Industry Pain Points

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  Unrecognized Data Value

  Data from meters, production equipment, and industrial robots partially enter production information systems or manufacturing execution systems, but they are mostly used for preliminary viewing and cannot be subjected to massive data analysis.

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  Disconnected Full-Link Data

  Various information systems in manufacturing, such as MES, CRM, and supply chain systems, contain a large amount of fragmented production and operation data, resulting in severe native data siloing.

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  Lack of Data Management Tools

  The manufacturing industry possesses numerous systems and equipment, with a focus on hardware. However, there is a neglect of investment in management software for production data, leading to an inability to effectively provide data guidance for production and operation.

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  Insufficient Guarantee for Data Quality and Security

  Throughout the entire data lifecycle, there is a lack of a series of management activities such as identification, measurement, analysis, and improvement for data at every stage, which ensures data availability and security.

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  Lack of a Comprehensive Data View

  There is no comprehensive inventory and monitoring of enterprise data assets. A large amount of enterprise data is scattered across various businesses and systems, making it impossible to obtain a comprehensive and timely understanding of what data the enterprise has, where it is located, and what changes have occurred.

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  Low Data Utilization Efficiency

  Enterprises lack a clear understanding of their data assets and do not know what data they have and where it is distributed. Most manufacturing data remains dormant and requires data mining.

Business Objectives

By precisely allocating big data resources, we aim to support management in rapidly analyzing data for decision-making, enhance production efficiency, and accurately predict supply and demand through algorithmic models. Additionally, we strive to achieve intelligent transformation in production, management, and decision-making based on data models designed with intelligence in mind.

Solution Architecture

Customer Value

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  Build a Full-Link, One-Stop Data Mid-Platform

  We will establish a full-link, fully visual, distributed, highly available, and horizontally scalable one-stop data mid-platform, covering the entire lifecycle of data processing. This platform will unify and integrate dispersed data from various business systems, establish a unified scheduling and monitoring system, support large-scale and efficient collaborative development, and complete the intelligent construction of business data across all lines.

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  Build a Comprehensive Data Asset with Unified Standards for the Enterprise

  We will develop a comprehensive data asset with unified standards for the enterprise, achieving unified data control, standardization, classification, and grading to meet the high level of reuse required by business operations.

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  Build a Closed-Loop Ecosystem

  By integrating application scenarios, we will establish a unified service system to meet the needs of intelligent application construction for business scenarios, continuously building a closed-loop ecosystem driven by data.