The twin figures forecast projected into the future, different simulation scenarios to predict future development, and make timely decisions and take appropriate measures. Adaptive predictive power only the fundamental requirement automation behavior and decision-making, while continuing to enable enterprises to achieve the self-adaptive response, in order to adapt to changes in their business environment as soon as possible. With the integration of the two Chinese Internet of things industry and many other initiatives to promote the construction, manufacturing enterprise digital capabilities significantly improve the quality, most companies are committed to vertical integration data. Deloitte survey results show that 81% of respondents completed a computerized stage, 41% in the connected stage, 28% in the visible phase, 9% in a transparent stage, and adaptive prediction stage companies are each 2%.
▲ respondents the stage (based on self-assessment companyEstimate)
Intelligent Manufacturing to significantly improve the profit contribution of 4.0 Advanced Industrial bring real and tangible benefits for manufacturers. 2013 nationwide survey Deloitte has 200 manufacturing enterprises showed that Chinese manufacturing enterprise intelligence at an early stage and profits. After five years of rapid development, intelligent manufacturing profitability of products and services significantly. 2013 Intelligent Manufacturing profits for the enterprises is not obvious, 55% of respondents intelligent manufacturing its products and services, net profit contribution rate in the 0-10% range, while in 2017, only 11% of respondents in this interval, while 41% of companies that intelligent manufacturing profit contribution rate of between 11-30%. Profit contribution rate of more than 50% of the enterprises by 2013 accounted for 14% of the companies surveyed raised to 33% in 2017. Intelligent Manufacturing profit contribution rate has improved significantly, the source of profits including the upgrading and enhance service value of production process efficiency.
▲ intelligent manufacturing products and services significantly improve the profit contribution rate
China to change lanes to overtake the two cards China for six consecutive years as the first industrial robot consumer. IFR data show that China’s industrial robot market size in 2017 was $ 4.2 billion, accounting for 27% of the world in 2020 will expand to $ 5.9 billion. 2018–2020 domestic robot sales will respectively 16,19.5,23.8 million units in the next three-year CAGR of 22%. Automotive, high-end equipment manufacturing and electronic industry is still the main users of industrial robots. What are the unique advantages of China have? The first is the amount of data. Upsurge in artificial intelligence machine-learning technology is extremely dependent on the data. Face recognition, language translation and testing driverless cars need a lot of “training data.” Because of the large number of China’s population and the number of devices, Chinese enterprises have a natural advantage in terms of data acquisition. Second, China’s manufacturing enterprise hardware equipment and plant relative European companies generally newer, relatively easy to implement device connectivity and plant transformation.
▲ key global markets of industrial robot sales
For China, the Internet industry is not “turn overtaking” but “change lanes to overtake,” China’s huge number of engineers, a sound industrial base and potential based on large amounts of data. – Li Yi Zhang, chairman cable system how to deploy the Intelligent Manufacturing Deloitte survey found that Chinese industrial enterprise intelligence systemFive were made to deploy the focus: Digital Factory (63%), and user equipment worth digging (62%), industrial things (48%), remodeling business model (36%) and artificial intelligence (21%).
▲ intelligent manufacturing companies surveyed deploy key areas
▲ technology companies surveyed are concerned about the
Digital Intelligent Manufacturing plant manufacturing sector is at the core of intelligence to-end data stream based, digital as the core driving force, so the digital factory is intelligent manufacturing enterprises as the primary task of deployment. At present, enterprises deploy digital factory to open up the flow of production data to perform the main task, and the product stream and supply chain data stream large room for improvement. Digital Factory by a new generation of information technology, data from all aspects of design, production, logistics and services such as thread, accelerate decision making, improve accuracy. Only open up the data stream can be achieved based on real-time data changes, the production process was analyzed and optimized, thus achieving business processes, collaborative processes and financial processes, and production resources (materials, energy, etc.) within the enterprise and between enterprises dynamic configuration. Open up the data stream is factory establish a “digital twin” of the premise, not only refers to digital twin digital products, but also contains a number of the plant itself and the process and equipment in order to achieve full traceability, physical and virtual two-way information sharing and interaction. Open communication data stream includes three types of data, i.e., production process data, product data, and supply chain data. Open up the production process production process data in addition to data from the data stream to the execution of the production plan (such as ERP to MES), further comprising a data flow between the MES and the control device and the monitoring device, data between the field device and the flow control device [123 ] Copyright control Engineering , and the data flow between the MES and the field devices and the like. The main types of data stream produced ▲
open up the product stream in a product stream mainly full life cycle of digital products and integration of the product lifecycle traceability. Product Lifecycle digital integration to shorten the development cycle as the core, the main applications for product development based on the model definition (MBD) technology, construction Product Lifecycle Management (PLM) and so on. R & D is the digital factory “data chain” starting point, the data generated by the R & D links between various systems in real-time transmission plant
Control Engineering Copyright , the data synchronization update to avoid the traditional manufacturing enterprises often due to poor communication error is generated, but also greatly enhance the efficiency of the plant makes, shorten the product development cycle. Product Lifecycle traceability to improve product quality control at the core. The main application is to make a product unique identification, application sensors, smart instrumentation, industrial control systems, such as automatic data collection quality management needs, carry out online quality testing and early warning systems by MES in the life cycle. Open up the supply chain data stream supply chain data stream mainly in the supply chain collaborative optimization, network collaborative manufacturing. The main application is to build cross-platform collaborative enterprise manufacturing resources to achieve integration and docking between R & D, management and service system, to provide access to corporate R & D, operations management, data analysis, knowledge management, information security services, to carry out manufacturing services resources and dynamic analysis and flexible configuration. Deloitte survey results show that companies committed to open up the flow of data from ERP to MES and even field devices, but it is only open from production to the implementation of the future needs to product data, supply chain data series. We will produce the data stream is divided into two areas: First, open up the data stream production planning and execution system; Second, the implementation and data flow monitoring and field devices. The results showed that 83 percent of respondents said has opened ERP and MES data stream to get through. 62% of enterprises continue to open up to the MES data stream to the next field devices. But only 47% of enterprises open up the product stream, 44% of enterprises open up the supply chain data stream. And taking into account our survey companies are better qualified and are above average size, this series of rate is clearly higher than the overall average Chinese. ▲ respondents enterprise data connectivity
From the industry perspective, the aerospace industry has opened up all of the companies surveyed data from production planning to execution, but the data field devices, product manufacturing and supply chain execution to chain connectivity is lagging behind, lifting the large space. Electronic components and electrical products manufacturing industry data flow and data flow communication with the supply chain is higher than other industries, the higher the overall level of the digital factory. Product quality can be described as the pharmaceutical industry’s life, and open up the flow of product data accounted for only 33% of pharmaceutical companies, industry needs to strengthen product lifecycle traceability, improve product quality management and control capabilities. Motor vehicles and parts and high-end equipment manufacturing areProduct data flow aspects leader (see below).
▲ respondents enterprise data connectivity (by industry)
open up “dimensional wall” future digital world and the real world will be one of the two sides, open up the underlying data stream is digital twin (digital twin) operation. Deloitte believes digital twin is a physical entity or quasi-real-time flow of digital images, will help to enhance business performance. Digital twin often contains “twin digital products”, “production process digital twin” and “device digital twin” different levels but can be highly centralized and unified data model. Twin fields of digital products, digital Tesla companies have established a model for the production of twins and every electric car sold, corresponding to the model data is stored in the company database. Each electric car daily reports of their daily experience, and to detect possible anomalies in the use of these data by digital simulation program twin and provide corrective action. By twin digital simulation, Tesla obtained the equivalent of a daily 1.6 million miles of driving experience and feedback to each vehicle in a continuous learning process. Production processes digital twin fields, some of the keen sense of smell and factory production line started to introduce digital twin, prior to construction, the plant simulation and modeling, virtual optimum process to build a factory, then the real argument to the actual plant construction, effective reduce errors and risk. After the workshops and production lines to be built, the daily operation and maintenance interact through digital twin that can quickly identify problems, improve work efficiency. Gartner survey of the United States, Germany, China and Japan 202 companies found that, by 2020, at least 50% of the annual income of more than $ 5 billion manufacturer of digital twinning project will be at least one of its products or assets to start, will participate in the use the number of enterprises twin digital technology will grow 3 times. Expected in the next few years, there will be hundreds of millions of users using digital twin operation, it will be used for business planning service equipment, production lines operating, predict equipment failures, improve operational efficiency, accelerate new product development. In the future, this technology is expected to complete the integration and industrial production, and promote intelligent industry entered a new stage. How to create a digital twin? Deloitte believes creating a digital twin contains two main areas of concern: data flow requirements and product life cycle design digital twins – from design assets to asset-site use and maintenance in the real world; to create enabling technologies, the integration of realIts twin digital assets, the sensor data and enterprise core system operations and real-time transaction information flow. Intelligent plant floor implementation depends on enterprise pain points, and some enterprises to improve product quality, some enterprises to achieve the digital product design and production management, as companies often unbearable “family bucket” solution, you can solve the immediate problem, but there must be long-term planning, in order to avoid later unable to achieve interoperability. – Zhu Yiming, chief engineer and Lee Group customer value and digging equipment manufacturing enterprises are facing increasingly fierce market competition and increasingly transparent product pricing, have to find new sources of value. Deloitte intelligent manufacturing survey results show that users value the depth of excavation equipment and intelligent manufacturing enterprise deployment of the second area of focus. 62% of respondents are actively deploy device and user value of the depth of excavation, 41% of companies focused on mining equipment worth 21% of business focused on customer value mining. Mining equipment worth around can be said that the nature of manufacturing enterprises. As in the R & D stage, embedding new technology to produce more intelligent or more diverse product; the sales stage, to provide equipment related financial services; in the after-sales stage, factory equipment and products for real-time data collection and monitoring, and performance analysis, predictive maintenance, both to enhance security, but also serve to create more opportunities for businesses. Despite a late start, manufacturing companies are also attempting to explore and customer value-depth mining, which C2M (customer-to-manufactory, manufacturing to customer) the most attention. C2M reflects the customized production characteristics, allows manufacturers to directly face the user, in order to meet the needs of individual users; while reducing costs by eliminating intermediate links, improve efficiency. Example: Red Group C2M by creating electronic business platform, flexible supply capability and large data capacity to achieve large-scale customization. Customers can choose the style, technology, materials and orders in its C2M electronic business platform. Fast platform to collect customer dispersed, the individual needs of data at the same time, big data and cloud computing technologies according to customer needs matching product data models, data models and process data that can satisfy more than one million one trillion kinds of design portfolio, covering 99.9% of personalization design requirements. When the determined version, the system automatically generates process data, process data sent to the factory, the factory for delivery of production. The whole process only seven working days from order placement to product factory, and do demandProduction, inventory, one person, one version, a clothing section. Ali Baba “Amoy factory” ten thousands assembly plant, the electricity supplier buyers orders with manufacturers capacity for docking, the flexible production schedule of networking, solve electricity supplier buyers have orders no factories, manufacturing companies have capacity without orders knot disease. Scene three things intelligent manufacturing industry requires manufacturing system includes sensing, analysis, and decision-making ability to perform, while the core of these things involve the ability of the related art, technology for sensing such IOT (sensor, RFID, chip), industry-oriented analysis of large data analysis and decision-oriented application platform and services. Deloitte survey results show that Chinese manufacturers were aware networking applications to focus on analysis and service blend will be the focus of future construction of things. Respondents generally established system to collect dynamic sensor data, but the data analysis and application platform is lagging behind. From the industrial application point of view, the sensor electronics and electrical industry and the most popular internet, 76% of respondents using the sensor data collection, use was 43% enterprise networked platforms, but only 33% for the use of large data analysis techniques data collection. Motor vehicles and parts manufacturing industry, sensor technology also has a high penetration rate of 73%, but the big data platforms and applications than other surveyed industries. Big Data pharmaceutical industry technology utilizes the most active, because the pharmaceutical industry has long faced the challenges of massive data and unstructured data (see below).
▲ respondents typical things related to technology application
perception is only the initial stage of networking applications, data insight to guide action to improve efficiency or service blend to create new value, is the Internet of Things core. Cloud platform by providing powerful data transfer, storage and processing capabilities to help manufacturing companies acquisition and processing large amounts of data. Industrial cloud platform not only be able to achieve business platform through the completion of design, engineering, manufacturing, purchasing, marketing and other aspects of the product, will change the traditional mode of production and manufacturing ecology, creating new sources of revenue and business models. What is the status of Chinese manufacturing enterprises deploy cloud? Deloitte survey found that manufacturers are not enthusiastic about cloud deployments China. 53% of respondents cloud industrial manufacturers have not yet deployed, being 47% of industrial enterprises cloud deployments, 27% of the private cloud enterprise deployment, deploying public cloud 14%, 6% Cloud deployment of the hybrid (see below). Cloud storage and can significantly reduce the computational cost of each unit, and evenBy straddling the creation of new business models, but it also brings complexity. Once enterprises are worried that the factory production process data, such as asset performance management into the cloud platform
CONTROL ENGINEERING China Copyright , information security, intellectual property issues will follow. In addition, many enterprises have yet to clear the cloud industry in commercial applications and related capacity also contributed to the lack of enterprise-level enterprise cloud deployments reason for the enthusiasm is not high. The choice of public or private clouds, to a large extent depend on the concerns of different companies. If the business is only focused their production, cost efficiency and often do not choose public cloud; if companies focused business model innovation and product restructuring, will naturally prefer public or hybrid cloud, because often involves service platform the need to achieve compatibility and integration of a certain degree. Due to the current deployment of the more common domestic industrial base of the cloud to cloud-based functions, the enterprise cloud seen as a virtual server, do stored in the cloud, computing, only a few companies to change the mode of production and manufacturing eco-through cloud deployments, a public cloud and hybrid cloud deployments are still a few companies. ▲ respondents industrial manufacturing enterprise cloud deployments
much of the future will come from value-added business activities across the enterprise, in the long run, public cloud, hybrid cloud is the trend, because the only way to achieve data exchange and resource sharing . Although private cloud security, but is likely to be isolated in the new business models and new ecosystem outside. – He Dongdong roots Internet CEO Deloitte think of things in the field of intelligent manufacturing scenarios divided into three categories: equipment and asset management, product and service innovation insight. Management equipment and assets with sensing and networking capabilities of the system combined with the large data can be implemented to monitor and manage devices, such as remote monitoring, predictive maintenance and interconnect the scene. In remote monitoring things replace conventional manual inspection mechanism, data transmission equipment to the remote operations center by the sensor. To break the traditional predictive maintenance plant as planned regular maintenance equipment the way they operate, through things throughout the life cycle of the device monitor the entire process, and predict equipment failures that may occur in the future, advance the development of preventative maintenance plans to reduce the failure rate and improve productivity effectiveness. Things may also be connected to the device and monitoring apparatus and industrial plant, obtained insightful analyzes to help cross industrial equipment, production linesAnd optimizing performance and efficiency throughout the plant. Of course, in addition to the new plant, the old plant and equipment in the absence of replacement, but also need to network monitoring, how things transform the existing equipment is worthy enterprise concern. Insight into product manufacturing companies often do not understand how their products are used, but things will change this situation. After the use of the product by the manufacturer Things establish and maintain contact with the product, collect dynamic data in a more systematic way to analyze products in real-time continuous usage. After understanding of customer use of the product, manufacturers can predict data based on customer demand, the development of personalized products and new services, higher value added products. Based on the data and service innovation platform providing market services, networking and service blend to achieve business model innovation. Things to assist manufacturing companies to more effectively capture and forecast market demand, creating a dynamic, personalized intelligent services, new types of services consulting services, data services, finance and insurance and other things. Such applications will break the original boundaries of the enterprise, as well as innovative thinking interactive optimization, customers and manufacturing side of the business model of manufacturing resources from the various dimensions of the whole society. Companies need to assess their business needs, clear business objectives and processes related to the scope of the expected results, consider the technical scalability, performance, bandwidth, after the economic and technological level of innovation in order to make informed data and processing architecture of the system of things select. Reconstruction of intelligent manufacturing business model will not only help manufacturing companies to achieve cost efficiency
CONTROL ENGINEERING China Copyright , but also gives a rethink of the value proposition and business model reconstruction opportunities. At the same time, new entrants are constantly challenging the status of the traditional market participants, many technology-based companies to join the battlefield on the promotion of industrial enterprises explore new and innovative business models. Deloitte survey found that companies plan for the future business model of roughly four categories: 30% of respondents future business model will be the platform as the core, 26% of companies take large-scale customization, 24% of “product + service” as the core business transformation program to solve 12% of the core intellectual property (see below). Platform-based business model positioned to provide a variety of services and software to build eco-system as the core, the future might not be such a giant similar to BAT, but no lack of vertical industry leader or platform. Large-scale fixedSystem model, such as C2M is no longer limited to clothing manufacturers, but extends to the automotive and equipment manufacturing industries. “Product + Services” as the core is designed to provide solutions focus on customer needs, many companies are currently doing. IP-core businesses, often through patent strategy, technical barriers dominate the market. ▲ respondents positioning the company’s future business model
value proposition and value of different business models to create different ways, the challenges are not the same (see below). Enterprises need to continue to examine their business models, improved by appropriately assess the situation and to regularly evaluate their operations to other business model is feasible.
▲ characteristics of different business models and challenges
AI Artificial Intelligence subversive influence the manufacturing and service industries in the manufacturing sector, mainly from two aspects: one is the use of artificial intelligence in manufacturing and management processes to improve product quality and production efficiency; the second is to overturn the existing products and services. With the improvement of the domestic level of manufacturing automation, robotics application in manufacturing processes and management processes in an increasingly pan, and artificial intelligence robot gives further self-learning ability. Combined with data management, networking import automation equipment and related equipment, robotics, machine analysis can be achieved with precision production lines, and more accurate forecasts and real-time detection of production problems. Application of Artificial Intelligence in the field of manufacturing products and services even more subversive. The product itself is the carrier of artificial intelligence, hardware and software combined with all kinds of perception, judgment and the ability to interact in real time with the user environment. The product’s features and services, will subvert the original ecosystem. Automobile industry, for example, the competitive landscape of the automotive industry is the traditional pyramid – OEMs at the top, at all levels of suppliers follow. But in the era of smart car, OEMs dominance will be a serious challenge, parts manufacturers, Internet giants, algorithms companies, chip manufacturers, sensor suppliers and other companies are all accelerate the pace of unmanned technology development and commercialization, and expect to break the ecological balance of the car industry by occupying high ground. How Chinese manufacturers of artificial intelligence applications? Intelligent Manufacturing Deloitte survey found that 51 percent of the companies surveyed use of artificial intelligence in manufacturing and management processes, 46% of the companies surveyed in the areas of goods and services have been or plan to deploy artificial intelligence (see below). Manufacturing and management processes using artificial intelligence is more interested in systemAutomation and lean manufacturing, aims to improve production efficiency and product quality, but people have been liberated, you can think about more complex problems. The main application scenario includes using a robot to automate processes, flexible manufacturing, customized production, quality inspection. Used in the field of artificial intelligence products and services more focused and interactive products and services to users, Typical applications include the development and testing of new, user behavior analysis, and so on autopilot.
▲ respondents artificial intelligence application and deployment (as a whole)
Of course, artificial intelligence companies still need more time early in its development, technological breakthroughs and commercial demonstration. In addition, the degree of perfection of artificial intelligence application environment and infrastructure, information and safety regulations, their own ability to have a major challenge for companies. We have found that artificial intelligence have not deployed the manufacturers, the lack of investment in artificial intelligence business case, the system does not have the ability to create and support of artificial intelligence, artificial intelligence is not clear precondition for the deployment of the main challenges (see below) .
▲ respondents artificial intelligence has not been the main reason for the deployment of
AI is rapidly permeate all walks of life. Motor vehicles and parts manufacturing, high-end equipment manufacturing, electronic and electrical manufacturing industry to adopt the proportion of three robots in the manufacturing process more than half. Motor vehicles and parts manufacturing industry companies use robots proportion reached 80%, indicating that future incremental market for industrial robots will come from non-automotive industries. In the field of goods and services have been or plan to deploy artificial intelligence industry is more evenly distributed, the higher the proportion of high-end equipment manufacturing and pharmaceuticals, other industries such as new materials, automobiles and auto parts, aerospace, electronics and electrical appliances are also planning to deploy or artificial intelligent.
▲ respondents Artificial Intelligence applications and deployment (by industry)
on artificial intelligence is also more in manufacturing applications, device-specific applications (such as logistics, disk capacity), but less demand for technology-related fields . – Zhao Yuan, Taiji Group Co., a general manager of the IT industry to understand the cause of artificial intelligence has been with the development of algorithms, technology and applications, more and more deepened. For businesses, should only be out of artificial intelligence “machine substitutions,” the established thinking, many deploy lean manufacturing, product quality and user experience.
AI business application scenario ▲
Three strategies across gap weight capacityStructure the business model is a complex and difficult task, we asked the company to achieve the ability to divide the idea of facing the business model to rate the whole, business model optimization, innovation management and capacity building for enterprise cloud deployments three key tasks Deloitte recommended lifting capacity respectively start from the following aspects:
▲ urgent need to improve the ability of the companies surveyed (corporate self-assessment score weight, the higher the score, the weaker ability)
to optimize the business model to optimize the business model may only need to change or improve some elements of the current model, it may involve changing the entire business model of a major transformation. In the past 15 years, due to the rapid advancements in technology, communications, logistics and transport, the whole business model of major in transition has become more common. Enterprises need to use proven methods and tools to start optimizing business model from the following workflow various aspects. Corporate restructuring and reorganization: optimization of existing business models, including procurement of raw materials to all aspects of the sales process involved, you can tap the link to be optimized as a whole or partial modification modified to support new business models. Reconfigure IT systems: companies need to explore, design and improvement of infrastructure and information technology systems implementation. Redeployment of staff: people make the best use is one of the key business transformation sustainability. Redeployment of staff focused on the design and implementation of staff scheduling, to support new business models and achieve a smooth transition from the old model to the new model. The link also includes the development of new key performance indicators and reporting relationships to support new business models. Reorganization of legal, financial and tax Architecture: Design and implementation of business model optimization typically involves many changes in a complex legal entity and tax structure. Management team need to analyze the pros and cons of different ways. Under the new business models such as income tax and transfer pricing matter what changes, the impact of VAT and customs duties on new business models that may arise. Innovation Management Innovation Management objectives include product optimization and innovation management, optimize life cycle costs, optimize capital efficiency and better risk management. Optimization of product innovation management: establish a unified management system products (including tangible goods and services), optimize decision-making processes, improve decision-making efficiency-optimized life cycle costs: through the product life cycle to optimize operations, optimize product investment costs and operating cost optimization of capital efficiency: by monitoring, evaluation and KPI management, optimization of product management, improve capital efficiency advantagesRisk Management: a lot of risk is worth noting that the effective management of market risk in the process of innovation and data security risks is a simple product innovation management and enterprise can not make long-term competitive advantage. Today, almost all product categories are in fierce competition, any new product will be any unique advantages quickly swallowed. Combination of various types of innovation can help companies have better financial returns. While these innovations can not be attributed to all of the company’s performance, but innovation can help enhance a company’s mechanisms, including investor expectations of its future. Cloud deployments merely transferred data and applications to the cloud is not enough, in most cases, the cloud will involve multiple business functions, affect suppliers, customers and the financial statements, companies need long-term planning, step by step execution. Enterprises also need to take full account of how human resources and the level of digital deployment with cloud. Planning: examine their existing business models and to explore whether there are other viable business models, cloud deployment strategy based on the business model, and demonstrate their ability to conduct business assessments. Execution: execution phase can be divided into four steps, the first step is SaaS deployment, including ERP, CRM, HR Transformation and other software deployment; the second step is a personalized deployments, including application development, infrastructure construction and deployment platform; third step cloud migration, during which applications may need to be updated and adjusted. The fourth step is the introduction of big data analytics platform. Today’s market has become increasingly diverse consumer needs are constantly changing. At the same time, products, production processes and services Digital Intelligence is a general trend, affected by trends affecting industrial enterprises are accelerating the intelligent manufacturing deployment, and continue to examine business models, and to develop effective strategies, in order to promote real value from an operational and strategic level creation. Summary With the new round of global technological revolution and the industrial revolution gave birth to intensify the rise, coupled with China’s manufacturing industry transformation and upgrading of the formation of today’s historic intersection. Intelligent Manufacturing rapid growth on a global scale, the manufacturing sector has become an important trend, a profound impact on industrial development and division of labor, promote the formation of new production methods, industrial patterns, business models. But the risks and opportunities, companies should optimize the business model, innovation management and cloud deployments three general direction of their own transformation and upgrading, to cope with future challenges.