Data Analysis Papers
The Quality Data Warehouse: Solving Problems for the Enterprise
Brad Klenz and Donna Fulenwider, SUGI Proceedings, 1999.
Abstract
Enterprise quality improvement is quite different from quality improvement at the process level. Enterprise quality improvement requires information from many departments within an organization, such as production, quality assurance, engineering, customer service, and purchasing. Typically, these groups collect large amounts of data from disparate and disconnected systems. The systems include a variety of Statistical Process Control (SPC) systems implemented on different production lines at different plants, as well as data collected in Laboratory Information Management Systems (LIMS), Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), and Manufacturing Resource Planning (MRP) systems. The disparity and disconnection of these systems poses a major problem for the implementation of enterprise quality improvement.
Data warehousing is a technology that is designed to facilitate analytical processing on data from disparate sources. This technology is being employed successfully today in many industries such as finance and retail where it aids, for example, in the understanding of customers and their buying habits. Now, forward thinking companies in the manufacturing industry are beginning to use data warehousing to address enterprise level quality. Transactional systems exist to do the job of gathering and storing the detail data, whereas the data warehouse exists to serve the decision-making needs of the enterprise. To achieve enterprise quality improvement, a Quality Data Warehouse must be built to serve the analytical needs of the manufacturing enterprise.
This paper discusses some of the emerging business problems in quality improvement and how the field has evolved to the current situation. Data warehousing is presented as a key technology for continued quality improvement. The presentation starts with what information is delivered with a data warehouse and how that information is delivered. Next, steps are laid out for building a Quality Data Warehouse, including appropriate sources of data and who needs to be involved in the data warehousing project. Finally, some important additional points on exploiting the Quality Data Warehouse are covered.
