Enterprise Verticals

The OriginTrail protocol brings many new possibilities to approaching challenges in supply chains using decentralized solutions. It brings an unprecedented level of scalability and makes use of decentralization cost-effective.

OriginTrail is a “low-level” technology that has very wide applicability in the real business world once applications are created on top of the protocol. Applications in different verticals are tailored to the particularities of the data being shared in them, ensuring that stakeholders are included in creating the business value they are pursuing.

Supply Chain Integrity

In order to ensure the extraction of value from end-to-end transparency in supply chains that are growing in complexities, we need data with integrity. With OriginTrail, you can integrate different data sets with GS1 standards to ensure interoperability. Data alignment and consensus checks ensure that every data set gets confirmed by relevant partners (cross-checking) and that it cannot be tampered with. Data objectivity can be further increased by introducing sensors, protective/tracking tags or even forensic laboratory tests.



Proposed solutions:

  • End-to-end product traceability and provenance in different industries, including food & beverage, automotive, pharmaceuticals, oil & gas, diamonds, and luxury goods);
  • Brand protection;
  • Product authenticity.

Supply Chain Management

Sharing of supply chain data creates opportunity for more efficient operations across the supply chain. With data integrity and consensus mechanism between data sets, quick detection of disparity and malfunctions in the supply chain is possible, which greatly enhances internal operational efficiency.


Proposed solutions:

  • Vendor inventory management;
  • Establishing accountability in supply chains;
  • Quick detection of supply chain data discrepancies for further investigation.

Trade Finance

Aligning financial and other dynamic data (Such as Data on payment delivery regarding accounts payables and receivables) and providing a “single version of truth” based on consensus checks between multiple organisations. This helps companies to protect and improve their financial position, through data integrity.

Example:

Data on payment delivery with accounts payables and receivables alongside other related data can be offered to financial institutions in order to tackle the pain points of today's financing processes (delayed payments, manual contract creation, manual AML review, multiple versions of truth, etc.)

Proposed solutions:

  • Optimising financial products
  • Optimising insurance policies
  • Mitigation of Financial

Data Ownership

Creating additional revenue streams by collecting data from various phases in the supply chain, to extract different business intelligence value propositions for participants in the supply chain and others.

Example:

Harvesting supply chain data through a connectivity hub in a supply chain (eg. Distribution channel), where producers on one hand are providing the data upstream the supply chain and retailer are providing data downstream the supply chain. This creates value through business intelligence data, which can be monetized by offering private access keys upon payment request to different stakeholders interested in the intelligence of the data (producers, institutions, research companies, etc.)

Proposed solutions:

  • Dynamic business intelligence data
  • Research data offered to research institutes

Compliance


Providing evidence and aligning it through standards and controls to meet regulatory conditions and mitigate compliance risks.



Proposed solutions:

  • Dynamic compliance through full audit trail of data;
  • Insurance claim prevention and management.

Supply Chain Integrity

Ensuring the extraction of value from end-to-end transparency

Show more

In order to ensure the extraction of value from end-to-end transparency in supply chains that are growing in complexities, we need data with integrity. With OriginTrail, you can integrate different data sets with GS1 standards to ensure interoperability. Data alignment and consensus checks ensure that every data set gets confirmed by relevant partners (cross-checking) and that it cannot be tampered with. Data objectivity can be further increased by introducing sensors, protective/tracking tags or even forensic laboratory tests.

Proposed solutions:

  • End-to-end product traceability and provenance in different industries, including food & beverage, automotive, pharmaceuticals, oil & gas, diamonds, and luxury goods);
  • Brand protection;
  • Product authenticity.

Supply Chain Management

Opportunities for more efficient operations across the supply chain

Show more

Sharing of supply chain data creates opportunity for more efficient operations across the supply chain. With data integrity and consensus mechanism between data sets, quick detection of disparity and malfunctions in the supply chain is possible, which greatly enhances internal operational efficiency.

Proposed solutions:

  • Vendor Inventory Management;
  • Establishing Accountability in supply chain;
  • Quick detection of supply chain data discrepancies for further investigation.

Trade Finance

Smart sensors and photosensitive QR codes are an outstanding solution for preventing fraud in the wine industry.

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Aligning financial and other dynamic data (Such as Data on payment delivery regarding accounts payables and receivables) and providing a “single version of truth” based on consensus checks between multiple organisations. This helps companies to protect and improve their financial position, through data integrity. Example: Data on payment delivery with accounts payables and receivables alongside other related data can be offered to financial institutions in order to tackle the pain points of today's financing processes (delayed payments, manual contract creation, manual AML review, multiple versions of truth, etc.)

Data Ownership

Our partnership with the BTC Logistic Center entails exploring new possibilities for blockchain-powered supply chain management.

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Creating additional revenue streams by collecting data from various phases in the supply chain, to extract different business intelligence value propositions for participants in the supply chain and others.
Example: Harvesting supply chain data through a connectivity hub in a supply chain (eg. Distribution channel), where producers on one hand are providing the data upstream the supply chain and retailer are providing data downstream the supply chain. This creates value through business intelligence data, which can be monetized by offering private access keys upon payment request to different stakeholders interested in the intelligence of the data (producers, institutions, research companies, etc.)

Compliance

Aligning the evidence through standards and controls

Show more

Providing evidence and aligning it through standards and controls to meet regulatory conditions and mitigate compliance risks.

Proposed solutions:

  • Dynamic compliance through full audit trail of data;
  • Insurance claim prevention and management.