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Tejas Bhatt, MS CFS

Institute of Food Technologists (IFT)

03.02.2015   3:00 ET | 2:00 CT

Vir tual Integrated Real-Time User AnaLytics(VIRTUAL) Tool

SUPPLY CHAIN SECURITY PROJECTS

OVERALL OBJECTIVES:  To create a continuously evolving food supply chain model that outputs distribution dynamics To create an ontological framework that would facilitate connecting the outputs of this model with existing (or under development) food defense models

IMPACT:  The model will be a development tool to minimize data collection activities and maximize  innovation potential within academia The model will be an analytics tool for the food industry to justify implementation and enhancement of food defense practices

DATES:  July, 2014 – June, 2016

PROJECT INTRO

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RESEARCH TEAM

Tejas Bhatt, PI Janet Zhang, co-PI Rosie Newsome, Senior Scientist

William Fisher, Industry Liaison

Brian Sterling, Technology Specialist

Active External Partners Department of Homeland Security (DHS) Chemical Security Analysis Center (CSAC)

Bumble Bee Foods

Other External Partners University of Minnesota / NCFPD

Purdue University Georgia Institute of Technology Battelle GS1USMetro Germany

PepsiCo

PROJECT PARTNERS

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Two primary deliverables: A food defense model An ontological framework

End Users Academic Community Researchers Model developers

Public Sector   Food and Drug Administration DHS Chemical Security Analysis Center

Private Sector Food Industry Non‐governmental Organizations

PRODUCT(S) & END USERS

BACKGROUNDERGLOBALIZED SUPPLY CHAINS

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BACKGROUNDERMODELING AND SIMULATION

Application of Modeling and Simulations (M&S)

Predictive microbiology

Processing conditions

Packaging operations

Sensory characteristics

Shelf-life studies

Supply chain dynamics

Advantages of Modeling and Simulations (M&S) Cost effective

Fast turnaround

Near real-time feedback

Measure secondary and tertiary effects

Impact of decision-making

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BACKGROUNDERLOCALIZED BARRIERS

Researcher

Dat

a?Joint P

roposal?

GovernmentIndustry

Academia

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BACKGROUNDER

Garbage In = Garbage Out

MODELING AND SIMULATIONS: VALIDATION

Statistical Significance Error rates

Confidence intervals

Software Development Lifecycle Requirements gathering

Design

Development

Deployment

Testing

Evaluation

Refinement

Modern Methodologies Automated learning techniques

Behavioral software analysis

Experimentation & Exercise Workshop

Seminar

Tabletop

Functional

Full-scale

Homeland security exercise evaluation plan (HSEEP)

Mixed reality environment

Verification, Validation and Testing

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Source: Giannakopoulou & Păsăreanu, 2010; FEMA, Chaturvedi, et al., 2008

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BACKGROUNDER

Reduced Resources

Faster Innovations

Incentivize Smarter Research

METHODOLOGYOVERVIEW

Source(Pre-harvest)

Transport(Post-harvest)

Storage Processing Retail & Consumption

Economic Impacts?

Impacts?

Public Health

Impacts?

Farm•Grains•Animals •Plants

Storage ProcessingDistributing

Center Retail

Restaurant

Consumer

Transport Transport

VIRTUAL World

REAL World

Transport

Transport

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METHODOLOGYENVIRONMENTAL SCAN

METHODOLOGYMODEL DEVELOPMENT

Inputs to the Model

Use existing data from

models

Identify gaps in data

Use industry data to fill

gaps

For each food For each company

For each critical tracking event Collect key data elements such as:• Supplier, Event Owner, Customer, Trailer, Carrier, Transporter• Origin, Location, Destination• Product, Commodity, Variety, Species, Quantity, Amount• Packaging Type, Materials, Style• Date, Time, Batch, Lot Code, Sell-by Date, Use-by Date• Bill of Lading, Invoice, Packing Slip, Purchase Order, Work Order• Others

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METHODOLOGYMODEL DEVELOPMENT

Supply Chain Dynamics

Build a VIRTUAL model of the supply chain using distribution dynamics and business intelligence extracted from the input data

Update VIRTUAL model of the supply chain as more data (and therefore more insights) becomes available

Continuous Evolution

METHODOLOGYMODEL DEVELOPMENT

Outputs from the Model Supply Chain Dynamics

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METHODOLOGYMODEL DEVELOPMENT

Outputs from the Model Comparative Analysis

Scenario 1 Scenario 2

METHODOLOGYMODEL DEVELOPMENT

Use Case 1: Academic Modeler IDEA: I think I have come up with a better way to assess supply chain

risks…

NEED: I need a complex supply chain to test my theory…

USE: Use the VIRTUAL model to create a supply chain of hundreds of nodes/entities and thousands of links/connections

IMPACT: Overlay a supply chain risk assessment model to measure impact/effectiveness/plausibility/gaps/refinement…

EXAMPLE: Food defense model at Purdue University

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METHODOLOGYMODEL DEVELOPMENT

Use Case 2: Student Development IDEA: Engage student learning through group mock exercises in

supply chain resiliency…

NEED: I need multiple complex supply chains to practice recall management scenarios…

USE: Use the VIRTUAL model to create multiple supply chains of different commodities

IMPACT: Overlay an educational model to measure student learning…

EXAMPLE: Food Safety and Defense Graduate Certificateat Kansas State University

METHODOLOGYMODEL DEVELOPMENT

Use Case 3: Industry Capacity Building IDEA: I want to test the capabilities of a new traceability software

vendor…

NEED: I need challenging complex supply chains to test product tracing scenarios without giving away my business confidential information

USE: Use the VIRTUAL model to create challenging complex supply chain that closely match own product commodity and supply chain systems

IMPACT: Overlay a capability-testing model to measure effectiveness of tool/technology in handling challenging complex food production scenarios…

EXAMPLE: Technology selection process at a large MNC such as PepsiCo

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METHODOLOGYMODEL DEVELOPMENT

Use Case 4: Industry Assessments IDEA: I want to test the resiliency of my own distribution system…

NEED: I need to mimic my own supply chain dynamics without the need to engage in complicated non-disclosure agreements

USE: Use the VIRTUAL model to recreate own product commodity and supply chain system with anonymized and approximated supply-customer relationships

IMPACT: Overlay a “what-if” scenario model to measure resiliency of the distribution system to compare baseline distribution dynamics with anomalies (such as single or multiple nodes/links going down)

EXAMPLE: Internal risk assessments at food retailerssuch as Metro

METHODOLOGYMODEL DEVELOPMENT

Use Case 5: Regulatory Analytics IDEA: I want to reduce the signal to noise ratio in the traceability records

collected during a food defense outbreak investigation…

NEED: I need to compare the distribution records collected during the investigation with “normal“ distribution dynamics

USE: Use the VIRTUAL model to create a parallel supply chain system and compare the baseline with the records collected to identify anomalies

IMPACT: Using comparative analysis, a more focused investigation could proceed…

EXAMPLE: Flagging seasonal variances in supply chain distribution dynamics in the tomato-pepper outbreak

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METHODOLOGYMODEL DEVELOPMENT

Use Case 6: Regulatory Functioning IDEA: I want to estimate the magnitude of the impact of an outbreak or

of new regulatory mandates…

NEED: I need a set of representative supply chains impacted by the outbreak or new rules

USE: Use the VIRTUAL model to create large scale supply chains with millions of nodes and edges representing a sector of the food industry within the USA (or globally)

IMPACT: Overlay a public health or economic model to estimate the impact of an on-going outbreak or new regulations…

EXAMPLE: Estimate the scale and scope of the PCA outbreak or the economic impact of requiring food defense mitigationstrategies

METHODOLOGYMODEL DEVELOPMENT

Ontology Development“An explicit formal specification of how to represent the objects, concepts and other entities that

are assumed to exist in some area of interest and the relationships that hold among them.”

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Got the work plan approved by DHS and NCFPD!

PRELIMINARY FINDINGSACCOMPLISHMENTS TO DATE

Stakeholder Buy-in

Model DevelopmentModel Rollout

YEAR 1 COMPLETION

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Education and Training

Model Enhancements

Publicize and Promote

YEAR 2 COMPLETION (15/16)

Model

Open source model development

Users/Modelers Manual

Documented case studies and use cases

Ontology

Publication (for example UC Davis BioPortal)

Best practices and guidance document

TRANSITIONING RESEARCH

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Know of existing research / models that could be… … studied

… incorporated

… utilized

… connected

Know of existing ontological developments in the area of food supply chains … learn from

… partner with

… cede control to

Know of other stakeholders who might be interested to participate … provide data to build the model

… provide scenarios to use the model

CALL FOR ASSISTANCE!

STAKEHOLDER RESPONSE

Defending the safety of the food system through research, education & transition.