Audits Reveal 90 Percent of Digital Product Data Tested Failed Company-Defined Quality Standards.

BOSTON--(BUSINESS WIRE)--Dec. 1, 1999--

Industry-wide productivity gains in product development hampered

by data quality problems, findings show

Prescient Technologies, Inc., a leading provider of software to manufacturers for improving engineering data quality, announced today the initial results from a set of engineering quality audits. Data gathered from manufacturing companies around the world indicate a pervasive problem with data quality in the process of engineering design.

The quality audit program showed a consistent level of error in engineering data tested across aerospace, automotive, consumer products and electronics industries. Quality audits were performed in companies with as few as 100 employees and as many as 160,000 employees. Each quality audit took approximately two weeks to complete and included a review of multiple sets of engineering data from each organization. Over 3,000 separate product models were analyzed during the study timeframe. Of these 3,000 models, only 225 passed the appropriate set of each company's defined standards. Seventy percent of the models failed standards that companies categorized as "critical."

The quality audit process is customized to look at the engineering data standards of most concern to each specific company. These can be common industry design standards and best practices, or a set of quality issues unique to the organization. The standards can be as straightforward as naming conventions or as complex as manufacturing requirements and guidelines for electronically building the geometry of a part.

"Data quality has been one of the most significant issues in the product development process for a long time," noted John MacKrell, a leading industry analyst with CIMdata, Inc. "To achieve the full benefits of advanced engineering and data management solutions, companies must have information of the highest possible quality."

"The audit has only scratched the surface of the issue of data quality in engineering," said Gavin Finn, president and CEO of Prescient Technologies, Inc. "Although there are more analyses to do, the overwhelming rate at which data failed defined standards was consistent across all the audits. The high percentage of errors is even more noticeable because the results are not limited to company size or market. This problem spans the entire manufacturing industry. The problem is a consequence of the increased pressure to utilize digital data throughout the automated product development process."

The issue of poor data quality and its cost

The issue of quality in engineering data is growing more crucial as digital modeling software becomes integral with automated product development. Inaccurate or incomplete design data affect the product development process in a number of ways. Models are increasingly used by other product development functions such as manufacturing, procurement, and documentation, so errors in design data add rework time and cost downstream. Design errors also reduce the capacity for data to be exchanged between different software systems. The result is that models are often re-created from scratch, or substantially reworked, and this limits a company's ability to utilize legacy models in new designs.

"Companies have already acknowledged that inaccurate design data cause additional costs and problems for people downstream of engineering who need to use the data," stated John Racine, vice president of customer service and implementation at Prescient Technologies. "But until recently the technology has not been available to quantify the size of the problem. We never expected the data failure rate to be this high. Neither did the companies that participated in the audit program."

The results of these quality audits are consistent with research conducted by industry organizations and the federal government. A March 1999 study by the National Institute of Standards and Technology (NIST) showed that interoperability problems due to data quality errors within the automotive supply chain alone could cost as much as $1 billion a year. (Go to www.nist.gov/director/prog-ofc/report99-1.pdf for the complete report.) The Automotive Industry Action Group (AIAG), in a 1998 report entitled "Best Practices in Supply Chain Product Development," revealed that the "highest priority issue (affecting the effectiveness of best practices in the automotive supply chain) was concern about the quality of product data, or rather, the lack thereof, which is directly related to the usefulness of that data for purposes beyond basic product representations." The report went on to note that "these kinds of product data quality problems lead to increased cost and time delays."

"Companies have historically accepted these costs as a side effect of business in the digital age," stated Finn. "However, given the constant pressure to keep product development costs down, it is very timely that technology can now help organizations put some quantitative numbers not only to understanding the problem, but to solving it."

Why 90 percent of digital models fall short of standards

Product modeling software systems are inherently flexible, offering engineers a number of ways to create, assemble, and annotate a digital model. Without a set of defined guidelines for model structure and design, however, the danger is that each designer will create models according to his or her own individual methodology, rather than in accordance with company standards. As the AuditQA program discovered, this causes problems for the organization, because even designers on the same team may not be able to make simple changes to one another's designs.

"As an example," said Finn, "consider how automobiles are physically assembled. What would happen if people on the manufacturing floor did not have a set of standards to follow? Each assembler would make different decisions about how to install parts, and the cars coming off the assembly line would be inconsistent and prone to defects. Think of an engineering model as a digital product. In engineering design today, no two models necessarily follow the same construction practices."

The audit programs showed that some companies have developed a set of design standards and best practices for engineers to follow, but reinforcing those standards has been a challenge. Software tools that can measure models against standards have only recently become available.

"It comes down to questions of training and the consistent use of best practices," added Finn. "Engineers want to do the best job possible, and they need tools to guide them toward the best way to use the system for the specific job at hand or for a particular customer."

For additional information on the quality audit process, visit www.prescienttech.com.

About Prescient Technologies, Inc.

Prescient Technologies, Inc., is the premier provider of engineering quality software solutions, which enable companies to minimize design iterations, reduce costs, and accelerate time to market. Prescient Technologies software solutions are found in aerospace, automotive, electronics, and other industries with complex design, manufacturing, and assembly processes. Prescient Technologies provides technologies, products, and services that help manufacturing companies leverage engineering assets, protect design information, and facilitate electronic design-to-manufacture interoperability. Headquartered in Boston, MA, Prescient Technologies products and services are supported through field offices in the United States, as well as through international partners outside North America. For additional information about Prescient Technologies, Inc., visit our web site at: www.prescienttech.com

DesignQA is a registered trademark of Prescient Technologies, Inc. PrescientQA, AuditQA, DriveQA, GeometryQA, CertifyQA, and PrescientQA are trademarks of Prescient Technologies, Inc. All other trademarks are properties of their respective companies.

Independent groups cited above can be reached as follows:

AIAG: [phone]

NIST: (301) 975-NIST