How we did it: BSI: Teradata Case of the Tainted Lasagna

BSI TERADATA
EPISODE 11:HOW WE DID IT
THE CASE OF THE TAINTED LASAGNA
WATCH THE EPISODE AT HTTP://BIT.LY/14P5RMO

We’re Getting A Lot of Questions …

Hi Everybody, BSI Teradata
We’re the brains behind the scenes
JODICE
and wanted to answer your
questions about “how we solved BLINCO
that lasagna case so fast.” DIRECTOR

This little write-up will give you an
idea of our client’s architecture and
some details about how we did the
investigation. BSI Teradata
Take a look, and if you still have MIKE
questions, shoot them to us!
RINALDI
Yours truly, Level 2

Mike Rinaldi and Jodice Blinco

2 © Teradata BSI Studios 2013

Story Synopsis
Case of the Tainted Lasagna

Situation Impacts

Huge worldwide consumer goods food
• Complex problem
producer, faced with 3-4 major and 5-6 identification is faster –
minor recalls per year. Increased from 2 weeks to 3 days
government oversight and food safety • Big data improves root
regulations. cause isolation and
hypothesis testing
Problem
• Notification and remedy:
Current approach – too slow, incomplete, recent recalls were 85%
because data is not integrated across the faster and at 99%
entire food chain. No advanced analytics. coverage
Impacts both sales and brand. High risk.
• Improvement from 75% to
Solution 86% bad units verifiably
destroyed
Used Teradata, Aster, Teradata Applications,
• Easy to satisfy regulators /
and Tableau to re-engineer their Risk and
Recall management system, built on top of prove issues were resolved
their current ERP system. Uses big data for • Lowers risk for the
tracking and tracing. company from bad PR and
lawsuits

CAST OF CHARACTERS
Great Brands:
•Chief Risk Officer: Wiley W. Harvey
•VP Supply Chain Management: June Davis

BSI:
•Jodice Blinco
•Mike Rinaldi

Jodice Blinco – Head of BSI
• Decided to “keep her feet wet” by working on this case
• Very interested in “emergency” uses of data
• Had food poisoning recently, so personally engaged!

BSI Teradata

JODICE
BLINCO
DIRECTOR


Mike Rinaldi – Principal Investigator
• Tech expert in Teradata, Aster, Teradata Apps, and Tableau
• Focuses on architecture improvements, uses of big data

BSI Teradata
MIKE
RINALDI
Level 2


Great Brands

• Wiley W. Harvey - Risk Officer –
very worried about new Food
Safety government regulations,
ability of Great Brands to
comply. Ongoing issues with
recalls, negative PR and
associated costs

• June Davis - Supply Chain VP –
knows her group is on the hook
to resolve this problem. Focus is
of course on better prevention,
but things do slip through,
requiring recalls. They need to
be faster and more precise.


SCENE 1
At Great Brand Corporate HQ

Problems: discussion of the problems, risks, unwieldy current
architecture and processes. Commissions BSI to help

The Problem – Yet Another Recall


Scene 1: Problem
Case of the Tainted Lasagna

Wiley and June from Great Brands have brought in Jodice and
Mike from BSI to get their help on a revamp of Great Brands
Risk/Recall system. They need to do better track and trace.

•Historical problems at Great Brands:
> Reaction time – situational analysis takes too long, especially when
issues are cross-company with upstream suppliers
> Root cause identification, scoping for recalls also takes too much time
> Execution of the recall, compliance proof
•Impacts:
> Number of incidents, complexity and cost of resolving - costly
> New issues:
– Government regulations – Food and Safety Administration rules, plus more
international rules coming
•Goals:
> New Track and Trace system
> Fresh ideas, incorporating the latest technologies


Food and Drug Administration
Shuts Down Peanut Factory in New Mexico

News:
Huffington Post:
http://www.huffingtonpost.com/2012/11/28/sunland-fda-
peanut-butter_n_2206353.html

FDA Statement:
http://www.fda.gov/food/foodsafety/corenetwork/ucm320413.h
tm


Governments are getting aggressive about food safety
Monitoring more closely, demanding compliance …

Read More At:
http://www.fda.gov/newsevents/newsroom/pressannouncements/ucm334156.htm

New Headlines Around the World

The Spanish cucumbers were not the problem.

Sources:
http://www.independent.co.uk/life-style/health-and-families/health-news/spain-takes-on-germany-after-cucumber-scare-
cripples-farm-exports-2292005.html

http://www.nbcnews.com/id/38741401/ns/health-food_safety/


More News Headlines
Labeling Issues – Horsemeat in Europe

Source:
http://www.thedailybeast.com/articles/2013/02/27/horsemeat-for-lunch-christopher-dickey-n-paris-s-horse-boucheries.html


The Food Industry Structure is Complex
Simplified Picture


The Real Picture from a CDC Talk

Source: CDC report at www.cdc.gov/about/grand-rounds/archives/2009/.../GR-121709.pdf

BSI Teradata’s Recipe for Success

• Food manufacturers must increase
collaboration with their trading partners
(on both ends of the value chain –
suppliers and retailers/customers)
• Invest in standardizing recall procedures
and tools
• Implement effective technology that
improves visibility across the value chain

Such improvements can serve to both manage the risk of
recall occurrence and reduce negative impact to your
company and brand should the improbable happen.


Checkpoint – How Long Does It Take Today
for Great Brands to Run A Recall?

Answer: it depends, but usually at least 30 days.

Sales Impact of Recalls Can Be Huge $ Losses


Recalls Also Impact GBRA Stock Price
3 Examples


SCENE 2

Back at BSI offices, Jodice and Mike tap into the data using
new technology to see what changes to recommend

BSI Analytics for Track and Trace
Goals – use tools to identify root causes fast (using big data
across the supply chain) and create/execute recalls faster

Simplified Process View


The “Real” Process at the CDC
http://www.cdc.gov/outbreaknet/investigations/figure_outbreak_process.html

When the CDC contacts
us we have to do a better
job on these steps:


Government Epidemiological Chart –
Case Outbreaks Drive Interviews Which Lead to Suspect
Foods

The process begins with a notification from the Government when they find from
interviews of sick people point that they all ate Great Brands Lasagna.

What Activities Occur at Great Brands?
Once notified by the Government, Great Brands steps:

1.Rapid data capture – load data with varying structures,
formats, sizes, and velocities more quickly into a Discovery
Platform – then find the needles in the haystack

2.Analysis and root cause –use fishbone analytics and
temporal sequencing (nPath) to “see” the flow of raw product
to consumers; isolation to bad product lots and possible
victims

3.Managing recall resolutions – use B2B and B2C campaigns
with workflows to guarantee recall notices go out to all
downstream participants, and monitor responses
(B2B – Business to Business; B2C – Business to Consumer)

Side benefit – Government audits – build a dashboard portal so everyone -
including regulators - can tap into the database, for easier auditing/risk
monitoring

Why Do This? Overview
• Process
> Great Brands needs end-to-end visibility, traceability to close its
part of the loop quickly and thoroughly.

• Data
> Great Brands has many data sources: CDC, Public Health inputs,
all manufacturing and transport data, access to supplier data,
social media, Retail Store reports, loyalty data – how much
depends on whether they can get access to both upstream and
downstream data sets, in addition to their own data

• Detailed Analytical Steps In this Episode
> Backtracking from sick people to manufacturing lots
> Backtracking analytics / traceback from manufacturing lots to
raw product providers
> Isolation to transportation introduction of pathogens
> Analytics on cooking temperature sensor data


Sourcing Data for Great Brands – Details

Internal to Great Brands
> Manufacturing process data, by lot, by worker, by equipment
> Sample testing reports, plant equipment maintenance inspections

Upstream – Supplier Information
> QA reports from Farms, raw goods suppliers
> QA reports from Transporters

•Downstream – Consumer
information could be
acquired much earlier,
potentially – from retailers
or directly from consumers


The Investigation/Backtracking
The BSI investigators go through this case, using analytic tools
•They started with this picture that goes from all external suppliers of inputs for
the lasagna on the left through Great Brands to customers on the right. This is
called a “fishbone” diagram. There can be thousands of potential problems.


The Investigation/Backtracking
From Sick Consumers to Suspect Manufacturing Product Lots
• We start with the consumers on the right who were known to be sick,
then look at which retailers sold them product, and backtrack.
• FINDING: the product all came from one plant and only selected
product lots. That’s good news because the contamination is not
widespread. We can next backtrack all the way back to farms.


Investigation/Backtracking to Farms
• Then we accessed farm data (with their permission, via their portals)
and drilled into the QA reports and data from those farms for the
time period when we sourced product. We started with broccoli
(inspected at the farms) – but didn’t find any issues with the farms.
• For a while, we were stumped. Looked at other ingredients but came
up empty, there, too.


Investigation/Backtracking, Adding Transport

• On a hunch, we realized we skipped one step in ourflow diagram -
the transportation of product from the farms to our plants
> So we added the extra transportation step from farm to plant as a new
column and pulled data from transport companies
> We pulled in data from the trucking companies used by Great Brands

• We found that all the sick people ate broccoli that was transported by
one truck, from one transport company – Jimmy Changa Transport.

• Upon investigation (not shown in the episode) and some testing of
swabs from the truck – they were the guilty party!
> Sometimes the trucks are used to transport other products, and are
supposed to be decontaminated between loads – but apparently was not on
this day


Added the Transportation Step


Drilling into Details:
Broccoli from Farms - Transportation - Manufacturing Plant

Jimmy Changa Transport
Truck: 12 Date: Dec 29, 2012
Truck Pickup Schedule

8:45 McDonnell Farm Corp
9:35 Shaw’s Broccoli & Spinach Farms
10:20 Gib’s Healthy Green Coop

Jimmy
Changa


Jodice Asked a Good Question
How did the bacteria pass through the Kill Step in the
manufacturing process? That step should have killed the
bacteria at 167 degrees Fahrenheit (or 75 degrees Celsius).

•They load temperature records for the implicated lots and
find that it was the first run of the day – so maybe equipment
is faulty, taking too long to heat up.
•They investigate, by pulling into the Discovery Platform all
the cooker sensor data.
•They found that on January 2nd (right after a vacation day)
the first 3 lots for Plant 21 Unit 1 - never hit the kill temp.
•Upon further investigation (not shown) compared to other
cookers, this unit is old and they recommended immediate
replacement.


Why Didn’t the Kill Step Solve the Problem?


Going Back to the Big Picture
We discovered the contamination sources, now on to the Recall!


Isolation for the Recall – Who To Contact?
• Using the sick people input and the Fishbone and pathing, we
discovered the probable cause. A few more questions:
> Any other pickups from that truck that day? (Answer: no – otherwise
that would widen the number of product units that need analysis)
> We’ve only heard about those who GOT sick. Who else MIGHT get sick
because they bought the suspect product?
> Is there unsold product in the warehouses or at stores that needs to be
pulled immediately?

• Great Brands must work next with the Retailers
> Some stores let us (under recall situations only) access the store loyalty
data. If people paid using a loyalty card, or with a credit/debit card – we
can create the list of people to notify immediately. This is fast.
> In other cases, we have to call the store’s BI people do the list runs for
us. This is slower.
> And some Retailers are happy for us to contact their customers; some
want to do the calls themselves. It can get complicated!

• Almost done. We also know from our own ERP system which lots
went to which distribution centers and which retail stores.

We create the Product Recall Notices
There will be variations for different audiences


Targets for Recall

Consumers - Who bought the product?
> Known? – loyalty or purchase card data (from Retailers)
> Unknown? – paid cash. This is where we have to go public.
•If all are known we can contact them all directly, avoid
media.

Retailers
> Unsold product still on shelves
> In Retailer Distribution Warehouses – not yet on shelves

Great Brands
> Distribution Centers – not yet shipped from our warehouses
> Transportation Companies – if product is enroute from either:
– Manufacturing location to Great Brands distribution centers, or
– GB Distribution centers enroute to Retailer Distribution Warehouses


Recall Communications and Monitoring
• For each target, we create the sequencing of events we
want to monitor:
> We communicate to them (telephone calls, emails, faxes)
> We want to monitor that they received the communication
> Then we want to monitor (with timeouts and follow-ups if
needed) whether they responded appropriately

• Responses will range, but can include:
> Consumers: they consumed the product and got sick/reported (data
could be enroute to the government through public health channels)
> Consumers: consumed product, did not get sick
> Consumers: did not consume product, will destroy – we need to
reimburse
> Retailers: pulled product from shelves
> Retailers: pulled product from warehouses and destroyed/return
> Great Brands internal: this is far easier.

• Goal is 100% communication and recall coverage.

Designed and kicked off the Recall Workflows


SCENE 3
BSI Readout back at Great Brands HQ

Scene 3: Readout at Great Brands HQ

• Our BSI investigators Jodice and Mike give the summary of
changes they’d recommend for a better Track/Trace/Recall
system to Wiley and June


Three Key Technology Requirements


Key Technology and Architecture Points

1 - Data Capture and Discovery Platform: Teradata UDA
> Any data, any type, any source, any volume
> Right toolkit for analytics
– Teradata - core enterprise-wide data warehouse
– Aster - Discovery Platform
– Hadoop – optional data storage layer
– Unified Data Architecture™ ties everything together with connectors, adaptors

2 - Recall Platform: Teradata Application - Aprimo
> Quickly create recall targets
> Quickly launch the recall notices with various workflows
> Capture the results

3 - KPI Reporting / Risk Monitoring:
> Tableau
> Portal for executives
> Could also be used for Government compliance reporting


Data Capture and Discovery
TERADATA UNIFIED DATA ARCHITECTURE™
Data Scientists Business Analysts Risk/Recall Managers Marketing/Sales
Engineers Customers / Partners Executives Operational Systems

VIEWPOINT LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS SUPPORT

DISCOVER
Y
PLATFOR
M
INTEGRATED
DATA
WAREHOUSE

CAPTURE | STORE | REFINE

COOKING PRODUCT CONSUMER SURVEY
46 FARM DATA TRANSPORT © Teradata BSI Studios 2013
MANUFACTURING SENSOR PLANNING
WAREHOUSE RETAILERS
WEB & SOCIAL

Key Technology Points
Data Capture and Discovery : Teradata UDA

> Teradata is the core repository of enterprise data – historical context, any
structured data, e.g., information from ERP systems, product data,
production data, sales data, retailer data

> Aster - fast hypothesis testing for multi-structured data, e.g., fast pathing
analysis, backtracking analytics, isolation insights. In this case, fast load
and hypothesis testing on cooking sensor data, upstream farm raw product
data (various formats, semi-structured including government reports from
farm inspections), electronic data exchange (EDI) with upstream and
downstream suppliers. Hypothesis testing using SQL Map/Reduce®.

> Hadoop as an optional component for fast, cheap ingest, e.g., Twitter feed
of social comments, distillation/aggregation for feeding into Aster

Unified Data Architecture™ ties all the platforms together.
Experimental results and data from discoveries in Aster or
Hadoop flow into Teradata.


Data Flows


Summary of Discovery Process
• There are 4 aspects for doing hypothesis testing: Data
Acquisition, Data Preparation, Data Analytics, and Data
Visualization.
> Acquisition can happen within the UDA through three platforms
Teradata (Structured), Aster Discovery Platform (Structured and
Unstructured), and if need be Hadoop (for more historical data)
> Data preparation can happen through proprietary technology
(SQL-MapReduce functionality though we do not have to
mention this technical detail) within the Discovery Platform
> Analytics such as the ones shown in the video can be done with
proprietary technology within the Discovery Platform
> Visualization can be done in unique ways through a Tableau-like
front end or some complementary visualization techniques in
Aster (see next slides)
• Analytical insights from Aster can then be operationalized in
the Teradata EDW from which we can trigger actions via
Teradata Campaign Interaction Manager and other
marketing automation tools

Goal: Faster Hypothesis Testing
Aster Discovery Platform

New business insights from all kinds of data with all
types of analytics for all types of enterprise users
with rapid exploration. Iterative hypothesis testing.

1 2 3 4

 Large Volumes  Relational/SQL  Business Users  Fast
 Interaction Data  MapReduce  Analysts  Iterative
 Structured  Graph  Data Scientists  Investigative
 Unstructured  Statistics, R  Easy
 Multi-structured  Pathing
 Hadoop


Teradata Aster Discovery Platform
New Capabilities in 5.10 release
Industry’s First Visual SQL-MapReduce ® Functions

FLOW VISUALIZER
Visualize paths & patterns

AFFINITY VISUALIZER
Visualize clusters & groups

Complementary Value
•BI: Batch Visualizations Outside the
HIERARCHY
Database, General & Generic
VISUALIZER
Visualize hierarchical •Aster: Rapid Visualizations, in-Database,
relationships for Specialized Analytics


Other New Visualizations for Big Data
Flow, Affinity, Hierarchy Visualizers

Home &
Home &
Garden,
Garden,
Bedding and
Bedding and
Bath & Fair
Bath & Fair
Trade have
Trade have
high affinity
high affinity

Low Affinity
Low Affinity
between
between
certain
certain
department
department
ss


Sample Analytics Modules in Aster
Fastest path to big data analytics

PATHING ANALYSIS TEXT ANALYSIS
Discover Patterns in Rows of Derive Patterns and Extract
Sequential Data Features in Textual Data

STATISTICAL
ANALYSIS GRAPH ANALYSIS
High-Performance Processing Discover Natural
of Common Statistical Relationships of Entities
Calculations

SQL ANALYSIS MAPREDUCE
Report & Analyze Relational
Data ANALYTICS
Custom-built, domain-
specific analysis


Aster Connectors / Adaptors

HADOOP ACCESS TERADATA ACCESS
Acquire unstructured data Acquire structured data for
for analysis analysis
SQL-H, Hadoop connectors Aster-Teradata connector

RDBMS ACCESS
Acquire structured data for
analysis
DB connectors

DATA ADAPTERS DATA
Interpret Data for Analysis TRANSFORMATION
Weblogs, XML, PST, Machine Prepare Data for Analysis
Logs, JSON Sessionization, Pivot, Unpivot,
Pack, Unpack


Architecture: Teradata Aster Discovery Platform
Fastest path to big data apps and new business insights

Analysts Customers Business Data Scientists

Interactive & Visual Big Data Analytic Apps
Growing the Development Bucket
SQL-H Unpack Pathing Flow Viz Attensity
•70+ pre-built functions for data
Hierarchy
Teradata Pivot Graph Viz Zementis acquisition, preparation, analysis &
Develop visualization
Apache Affinity
RDBMS Statistical Viz SAS, R
Log Parser •Richest Add-On Capabilities:
Data Data Analytics Viz Partner & Attensity, Zementis, SAS, R
Acquisition Preparation Add-On
Module Module
Module Module Modules •Visual IDE & VM-based dev
environment: develop apps in minutes

• SQL-MapReduce framework
Process • Analyze both non-relational +
relational data

• Integrated hardware and
software appliance
Store Row Store Column Store • Software only and cloud options
• Relational-data architecture can
be extended for non-relational types


Tying Everything Together - Recall Campaign
Data, Discovery, Insights, Context, and Communications via Workflows

Consumer and
Retailer Data
Big Data Analytics
Discovery

CookieID UserID Attribution_Path

Customer Recall Product
Marketing Digital
Spend Integrated Marketing Reports In Progress QA History
Marketing
Management Previous Recalls Recall Costs
Campaign Real-time
Management Interactions


Recall Workflows


Recall Monitoring and Reporting
• The Teradata Campaign Interaction
Manager collects information about
workflow responses and creates summary
tables
• These can be visualized with reporting tools
• BSI Teradata investigators used Tableau in
this episode to mock up dashboards
> Impacts on sales compared to other recalls
> Waterfall diagrams showing the recall
precision
> Effectiveness and efficiency reports for
both B2C and B2B recall campaigns
Photo: Tableau

• These results link to key performance
indicators (KPIs) for Great Brands
• Wiley’s next step is to come up with mobile
executive dashboards so they can self-
59 monitor on tablets how Teradata BSI Studios 2013
© recalls are going

Reporting: Tableau Recall Dashboard


SCENE 4

A year later, Jodice and Wiley link up at a coffee shop

What was the impact of building the new Track and Trace
system?

Scene 4: Impact

• 1 year later – system has been used for more recall cases
and we take a look at the impacts. Some key KPIs are:

> Speed and Accuracy of Exploration, Root Cause Analysis
> Speed, Precision, and Accuracy of Recalls

• Jodice asks Wiley for an example recall they did with the
new system and he shows her the results for a pepper-
crusted salami product. The problem was bad spices that
were imported from overseas. International tracing can also
be included in the system.

Overall result: much more in control and reduced risk!!!


Salami Recall
Process Improvement Faster isolation and
Compared to Previous System recall design steps:
from 13 to only 3 days

2 Days to
GET DATA

Time

2 days to 1 day to 8 days –
ISOLATE Design Run RECALL:
RECALL responses


Tableau – Waterfall Isolation of Targets


Social Listening Platform
• Great Brands is also now using
social media analytics to track all
mentions of Great Brands on
Twitter
> Subscription service to the
Twitter Firehose
• Keywords include “Great Brands”
“sick” “ill” “food poisoning”
> Can also use this to track
comments about competitors
• Provides very early warning
about potential problems
> In this case, Great Brands
spotted the salami problem
before the government health
officials knew there was a
problem


For more information – UDA

• Teradata UDA
> http://www.teradata.com/products-and-services/unified-data-
architecture/


For more information - Aster

Teradata Aster: www.asterdata.com


For more information – Teradata Applications
www.aprimo.com – Component used is “Teradata Customer
Management Interaction Manager”


For more information: Teradata
• www.teradata.com


For more Information: our partner Tableau
• www.tableausoftware.com


For more information
Consumer Goods Manufacturing Industry …


Thanks for watching!
• This episode appears at:
http://bit.ly/13M0SMl

• You can see all our episodes at www.bsi-teradata.com on Facebook:
links to 11 Videos and “How We Did It” Powerpoints


Other BSI: Teradata Episodes
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4.CASE OF THE CREDIT CARD BREACH
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5.CASE OF THE FRAGRANT SLEEPER HIT
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6.CASE OF DROPPED MOBILE CALLS
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7, 8, 9: The SAD CASE OF STAGNOBANK
Customer service is lousy, most marketing offers are rejected by customers, and
the bank has lost its appeal to younger households. BSI is engaged to work on
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