In this year’s report on the work of the central government, special emphasis has been placed on major structural changes in the economic structure over the past five years and the annual growth of the high-tech manufacturing industry by 11.7%. Innovation driven development has yielded fruitful results, and the R & D investment of the whole society has increased by 11% annually, ranking the second largest in the world. The contribution rate of scientific and technological progress increased from 52.2% to 57.5%.
Over the past few years, various industries have been undergoing digital transformation, that is, our widely recognized investment in “industrial 4”. From the pilot projects, the trend of digital transformation is accelerating. So far, most of these investments are aimed at improving business reliability in terms of asset reliability, product quality and manufacturing efficiency.
The following ten trends will be active in China’s manufacturing industry technology and management chart in 2018. Some of the trends are very obvious, some are not, but I believe these trends are likely to have a profound impact on the management of the manufacturing industry and its contribution to excellent operation.
1, industrial big data and quality acceleration industry upgrade
Quality is a top application scenario of IIoT technology. The awareness of the Internet of things is increasing substantially every year. Most enterprises have started or planned to start a plan related to the industrial Internet of things. This change in the market is partly due to the growing number of successful cases and the increasingly mature and easy to deploy solutions.
The case of IOT business around intelligent manufacturing and digital twin has increased significantly in the past year. Quality initiatives such as zero defects are based on these industrial IOT methods to increase substantial operational and financial returns. Zero defect is based on the quality data of manufacturing enterprises.
It is an inevitable trend to combine quality data with testing results, manufacturing execution system, manufacturing operation management data, warranty data, and sensor data in intelligent manufacturing. Continuous real-time data statistics can help identify products, batches or processes that have passed quality checks but still lead to service interruption. With time and rapid accumulation of data, it allows real-time identification of previously undetectable failures. In 2018, the government’s work report emphasized: strengthening product quality supervision. We should carry out quality improvement actions in an all-round way, push forward benchmarking with international advanced standards, carry forward the spirit of craftsmen, and create a quality revolution made in China.
2, supplier and value management get new traction
Digital transformation and management of supply chain is one of the most critical areas of enterprise success, and it is also one of the most immature areas of manufacturing digitalization at present. More industry consulting research shows that nearly 20% of enterprises adopt digital technology to realize the automation of supply chain quality management.
Since 2017, with the drive of information technology, this field has received unprecedented attention. It is expected that many enterprises will increase their investment in supply chain and quality management in 2018, and the main purpose of capital or management costs is to convert traditional supply chain management into automatic data driven processes.
3. Cooperative drive manufacturing
The risk of manufacturing enterprises has never been so high. Enterprises face pressure such as increasing product complexity, changing the global market, requiring regulatory agencies, changing labor demand and complex value chain, all of which are fragmented to the quality management process at the same time that the product release cycle is shrinking. Today’s business customers have unprecedented visibility and analytical ability, and consumers share experience in social media and the Internet market, often two extremes, either very positive or very negative.
The market is facing these challenges, and more enterprises are implementing the digital management of collaborative collaboration. Many companies are moving from traditional analysis and standards to modern analysis and measurement. These measures, combined with edge data, historical data, and business metrics, require the deployment of cross functional processes and teams in design, manufacturing and services to enable high-level analysis to see the typical situation within a factory within a short period of time. And it begins to deliver predictive ability for a long time.
4. The new goal of asset management is to “optimize the assets”
Since the advent of asset performance management, whether it is computerized maintenance management system (CMMS) or enterprise asset management (EAM), the emphasis has been to improve reliability, reduce downtime and reduce the maintenance of out of plan. There is an assumption that minimizing downtime can improve profitability, and the best way to reduce downtime is to “repair” devices before disasters occur. The problem with this assumption is that maintaining the behavior of a device itself can lead to further failure.
On the 2017, a few world-class smart manufacturers began to pay attention to the “profit” and “operating performance” products based on industrial large data analysis that had been used to predict when a device failed, and then on the basis of predictive maintenance. The most important transformation in 2018 will be to break away from the idea of maintenance centered, and the new focus is to optimize the profitability of equipment. Machine self-learning (ML) and industrial large data analysis will enable enterprises to determine the best operation of the factory based on the backlog of orders, reliability problems and the factory’s digital twin models. In 2018, asset management focused on digital twins and normative analysis, especially non-standard industrial production equipment, was more about asset optimization.
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