Manufacturing Industry

Manufacturing Industry Solutions

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

The manufacturing industry has gone through several industrial revolutions, in which manufacturers were leveraging new technologies to deliver better products at lower cost to maximize profit margins. As the industry is evolving in the era of digital transformation, manufacturers are trying to create competitive benefits and advantages by using data produced across the manufacturing value chain.  Many established information systems, such as CRM, ERP, MES, CMMS, etc., as well as mechanization and automation in manufacturing have produced huge amounts of data for manufacturers to explore and extract valuable insights to guide data-driven decision-making. Big data has gradually become the driving force of a new round of industrial revolution.

01

Although machine sensors have collected and accumulated an abundance of data, it takes too long for traditional data management systems to get insights due to the volume and the variety of data.

02

As more manufacturers are integrating Internet of Things (IoT) technology to improve the efficiency of industrial processes, they now have to work with a lot of high-speed real-time streaming data.

03

Machinery malfunction can slow down an entire production flow and unexpected repairs can result in unplanned downtime, which lowers manufacturing productivity and increases maintenance costs.

04

As manufacturing processes are becoming more and more complex, workforce training costs continue to rise.

05

Production planning is not independent.  Data silos would make it extremely difficult to unleash the true value of data.

01

As a distributed in-memory big data platform, RDP offers a flexible, scalable big data architecture that can grow with business needs in a reliable and cost-effective way.

02

The optimized SQL engine of RDP effectively handles data quality and performance.

03

StreamDB is an in-memory and distributed stream database that can continuously and process and analyze streaming data within milliseconds.

04

ParallelAI enables manufacturers to apply machine learning algorithms to build models to identify hidden patterns of data.

05

As a unified big data platform, RDP helps to break data silos by consolidating different types of data across a wide variety of data sources.

Manufacturing companies expect a high return on investment in a timely manner. RDP is a one-stop big data platform that provides a simplified architecture to help manufacturers reduce processing flaws, improve production quality, increase manufacturing efficiency, and manage supply chain risk. It significantly reduces data application development time as well as capital investment cost.

 

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

Leveraging in-memory data computing technology, RapidsDB can analyze massive volume of data while ensuring the high concurrency and low latency of the database.

02

RDP ensures the completeness, integrity and consistency of data so that the system can deliver actionable insights that manufacturers can trust.

03

Production anomalies can be detected and reported to the system instantaneously to minimize downtime and waste in the event of an unexpected failure.

04

The automated process greatly minimizes human error. Real-time insights can be delivered to optimize manufacturing processes or predict maintenance needs to ensure product quality, increase yield and control costs.

05

With the integration of real-time and historical data, manufacturers can conduct comprehensive quantitative assessments to analyze data and get a holistic view of the manufacturing process.

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

Data source layer

Consolidates heterogeneous data that resides in different business information systems.

ETL layer

Provides a standard API interface for data extraction, transformation and loading process.

Warehouse mart layer

Stores subject-specific data based on different business needs in star or snowflake model.

Application layer

Provides rich data applications to support ad hoc queries, multi-dimensional analysis, in-depth analysis, etc.

Presentation layer

Generates comprehensiveness reports and presents analysis results to users in a visual and interactive way.