What is supply chain data analytics? Uses, Types & Dataset Examples
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Supply Chain Data?
Supply chain data is throughout the entire supply chain process. It includes data related to procurement, production, inventory, logistics, and customer demand. It helps organizations optimize their supply chain operations, improve efficiency, reduce costs, and make informed decisions. Examples of supply chain data include information on product demand, inventory levels, transportation routes, supplier performance, and customer feedback.
Best Supply Chain Databases & Datasets
Here is our curated selection of top Supply Chain Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Public Company Supply Chain Data | Trademo
Supply Chain Data | Business Supplies & Equipment Professionals Worldwide | Verified Global Profiles from 700M+ Dataset | Best Price Guarantee
US Supply Chain Data of Industrial Contract Manufacturers | Verified Medium & SMB Contact Data | Supply Chain Equipment
Indonesia Detailed 100% Customs Import Export Data(2019 till present) with Monthly Updates | Supply Chain Data | Global Trade Data
Competitive Intel-Supply Chain Intel - US nationwide +115,000 facilities tracked
Satellite US Supply Chain Dataset Package (Amazon, Fedex, Walmart) + Research Report Available
Agricultural | Product Data | Commodity Prices Data | Benchmarking | Region | Purchase Volume | Cost Planning | Market Trends | Supply Chain
Supply Chain Data of Sanctioned Entities Globally | Trademo
Asian Continent (51 Countries) Detailed Customs Data | Trade Activities Insights | Trade data API | Supply Chain Analysis
Transport and Logistics Data | Logistics Professionals Worldwide | Verified Professional Histories & Contact Details | Best Price Guarantee
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Top Supply Chain Data Providers & Companies
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Predik Data-driven
CoverageUSAUK+247VWe wanted to analyse the foot traffic data across Sydney. Predik provided quality traffic density heatmap and raw foot traffic data across Sydney Australia to support us with the use case. Highly recommended.
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Market Inside Data
CoverageUSAUK+247VI use Market Inside data for business expansion by analyzing international trends, assessing the prices of raw materials from importers, and comparing the imports of our competitors. Apart from that, it helps to analyze trends in the international market, find new buyers, and close deals by knowing our competitors' prices.
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Opoint
Our suite of solutions equips you with the data to monitor, analyse, and react to narratives around brands, industries, or markets swiftly. With unparalleled news data coverage and advanced analytics, ensure every decision you make is informed, strategic and a leap ahead.230000+Sources185Languages< 5 minuteSpeed of Publication to index
Popular Use Cases for Supply Chain Data
Supply Chain Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for Supply Chain Data.
Main Attributes of Supply Chain Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The industry classification of a company. | View 62 datasets | |
| String | The name of a company or business, might be the legal or brand name. | View 61 datasets | |
| String | The name of a country. | View 59 datasets | |
| String | The approx. number of employees working for a company. | View 58 datasets | |
| String | The official website of a company. | View 54 datasets | |
| String | The LinkedIn profile of a company. | View 51 datasets |
Supply Chain Data Explained
Supply Chain Data Collection
There are various methods for collecting data in the supply chain, including manual data entry, sensors, and electronic data interchange (EDI). Manual data entry involves manually entering information into a computer system, such as tracking numbers, quantities, and product details.
Sensors, such as RFID tags, can be attached to products or placed along the supply chain to automatically collect data on location, temperature, humidity, and other factors. EDI involves the exchange of electronic documents, such as purchase orders and invoices, between different systems and stakeholders in the supply chain.
Other methods for collecting supply chain data include the use of barcodes, GPS tracking, and cloud-based systems that allow for real-time data collection and analysis. By collecting data from various sources and analyzing it in a systematic way, businesses can gain valuable insights into their supply chain operations and make informed decisions to optimize processes and improve efficiency.
Use Cases for Supply Chain Data
There are many ways in which businesses are using supply chain data to improve their operations and drive success. One common use of supply chain data is in demand forecasting, which involves predicting future demand for products or services based on historical data, trends, and other factors. This information can help businesses plan their production, sourcing, and distribution activities more effectively and reduce the risk of overstocking or understocking.
Another common use of supply chain data is in the optimization of logistics, including the identification of bottlenecks and the selection of the most efficient transportation routes. Supply chain data can also be used to identify opportunities for cost savings, such as by reducing waste or identifying more efficient suppliers.
Additionally, businesses may use supply chain data to enhance their relationships with customers by providing more accurate delivery estimates and identifying opportunities for customization or personalization. Overall, the effective use of supply chain data can help businesses make informed decisions, improve efficiency, reduce costs.
Frequently Asked Questions
How is the Quality of Supply Chain Data Maintained?
The quality of Supply Chain Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is Supply Chain Data Updated?
The update frequency for Supply Chain Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is Supply Chain Data Secure?
The security of Supply Chain Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is Supply Chain Data Delivered?
Supply Chain Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for Supply Chain Data.
How Much Does Supply Chain Data Cost?
The cost of Supply Chain Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of Supply Chain Data for your needs.
What Are Similar Data Types to Supply Chain Data?
Supply Chain Data is similar to other data types, such as Aviation Data, Trucking Fleet Data, Freight Data, Road Data, and Shipment Data. These related categories are often used together for applications like Supply Chain Monitoring and Supply Chain Analytics.
