FAIRFAX, Va. - Feb. 1, 2017 - InfinityQS® International, Inc. (InfinityQS), the global authority on data-driven enterprise quality, challenges manufacturers to Re-imagine Quality as a competitive advantage to drive profits. Instead of perceiving quality issues as factory floor problems, manufacturers must understand that the quality data that identifies these problems is also an untapped source of insight to drive strategic transformation across their entire enterprise.
Manufacturers across all industries face the challenges of producing the highest quality products at the lowest possible cost, using supplies sourced from around the globe — all in the face of increasing global competition. These organizations spend enormous time and energy collecting and responding to an avalanche of quality-related data that ends up in disparate spreadsheets, physical filing cabinets, or siloed in databases or file servers across the organization. But, when data is scattered and non-standardized, it's impossible to visualize and analyze what's happening — in real time or over time. As a result, quality becomes a constant problem.
Instead, manufacturers must realize that quality data is the company's greatest competitive advantage. With an aggregated, end-to-end view of production sites across the entire enterprise, manufacturers can formulate a cyclical, continuous improvement program that uses valuable quality data in three ways:
•Enterprise Visibility: When all quality-related data is unified, from all sources, into a standardized and centralized database, it's possible to visualize more than the quality of a single product. The outcome is real-time visibility of the entire enterprise, from end to end — including suppliers, incoming inspection, raw materials, in-process checks from shop floor operators and the quality lab, process data, packaging, and finished products.
•Operational Insight: With visibility of the entire operation, useful and actionable insight is generated about the enterprise's processes, suppliers, and manufacturing operations. Improved analytics and reporting help to apply best practices consistently across all plants, lines, processes, and products.
•Global Transformation: Finally, the resulting insights can be applied to streamline, optimize, and transform processes and operations across the enterprise, elevating product quality, improving efficiency, impressing customers, and creating exponential cost savings.
Michael Lyle, president and CEO, InfinityQS, said, "When you shift your thinking about quality from 'how can I fix this' to 'how can I use this to my advantage,' you are already ahead of your competition. You no longer think just about what you reacted to yesterday, but instead about what you can proactively do tomorrow, or next year. Now you're turning today's quality data into tomorrow's strategic operational decisions to drive real, measurable benefits to your bottom line."
To learn how to do more than just "manage" quality, download the InfinityQS white paper, "Quality Re-Imagined": https://bit.ly/2jE9DSK.
About InfinityQS International, Inc.
InfinityQS International, Inc. is the global authority on data-driven enterprise quality. The company's Manufacturing Intelligence solutions deliver unparalleled visibility across the enterprise, from the shop floor to the boardroom, enabling manufacturers to Re-imagine Quality and transform it from a problem into a competitive advantage. Powered by centralized analytics, InfinityQS's solutions provide operational insight to enable global manufacturers to improve product quality; decrease costs and risk; maintain or improve compliance; and make strategic, data-driven business decisions. Headquartered near Washington, D.C., with offices in Seattle, London, and Beijing, InfinityQS was founded in 1989 and now services more than 40,000 active licenses with more than 2,500 of the world's leading manufacturers, including Ball Corporation, Boston Scientific, Graham Packaging, and Medtronic. For more information, visit www.infinityqs.com.
More Info: https://bit.ly/2jE9DSK
