DM_Case_Studies_for_Students

DM_Case_Studies_for_Students - Data Mining Industrial...

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Data Mining Industrial Projects and Case Studies Kwok-Leung Tsui Industrial and Systems Engineering Georgia Institute of Technology
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1. AT&T business data mining 2. Inventory management in military maintenance 3. Sea cargo demand forecasting 4. SMATRAQ project in transportation policies 5. Location problem of letterbox 6. Home improvement store shrinkage analysis 7. Hotels & resorts chain data mining 8. Used car auction sales data mining 9. Fast food restaurant call center Industrial Projects
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Data Mining in Telecom. (Funded AT&T project) z ~160 billion dollar per year industry (~70 B long distance & ~90 B dollars local) z 100 million + customers/accounts/lines z >1 billion phone calls per day z Book closing (Estimating this month price/usage/revenue) z Budgeting (Forecasting next year price/usage/revenue) z Segmentation (Clustering of usage, growth, …) z Cross Selling (Association Rule) z Churn (Disconnect prediction & Tracking) z Fraud (Detection of unusual usage time series behavior) z Each of these problems worth hundreds millions dollars
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z A contractor manages parts inventory for aircraft maintenance z Characterization and forecasting of demand and lead time distributions z 60,000 different parts and 500 bench locations z Data tracked by an automated system z Demand data not available & stockout penalty Inventory Management in Air Force (Funded project)
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z Sea cargo network optimization z Contract planning & booking control z Characterize & forecast sea cargo demand distribution & cost structure z Improve ocean carrier and terminal operation efficiency Data Mining in Sea Cargo Application (Funded TLIAP project)
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z Strategies for Metropolitan Atlanta’s Regional Transportation & Air Quality z Five-year project sponsored by Transportation Dept., Federal Highway Admin., EPA, CDC, etc. z Assess air quality, travel behavior, land use & transportation policies z Reduce auto-dependence and vehicle emissions SMARTRAQ Project for Transportation Policies
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z Improve performance of express mail dropoff letter boxes z 50,000 letter boxes & 8 month transaction data z Relate performance with important factors, e.g. regions, demographic, adjacent competition, pick-up schedule z Comparison with direct competitors z Customer demand analysis and forecast Mining of Letter Box Transaction Data
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z Inventory shrinkage costs US retailers 32 billions z Shrinkage = book inventory – inventory on hand z Working with a home improvement store’s Loss Prevention Group z Develop predictive model to relate shrinkage to important variables z Extract hidden knowledge to reduce loss and improve operation efficiency Data Mining for Shrinkage Analysis in Retail Industry
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z Manage chain hotels and resorts in different scale z Evaluate impact of promotional programs z Forecasting of customer behavior in frequent stay program z Monitor performance in customer survey z Predict performance with important factors Data Mining for Hotels and Resorts Chain Business
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z Maintain all used car auction data in last 20 years z Provide service to customers and dealers on auction
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This note was uploaded on 11/13/2010 for the course ISE 680 taught by Professor Santanu during the Spring '10 term at Purdue University Calumet.

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DM_Case_Studies_for_Students - Data Mining Industrial...

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