Periodic Reporting for period 1 - MINESPIDER (Blockchain Protocol for Responsible Mineral Sourcing)
Reporting period: 2020-10-01 to 2021-09-30
Minespider already has a deep understanding of how information can be shared along a supply chain electronically and supported by the Minespider Certificates. But in order to also be able to answer questions and to provide advice and consulting services to our actual or potential customers on best practices and tools how to physically track minerals and products and how to screen, verify and assess information on a large scale basis, we need to master and demonstrate deep knowledge, competence and experience in the field of physical tracking minerals and products, for example (but not limited to) with regards to tools and practices related to all kinds of markers, fabrics, labels, sensors and other biological, chemical or electronic devices and the so called “internet of things” (IoT), as well as in the field of Artificial Intelligence (AI) that can help to screen, verify and assess all the data provided in large scales based on the input of all the devices.
For this we implemented the Innovation Associate and ran extensive R&D in tools and best practices related to the physical tracking of minerals and other products (including, but not limited to the aspects of the IoT) as well as in artificial intelligence/machine learning (AI) related applications and best practices.
The ambition and innovation potential is not only to provide our customers with guidance and consulting services on the physical tracking of minerals and other products and the highly automated collection and input of data related to them, but also to have this screened, verified, assessed by different sources, including AI.
Besides successful onboarding as well as internal and external training of the Innovation Associate (IA) the work in a first step focused on exploring the environment to understand the state of the art in physical tracking, screening, verification and assessment etc. as well as its limitations and identify the main players as well as the best practices and applications. This included the structuring of the screened input and a development and update of the research roadmap and in consideration of our guidelines and workflows, pilots, projects and services as well as customer requirements and requests.
In a second step, we proceeded with the development of business cases related to physical tracking and Artificial Intelligence / machine learning (AI). This in particular included to explore and develop relations to third party suppliers for physical tracking and AI tools and services. In addition this comprised internal documentation, communication and training as well as the preparation of management guidelines and decisions based on the findings.
In particular we developed an internal database and business matrix to support our consulting and project related activities. We considered the majority of tracking technologies to be part of the IoT ecosystem and analysed their key features, benefits and use cases in the mining industry as well as downstream the supply chain. For example, we have analysed the following most common physical tracking technologies:
- Marking technologies, from DNA to ink markers Tag identification
- Labels/tags attached to tracked asset: QR/Barcode Identification Labels
- Tag Identification using GPS/Bluetooth Low Energy/RFID/ NFC
- Asset Identification with 0G, LT 1 Low power wide area network
- Telco network services: Geolocalisation, Mobile coverage, 5G with IOT
- Hybrid positioning technologies blending: Wi-Fi Signals, Cellular Signals, GPS
- Satellite observation & drones
- Private radio solutions such as Motorola Solutions Tetra/DMR
- Mobile app providers
- Computer vision/AI and video surveillance
- Scan Technologies to transfer paper documents to digital data points
Besides building internal expert knowledge, our research also identified and assessed potential ecosystem partner companies and strategic alignments. Furthermore the communication to third parties, for example via publications as well as participation in online events has been planned and performed.
Moreover, to explore AI related use cases and to develop related business products and opportunities, our research focused on developing use cases, methodologies, test scenarios and processes as well as establishing a test environment to perform machine learning tests. We used AWS services, in particular the machine learning services and test environments to explore and implement the technology in a Minespider environment. This in particular included the AWS machine learning techniques and services such as Textract, Recognition, Comprehend and SageMaker.
2. Main results achieved
Work resulted not only in building an extensive internal knowledge base with the Minespider Team but also extended our network to experts, service providers and partners, strengthened our relationships to customers and supported us in the acquisition of new customers. This has contributed to creating an international ecosystem of various industry players that enhance the blockchain landscape. Moreover, we developed a taxonomy of physical tracking technologies in the context of the Internet of Things and a blockchain based supply chain tracking environment.
The main (non-confidential) findings were presented on the RawMat 2021 event and in an expert article published on the Material Proceedings (DOI 10.3390/materproc2021005001). Also we published non-confidential datasets to the suggested taxonomy of tracking technologies to effectively capture and input key data on the blockchain (DOI 10.5281/zenodo.5510478).
as not only as the key provider of a decentralised traceability and impact assessment infrastructure, but also as an educator and consultancy provider on all aspects related to tracking and assessing supply chains.
The socio-economic impact of using Minespider technology and services can be significant. In terms of the European Green Deal, digitization and sustainable and transparent supply chains are among the most important elements of the socioeconomic development goals in Europe. However, achieving these goals requires not only the appropriate technical solutions, but also the necessary know-how in the industry and among supply chain stakeholders and building the relevant ecosystem. This INNOSUP project makes a valuable contribution to these objectives.