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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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 data collection inventory tracking


Meeting the Challenges of Product Traceability with Automated Data Collection
An effective traceability system involves determining which product and manufacturing process attributes to collect and maintain@and deciding when during the

data collection inventory tracking  Product Traceability with Automated Data Collection The backbone of Radley's WorkForce Productivity Solutions is x/DC, a .Net, XML based data collection development and integration platform. x/DC’s client/server architecture offers scalability, performance and availability unachievable by other solutions. Source : Radley Corporation Resources Related to Meeting the Challenges of Product Traceability with Automated Data Collection : Traceability (Wikipedia) Data Collection (Wikipedia) Meeting the

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

Merchandising Systems

Merchandising systems are the enterprise back and front-office software solutions upon which the majority of retailers rely to manage and support their daily tasks. These systems typically record product performance, which allows buyers to purchase merchandise according to that information and to make accurate merchandise decisions. Moreover, retail systems have capabilities for tracking inventory, capturing sales data, and managing retail prices. 

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Documents related to » data collection inventory tracking

Zooming into an Inventory Free Flow


FreeFlow, a provider of business services with a patented technology, aims to help companies improve product life cycle profitability by providing a unique, online auction platform where companies can reduce their at-risk and excess inventory.

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How Bar Codes Can Optimize Data Recording and Information Analysis


Bar code technology allows users to analyze information to develop more accurate maintenance, personnel, and financial planning. In particular it can hasten the data recording processes in a maintenance system, as seen in its use in the aircraft maintenance process.

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What Are Manufacturing Execution Systems?


A manufacturing execution system (MES) can be defined as a collection of business processes providing event-by-event, real-time execution of planned production requirements. However, market ambiguity and functional overlap with enterprise resource planning systems obscure what an MES can bring to an enterprise.

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Is There a Smarter Way to Handle Excess Active and Obsolete Inventory?


Imagine the convenience of a 24x7 intranet marketplace where companies could sell off their inventory to the highest bidder. FreeFlow is one such business service provider, helping companies find buyers to sell their products to and increase inventory asset recovery.

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Newman Technology Cuts Inventory Costs by 25 Percent; Achieves 98 Percent Inventory Accuracy


Newman Technology was using a paper trail system to manage a lot of traffic, and realized that it needed a modern-day solution for production accuracy and accountability. It needed a robust tool and disciplined processes for managing its inventory, purchase orders, and accounts receivables. Learn how Newman Technology improved its communication with suppliers and gained visibility into suppliers’ operations.

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The Value of Big Data


As the use of big data grows, the need for data management will also grow. Many organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. This white paper looks at what big data is, the value of big data, and new data management capabilities and processes, required to capture the promised long-term value.

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Scalable Data Quality: A Seven-step Plan for Any Size Organization


Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

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Inventory Reduction: Effectively Turning Excess Into Cash


In virtually all manufacturing companies, there is a direct correlation between inventory levels and overall business performance. In fact, CEO's and CFO's believe that their companies consistently carry 25 to 40 percent or more inventory than is needed.

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Considerations for Owning versus Outsourcing Data Center Physical Infrastructure


When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, data center life expectancy, regulatory requirements, and other strategic factors. This paper discusses how to assess these key factors to help make a sound decision.

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Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization


Read this white paper to learn about a lean form of on-demand data integration technology called data virtualization. Deploying data virtualization results in business intelligence (BI) systems with simpler and more agile architectures that can confront the new challenges much more easily.

All the key concepts of data virtualization are described, including logical tables, importing data sources, data security, caching, and query optimization. Examples are given of application areas of data virtualization for BI, such as virtual data marts, big data analytics, extended data warehouse, and offloading cold data.

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