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Dr. Peng allies with Guizhou CASI Cloud to lead intelligent manufacturing by big data

2019-05-30

On May 28, Dr. Peng and Guizhou CASI Cloud signed a strategic cooperation agreement at Big Data Expo 2019, to conduct in-depth integration and cooperation within the big data sector. The latter provides its cloud platform while Dr. Peng furnishes one-stop industrial big data solutions, to effectively help enterprises improve their operational capacity, and to thus achieve enhanced efficiency and less loss. The partnership marks Dr. Peng initiating the era of industrial big data, greeting a new start in the field of intelligent manufacturing.

Currently, big data has become a key technology element for industrial upgrade as is broadly recognized within the industry, and is a core power for supporting intelligent manufacturing. By using respective advantages and technological resources, Dr. Peng and CASI Cloud jointly built a "cloud computing + big data" technology system. Relying on distributed cloud computing and storage, the system allows collecting and mining of massive data to perform intelligent data analysis and decision-making over industrial big data, thus powering the development of the manufacturing sector, and promoting the integration between entity economy and digital economy.

At the product level, Dr. Peng and CASI Cloud could complement each other's advantages and jointly construct a product eco-system on the big data cloud network. With Dr. Peng's data center and network access services as the underlying infrastructure, with the "multi-cloud + networking + edge" support as converged platform, and by business applications of big data algorithm and industrial mechanism model, the system can aid enterprises of various industries to connect to IT infrastructure service providers and big data vendors, and provide them with personalized and custom solutions, thereby enabling them to achieve industrial Internet intelligence.

Big data is involved in many aspects throughout an enterprise's industrial Internet process, including management, cost, quality, and delivery time. One could raise the efficiency and achieve industrial Internet intelligent evolution only by getting through all aspects and all departments to perform big data digital transformation, and opening up the value chain.

 

Big data to work out corporate management
and to conduct real-time data governance

In a traditional enterprise, information of each department can not be shared in real time; the accuracy, timeliness and authenticity of various types of data and indicators can not be guaranteed; the current operating capacity is difficult to support higher management objectives; all kinds of information asymmetry problems occur one after another. How to achieve full integration of business flow and data flow among all departments, to form an accurate and unified system from the strategy level to the management and execution levels - that seems particularly important.

Based on the core concept of big data - "integrating business and data," and by means of the visual and unified cross-departmental big data management platform, the enterprise can make full use of data interworking to mobilize collaboration among departments, improve the operational performance, and thus promote the enterprise's governance by "people" into governance on "data," and ultimately achieve improved management efficiency, reduced labor costs, higher execution efficiency, aiding for strategic decision-making, and other management purposes.

 

Big data to do reasonable cost control
and full optimization of equipment, materials and supplies

Device management is the base underlying production, quality, safety, and all other result-oriented demands, but is often overlooked due to big investment or being complicated to implement, or lacking indicators that tell immediate changes.

Enterprises go after maximized output of equipment investment, that is, to maximize equipment utilization and operational efficiency. To minimize capital investment recovery period of plant, stock capital occupied, and faults for timely diagnosis and maintenance - these are practical problems that a business operator need to solve immediately.
The big data device management system integrates multi-source data, fast optimization algorithms, visual on-site management, prevention, diagnosis and treatment of equipment faults, stable and healthy operating environment, to ensure maximum production efficiency.

As for the raw material consumption optimization system, the production department, equipment department, QC department are connected by unified flow of information, to provide real-time data correlation analysis, algorithm models for a variety of industries, online optimization, etc., thereby reducing the operating costs.

 

Big data to improve quality management system
and to build whole-process traceability

 It is common that an enterprise may only analyze and adjust its process when problems are detected; inspectors of varying proficiency handling a large number of finished products, there'd be still defective goods entering the warehouse; as quality tracking is performed, the source cannot be traced...

With big data, the quality management system can be effectively improved. And two-way manufacturing information can be reflected via the Manufacturing Execution System (MES). An ERP platform can be used to monitor the quality of products in the strategic business level. Of course, analysis of internal quality data only is not enough for an enterprise; rather, it is required to combine internal and external data to build up big data on quality.

As the enterprise sets up corresponding data centers for costs, equipment, materials, production and inventory, it forms an internal big data platform within the enterprise. And by combining the external industry standards, market conditions, distributors at all levels, consumers, network platform, and so on, it forms an external big data platform. Upon combination of internal and external data, and by organization and correlation of data and information from different aspects, such as customer, product and business, the corporate big data model shall be built. The data model can achieve traceability and recall whenever a quality issue occurs.

 

Big data to avoid delivery risks
And to integrate production and sales

For production planning and scheduling, big data can integrate ERP, MES and, combined with APS software, quickly and dynamically adjust and iterate the production plan, to help solve such issues as inaccurate delivery time, frequent order inserting, delayed orders, and insufficient production capacity. APS can help improve the on-time delivery rate by 20%, raise the key resource utilization rate by 25%, and increase the speed of response to customer demand by 30%.

The production and sales integrated system, unlike ERP, MES, APS, is an information system featuring small investment and quick effect, fit for enterprise of varied information levels. The production and sales integrated system fetches data from relevant information systems, accesses directly to the data flow between information systems and customer demand, sales staff, field production personnel, achieving complete interconnection of end-to-end information, no longer required to find amongst mass information. Therefore, the system can significantly improve the communication efficiency, and also the order delivery rate.

For whatever size of the enterprise, whichever stage among automation, information and intelligence, there are corresponding big data solutions. The enterprise needs to choose the most suitable one according to its business, to gradually get improved from human efficiency to production efficiency, and finally to upgrade in all aspects, thereby achieving the ultimate benefit of higher efficiency and reduced losses.

Currently, Dr. Peng's big data has been serving for many well-known customers from the government, electronics, automotive, textile, finance, chains, FMCG and other sectors, providing core support of big data for the digitization process of various industries and enterprises, helping enterprises to standardize and optimize their business processes, and to obtain external sales and market information timely in order for prompt response to market changes, thus driving the enterprise to perfect management, reduce operating costs, and improve economic efficiency from various aspects of daily operations.

As the manufacturing industry is undergoing transformation and upgrading, it is inevitable that big data drives the whole industry to enhance production efficiency, improve product quality, save resource consumption, guarantee production safety, and optimize sales services. By collaborative development with AI, mobile Internet, cloud computing, IoT and other technologies, the industrial Internet driven by industrial big data is bound to fit deep into the entity economy, and turn to be a new engine for the era of digital economy.