The U.S. agriculture sector extends beyond the farm business to include a range of farm-related industries. The largest of these are food service and food manufacturing. Food accounts for 12.6 percent of American households’ expenditures Food manufacturing accounts for 14 percent of all U.S. manufacturing employees. In 2014, the U.S. food and beverage manufacturing sector employed about 1.5 million people, or just over 1 percent of all U.S. nonfarm employment. Over 31,000 food and beverage manufacturing plants located throughout the US are engaged in transforming raw agricultural materials into products for intermediate or final consumption. Food processing is the transformation of raw ingredients, by physical or chemical means into food. Food processing typically involves activities common to manufacturing such as transportation, processing, testing, packaging, and storage. Large amounts of data are needed drive the food manufacturing supply chain, from raw materials sourcing to plant production to end consumers. That information is critical in enabling food companies to meet regulatory requirements unique to the industry. Today, more data than ever is being generated through sensors in manufacturing and transport equipment and electronically tagged items across the supply chain. This emerging information-driven enterprise architecture offers food manufacturers tremendous potential to improve operations in terms of traceability, compliance, energy costs, and partner collaboration. However, achieving some of these improvements will require blending data from multiple participants into a single view of a food product’s history. This blending is often impossible to perform currently due to differences in perspectives, gaps in types of data collected, and differences in data formats used by different food supply chain stakeholders. This project will focus on models for key aspects of food supply chains to enable blending of information, and it will focus on new standards and resources to improve collection of data and integration of food manufacturing information systems.
Objective: Deploy new standards and reference data supporting agri-food manufacturing systems to enable industry to more efficiently and cost effectively produce food-based products of higher quality and with improved food safety.
Technical Idea: Today, food producers face many challenges include adapting to uncertainty about input materials; adapting product recipes for different production environments; and measuring, tracking and tracing product information. Fortunately, many recent advances in information technology may provide a means to address these challenges, including: advanced sensors technology; the Internet of Things (IoT); cloud computing; ubiquitous geographic information system (GIS) technology and; powerful new platforms for integration and analytics. Vendors are providing solutions for the agri-food manufacturing domain that exploit these opportunities for targeted purposes. However, many of the challenges in food production require sharing and merging data across supply chains. Currently it is difficult to share and merge data across the food supply chain due to lack of common practices, standards, platforms and even common IT readiness levels among supply participants. This project will perform studies to identify these problems and opportunities for high impact improvements, use a systematic Model-Based Interoperability Improvement approach to create standards to fill key standards gaps, and investigate platforms and IT readiness assessment methods to accelerate deployment across agri-food domain stakeholders who must collaborate electronically to address challenges in this space.
Research Plan: The research supporting the technical idea will begin with a study of the state of traceability in food supply chains to be conducted with an academic partner who has expertise in this area. This study will result in a report identifying the needs of the food industry for traceability which will be used, along with input from industrial partners, to guide the standards and technical work for the project. The study will be followed by parallel efforts to: 1) develop standards to fill gaps identified by industry and academic partners, and 2) organize and conduct a series of workshops to engage industry, to refine the results of the survey, and to continue to identify needs for standards and technology developments as gaps are filled and market conditions change. The project will produce publications reporting on these efforts that motivate and explain the capabilities added through standards and technologies developed and investigated.
The project will employ an approach that NIST staff on this project have adopted that we call Model-Based Interoperability Improvement (MBII). This is a methodology with the following elements: