Use Case
Manage Supply Chain Data to Optimize Efficiency and Decision Making
Supply chains are increasingly complex, involving vast amounts of interconnected data, much of it stored in relational formats. Traditional graph solutions require costly, time-consuming data transformation, while legacy systems struggle to provide real-time insights, traceability, and a unified view.
Timbr enables organizations to unify and visualize supply chain data as a web of interconnected relationships directly from relational systems. By leveraging semantic modeling, Timbr provides a comprehensive, easy to understand view of the supply chain data, ensuring transparency, resilience, and actionable insights.
USE CASE TEMPLATE
Timbr Github repository contains a collection of SQL ontology-based semantic data models. These ontologies serve as customizable templates, designed to help users jump-start their modeling process tailored to their specific business domains.
USE CASE ONTOLOGY MODEL
The ontology semantic model makes it easy to understand and find data across tables and datasets. Timbr speeds up data discoverability by displaying data as a network and in a business-friendly manner.
Challenges
Need a Unified View of Your Supply Chain Data?Supply chains span multiple regions, systems, and datasets, creating silos that prevent a cohesive understanding of operations. Much of this data resides in relational systems, where identifying connections and patterns is cumbersome, limiting visibility and traceability.
Struggling with Graph Solution Implementation and Maintenance?
Graph solutions require data to be transformed from relational formats into graph databases, incurring significant costs and time delays. This complexity often deters organizations from adopting graph-based insights, leaving valuable connections untapped.
Untimely Supply Chain Decision-Making?
Legacy systems rely on rigid schemas and slow processes, making it difficult to handle the scale and complexity of supply chain data. Extracting and transforming data for analysis delays critical insights, impeding the ability to respond to disruptions or optimize operations.
Why Timbr
Ontology-Based Semantic Modeling for Supply ChainsTimbr’s ontology-based semantic layer unifies supply chain data across systems and sources, including relational databases, without requiring data transformation. This approach breaks down silos and enables a 360-degree view of the supply chain.
Seamless Integration Without Data Transformation
Unlike traditional graph solutions, Timbr allows organizations to visualize and explore supply chain relationships directly from relational data. This eliminates the need for costly and time-consuming data transformations, accelerating implementation and reducing complexity.
Real-Time Insights Through Virtualization
With Timbr’s data virtualization capabilities, supply chain professionals can access and analyze data in real-time without moving or duplicating it. This ensures immediate visibility into operations and enables rapid responses to disruptions and changing conditions.
Impact
Enhanced Supply Chain Visibility and TransparencyTimbr provides a comprehensive view of supply chain relationships, improving traceability and ensuring compliance with regulatory and ethical standards. This transparency builds trust with customers and stakeholders.
Improved Agility and Resilience
By unifying data and providing real-time insights, Timbr helps organizations quickly adapt to changes or disruptions in their supply chain. This agility reduces downtime and operational risks.
Reduced Costs and Optimized Operations
Timbr’s ability to work directly with relational data eliminates the costs associated with data transformation into graph databases. This streamlines operations, reduces infrastructure expenses, and enables scalable, efficient supply chain management.
