Many organizations have been able to gather most of
the needed data, but their traditional analytic
technologies are proving to be too slow, too expensive,
and generally incapable of analyzing the massive volume
of partner, route, transaction, and other data stored
across various locations, formats, and protocols. Most of
the traditional supply chain analytics solutions are built
on relational databases.
Real-time analysis of supply and
demand changes requires expensive database joins
across the tables with the data for suppliers, orders,
products, locations and the Inventory for parts as well as
sub-assemblies. Global supply chains have multiple
manufacturing partners, requiring integration of the
external data from partners with the internal data. Given
their rigid schema, traditional supply chain analytics
solutions based on the relational databases require
significant time and effort to integrate external data into
the supply chain analysis.
Why TigerGraph, a Native Parallel Graph Database for Supply Chain Analysis?
Deep Link Supply Chain Analytics
Supply and delivery pipelines have dozens if not
hundreds of stages, so the ability to analyze and
understand the impact across many levels is essential.
TigerGraph’s Deep Link Analytics powers advanced
analysis and pattern recognition to identify product
delays, shipment status, and other quality control and
risk issues. Powerful event impact capabilities notify
personnel when a relevant action has taken place and
reveal the updated consequences down the chain, such
as how a production slowdown impacts manufacturing,
order fulfillment, pricing and revenue down the line.
fast, real-time insight allows them to optimize orders and
shipping routes, and also quickly respond to changing
demand patterns as events unfold. TigerGraph features
high availability, system monitoring, and other enterprise
readiness capabilities that ensure that real-time shipping
status and other mission-critical information is always
TigerGraph delivers real-time visibility and analytics into
key supply chain operations including order
management, shipment status, and other logistics.
Organizations can rapidly model their supply chain
functions and business processes in real-time, through
the use of a Native Parallel Graph, allowing propagation
of demand and supply changes through the 10+ level
deep value chain to calculate potential supply outages
and create the recommendations for addressing those in
a timely manner.
Improve Supply Chain Analytics with AI
and Machine Learning
Traditional supply chain optimization approaches work very
well for routine operations. Humans are still asked to make decisions when extraordinary disruptions occur. AI-assisted
supply chain analytics can provide vital advice in such cases.
TigerGraph generates new features for machine learning
based on the analysis of as many as 10 or more levels in the
supply chain. These graph computed features are fed into the
machine learning solution as training data, improving the
accuracy of the machine learning solution for the prediction of
supply chain disruptions.