Digitizing Mining Throughout Africa

EXECUTIVE SUMMARY

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 the government to ensure compliance throughout the value chain to ensure tax revenues derived from all mining activities are fair and accurate.

The problem with previous traceability solutions have been as follows: 1) previous generations of digital traceability systems are built on old-style databases, making it difficult for mining participants to use their current systems to contribute traceability data and 2) force feed standards on participants of the supply chain, making it costly and unattractive for all suppliers to participate.

RippleNami’s Mining Traceability System (MTS) program is designed to resolve these two significant obstacles and enable wide adoption by all mining participants in the value chain. It corporates the latest, 1) mobile data collection and 2) data visualization technologies, 3) blockchain and 4) modern architecture and database specifically designed for optimal traceability capabilities.

At the heart of the MTS system is RippleNami’s rTRACE, a robust, highly available, and universal web application architected for the specific purpose of capturing and recording every change and event associated with the Mining Value Chain. Suppliers along the value chain can provide traceability data from their current systems without adopting new standards. rTRACE can quickly develop new APIs and rapidly ingest manufacturing data using any manufacturing data. rTRACE leverages the latest lineage technology and proprietary matching algorithms to enable relationships between mineral assets, owners, location, different processes and processors to be linked and thus determine the movement of assets from one stage of production to another without the limitations of utilizing restrictive relational databases. From origination through a period of processing and subsequent export, the system provides the necessary tools and processes for users to accurately record, search and display the current and historical lineage of every asset maintained within the system.

Previous traceability platforms using traditional relational databases for describing relationships between multiple entities have proven to be limited, particularly as systems need to expand beyond tracking just the mining operation 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.

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. 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 models and connecting new entities or processes during development with continual adaption to new requirements
2. Adapt various manufacturers in converting the data from their systems into data that a centralized monitoring and traceability system can use. Very little additional work from their IT systems to contribute data that naturally is available in their system
3. Enable visualization in a natural intuitive manner using a node-link diagram, analyzing the mining source to final export and all the interconnected entities between the two nodes.
4. Support millions or hundreds of millions of records without impact to performance, as the data “traverse” across the various entity relationships.
5. Easily add new inspection or compliance analysis across the application
6. Inherent use of using photos and geo-coded information as confirmation of inspections and volume of minerals being mined
7. Create blockchain architecture so that data is immutable that allows contributions by all value chain participants that the government can track

DIGITIZATION OVERVIEW

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

TRACEABILITY: INTRODUCING rTRACE

The heart of the system, rTRACE, is designed around graph database technology, a unique database characterized by nodes, with nodes being entities. A node example includes a mine, mine owner, minerals from the mine, the processor, the distributor, retailer, an inspector, or an exporter. 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. You get a visual representation of nodes and linkages with results showing the data flowing through different nodes.

By leveraging graph design as the core of traceability, RippleNami’s MTS 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 Mining lineage, real-world scenarios make the issue to be significantly more complex than most systems can accommodate. When a mining batch or a collection of mining batches are moved to a production facility, they will be processed and tracked differently than how the mines track minerals, creating challenges for a structured system. Production facilities often combine multiple lots into a single production batch or split a single production batch into multiplebatchesfordistributiontodifferentcustomers. TheRippleNamisystemisinherentlydesigned 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.

DATA COLLECTION: INTRODUCING rCAPTURE

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.

In addition, rCAPTURE 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 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

DATA VISUALIZATION: INTRODUCING rWAVES

rWAVES is RippleNami’s intuitive data visualization application, designed so users can see a representation of the data across various viewpoints, owners, mines, and processors. Each major category has significant details, providing users with an immediate point of interest reports. For instance, in the Place Subtab, rWAVES, 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. 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. Each item for concessions contains the company name, mineral, date and all other data fields contained on a concession.

ARTIFICIAL INTELLIGENCE ANALYTICS: INTRODUCING rANALYZE

Analytic 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 analyzed to ensure compliance. rANALYZE will 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.