Data mining in the chemical industry

ABSTRACT

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.

REFERENCES

• 1: Anderson, E. W. and Mittal, V., Strengthening the Satisfaction-Profit Chain, Journal of Service Research, Volume 3, No. 2, (Nov. 2000), 107--120.
• 2: Michael J. Berry , Gordon Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., New York, NY, 1997
• 3: George Edward Pelham Box , Gwilym M. Jenkins, Time Series Analysis: Forecasting and Control, Prentice Hall PTR, Upper Saddle River, NJ, 1994
• 4: Breyfogle, W. III, Implementing Six Sigma - Smarter Solutions Using Statistical Methods, Wiley-Interscience, 1999.
• 5: Vasant Dhar , Roger Stein, Seven methods for transforming corporate data into business intelligence, Prentice-Hall, Inc., Upper Saddle River, NJ, 1997
• 6: Dick, A. S. and Basu, K., Customer Loyalty: Toward an Integrated Conceptual Framework, Journal of the Academy of Marketing Science, 22 (2), (1994), 99--113.
• 7: Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth, From data mining to knowledge discovery: an overview, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 8: Gale, B. T., Managing Customer Value, The Free Press, New York, New York, 1994.
• 9: David J. Hand , Padhraic Smyth , Heikki Mannila, Principles of data mining, MIT Press, Cambridge, MA, 2001
• 10: Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall PTR, Upper Saddle River, NJ, 1998
• 11: Johnson, M. and Gustafsson, A., Improving Customer Satisfaction, Loyalty and Profit: An Integrated Measurement and Management System. San Francisco: Jossey-Bass, 2000.
• 12: Lee, C., Mentele, J., Gaver, Rey, T.D., Structured Neural Network Techniques for Modeling Loyalty and Profitability, SUGI 2005.
• 13: Oliver, R. L., Satisfaction: A Behavioral Perspective on the Consumer. New York: McGraw-Hill, 1997.
• 14: Dorian Pyle, Data preparation for data mining, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1999
• 15: Readers' Advantage, Business Modeling and Data Mining, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2003
• 16: Reichheld, F. The Loyalty Effect: The Hidden Source Behind Growth, Profits, and Lasting Value. Boston: Harvard Business School Press, 1996.
• 17: Rey, T. D. and Johnson, M., Modeling the Connection Between Loyalty and Financial Impact: A Journey. In Earning a Place at the Table, 23rd Annual Marketing Research Conference, American Marketing Association, Chicago, IL, September 8-11, 2002.
• 18: Rey, T. D., Tying Customer Loyalty to Financial Impact. In Symposium on Complexity and Advanced Analytics Applied to Business, Government and Public Policy Society for Industrial and Applied Mathematics, Great Lakes Section, University of Michigan, Dearborn Campus, October 23, 2004.
• 19: Rust, Z. and Kenningham, Return on Quality: Measuring the Financial Impact of Your Company's Quest for Quality, Probus Professional Publishing, 1993.
• 20: Xue Zhang Wang , C. McGreavy, Data Mining and Knowledge Discovery for Process Monitoring and Control, Springer-Verlag, London, 1999
• 21: SAS Institute, http://www.sas.com/technologies/analytics/~datamining/miner/semma.html, 2005.
• 22: Shearer, C., The CRISP-DM Model: The New Blueprint for Data Mining. In Journal Data Warehousing, Vol. 5, No. 4, (2000), 13--22.
• 23: Ian H. Witten , Eibe Frank, Data mining: practical machine learning tools and techniques with Java implementations, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.5 Model Development
          Subjects: Modeling methodologies

General Terms:
Design, Documentation, Human Factors, Management, Measurement, Performance, Standardization, Verification

Keywords:
chemical industry, data mining, manufacturing