Visual and analytical mining  of transactions data  for production planning  and marketing   Gurdal Ertek, Can Kuruca, Cen...
Introduction <ul><li>Motivation </li></ul><ul><ul><li>Large amounts of enterprise data available </li></ul></ul><ul><li>Da...
Motivation: Explosion of Data <ul><li>Data from marketing </li></ul><ul><ul><li>Barcode systems, accounting software, ERP ...
Data Mining <ul><li>Effective collection, management, reporting, interpretive analysis and mining of enterprise data: </li...
Sales Transaction Data <ul><li>Collected and archieved in almost every firm </li></ul><ul><li>Essential input for both mar...
Literature Review <ul><li>… -1990’s: Scatterplot, boxplot, … </li></ul><ul><li>1990-2000’s: Information visualization </li...
Proposed Framework <ul><li>Filtering </li></ul><ul><li>Clustering </li></ul><ul><li>Comparison </li></ul>
Implementation of the Proposed Framework: CuReMa
Filtering: Interactive Visual Querying
Clustering: Analytical Data Mining
Comparison: Visual Data Mining
Comparison: Visual Data Mining
Future Work <ul><li>Other visual metaphors and analytical approaches can be used to extend the framework </li></ul><ul><ul...
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Visual and analytical mining of sales transaction data for production planning and marketing

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Ertek, G., Kuruca, C., Aydin, C., Erel, B.F., Dogan, H., Duman, M., Ocal, M., and Ok, Z.D. (2004). “Visual and analytical mining of sales transaction data for production planning and marketing”. 4th International Symposium on Intelligent Manufacturing Systems, Sakarya, Turkey.

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Visual and analytical mining of sales transaction data for production planning and marketing

  1. 1. Visual and analytical mining of transactions data for production planning and marketing Gurdal Ertek, Can Kuruca, Cenk Aydin, Besim Ferit Erel, Harun Dogan, Mustafa Duman, Mete Ocal, Zeynep Damla Ok SABANCI UNIVERSITY
  2. 2. Introduction <ul><li>Motivation </li></ul><ul><ul><li>Large amounts of enterprise data available </li></ul></ul><ul><li>Data mining </li></ul><ul><ul><li>Deriving necessary and meaningful information out of data </li></ul></ul><ul><li>Framework combining visual and analytical data mining </li></ul><ul><ul><li>Filtering --- Interactive pie charts </li></ul></ul><ul><ul><li>Clustering --- k-means algorithm </li></ul></ul><ul><ul><li>Comparison --- Parallel coordinate plot </li></ul></ul>
  3. 3. Motivation: Explosion of Data <ul><li>Data from marketing </li></ul><ul><ul><li>Barcode systems, accounting software, ERP software, e-commerce data (B2B and B2C) </li></ul></ul><ul><li>Data from manufacturing </li></ul><ul><ul><li>CIM systems, barcode, radio frequency technologies </li></ul></ul>
  4. 4. Data Mining <ul><li>Effective collection, management, reporting, interpretive analysis and mining of enterprise data: </li></ul><ul><ul><li>Establishing effective control of manufacturing activities </li></ul></ul><ul><ul><li>Achieving effective production planning and increased sales, and consequently increasing the firm’s profitability </li></ul></ul><ul><ul><li>Increasing customer satisfaction by offering and timely delivering them products that they are willing to purchase. </li></ul></ul><ul><li>CRM: Customer Relationship Management </li></ul>
  5. 5. Sales Transaction Data <ul><li>Collected and archieved in almost every firm </li></ul><ul><li>Essential input for both marketing and production planning </li></ul><ul><li>Framework and prototype implementation CuReMa </li></ul>
  6. 6. Literature Review <ul><li>… -1990’s: Scatterplot, boxplot, … </li></ul><ul><li>1990-2000’s: Information visualization </li></ul>
  7. 7. Proposed Framework <ul><li>Filtering </li></ul><ul><li>Clustering </li></ul><ul><li>Comparison </li></ul>
  8. 8. Implementation of the Proposed Framework: CuReMa
  9. 9. Filtering: Interactive Visual Querying
  10. 10. Clustering: Analytical Data Mining
  11. 11. Comparison: Visual Data Mining
  12. 12. Comparison: Visual Data Mining
  13. 13. Future Work <ul><li>Other visual metaphors and analytical approaches can be used to extend the framework </li></ul><ul><ul><li>Ex: Drawing association rules </li></ul></ul><ul><li>Other data fields can be incorporated </li></ul><ul><ul><li>Ex: Ages and income levels of customers </li></ul></ul><ul><li>Other clustering algorithms can be used </li></ul><ul><ul><li>Ex: Self-organizing maps </li></ul></ul><ul><li>Other criteria for clustering can be implemented </li></ul><ul><ul><li>Ex: Recency and frequency of purchases </li></ul></ul><ul><li>Localization issues </li></ul><ul><ul><li>Ex: Inflation and local holidays </li></ul></ul>

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