Journal of American Science, 2010;6(11) http://www.americanscience.org http://www.americanscience.org editor@americanscience.org 999 Data Mining Methodology in Perspective of Manufacturing Databases Muhammad Shahbaz 1* , Syed Athar Masood 2 , Muhammad Shaheen 3 , Ayaz Khan 4 1,3 Department of Computer Science & Engineering, UET Lahore, Pakistan 2 Department of Engineering Management, NUST College of E&ME, Rawalpindi, Pakistan 4 Forensic Expert/ Project Coordinator, National Response Center for Cyber Crimes (NR3C), FIA, Islamabad 1 m.shahbaz@uet.edu.pk, 2 atharmasood2000@hotmail.com , 3 m.shaheen@uet.edu.pk , 4 chaudhary.ayaz@gmail.com Abstract: In recent years data mining has become a very popular technique for extracting information from the database in different areas due to its flexibility of working on any kind of databases and also due to the surprising results. This paper is an attempt to introduce application of data mining techniques in the manufacturing industry to which least importance has been given. A taste of implement-able areas in manufacturing enterprises is discussed with a proposed architecture, which can be applied to an individual enterprise as well as to an extended enterprise to get benefit of data mining technique and to share the discovered knowledge among enterprises. The paper proposes conceptual methods for better use of different data mining techniques in product manufacturing life cycle. These techniques include statistical techniques, neural networks, decision trees and genetic algorithms. An integrated and unified data mining platform is anticipated then to improve overall manufacturing process. [Muhammad Shahbaz, Syed Athar Masood, Muhammad Shaheen, Ayaz Khan. Data Mining Methodology in Perspective of Manufacturing Databases. Journal of American Science 2010;6(11):999-1012]. (ISSN: 1545-1003). Keywords: Data Mining, Manufacturing, Industrial application, Data Mining methodologies, Data Warehousing 1. Introduction The progress in data acquisition and suc- cessful development of storage technology at cheaper rates, along with limited human capabilities in ana- lyzing and understanding big databases have tempted scientists and researchers to move forward towards the specific field of knowledge discovery in data- bases (KDD). This recently emerged discipline, lies at the intersection of data management, artificial in- telligence, machine learning and statistics. Data Min- ing is the search for valuable information in large volumes of data. It is a cooperative effort between humans and computers. Humans design databases, describe problems and set goals; computers sift through data, looking for relationships and patterns that match these goals. The central step within the overall KDD process is data mining, the application of computational techniques in the task of finding patterns and models in data. The major areas enjoy- ing the benefits of KDD include banking, finance, business and medical sciences. Many companies in- cluding manufacturing enterprises all over the world, are now giving attention to the utilization of KDD technology for the improvement in their current status. KDD has not commonly been used in manu- facturing enterprises. The reasons for this are not certain, but it may be because of the long time scales and expenses involved in introducing new techniques in this area and also because of lack of awareness of the benefits offered by this new data mining technol- ogy. An alternative possible reason might be the complexity and diversity of different manufacturing processes as these make it very difficult to devise a generic data mining process that can be used for all kinds of manufacturing processes and can handle all types of manufacturing problems. Data is stored in most manufacturing enterprises for quality control and traceability reasons or sometimes for simple sta- tistical analysis to provide information on where the enterprise is currently standing against its past per- formance or its competitors. These databases may also be consulted if any problem occurs in the manu- facturing process but the operational knowledge that exists within these databases is generally not ex- ploited beyond these types of activities. Competitive improvement can be achieved in many ways, for example by improving the quality of prod- ucts or by reducing the material waste, production or overhead costs, or by decreasing the time to launch a new or improved product. Data Mining can support these improvements, through the extraction of know- ledge from either existing data warehouses, or from current production data. Applying this knowledge can help to improve the quality of products by better con- trolling the manufacturing processes and methodolo- gies, and by keeping product and production parame- ters in range. Computer integrated manufacturing systems as well as more simply controlled enterprises, gener-