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Compare Epicor (Vantage, e by Epicor) side-by-side with BAAN, SAP, J.D. EDWARDS, EPICOR, ORACLE, QAD, and 80+ other ERP vendors
Nov 24, 2009
Today's usage of Decision Support Systems (DSS), combined with vetted ERP knowledge bases, allows organizations to save time and money, achieving better and more reliable/fully-documented decisions, a quantum improvement over the widely-used subjective process of selecting complex enterprise software...
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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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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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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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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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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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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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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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Business Basics: Unscrubbed Data Is Poisonous Data (4 Pages)
by J. Dowling
Jun 13, 2001
Abstract : Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data quality is low, and the short- and long-term benefits are great.
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Business Basics: Unscrubbed Data Is Poisonous Data ( Pages)
by J. Dowling
Nov 26, 2003
Abstract : Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data quality is low, and the short- and long-term benefits are great.
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