Enabling and Using Traceability and Data Linking for Sustainable and Efficient Supply Chains

Summary

Traceability is becoming increasingly crucial in and across multiple sectors and is critical to identifying and connecting value chain data. For example, tracking all the components that contribute to the environmental impact of producing and delivering a product to a final customer involves many producer subdomains. However, these subdomains have different conceptualizations, use different standards, and have different priorities with respect to traceability. This makes finding and usefully combining needed data difficult and is leading to diverging traceability models emerging in different sectors, worsening the situation. Yet, traceability data is needed to address many diverse use cases beyond environmental impact verification, such as track and trace for food safety, counterfeit detection, cybersecurity, and documentation of processes or inputs for increased product value, to name only a few.
NIST has previously developed a semantic model for traceability for bulk grain that has been shown to enable tracking of material from harvest to processing. Elements of this model are now being incorporated into standards for this domain. Although the model was designed as a general-purpose model for traceability, it has only been verified for bulk material in agriculture. The model will be enhanced and used as a seed standard to support semantic interoperability across domains. 

NIST’s broad mission “To promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life” and its role “as an impartial technical authority when it is developing standards and guidelines”[1] makes it uniquely suited to addressing models and standards needed to gather, process, and analyze data for traceability that, by their nature, span domains.

Description

Objective
Develop a robust traceability model that will support standards and tools with consistent semantics, enabling low-effort sharing and integration of traceability data for insight across value chains.

Technical Idea
Refine and broaden the traceability ontology NIST created for bulk grain to support both bulk and discrete material types to serve as a domain-independent model for traceability across two or more domains and leverage work and partners at NIST and IOF to find one or more non-agricultural use cases (such as non-ag Circular Economy, bioMfg, additive, or pharma) in which to test, validate, and demonstrate the model. Work to incorporate elements of these models into standards in and beyond agriculture.

Research Plan
The research plan has multiple threads:

1.  Refining and extending the existing traceability model (initially with domain experts in the agriculture domain).
2. Exploring traceability needs and potential new partners in other domains.
3. Working in standards groups to publish specifications related to more mature parts of the traceability model.
4. Working with tool experts to demonstrate the value of the model.
5. Fostering a mature infrastructure for dissemination and use of a semantic model.
6.  Investigating technologies that may enable traceability and/or the model’s impact.

The research process for the model development includes identifying and documenting use cases and associated require ments (as competency questions), acquiring or generating data through simulation, developing the traceability models needed (in the form of ontologies or rules) to meet requirements, ingesting the data into knowledge graph tools, querying or reasoning over resulting graphs to answer competency questions to validate the model, building tools that provide insight for domain experts from the graphs, and publishing papers describing these results.

Standards engagements provide contact with domain users and tool developers. This is our key source of industry pain points, use cases, and requirements, in addition to being a means to diffuse our research into a form for uptake in applications and operations for industry impact.

We are working with industry groups at AgGateway to standardize a Traceability API for harvest data and to document new use cases and requirements related to sustainability data. This year, a subproject has been stood up with MML investigating traceability and other information requirements needed to support circular economy (CE) value chains in various domains. We have partnered with ASU to build a tool to visualize planting to harvest traceability data created by simulation in FY23.

The CE subproject will determine if there is a need for extensions to the traceability model to support circular economies. However, other domains have also been identified for investigation in later years regarding traceability needs independent of circularity. These domains include critical infrastructure control systems production, biomanufacturing, additive manufacturing, and pharmaceuticals. Partners or industry groups have also been identified for each of these domains.

Highlights
Standards
ASTM F3682 Standard Terminology for Goods Movement Process Precise Foundational Definitions

OMG Pedigree and Provenance Model and Notation (PPMN) beta specification (dtc/22-11-05)

Industrial Ontology Foundry (IOF) Supply Chain Ontology
https://github.com/iofoundry/ontology/tree/master/supplychain

Datasets
Traceability dataset for elevator to processor scenario.
Traceability dataset for planting to harvest scenario.

Selected Publications
Ameri Farhad; Wallace Evan K.; Yoder Reid; Riddick Frank H., “Agri-Food Supply Chain Traceability Supported by a Formal Ontology, A Grain Elevator to Processor Use Case”, ASME 2023 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, DETC2023-108860, V002T02A051: 11 pages. Boston, Massachusetts, August 20–23, 2023

Ameri Farhad; Wallace Evan K.; Yoder Reid; Riddick Frank H., “Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach,” Journal of Computing and Information Science in Engineering (JCISE), October 2022, Vol. 22. DOI: 10.1115/1.4054092

Stouffer, K.; Pease, M.; Lubell, J.; Wallace, E.; Reed, H.; Martin, V.; Granata, S.: Noh, A.; Freeberg, C. (2022) “Blockchain and Related Technologies to Support Manufacturing Supply Chain Traceability: Needs and Industry Perspectives,” NIST Internal Report, NISTIR 8419. DOI: 10.6028/NIST.IR.8419.