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The Use of Big Data and Data Mining in Supply Chains

Explore the use of big data and data mining in supply chains, including the types of data used, examples of its application, and the potential benefits and opportunities it presents. Discover how companies can leverage big data to improve decision-making, optimize supply chain operations, and enhance customer service. Gain insights into the role of vertical and horizontal data scientists in implementing big data solutions, and learn about the various supply chain functions that can be improved through big data analytics.

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The Use of Big Data and Data Mining in Supply Chains

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  1. The Use of Big Data and Data Mining in Supply Chains David L. Olson College of Business Administration University of Nebraska-Lincoln

  2. 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 warehouse • THE MOST IMPORTANT ASPECT IS LACK OF STRUCTURE, NOT SIZE • The point is to ANALYZE • Convert data into insights, innovation, business value • Waller & Fawcett (2013) • Shed obsession for causality in exchange for simple correlations • Not knowing why, but only what

  3. Governmental & Non-Profit ExamplesDobbs et al. 2014, McKinsey Report • European & US food safety regulations • Need to monitor, gather data • Need to analyze • Hospitals • Biological data • Operational data • Insurance data • Schools • Government • Monitor Web site use • Monitor use of apps

  4. Data Types (Davenport, 2014) • Text & Voice • Been around forever • Internet presence initiates a new era (text mining) • Social Media data • Sentiment analysis – identify opinions from posted comments • Sensor data • The “Internet of Things” • Digital cow – sensors in 2nd stomach • Humans – sensors for fitness, productivity, health • Industrial – manufacturing, transportation, energy grids

  5. Contemporary Big Data Examples • Baseball • Moneyball • Flu detection • Google searches • Wal-Mart disaster relief • Hurricane Katrina • Pop-tarts & water

  6. Sathi (2012) • Internal Corporate data • Generated by e-mails, logs, blogs, documents • Business process events • ERP • External to firm • Social media • Competitor literature • Customer Web data • Complaints

  7. Mayer-Schonberger & Cukier (2013) • Logistics firm • Masses of data – product shipments • Turned into a source of revenue • Accenture • Big data provides • Better customer service • More effective order fulfillment • Faster response to supply chain problems • Greater overall efficiency • Zillow • Masses of real estate data

  8. Supply Chain Analytics • Big data supports real-time decision making • Grocery stores • Wal-Mart • American Airlines – yield management • Trucking – monitor real-time breakdown response • SUPPLY CHAIN ANALYTICS (Chae 2014) • Data management resources • Data acquisition & management (RFID, ERP, database) • Analysis (data mining) • IT-based supply chain planning resources • Performance management resources • Statistical process control, Six Sigma, etc.

  9. Supply Chains & Big Data • RFID/GPS • Tracking now affordable • Manufacturing links to supply chains • Discrete manufacturing has for some time • Process industries (oil refining) behind

  10. Example Supply Chain Big Data SourcesWaller & Fawcett (2013a) – Journal of Business Logistics

  11. Supply Chain Analytics Objectives • Cost reduction • Develop innovative new products & services • LinkedIn – developed array of offerings • Google • Zillow real estate site • Reduce time needed to analyze • Department store chain – 73 million items • Reduced 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 minutes • Better discovery process • Support Internal Business Decisions • United Healthcare – insurance • Analyze customer attrition • Wells Fargo, Bank of America, Discover use for multichannel CRM • Unstructured data – website clicks, transaction records, banker notes, voice recordings from call centers

  12. Responsibility Locus for SCAProjects

  13. Vertical vs. Horizontal Data Scientists • VERTICAL • In-depth technical knowledge of narrow field • Econometricians • Software engineers • HORIZONTAL • Blend: 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 TEAM • WANT TO AUTOMATE AS MUCH AS POSSIBLE

  14. Big Data Opportunities to Improve:Waller & Fawcett (2013b) - Journal of Business Logistics • Demand forecasting • Link real-time sensors to machine-learning algorithms • Bar-coded checkout & Wal-Mart RFID chips already exist • Enables real-time response • Warehouse design & location • System design for optimality • A classical operations research problem • Can use network analysis to be more complete • Supplier evaluation & selection • Probably the most commonly researched supply chain function • Can consider more factors, more up-to-date data • Selection of transportation nodes • Real-time truck/rail assignment • Already exists

  15. Company Examples (Davenport, 2014)

  16. US • Great economic changes • Wages too high • Outsourcing • Computer programming (service) to India • Manufacturing to China • Technology • Robotics – no health benefits, no vacations, no complaints • Computers • ERP systems replacing multiple legacy systems • Layoff most human IT people • Business Analytics • BIG DATA

  17. 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 Economy • Computer progress advancing exponentially • AFFECT ON • Jobs • Skills • Wages • The Economy

  18. Supply Chain Areas with Big Data Impact • Globalization • Japan; Asian Tigers; BRIC Supply Chain involvement • Digitization • Enterprise systems Supply Chain Enabler • Paradox: More Integrated Systems ˃˃ Fewer Systems People • Energy supply • Peak Oil (Fracking) Big Data won’t predict major shifts • Global warming • Complexity • Unintended consequences Medicare false positives • DEREGULATION/PRIVATIZATION • Home mortgage crisis Reliance on statistics gone wrong

  19. Potential Areas of Interest – SCA & Big DataFriedman (The World is Flat) • THREE CONVERGENCES • New players (through global access) • BRICS • New playing field (Web economy) • Global warming • Green emphasis • Cultural conflicts • Ability to develop new ways

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