Overcoming Traceability Obstacles


There are numerous examples of robust traceability requirements in multiple industries for consumer safety, ensuring compliance and tracking revenues. For instance, in mining, the Dodd-Frank Act passed by the US Congress in July 2010 (Section 1502) imposes on all Congolese mining companies a disclosure requirement that companies must determine whether their products contain conflict minerals – subject their supply chain to due diligence – and report to the Securities and Exchange Commission (SEC). In addition, traceability from source to export allows African governments to ensure compliance throughout the value chain to ensure tax revenues derived from all private mining activities are fair and accurate. In the beef industry, policymakers in many countries have proposed or adopted mandatory systems to track animal feed to control the risk of mad cow disease and to improve meat safety. In the seafood industry, the Office International des Epizooties (OIE) Aquatic Animal Health Code defines traceability as a key to determining history and movements of aquatic animals to minimize illegal fishing and create sustainability, in an industry where one-third of global fish stocks are overfished.

Unfortunately, no industry has created a solution that makes it easy or economically viable for all contributors in the value chain to participate in any traceability ecosystem. The problem with previous traceability solutions have been as follows: 1) traceability efforts are built on the notion of a standard for data collection and force feed these standards across all participants of the supply chain, making it costly and unattractive for all suppliers to participate 2) force feeding standards is required because earlier generations of digital traceability systems are built on old-style relational databases. Using a standard data format is required for relational databases to work efficiently, and link participants through the value chain. However, old-style relational database is unable to quickly adapt to the variety of data generated by different value chain participants. Standards do not take into account the natural way that each processor tracks their internal manufacturing workflows. None of the traceability systems today allow these value chain participants to use their natural output from their current manufacturing systems to contribute traceability data. Instead, they require each participant to conform, which leads to long development timelines, high implementation costs significant resource drain, complexity in the manufacturing workflow and ultimately a decision not to participate.

RippleNami’s Traceability System (RTS) program is designed to resolve these two significant obstacles and enable wide adoption by all traceability participants in the value chain. It is designed from the ground up to perform linkage between the various value chain participants using their natural data output from their own workflow processes. It corporates the latest, 1) mobile data collection and 2) data visualization technologies, 3) blockchain and 4) modern database architecture specifically designed for optimal traceability capabilities.

Why Current Systems Fail for Broad Adoption of Traceability

Previous traceability platforms use traditional relational databases for describing relationships between multiple entities and have proven to be limited, particularly as systems need to expand beyond tracking just the source asset only. Relational databases are designed for tabular structured data, with a consistent structure and a fixed schema. They work best for problems that are well defined with a minimal number of relationships between multiple entities. However, answering questions about data relationships with a relational database becomes difficult and complex as the number of inter-relationships grows between data tables. With both the database growing and the number of relationships between entities, the system responsiveness is severely impacted by the multiple JOINs between the database table. In addition, relational databases have fixed schemas, so they don’t adapt well to changes. A strength of relational databases is the carefully engineered relationships and elimination of redundancies that make the system work very quickly as a whole. But the Achilles heel of this approach is that a small change to one table can cause a ripple of changes across the system that must be carefully accounted for. And as such, schema changes are problematic and take a great deal of time for ensuring changes do not break the system. Unfortunately, a simple change like adding or replacing a column in a table might be a million-dollar task when using a relational database.

The Ripplenami Solution Accounts for Inherent Data Differences in the Value Chain

To meet the high standards of traceability from source to the consumer, RippleNami developed a technology platform designed from the ground up to leverage the advantages of graph databases specifically for traceability applications built on connected relationships between tens or hundreds of different entity types. In addition, the system utilizes proprietary machine learning algorithms to perform the linkage from various different data silos for traceability using their inherent data structures. This is a major paradigm shift. This new technology is designed specifically for recording, defining, searching and visualizing a genealogy linkage between assets, owners, places, and events. In addition, the core database utilized by RippleNami is a mixed-model NoSQL database, having characteristics of a graph database, document database, key/value pair database and SQL database. Utilizing this database has numerous advantages over traditional relational databases in traceability applications due to:

  1. Quickly adapt to new data formats, new data models and connecting new entities or processes through all phases of any value chain from source to final destination.
  2. Allow various participants across the value chain to create natural data output from their IT systems into input data to a decentralized traceability system. Very little additional work from their IT systems is required to contribute data that naturally is available in their system.
  3. Provide the highest level of user and data security, so that data contributors can only see their own data contribution and no other data, delivering the utmost in security and privacy.
  4. Enable visualization in a natural intuitive manner using a node-link diagram, exposing any attribute within a node and lines that represent the source to final export and all the interconnected entities between the two nodes.
  5. Support millions or hundreds of millions of records without impact to performance, as the data “traverses” across the various entity relationships.
  6. Easily add new inspection, new value chain participant or compliance points across the value chain workflow without adversely impacting the application or design.
  7. Use of mobile applications that work offline as well as online to fill data gaps in the value chain, record inspections, determine compliance, or enable small business owners to participate in passing digital data for the value chain.
  8. Inherent use of using photos and geo-coded information as an independent confirmation of activity, inspections and volume of assets being processed
  9. Create blockchain architecture so that data is immutable that allows contributions by all value chain participants that the government or independent inspectors can track


The RippleNami Mining Traceability System consists of five different RippleNami modules; rCAPTURE®, rTRACE™, rWAVES®, rBLOCK®, and rANALYZE™.


The heart of the system, rTRACE, is designed around graph database technology, a unique database characterized by nodes and edges. Nodes being entities and edges are links between nodes (traceability). A node example includes a mine, mine owner, minerals from the mine, the processor, the distributor, retailer, an inspector, or an exporter. An edge is common data that is transferred between two entities. Due to the in-built relationships in graph databases, tracing data is much more straightforward than in relational databases requiring complex queries and a complex set of JOIN tables. Graph databases are ideally suited for complex traceability applications with high performance, scalability, easy expansion. They can accommodate an unlimited number of nodes and linkages for any network scenario versus traditional relational databases. With graph systems, single queries that track genealogy data for compliance purposes can run in seconds. Users can see a visual representation of nodes and linkages with results showing any detail data flowing through different nodes. Below is an example for a mining traceability system.

By leveraging graph design as the core of traceability, RippleNami’s RTS system can readily accommodate two primary core functionality of an accurate traceability system: Chain-of-Custody and SCCS traceability patterns. The Chain-of-Custody pattern requires that every asset is in the custody of a given party until such time that the party relinquishes custody to another party through a Transfer. All observations and actions for an asset are linked to the party with custody of the asset at the time.

SCCS Traceability requires a more detailed explanation. While it may seem that traceability is an easy problem for determining lineage, real-world scenarios make the issue to be significantly more complex than most systems can accommodate. Resources at the beginning of a value chain are tracked in a completely different manner than those toward the end of the value chain. For instance, a cow is tracked individual via an RFID attached to their ear identifying the cow itself and associated vaccinations and inspections. Beef production facilities often combine multiple lots into a single production batch or split a single production batch into multiple batches for distribution to different customers. The RippleNami system is inherently designed to accommodate these real- world scenarios. The RippleNami key SCCS traceability patterns include Source, Copy, Combination and Split,

The Source pattern drives traceability between assets within the system. It requires all derivative or downstream assets to be stored as unique assets, each with permanent links to its preceding assets.

The Copy pattern addresses the need to replace or relabel an existing asset. Common business cases may be the transfer of asset contents from one container to another or assigning a new label to an item. The new asset is created with properties copied from the original and a new ‘source’ association to the original. Any activities events recorded against the original asset are not transferred to the new asset.

The Combine pattern addresses the business case where multiple assets may be combined into a new asset. An example of this may be leveraging multiple mining batches into a single batch to be processed.

The Split pattern addresses the business case where an asset is divided into separate derivative assets. An example of this may be the division of a single mining batch into smaller individual lots for distribution to various exporters. Each derivative asset is a new asset in the system with a unique identifier and similar data differing in quantity or amount. The new asset retains a custody association with the original asset’s custodian party and a new ‘source’ association to the original asset.

The derived asset is new in the system with a unique identifier and similar data differing in quantity or amount. The new asset retains a custody association with the original asset’s custodian party and individual new ‘source’ association to each original asset.


RippleNami’s rCAPTURE is a flexible, intuitive data collection application residing on mobile devices or tablets. Customized data fields can be distributed to different groups to meet rapid deployment needs. Data can be captured online or offline, whereby offline data is automatically synced to the central database when network access is available. rCAPTURE can also be attached to electronic devices such as RFID readers, bar scanners, or QR code readers, allowing scanned/tagged asset data to be transmitted to your database along with data collected from rCAPTURE.

rCAPTURE can be used customized for a variety of purposes to bring benefits to the value chain.

  • Provide tools to smaller value chain participants that automatically digitizes source data and decreases their own operational overhead costs.
  • Photo evidence/confirmation of asset volume and activity, that geo-codes location to be used to confirm location activity against an independent 3rd party database to minimize corruption and bribery issues.
  • Real-time updates in the field for asset data collection including the transfer of assets.
  • Collection of inspections, electronic health records, documentation of disease while in the field for livestock or any food product.

rCAPTURE also includes integrated customizable reports. Reports can be generated in the dashboard, graphical, or tabular formats to enable analysts to see the results of the captured data from all devices from a top-down viewpoint.

Key Benefits:

  • Real-time data upload from an unlimited number of users.
  • Offline data collection (out of area cell service, no Wi-Fi).
  • Synchronizing offline data to the central database occurs automatically when coverage is
  • available; no manual uploads of data to a computer is necessary.
  • Ensures accurate data capture and eliminates redundancy.
  • GPS-based location information of every transaction captured for data visualization mapping
  • (even when offline).
  • Ability to add photos to any record.
  • Powerful smart logic: with specific events triggering automated notifications to targeted users for critical events, such as compliance violations.


page7image51852416Analytic reports will be available through RippleNami’s rANALYZE artificial intelligence web application module providing instant access to link charts and management reports displaying entity genealogy information and production results in an intuitive graphical and tabular format. Machine learning is written in Python code and reinserted into the multi-model database when the analysis is complete. Calculations to account for material loss can be analysed to ensure compliance. rANALYZE also enable users to customize reports to their preferences, providing a breadth of information at the user’s fingertips, supporting sophisticated search queries using multi-operand Boolean logic filters. Each tabular report can be customized and persisted for each user, with the ability to choose which columns and the order to display. Node-link charts like those below are drillable with specific data to each node or edge.



rWAVES is RippleNami’s intuitive data visualization application, designed so users can see a representation of the data across various viewpoints; owners, locations, processors, exporters. The application was designed from the ground up to import new data sources and quickly change the user interface or new reports to accommodate changes in the application. Each major category has significant details, providing users with an immediate point of interest reports. For instance, in a livestock application; owners, places (farms, slaughterhouses, transportation), cattle are key elements that are tracked with different views for the user based on the same data. This is called the 360 view. in a mining application, the Place Tab, users can quickly see a list of all the mining batches with a familiar location, perhaps identified by a mineral analysis within a specific time frame. Or, in the Owner tab, view a list of all mines owned without performing a query but with sophisticated filter functions to find investigation results within seconds or minutes.

page9image52267088 For example, the Democratic Republic of Congo’s open-source data regarding natural resources from mining, oil and forest permits issued along with protected parks is illustrated below can be displayed. Each item for concessions contains the company name, mineral, date and all other data fields contained on a concession. Sophisticated search queries using multi-operand Boolean filters are available across the application enabling the easiest of analysis. Different viewpoints from the same data are presented.

Map depicts resources such as mining, oil and forest permits issued along with protected parks. Each item contains data such as company name, mineral, date, etc.


A blockchain is a peer-to-peer distributed ledger forged by consensus, combined with a system for “smart contracts” and other assistive technologies. Together, these can build a new generation of transactional applications that establish trust, accountability, and transparency at all the transactional data while streamlining business processes and legal constraints. Blockchain by itself is not the solution to current traceability adoption but enable more value chain contributors to participate in a robust traceability solution The key characteristics of an actual blockchain are consensus, provenance, immutability and finality.