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

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

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Great Brands, a major food producer, faces yet another recall. The government is pointing at Turkey Broccoli Lasagna as the culprit, so the Chief Risk Officer and Chief Supply Chain officer bring in ...

Great Brands, a major food producer, faces yet another recall. The government is pointing at Turkey Broccoli Lasagna as the culprit, so the Chief Risk Officer and Chief Supply Chain officer bring in BSI investigators to help them build a better/faster track and trace system, using Big Data analytics.

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  • 04/01/13
  • 04/01/13 FSMA –Food Safety Modernization Act
  • Side note: Harvey W. Wiley was the originator of the FDA! 04/01/13
  • 04/01/13
  • From presentation on Grand Rounds by the CDC, Doyle of U-Ga author, p. 61 04/01/13
  • See Excel spreadsheet 04/01/13
  • 04/01/13
  • 04/01/13
  • SAX - Enables Machine Data Analysis, such as analysis of sensor data in Manufacturing. Identify anomalies in manufacturing production process or performance of devices. Sequential Pattern - Automatically identify frequent patterns in sequential data. Attensity ASAS - Entity/event extraction, classification, sentiment analysis. Confusion Matrix - Used in machine learning for quantifying the performance of an algorithm and helps improve predictive models. Returns true/false positives/negatives. Single Decision Tree - Build and apply a single decision tree for classification. Identify important variables (and disregard irrelevant) that play role in making a decision. Distribution Matching – Test the hypothesis that the data is distributed from a certain distribution and estimate the parameters of several distributions that may fit the data. LARS – Selects a set of variables that are the most important in the context of a linear regression analysis. Can be used as LARS or Lasso. Fpgrowth – An association mining algorithm for recommendation engines. Discover elements that co-occur frequently in large datasets. Histogram –Counts the number of observations that fall into each of the disjoint intervals. IP Geo/Mapping - Identify the location using IP address New Slide: Synergistic multi-genre analytics Combine: Mfg Yield Management = SQL + Statistical + Time Series + … Location analytics = geospatial + time series + sql Digital Mktg analytics = sql +time series + statistics + Text + Graph.. Social Media Analytics = SQL + Graph +Text (Attensity) +Statistics Churn Analytics = SQL + Time series + statistics + text Recommendation/Affinity engines = SQL + Statistics + Graph + Time Series Fraud Analytics = SQL + Statistical + Graph
  •   This slide represents the high-level Aprimo vision for Integrated Marketing Management. We have been in this space for 13 plus years and we recognize the value and the viewpoint around a vision to create integrated marketing for organizations so they can successfully communicate across multiple channels to reach their customers efficiently and effectively. Looking at the center circle of this slide – we realize there are many functions within marketing - and we have to support those various levels within each organization. From the corporate marketing levels to the field and regional managers, to brand managers and business units. You also have to be able to reach and collaborate with the external suppliers and other external functions within the company yet outside of marketing (c-levels). And, after 13 years in the IMM space, we have also recognized the ability to utilize the number of channels continues to grow and the ability to communicate and how you communicate has evolved over the years.
  • 04/01/13
  • 04/01/13

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

  • BSI TERADATAEPISODE 11:HOW WE DID ITTHE CASE OF THE TAINTED LASAGNAWATCH 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 2Mike Rinaldi and Jodice Blinco2 © Teradata BSI Studios 2013
  • Story Synopsis Case of the Tainted LasagnaSituation 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 testingProblem • 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% toSolution 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 lawsuits3 © Teradata BSI Studios 2013
  • CAST OF CHARACTERSGreat Brands:•Chief Risk Officer: Wiley W. Harvey•VP Supply Chain Management: June DavisBSI:•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 DIRECTOR5 © Teradata BSI Studios 2013
  • 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 26 © Teradata BSI Studios 2013
  • 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.7 © Teradata BSI Studios 2013
  • SCENE 1At Great Brand Corporate HQProblems: discussion of the problems, risks, unwieldy currentarchitecture and processes. Commissions BSI to help
  • The Problem – Yet Another Recall9 © Teradata BSI Studios 2013
  • 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 technologies10 © Teradata BSI Studios 2013
  • 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 tm11 © Teradata BSI Studios 2013
  • Governments are getting aggressive about food safety Monitoring more closely, demanding compliance … Read More At: http://www.fda.gov/newsevents/newsroom/pressannouncements/ucm334156.htm12 © Teradata BSI Studios 2013
  • 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/13 © Teradata BSI Studios 2013
  • 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.html14 © Teradata BSI Studios 2013
  • The Food Industry Structure is Complex Simplified Picture15 © Teradata BSI Studios 2013
  • The Real Picture from a CDC TalkSource: CDC report at www.cdc.gov/about/grand-rounds/archives/2009/.../GR-121709.pdf16 © Teradata BSI Studios 2013
  • 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.17 © Teradata BSI Studios 2013
  • Checkpoint – How Long Does It Take Today for Great Brands to Run A Recall? Answer: it depends, but usually at least 30 days.18 © Teradata BSI Studios 2013
  • Sales Impact of Recalls Can Be Huge $ Losses19 © Teradata BSI Studios 2013
  • Recalls Also Impact GBRA Stock Price3 Examples20 © Teradata BSI Studios 2013
  • SCENE 2Back at BSI offices, Jodice and Mike tap into the data usingnew technology to see what changes to recommendBSI Analytics for Track and TraceGoals – use tools to identify root causes fast (using big dataacross the supply chain) and create/execute recalls faster
  • Simplified Process View22 © Teradata BSI Studios 2013
  • 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:23 © Teradata BSI Studios 2013
  • Government Epidemiological Chart – Case Outbreaks Drive Interviews Which Lead to Suspect FoodsThe process begins with a notification from the Government when they find frominterviews of sick people point that they all ate Great Brands Lasagna.24 © Teradata BSI Studios 2013
  • 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 monitoring25 © Teradata BSI Studios 2013
  • 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 data26 © Teradata BSI Studios 2013
  • 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 consumers27 © Teradata BSI Studios 2013
  • 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.28 © Teradata BSI Studios 2013
  • 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.29 © Teradata BSI Studios 2013
  • 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.30 © Teradata BSI Studios 2013
  • 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 day31 © Teradata BSI Studios 2013
  • Added the Transportation Step32 © Teradata BSI Studios 2013
  • 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 Changa33 © Teradata BSI Studios 2013
  • 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.34 © Teradata BSI Studios 2013
  • Why Didn’t the Kill Step Solve the Problem?35 © Teradata BSI Studios 2013
  • Going Back to the Big Picture We discovered the contamination sources, now on to the Recall!36 © Teradata BSI Studios 2013
  • 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.37 © Teradata BSI Studios 2013
  • We create the Product Recall Notices There will be variations for different audiences38 © Teradata BSI Studios 2013
  • 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 Warehouses39 © Teradata BSI Studios 2013
  • 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.40 © Teradata BSI Studios 2013
  • Designed and kicked off the Recall Workflows41 © Teradata BSI Studios 2013
  • SCENE 3BSI 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 June43 © Teradata BSI Studios 2013
  • Three Key Technology Requirements44 © Teradata BSI Studios 2013
  • 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 reporting45 © Teradata BSI Studios 2013
  • 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 SURVEY46 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.47 © Teradata BSI Studios 2013
  • Data Flows48 © Teradata BSI Studios 2013
  • 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 tools49 © Teradata BSI Studios 2013
  • 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 Hadoop50 © Teradata BSI Studios 2013
  • 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 Analytics51 © Teradata BSI Studios 2013
  • 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 ss52 © Teradata BSI Studios 2013
  • Sample Analytics Modules in AsterFastest 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 analysis53 © Teradata BSI Studios 2013
  • 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, Unpack54 © Teradata BSI Studios 2013
  • Architecture: Teradata Aster Discovery PlatformFastest 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 frameworkProcess • 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 types55 © Teradata BSI Studios 2013
  • 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 Interactions56 © Teradata BSI Studios 2013
  • Teradata Application: Aprimo Components Used To Create and Run Recall Workflows57 57 © Teradata BSI Studios 2013
  • Recall Workflows58 © Teradata BSI Studios 2013
  • 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 Dashboard60 © Teradata BSI Studios 2013
  • SCENE 4A year later, Jodice and Wiley link up at a coffee shopWhat was the impact of building the new Track and Tracesystem?
  • 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!!!62 © Teradata BSI Studios 2013
  • 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 responses63 © Teradata BSI Studios 2013
  • Tableau – Waterfall Isolation of Targets64 © Teradata BSI Studios 2013
  • 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 problem65 © Teradata BSI Studios 2013
  • WRAPUP
  • For more information – UDA • Teradata UDA > http://www.teradata.com/products-and-services/unified-data- architecture/67 © Teradata BSI Studios 2013
  • For more information - Aster Teradata Aster: www.asterdata.com68 © Teradata BSI Studios 2013
  • For more information – Teradata Applications www.aprimo.com – Component used is “Teradata Customer Management Interaction Manager”69 © Teradata BSI Studios 2013
  • For more information: Teradata • www.teradata.com70 © Teradata BSI Studios 2013
  • For more Information: our partner Tableau • www.tableausoftware.com71 © Teradata BSI Studios 2013
  • For more information Consumer Goods Manufacturing Industry …72 © Teradata BSI Studios 2013
  • 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” Powerpoints73 © Teradata BSI Studios 2013
  • Other BSI: Teradata Episodes1.CASE OF THE DEFECTING TELCO CUSTOMERS A Telco uses analytics to see why they have a big customer retention problem2.CASE OF THE MIS-CONNECTING PASSENGERS An Airline improves its customer rebooking engine using analytics3.CASE OF THE RETAIL TWEETERS A Fashion Retailer uses social media tweets to get insights on hot and cold products and to find the FashionFluencers!4.CASE OF THE CREDIT CARD BREACH A Bank and a Retailer collaborate to solve a stolen Credit Card case5.CASE OF THE FRAGRANT SLEEPER HIT A Consumer Goods Manufacturer uses Social Media to recalibrate Manufacturing and Marketing plans6.CASE OF DROPPED MOBILE CALLS A major Telco digs into real-time dropped call data to understand high-value and high-influence customers, where to place new towers. Create 5 campaigns to retain their most valuable and influential customers .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 new ideas for Better Marketing, Better Customer Service, and New Mobile Apps.10. CASE OF THE RETAIL TURNAROUND A Big-Box Retailer learns how to use web path purchase and bailout analytics to create ways of driving shoppers into stores.74 © Teradata BSI Studios 2013