BigData & Supply Chain: A "Small" Introduction
4 likes3,514 views
The document discusses the concept of big data as a transformative force in supply chain management, emphasizing its significance in innovation and decision-making through the analysis of large and diverse information sets. It highlights the three key aspects of big data: volume, velocity, and variety, and illustrates its impact on various industries, particularly through case studies like eBay and Zipcar. Additionally, it addresses the challenges and misconceptions associated with big data, such as the importance of understanding correlation versus causation.
BigData & Supply Chain: A "Small" Introduction
- 1. 1 Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain
- 2. 2 …let’s start with what is not: Gathering all the data generated Storing all the data for years and years Predicting the future (demand, offer, stocks,… and thus prices) Necessarily so «BIG» (Lean IT approach) Bold Titles: “BIG DATA = BIG MONEY” (Source: Yahoo Finance) What does BigData mean? Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain
- 3. 3 5 Billion Mobile Phones (2010) $600 to buy a disk drive that can store all the world music 30 Billion pieces/month of content shared on Facebook $600 Billion potential annual consumer surplus from using personal data globally 40% year projected growth in global data 5% growth in IT spending globally 60% potential increase in retailers operating margins 190.000 job positions with deep analytical skills BigData Facts & Figures Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Source: McKinsey Global Institute M & A BIGDATA: Top Mislead Tech Buzzword 2013!
- 4. 4 “Big data” is high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” (Doug Laney – Gartner) BigData: a definition Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Source: McKinsey Global Institute “Big data” 3Vs Volume Variety Velocity
- 5. 5 BigData 3V: Volume Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Facts: every day are generated 2.5 10^12 bytes (trillion) of new data (…that does not mean necessarily also new information: redundancies) hourly transactions of Walmart generates a volume of data that is equal to the data stored in the Library of Congress cost of storing per gigabytes: from $1 billions (1980) to less than $0,05 (2010) Source: BCG's Perspectives, «How to get started with BigData»
- 6. 6 BigData Volume: a business case Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain What? Time? Positi- -on? DATA
- 7. 7 …it’s a question of “lead” time! BigData 3V: Velocity Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain
- 8. 8 BigData 3V: Velocity Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Facts: the “Same day delivery” battle between EBay andAmazon: EBay doesn’t own warehouses like its competitor Amazon …but, EBay has been able to provide to its customer a “same day of delivery” service by establishing partnership with retailers and by handling a network of retailers as a distributed inventory AlsoWal-Mart and Google (Google Shopping) are developing such a business model like EBay Source: Reuters, «EBay expands same-day delivery in local battle with Amazon»
- 9. 9 Managing different data sources altogether (ERP, GPS devices, RFIDs, social networks,…) Structured Vs UN-structured data: Structured: inventorying all the items in a warehouse or a vendor list are structured data that can be handled by designing a data model for a relational database (RDBMS) UN-structured data coming from blogs and social network they need database architectures that are not relational (NoSQL Databases) BigData 3V: Variety Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain See: http://nosql-database.org/
- 10. 10 SEO: Search Engine Optimization (“semantic” search) BigData: an example Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Content (words) Context (NW) SEO Where IS the PROBLEM: CONTENT or CONTEXT? CANDIDATE? Where IS…? …A GOOGLE SEARCH: MIB? Source: David Amerland, «Google Semantic Search»
- 11. 11 BigData as a tool… Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain BigData Analytics DATA INSIGHTS PREDICTION MODELS: MA, AR,ARMA, Kalman Filter, Neural Networks, Customized,… Assumptions Raw Materials Products
- 12. 12 Data Science Pyramid How to «BigData»? Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Visualization Knowledge Extraction Feature Extraction Cleaning Integration Storage Data GatheringSelection Ground Level: the Question? Source: Data Community DC, «The Pyramid of Data Science»
- 13. 13 Common «S.L.I.P.S.»… Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain BIGDATA 4) PSYCHOLOGY 1) STATISTIC 2) LEARNING 3) INFORMATION 5) SOURCES …when purchasing a B.I. tool! BIGDATA S.L.I.P.S
- 14. 14 Mislead Correlation with Causation Discovering correlations among variables doesn’t necessarily imply a cause-effect relation Mathematically speaking, correlation is a necessary but not sufficient condition for a cause-effect relationship BigData SLIP N.1: Statistic Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Aspects: Correlation among data: must be estimated and verified Representativeness of the data used for estimating correlationcausation relationship. (statistical inference)
- 15. 15 A cause-effect relationship exists: Validation: which modelalgorithm to chose in order to describe the relationship? A model validation is not valid “forever”: constantly monitor and measure the error of prediction = estimated – actual value Choosing a model implies making assumptions: be open to break them BigData SLIP N.2: Learning Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Aspects: The modelalgorithm used by the prediction tool (Assumptions) Error of prediction (learning)
- 16. 16 What information actually is? BigData SLIP N.3: Information Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain DATA (Facts) Assumptions (Culture, Mindset) Information Case Study: how a western say YES or NOT in India by moving the head and viceversa
- 17. 17 BigData SLIP N.3: Information Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Aspects: Which is the data that is relevant in order to provide (the) Information? Information is the answer to the question (the base of the data science pyramid) Relevant data: 1) What + 2) Time + 3) GEO Position and… that’s it! Information is an interpretation of data (facts): are the assumptions behind information constantly verified? …and thus be able to learn (break assumption)
- 18. 18 What about Sentiment Analysis? Key Aspects (issues): eco-chamber effects social influence: social media might amplify irrational behaviors. individuals base its decisions also on the actions of those who act before them (influencers) negative and positive SEO tactics sentimenters: treat them carefully. The risk is to gather datainformation intrinsically biased BigData SLIP N.4: Psychology Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain
- 19. 19 Handling different Sources (remember the “V” Variety) What about the reliability of the sources of data for the analysis? There is always a bias that can not be eliminated: the variance of the data sample How to improve predictionanalysis reliability? By gathering data from different (independent) sources and weight them accordingly to its “quality”: the variance (e.g.: MIMO antennas array) BigData SLIP N.5: Sources Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Key Aspects: Variance as a measure of the quality of data Data redundancy (variety of sources) is not useless. A lot of high volatile data from independent sources might be together a good source of data (low variance)
- 20. 20 Thanks to conversation with experts… A Golden Role from Data Scientist K.Borne Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain SEO & Semantic AuthorExpert Data Scientist 1° BigData influencer “Semantic” SEO SEO = CONTENT + CONTEXT ASSUMPTION: NO BIASES Source: D. Amerland, K. Borne and I.Gruer, Twitter conversation
- 21. 21 The Double Side of IT.. BigData, IT and the value chain… Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain NEEDS BigData (IT) 1- Automation (Effinciency) 2-Value Creation (Effectiveness)
- 22. 22 An Example How BigData Changes Business’ Models Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain How ZipCar’s changed car sharing Business Model Source: MIT Sloan: Management Review, «Video: What DigitalTransformation Means for Business»
- 23. 23 Value Chains Network and… Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain RISK: New Disruptive Business Model = A Disrupted Supply Chain
- 24. 24 A BigData initiative method to anticipate things… …how to survive from disruptions? Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain NEEDS BigData (IT)1- Automation (Effinciency) 2-Value Creation (Effectiveness) Why NOT NPD? PUGH Matrix QFD Matrix KJ Method New Product Development Source: I.Gruer, «IT and Procurement: Opportunities and Implementation of New analytics thecnology fro BigData»
- 25. 25 Is «BigData» different from Business Intelligence tools? (‘90s) What has changed in the economic enviroment (technology) from ‘90s to present? BigData & Supply Chain… So What? Fuel for Innovation: Data, Information and ICT Tools in the Supply Chain Contact Info Mail: ivan.gruer@gmail.com it.linkedin.com/in/ivangruer/
- 26. 26