Manufacturing Industry

Manufacturing Industry Solutions

Manufacturing is one of the main drivers of a country's economic growth. Big data analysis plays an important role in the manufacturing revolution process.

Manufacturing Image

Industry Background
Pain Points
Solutions
Strengths of Rapids Data Solutions
Value Realization
Technical Architecture

Manufacturing drives economic growth. The IT industry plays a very important role in the manufacturing process. Automated processes and mechanization have produced huge amounts of data, but most manufacturers are unable to make good use of it. How to fully tap the value of massive data, and use big data analysis technology to guide business decision-making has become the top concern of manufacturing enterprises. As the key to a new generation of information technology, big data has gradually become the core of a new round of industrial revolution.

Currently most large and medium-sized manufacturers have established relatively complete basic information systems such as CRM, ERP, MES, MRP, etc. helping manufacturers to collect large amounts of historical data. Machine automazation improves production quality and operation efficiency.

  • Unconnected data islands                                                                                    With the gradual improvement of information technology, progressive improvement has been made in the financial information system, MRP system, ERP system, etc., but there is a lack of a unified platform between internal information systems for real-time data analysis, consolidation and processing. This phenomenon has resulted in the inability to effectively collaborate between the different areas such as production, sales and inventory control which has made it very difficult to completely unleash the true value of data.
  • Lack of data management mechanisms and assurance Although enterprises have accumulated a certain amount of historical data, the lack of data management mechanisms resulted in disparate data quality, decentralized, nonuniform and inconsistent data, ill-suited to provide support for statistical analysis applications of upper-layer data.
  • Slow response to data analysis requests Different levels of users might have very different needs for data analysis. The analysis reports that currently can be provided by a manufacturing company are mainly forms with one-dimensional analysis. These simple and rigid forms are unable to meet the user demand for fast, flexible, and variable data analysis.
  • Costly and risky project investment Manufacturing companies expect high return on investment in a timely manner. Traditional big data projects are risky and costly with a long payback period. Senior mangers are unable to see the value brought about by big data after a lengthy period of data warehouse construction and modeling. As a result, they are discouraged from investing in data mining technology.

Rapids DB as an in-memory MPP data warehouse and Rapids StreamDB as a distributed in-memory streaming database, can quickly create data applications to achieve the connectivity of the existing corporate data, application systems, software equipment and resources. The pioneered in-mememory MPP data warehouse engine helps to reduce processing flaws, improve production quality, increase manufacturing effiency and managing supply chain risk.

  • 01 Identify initial patterns to prioritize data collection and define strategic objectives
  • 02 Index data from multiple sources and have massive amounts of data analyzed easily
  • 03 One-stop big data platform, transforming data application development into an experience similar to building blocks
  • 04 Put data projects into practice and reveal opportunities to increase yield
  • 01 One-stop big data application development platform greatly reduces the data application development threshold resulting in lower time and
    •        capital investment.
  • 02 Promote the internal application of big data analysis technology to improve the accuracy and timeliness of data analysis, boost product and
    •       service innovation, reduce corporate operational risks and improve yield.
  • 03 Establish a data operating system for our clients to build up their capabilities in making manufacturing decisions based on data-driven quantitative
    •       assessments and set them far apart from competitors.

The technical architecture of the entire platform comprises the following five layers:

  • Data source layer Dock data from different business information systems and channels to achieve relevance consolidation of heterogeneous data sources.
  • ETL layer Define a unified data API standard interface for data cleaning, conversion and loading operation.
  • Warehouse mart layer Store detailed and specific data after ETL in the data warehouse in the star or snowflake model, and according to the needs of business analysis themes, after theme classification on the data models, input into the Rapids Data MPP data mart for real-time analysis and processing.
  • Application layer Oriented to different levels of operational staffs and analysis theme needs, establish rich data application scenarios including immediate inquiry, multi-dimensional analysis, data reporting and in-depth analysis, etc.
  • Presentation layer Present analysis results to decision-maker, management, IT staff and business users with a rich graphical presentation, in flexible and variable interactive ways. All users can have access to the system via mainstream browsers or mobile terminal devices.