Seven methods for transforming corporate data into business intelligence
Seven methods for transforming corporate data into business intelligence
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data preparation for data mining
Data preparation for data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Principles of data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Business Modeling and Data Mining
Business Modeling and Data Mining
| Hi-index | 0.00 |
In this paper we describe the experience of introducing data mining to a large chemical manufacturing company. The multi-national nature of doing business with multiple business units, presents a unique opportunity for the deployment of data mining. While each business unit has its own objectives and challenges, which may be at odds with those of other units, they also share many common interests and resources. In this environment, data mining can be used to identify potential value-creating opportunities, through large site integration of multiple assets and synergies from the use of common assets, such as site-wide manufacturing facilities, and world-wide supply-chain, purchasing and other shared services. However, issues arise, on one hand from overly complex systems, and on the other hand, from the danger of reaching sub-optimal solutions, if a big enough picture is not considered when executing projects. The company-wide initiative and use of Six Sigma at all levels of the company provided a fertile ground for making the case for data mining and facilitating its acceptance. The Six Sigma mindset of measuring the performance of processes and analyzing data promotes data-based decision making, therefore making data mining a natural extension of this methodology. We will describe the approach for launching a data mining capability within this framework, the strategy for securing upper management support, drawing from internal modeling, statistical, and other communities, and from external consultants and universities. Lessons learned from industrial case studies, enterprise-wide tool evaluation and peer benchmarking will be discussed.