David L Olson College of Business Administration University of NebraskaLincoln BIG DATA Davenport 2014 Data too big to fit on single server Too unstructured to fit in rowandcolumn database ID: 528215
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Slide1
The Use of Big Data and Data Mining in Supply Chains
David L. Olson
College of Business Administration
University of Nebraska-LincolnSlide2
BIG DATA (Davenport, 2014)
Data too big to fit on single server
Too unstructured to fit in row-and-column database
Too continuously flowing to fit into static data warehouseTHE MOST IMPORTANT ASPECT IS LACK OF STRUCTURE, NOT SIZEThe point is to ANALYZEConvert data into insights, innovation, business valueWaller & Fawcett (2013)Shed obsession for causality in exchange for simple correlationsNot knowing why, but only whatSlide3
Governmental & Non-Profit Examples
Dobbs et al. 2014, McKinsey Report
European & US food safety regulations
Need to monitor, gather dataNeed to analyzeHospitalsBiological dataOperational dataInsurance dataSchools
Government
Monitor Web site use
Monitor use of appsSlide4
Data Types (Davenport, 2014)
Text & Voice
Been around forever
Internet presence initiates a new era (text mining)Social Media dataSentiment analysis – identify opinions from posted commentsSensor dataThe “Internet of Things”Digital cow – sensors in 2nd stomachHumans – sensors for fitness, productivity, health
Industrial – manufacturing, transportation, energy gridsSlide5
Contemporary Big Data Examples
Baseball
Moneyball
Flu detectionGoogle searchesWal-Mart disaster reliefHurricane KatrinaPop-tarts & waterSlide6
Sathi (2012)
Internal Corporate data
Generated by e-mails, logs, blogs, documents
Business process eventsERPExternal to firmSocial mediaCompetitor literatureCustomer Web dataComplaintsSlide7
Mayer-Schonberger & Cukier
(2013)
Logistics firm
Masses of data – product shipmentsTurned into a source of revenueAccentureBig data providesBetter customer serviceMore effective order fulfillmentFaster response to supply chain problemsGreater overall efficiencyZillowMasses of real estate dataSlide8
Supply Chain Analytics
Big data supports real-time decision making
Grocery stores
Wal-MartAmerican Airlines – yield managementTrucking – monitor real-time breakdown responseSUPPLY CHAIN ANALYTICS (Chae 2014)Data management resourcesData acquisition & management (RFID, ERP, database)Analysis (data mining)IT-based supply chain planning resources
Performance management resources
Statistical process control, Six Sigma, etc.Slide9
Knowledge Management
Elaboration
Performance management resources
How things are done (tacit knowledge, BPR)
Process control
Six Sigma
Information systems
Database, reports, decision support
Cloud computing
Data sources
ERP & related systems
External sources
Big data
RFID
Government publications
Social media
Analytics
Descriptive analysis
Data mining
Operations Research
Classification
PredictionClusteringLink analysisText miningMathematical programmingStochastic modelingMonte Carlo Simulation
Slide10
Supply Chains
& Big Data
RFID/GPS
Tracking now affordableManufacturing links to supply chainsDiscrete manufacturing has for some timeProcess industries (oil refining) behindSlide11
Example Supply Chain Big
Data Sources
Waller & Fawcett (2013a) –
Journal of Business LogisticsData Type
Volume
Velocity
Variety
Sales
More detail – price, quantity,
items, time of day, date, customer
From monthly & weekly to daily
& hourly
Direct sales, Distributor
sales, Internet sales, international sales, competitor sales
Consumer
More detail – items browsed & bought, frequency, dollar value, timing (RFM+)
From click through to card usage
Shopper
identification, emotion detection, “Likes”, “Tweets”, product reviews
Inventory
Perpetual inventory by style,
color, size
From monthly updates to hourly updates
Warehouse, store, Internet store, vendor inventories
Location/Time
Sensor
data to detect location, better inventory controlFrequent updates within store and in transitNot only where, but what is close, who moved it, path, future path, mobile device evidenceSlide12
Supply Chain Analytics Objectives
Cost reduction
Develop
innovative new products & servicesLinkedIn – developed array of offeringsGoogleZillow real estate siteReduce time needed to analyzeDepartment store chain – 73 million itemsReduced pricing optimization from 27 hours to around 1 hour
SAS high-performance analytics (HPA) – takes data out of Hadoop cluster, places in-memory on parallel computers
Financial asset management company
Analyze single bond issue, risk analysis using 25 variables, 100 simulations
With big data system can run 100 variables and 1 million simulations in 10 minutesBetter discovery process
Support Internal Business DecisionsUnited Healthcare – insuranceAnalyze customer attrition
Wells Fargo
,
Bank of America
,
Discover
use for multichannel
CRM
Unstructured data – website clicks, transaction records, banker notes, voice recordings from call centersSlide13
Responsibility Locus for SCA
Projects
DISCOVERY
PRODUCTION
Cost Savings
IT innovation group
IT architecture & operations
Product/Service Innovation
R&D/product
development group
Product development
Or Product management
Faster Decisions
Business unit or function
analytics group
Executive
Better Decisions
Business unit or function
analytics group
ExecutiveSlide14
Vertical vs.
Horizontal
Data Scientists
VERTICALIn-depth technical knowledge of narrow fieldEconometriciansSoftware engineersHORIZONTALBlend: business analysts, statisticians, computer scientists, domain experts
Vision with some technical knowledge
Focus on robust, efficient, simple, replicable, scalable applications
Horizontal more marketable
NEED A TEAMWANT TO AUTOMATE AS MUCH AS POSSIBLESlide15
Big Data Opportunities to Improve:
Waller & Fawcett (2013b) -
Journal of Business LogisticsDemand forecastingLink real-time sensors to machine-learning algorithmsBar-coded checkout & Wal-Mart RFID chips already existEnables real-time responseWarehouse design & location
System design for optimality
A classical operations research problem
Can use network analysis to be more complete
Supplier evaluation & selectionProbably the most commonly researched supply chain functionCan consider more factors, more up-to-date data
Selection of transportation nodesReal-time truck/rail assignmentAlready existsSlide16
Company Examples (Davenport, 2014)
LinkedIn
Start-up
Coined “data scientist– unified searcheBay
Start-up
Data hub,
virtual data marts
Kyruus
Start-upData about physician networks – track patient leakage
Recorded
Future
Start-up
Use Internet data to help predict
UPS
Established
Track packages,
monitor vehicles & route them
United Healthcare
Established
Take voice calls, put in text, text-mine
Macys.com
Established
Personalization of ads
Bank of America
Established
Better understand customers by channel
CitigroupEstablishedMonitor customer credit riskSears HoldingsEstablishedReal-time retail monitoringVerizon WirelessEstablishedSell data on mobile phone user behavior (movement, buying)Schneider InternationalEstablishedTrucking – sensors for location, driver behaviorSlide17
US
Great economic changes
Wages too high
OutsourcingComputer programming (service) to IndiaManufacturing to ChinaTechnologyRobotics – no health benefits, no vacations, no complaintsComputersERP systems replacing multiple legacy systemsLayoff most human IT peopleBusiness Analytics
BIG DATASlide18
Erik Brynjolfsson
and Andrew McAfee 2011 Digital Frontier Press
Race Against The Machine:
How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the EconomyComputer progress advancing exponentiallyAFFECT ONJobsSkillsWagesThe EconomySlide19
Supply Chain Areas with Big Data Impact
Globalization
Japan; Asian Tigers;
BRIC Supply Chain involvementDigitizationEnterprise systems Supply Chain EnablerParadox: More Integrated Systems ˃˃ Fewer Systems People
Energy
supply
Peak Oil (Fracking
) Big Data won’t predict major shiftsGlobal warming
ComplexityUnintended consequences
Medicare false positives
DEREGULATION/PRIVATIZATION
Home mortgage
crisis
Reliance on statistics gone wrongSlide20
Potential Areas of Interest – SCA & Big Data
Friedman
(The World is Flat)
THREE CONVERGENCESNew players (through global access)BRICSNew playing field (Web economy)Global warmingGreen emphasisCultural conflicts
Ability to develop new ways