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1/7/2005
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Convergence Up Close

These real-world BPM, text mining and EII deployments demonstrate the value of uniting data and content. But which technologies are ready for your enterprise?

Hasbro completed its first BPM pilot project in 2002, and using Lombardi Teamworks software, it has since extended procurement processes to thousands of products made by more than 100 different suppliers and delivered by 25 shippers. The process rules are designed around metrics such as service-level agreements and ship dates. Hasbro managers and suppliers log into a Web-based Teamworks process interface to monitor tasks and deadlines. When transactions fall out of a predefined tolerance range — say a shipment is a day late — the software raises red flags and sends out e-mail alerts to the appropriate managers.

Exceptions usually demand collaboration, and that's when the content comes into play. Teamworks taps into SAP data as well as related invoices, purchase orders and service agreements, which can be called up within the BPM software interface.

Hasbro's "low-seven-figure" investment in BPM helped it consolidate two supply organizations in Hong Kong, and Adams says the company is now managing twice the volume of goods with the 100-employee staff of just one of the original operations.

Adams says a next step being considered is deploying a performance database feature from Lombardi. "We're trying to develop reports on vendor responsiveness and scorecards on how well they're meeting service levels, particularly when it comes to shipping," Adams says. "'On schedule' is king, and we have to make sure Wal-Mart gets it on time."

BI and KM: The Eyes and Ears of Intelligence

Business intelligence (BI) is derived from structured data, and knowledge management (KM) is derived from content, but predictions have been kicking around for years that the two will inevitably converge. "It hasn't happened yet, but in the end, it's all intelligence, so businesspeople view it as one thing," observes Gartner analyst Ted Friedman.

One area in which the two worlds are colliding is in text mining, which covers a number of techniques for analyzing documents. Supporting technologies can identify and apply metatags to references to people, organizations, locations or even facts or events buried in the text of electronic documents, news articles, e-mail messages, Web pages or paper documents that have been scanned and turned into computer-readable text. The results can then be dissected and better understood through text analytics or popular BI tools.

How is text mining put to use? In one example, the NASD (formerly known as the National Association of Securities Dealers), the body that writes rules governing much of the financial securities industry, is using text mining to monitor member activity and ensure compliance. NASD monitors all trading on the Nasdaq and other financial markets worldwide, but data alone isn't enough. To better understand trading activity in the context of company and industry news, NASD developed SONAR, an application that analyzes trade data as well as news announcements, SEC filings and other textual documents using text mining software from ClearForest. The goal is to detect insider trading and other illicit activity.

Did text mining help uncover insider trading at Imclone and conflicts of interest among analysts and executives touting stocks while unloading the very same shares? NASD declines to discuss its use of text mining, but the SONAR application described on the organization's Web site would seem to be built for the task. Click here to see Intelligent Enterprise's findings on the current breadth of analytics and text mining deployments among enterprises.

One ClearForest customer that does talk about text mining is EDS. The systems integrator has been using the technology since 2001 as part of its "Bank of Knowledge" purchasing application. With more than 9,000 corporate customers, EDS buys servers, PCs, networking gear, software and services at volume discounts, but coordinating purchasing across offices in 65 countries and tens of thousands of suppliers was understandably difficult. Bank of Knowledge started as a BI initiative, but EDS soon turned to text mining to analyze purchasing contracts, investigate adherence to terms and determine best-possible sources depending on volumes, deadlines and delivery logistics.

EDS's main problem was interpreting all the content in its more than 35,000 active supply contracts. "Each contract is typically 40 to 60 pages, so it's just not possible to read every contract and apply that insight to buying activities in a reasonable timeframe," says Kas Kasravi, whose formal title is EDS Fellow.

The company started with high-value contracts (such as enterprise software) that offered the biggest potential for savings. Some contracts were available electronically while others were scanned and converted to searchable PDF files. Linguistic rules were then developed to guide ClearForest's software to look for word patterns identifying items, terms, prices, contact information and other essential contractual details.

Analysis is done when, say, a vendor notifies EDS that it's raising prices. If a contract specifies fixed pricing for three years, text mining quickly ferrets out the clause so it can be enforced by purchasing agents, saving the company money. When an EDS customer orders telephones or desktop PCs, text mining determines the best source, examining variables such as supplier location, past performance, product availability, discounts, order volume, margins and contract termination clauses.

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