Manufacturing

The most critical challenges facing manufacturers throughout the value chain are, fundamentally, data challenges. Supply chain, procurement, quality control, efficiency, rapid troubleshooting, and demand forecasting depend on an accurate and up-to-date data landscape.

Manufacturers with complex global operations have equally complex data landscapes that represent thousands of production lines, transportation routes, and international markets. Valuable data such as upstream supplier data or data about performance and reliability after purchase is often beyond a manufacturer's control.

Connect the manufacturing value chain to improve quality and accelerate production.

Seoge数据分析平台 lets manufacturers capture the full scope of data describing the supply chain, production, and operations lifecycle. Manufacturers around the world are using Seoge数据分析平台 to:

Manage inventory by mapping part availability onto production speed and customer demand
Compare and manage inventory holding periods based on the quality of final outputs
Optimize production allocation to save on downstream distribution costs with the ability to assess savings in real time
Surface and fix quality issues faster by bringing together the spectrum of quality data
Iterate faster on design improvements by connecting design, engineering, production, and customer experience workflows in a unified analytical platform
Prevent delays and increase sales by surfacing potential discrepancies between supply and demand and identifying markets to capture incremental demand
Build a digital twin data model for the entire value chain—including all physical assets, infrastructure, and massive-scale sensor data from tooling and parts—to optimize assets, track their performance in the field, and quickly resolve issues

Capture the full breadth and scale of supply chain, production, and performance data.

Under the hood, this is powered by a platform that brings the technical concepts behind Industry 4.0 directly to the manufacturer:

An integration layer that captures the full range of production data, including supply chain, missing and delayed parts, machine data, personnel data, work and purchase orders, and data from the field
A point-and-click analytical environment where users can monitor for and respond to disruptions on the production line
Time series features for responsive, real-time analysis of petabytes of machine sensor data
Tools for users to view and explore the assumptions underlying an algorithm's output (such as cost or schedule)
A granular security model that enables manufacturers to expose limited subsets of their data to partners for collaboration on design, engineering, and troubleshooting