Abstract: To ensure your
warehouse management
system is implemented as painlessly as possible, you must assess your
warehouse situation before you decide on a
warehouse solution. Using the Pareto Principle, where a minority of inputs yields the majority results; examining your processes; evaluating your personnel; monitoring the progress of implementation; and testing are the best ways to ensure both a successful launch and long term return on investment.
PubDate: 10/1/2004
Abstract: You’re probably already familiar with the role of a warehouse management system (WMS). But a warehouse control system (WCS)? In your warehouse, a WCS can play the role of a conductor by ensuring the individual pieces of material-handling equipment—such as conveyors and sorters—perform with harmony, precision, and efficiency. Find out how implementing a WCS execution system can complement your WMS’s planning abilities.
Abstract: Data leakage and data breach are two disparate problems requiring different solutions. Data leakage prevention (DLP) monitors and prevents content from leaving a company via e-mail or Web applications. Database activity monitoring (DAM) is a data center technology that monitors how stored data is accessed. Learn why DAM complements DPL, and how you can benefit by making it part of your overall data security strategy.
Abstract: Every supply chain professional must consider certain factors when comparing enterprise resource planning (ERP) and best-of-breed warehouse management system (WMS) solutions for warehouse management. Effective management of warehouse, fulfillment, and distribution operations is key to business success. With so much riding on your decision, you need to thoroughly compare ERP warehouse modules and best-of-breed WMS.
Abstract: Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.
Abstract: Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.
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.
Abstract: You can blame your sales people all you want, but if the lead data is bad, they’re not going to bring in business. You can blame your product managers for ineffective promotions, but if the target lists are redundant, the pitches fall on deaf ears. You can blame your customer service representatives for low satisfaction scores, but if customer data is missing, then no wonder the complaint resolution pipeline is backed up. Think it’s your customer resource management (CRM) system? Think again. It’s bad data, and it’s costing you millions. Request your copy of The Bottom Line on Bad Customer Data that delivers detailed advice from Jill Dyche, partner and co-founder of Baseline Consulting, about what you can do to address the impact of bad data on your company. The report gives you insight into how bad data is impacting your company and what you can do about it. How to identify where the bad data is and quantify its impact, and different approaches to determine the sources and causes of bad data are all offered in this paper.
Abstract: SAS Institute has announced the production availability of SAS/Warehouse Administrator software, Version 2.0. This new version provides IT the ability to proactively publish data warehouse information and track its usage, plus aggressively manage the process of change in the data warehouse.
Abstract: Although voice-directed picking may take distributors to higher logistics levels someday, operations managers should try listening to their warehouse personnel for now. Warehouse workers are the real experts on a company's warehouse, its product, and its customer.
Abstract: Once a revolutionary concept, data warehouses are now the status quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency.
Abstract: Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.
Abstract: Data auditing is a form of data protection involving detailed monitoring of how stored enterprise data is accessed, and by whom. Data auditing can help companies capture activities that impact critical data assets, build a non-repudiable audit trail, and establish data forensics over time. Learn what you should look for in a data auditing solution—and use our checklist of product requirements to make the right decision.
Abstract: Rising data volume is not the only reason companies are concerned with issues of data integration and data quality. The growing numbers of disparate systems that produce and distribute data add to the complexity. But in many companies, data quality management has not kept pace with the growth of data integration projects, and its use is immature. Find out how moving toward a single data services architecture can help.
Abstract: When searching for a warehouse management system (WMS), a number of myths surface.
Abstract: Companies today are challenged to maintain their data safe and secure from hackers and others with unauthorized access. In his article, TEC business intelligence (BI) analyst Jorge García looks the risks and issues that companies face with securing their data, the importance and advantages of data security, and outlines a path that companies can follow to achieve data security as part of an overall data governance initiative.
Abstract: Companies are fighting a constant battle to integrate business data and content while managing data quality. Data quality serves as the foundation for business intelligence (BI), enterprise resource planning (ERP), and customer relationship management (CRM) projects. Learn more about software that unifies leading data quality and integration solutions—helping your organization to move, transform, and improve its data.
Abstract: Before an important game, you create a game plan. Before you start building a house, you have a blueprint. And before you start looking at a warehouse management system (WMS), you must define how you want your warehouse to be organized and function. This article looks at basic warehouse strategies that need to be understood to ensure that the WMS software effectively and efficiently supports the activities of the warehouse, now and in the future. This is not to say that you will not consider the best practices of the new
Abstract: Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.