| 1. |
Pull vs Push: a Discussion of Lean, JIT, Flow, and Traditional MRP Part Two: Challenges and User Recommendations ( Pages)
by P.J. Jakovljevic
Jan 15, 2004
Abstract : While lean/flow leverages practices to stay ahead of actual demand, traditional approaches better coordinate secondary, back-office systems like accounting and HR. Moreover, flow should be a company-wide strategy that impacts more than manufacturing.
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| 2. |
Pull vs Push: a Discussion of Lean, JIT, Flow, and Traditional MRP Part 1: Tutorial ( Pages)
by P.J. Jakovljevic
Jan 14, 2004
Abstract : Flow manufacturing leverages techniques to help manufacturers create any product on any given day, in any given quantity including the
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| 3. |
A Definition of Data Warehousing ( Pages)
by M. Reed
Aug 18, 2002
Abstract : There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
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| 4. |
A Definition of Data Warehousing (6 Pages)
by M. Reed
Aug 24, 2000
Abstract : There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
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| 5. |
Distilling Data: The Importance of Data Quality in Business Intelligence (0 Pages)
by Anna Mallikarjunan
Jul 17, 2009
Abstract : As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence environment. This article looks at issues in data quality and how they can be addressed.
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| 6. |
Distilling Data: The Importance of Data Quality in Business Intelligence (0 Pages)
by Anna Mallikarjunan
Oct 20, 2008
Abstract : As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence environment. This article looks at issues in data quality and how they can be addressed.
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| 7. |
EXE Latest Vendor to Join IBM Supply Chain Club ( Pages)
by Steve McVey
Nov 17, 1999
Abstract : IBM and EXE Technologies today announced a global strategic relationship in which the two vendors will provide supply chain customers with integrated solutions that will help them transform into e-businesses. These solutions will be initially targeted to customers in the automotive, consumer packaged goods, electronics, retail and wholesale distribution industries. In addition, EXE and IBM announced that Pep Boys, a large automotive products retailer in the United States, and Metro Richelieu, one of Canada's largest grocers, are the first customers to take advantage of this relationship.
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| 8. |
Lessons Learned on the Inca Trail ( Pages)
by Carla Reed
Jan 28, 2005
Abstract : Peru, a country with a glorious past and an uncertain future, stands at the crossroads. The inability to attract significant trade or investment due to the crime and lack of democratic principles in the nation should be addressed as a matter of urgency. Peru can learn from countries that share the legacy of lost empires -- for example China -- and examine the principles of their ancestors to create a sustainable economic environment.
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| 9. |
Data Quality: Cost or Profit? ( Pages)
by Kevin Ramesan
Mar 8, 2004
Abstract : Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.
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