According to recent news reports, a growing number of Americans have reduced their food intake to a bare minimum, following the lead of animal research that suggests consuming 40 percent less calories than normal can dramatically extend one’s life.

While I’ll avoid commenting on the relative truth behind such a diet plan, there is a business parallel – supported by far more substantive evidence – in the manufacturing industry’s embrace of a concept called “Lean Manufacturing.” Lean manufacturing is the methodology that cuts the fat out of manufacturing costs at every stage of the operation. More than just a buzzword, many manufacturers have found lean manufacturing particularly effective as a weapon against low-cost competitors, which is definitely lengthening their corporate life-spans.

Lean manufacturing is defined as a process to improve manufacturing and service operations, reduce waste, improve quality and drive down costs. One of the first areas that manufacturers and their consultants have examined is the supply chain, where storage, shipping, packaging, resellers and multi-national regulations can sap profits if they are not intelligently managed.

In today’s Web-enabled economy, virtually all aspects of modern supply chains are defined by business data – data on many fluctuating factors, such as the costs of raw materials, labor, fuel, real estate, warehousing and shipping, plus consumer patterns, reseller-arrangements, insurance rates, security needs – even weather patterns. A constant flow of varying data needs to be continually weighed and evaluated to determine if, when and how a manufacturing supply chain should be adjusted, or how efficiencies might best be gained.

While a great deal of attention has been paid to harnessing all of this data through data integration, business intelligence and business process management systems and portals, lean manufacturing is also a cry for data quality. Data is the raw material at the very heart of all of this fluctuating information, feeding analytical tools and systems. But if that data is inaccurate or out of date, all of the down-stream results that emanate from its analysis will be skewed.

One large, multinational manufacturer that has successfully applied data quality solutions to its supply chain management began the process by building a central data warehouse. The company was challenged by multiple manufacturing and service branches on four contents, by thousands of suppliers around the world, and by more than 3 million parts and components in inventory at any given time.