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

Introduction Motivation Large amounts of enterprise data available Data mining Deriving necessary and meaningful information out of data Framework combining visual and analytical data mining Filtering --- Interactive pie charts Clustering --- k-means algorithm Comparison --- Parallel coordinate plot

Motivation

Large amounts of enterprise data available

Data mining

Deriving necessary and meaningful information out of data

Framework combining visual and analytical data mining

Filtering --- Interactive pie charts

Clustering --- k-means algorithm

Comparison --- Parallel coordinate plot

Motivation: Explosion of Data Data from marketing Barcode systems, accounting software, ERP software, e-commerce data (B2B and B2C) Data from manufacturing CIM systems, barcode, radio frequency technologies

Data from marketing

Barcode systems, accounting software, ERP software, e-commerce data (B2B and B2C)

Data from manufacturing

CIM systems, barcode, radio frequency technologies

Data Mining Effective collection, management, reporting, interpretive analysis and mining of enterprise data: Establishing effective control of manufacturing activities Achieving effective production planning and increased sales, and consequently increasing the firm’s profitability Increasing customer satisfaction by offering and timely delivering them products that they are willing to purchase. CRM: Customer Relationship Management

Effective collection, management, reporting, interpretive analysis and mining of enterprise data:

Establishing effective control of manufacturing activities

Achieving effective production planning and increased sales, and consequently increasing the firm’s profitability

Increasing customer satisfaction by offering and timely delivering them products that they are willing to purchase.

CRM: Customer Relationship Management

Sales Transaction Data Collected and archieved in almost every firm Essential input for both marketing and production planning Framework and prototype implementation CuReMa

Collected and archieved in almost every firm

Essential input for both marketing and production planning

Framework and prototype implementation CuReMa

Literature Review … -1990’s: Scatterplot, boxplot, … 1990-2000’s: Information visualization

… -1990’s: Scatterplot, boxplot, …

1990-2000’s: Information visualization

Proposed Framework Filtering Clustering Comparison

Filtering

Clustering

Comparison

Implementation of the Proposed Framework: CuReMa

Filtering: Interactive Visual Querying

Clustering: Analytical Data Mining

Comparison: Visual Data Mining

Comparison: Visual Data Mining

Future Work Other visual metaphors and analytical approaches can be used to extend the framework Ex: Drawing association rules Other data fields can be incorporated Ex: Ages and income levels of customers Other clustering algorithms can be used Ex: Self-organizing maps Other criteria for clustering can be implemented Ex: Recency and frequency of purchases Localization issues Ex: Inflation and local holidays

Other visual metaphors and analytical approaches can be used to extend the framework

Ex: Drawing association rules

Other data fields can be incorporated

Ex: Ages and income levels of customers

Other clustering algorithms can be used

Ex: Self-organizing maps

Other criteria for clustering can be implemented

Ex: Recency and frequency of purchases

Localization issues

Ex: Inflation and local holidays