Making Supply Chain Data Dynamic

Today’s supply chain managers are hot for more data and greater integration. Their motive is simple. They recognize that they can wring profit by reducing complexity or isolating actions. It is hard to argue against this point of view, especially when companies integrate data with the goals of cutting costs, reducing working capital by holding less inventory, keeping service levels up while achieving the first two goals, and gaining systems flexibility in a global business environment.

But global business is not conducted on an even playing field. Companies require varied details and degrees of data to manage logistics parts or the supply chain as a whole. Channeling mass amounts of information to appropriate decison-makers warrants a funnel, not a cylinder, for aggregating, integrating, and digesting data that enables companies to be more responsive to both internal and external pressures.

The world recession has inflicted levels of pain on many internal parts of the supply chain. The global warehouse management systems market, for example, is feeling only minor pain; it shrank one percent in 2008, according to ARC Advisory Group. Given the times, that one percent looks good.

In other areas, however, the economic impact is far from moderate. Gross Domestic Product figures for the fourth quarter of 2008 were less-than-admirable around the world (see chart) and essentially a response to spikes in global oil prices, says Alan Reynolds, senior fellow in the Cato Institute. Think things are bad in the United States? Take a look at Taiwan.

Here’s some more evidence of the uneven playing field. Dennis Lockhart of the Federal Bank reports that since January 2009 there has been a 31-percent decline in trade globally compared to last year. As a consequence, shipping rates have dropped in a tight credit period. As major exporters, China, Japan, and South Korea have been particularly hard hit, Lockhart adds.

These differing market and economic forces, internal and external, shape how logisticians and supply chain managers leverage the power of information and integration.

While transportation management and warehouse management systems can heal some economic pain by helping companies more efficiently manage their truck fleets or distribution centers, expecting to conquer global supply chain management is a matter of different scale. It challenges companies to gather the right information and make it accessible for both function specific and end-to-end optimization. But without proper integration even the best data can be detrimental.

When data appears in silos—in different parts of the supply chain, for example—its integration can create quality issues over a product or service’s entire life cycle in a global marketplace. Identifying the relevance of data from world sources involves reconciliation, discovery, and profiling; in the right hands, these steps can lead to data quality improvement.

Integrating the operational systems used by manufacturing, distribution, and retail partners can lead to better supply chain efficiency. Higher-quality data further improves the efficiency and effectiveness of various processes. Poor data quality, for instance, results in extraneous costs when goods are manufactured or shipped incorrectly.

Informatica, based in Redwood City, Calif., specializes in the integration and quality control of business data. Its system comprises five steps in the data life cycle:

  1. Access: The system enables access to almost all forms of data stored in a broad array of systems.
  2. Discover:In the discover stage, the system helps to quickly identify the most relevant information.
  3. Cleanse:Informatica puts data through a cleansing process to measure and improve quality.
  4. Integrate:Through automation, the system reconciles and integrates structured and unstructured data.
  5. Deliver:The system delivers data at the right time in the right format.

By automating the entire life cycle, Informatica addresses data integration needs at any scale, quickly and cost effectively.

Data quality improvement requires a continuous closed-loop process, which means working at it constantly. But the dividends are great. Better data can lead to better decision-making and better supply chain efficiency and customer service.

To tame the global supply chain, we will have to live in a world of giant volumes of data and data processing costs. We will pay for every byte we use. It had better lead to data integration.

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