Abstract Views :169 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal University, Manipal, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 5 (2013), Pagination: 209-214
Abstract
Supply planning is an integral part of any manufacturing company. The fundamental need of having a supply planning framework arises from various issues during the procurement of goods. A proper framework is always required to ascertain exact quantity of goods emanating from one end to the other end. In order to handle data coming from multiple resources and to create a home for all, Data Staging (DS) is one of the finest approaches. The primary aim of DS is to develop a home for data thus manifesting a controlled and monitored flow of data along a supply chain. Besides, DS maintains the shared data across the network. As planning processes became more integrated and more sophisticated, common data models to support integration and databases to manage the planning data and integrate among systems became necessary. The data when comes from one source and it is processed and sent to the target, is used ultimately to decide what quantities of goods are going to be manufactured or shipped. These quantities are known as schedules which indicate the future volume to be produced by a factory and represent the build plan at a given point of observation in time. Actual subject area deals with bringing in lot level transactions from the source for different factories aligned with the respective factory cut off times. Actual quantities of items produced for each manufacturing stage are calculated based on the lot movement from one operation to another operation and rolled up to work week based on the factory cut off time that are stored in the core table. Future scenario will be capturing the quantities of items produced for each manufacturing stage from various external locations. The maintenance issue rests with the DS and it also takes care of sharing data along the network so the sync issue is resolved by the functionality provided. Using this feature one can enable the user to maintain data quality as there is an automatic and early detection of data inconsistencies and there is a comprehensive proactive resolution to be applied to get the data fixed. Teradata Master Data Management Studio (Teradata MDM) tool is used to develop the staging systems.
Keywords
Data Staging, Master Data Management, Teradata Master Data Management Studio, Data Mining.
