Managing Automation - December 2008 - (Page 40)

[ INDUSTRIES ] Transformation Integration TechWatch Industries ma December 40 2008 Photo courtesy: DataNet Quality Systems While Moll is managing all of the quality data associated with the defect detection process, there are new products that Dyadem and others now offer that serve as a single repository of design and manufacturing data, including change management, risk assessment, hazard analysis, and data transfer and validation. This type of hub is part of an emerging technology trend in the medical device industry centered on quality lifecycle management (QLM). Until recently, quality management (QM) applications were delivered in solution silos, such as corrective and preventive action (CAPA) software, traceability functionality within manufacturing execution systems (MES), FMEA applications, and even via the basic quality principles as defined by the ISO 9000 standard for QM systems. But quality can’t be effectively managed in isolation, because each process impacts the next. Therefore, quality data has to flow from product design conception, to manufacturing, and out to field service. And, more important, quality management must extend to the entire supply chain, including outsourcing contractors, because everyone involved in making the product — even if it is a small component — is accountable. “Within the medical device industry, supplier quality has always been a big thing, but the challenge has been having the capability to manage multiple [entities],” says Simon Jacobson, a senior research analyst at AMR Research. “It gets down to creating a bill of compliance that travels with the product from supplier to supplier and to the customer for complete traceability, and having an audit trail that provides the appropriate documentation to drive it forward, as well as to understand the compliance procedures, and, if there is a failure, to investigate the root cause.” The problem is that so many different data models are generated from the variety of systems used to design, plan for, and produce a product — for example, PLM, ERP, and MES. Each of these systems defines quality based on its functional role, causing confusion. “Data quality is a broad and scary term that means so many things to many different people,” says Stephen Arnett, CEO of DataNet Quality Systems, a quality control software vendor. That’s why manufacturers have to approach QM with an enterprise-wide strategy, Jacobson wrote in a recent research report, forming business processes and accountability assembled from existing products and technologies to align not just intra-company functions, but inter-company ones as well. “A common enterprise QM hub is required to support this framework,” Jacobson wrote. While a QM hub connects all of the systems together in order to deliver information leading to the right conclusions or detecting areas in need of improvement, the data that is fed into the master data management (MDM) repositor y must be of high quality. To ensure that data integrity, medical device manufacturers are turning to technology that collects engineering and manufacturing design data and tests product specification execution on the factory floor, and to predictive and preventive manufacturing measures that include trend analysis, early-warning mechanisms, component relationships, and supplier performance scorecards. CLOSING THE QUALITY LOOP Quality lifecycle management is an end-to-end exercise that examines the correlation between failures in the field and manufacturing, component, or even design defects. The goal is to “look at the behavior of a product over its lifecycle to dial quality up as high as possible,” says Chris Rehl, director of marketing at CIMTEK, a maker of QLM software and manufacturing test systems that Rehl describes as a “quality hub.” Another product, CIMTEK’s Magellon software, sits in the middle of ERP, PLM, SCM, and MES applications and maintains a quality record of a product as it moves through its lifecycle. That information syncs up with the data extracted from a control test system, hardware that sits on the assembly line, and ensures that production complies with the product spec. The CIMTEK software also performs predictive analysis that spots trends where anomalies may occur, Rehl says. CIMTEK’s QLM software is delivered in a soft- “Data quality is a broad and scary term that means so many things to many different people.” — DataNet’s Arnett ware-as-a-service (SaaS) model, automating the collection of data via a Web-based interface that can reach into field service or outsourcing partners. Having visibility into what is happening at the manufacturing contractor site is critical to quality. “The problem with outsourcing production has been maintaining control of products because it’s not something that you can go take a look at on the shop floor downstairs,” Rehl says. That same mentality applies to contract manufacturers, many of which are taking more responsibility for their partnership role. “You have to fully understand every product

Table of Contents Feed for the Digital Edition of Managing Automation - December 2008

Managing Automation - December 2008 Contents Take 1 Business Objects Chief Says Union with SAP Meets Objectives After One Year Yes, Emerson, Too, Is in the MES Market Infor Chief Puts Off IPO, Restarts Buying Plans Kronos Now Tracks Shop Floor Machines IQMS Rolls Out User Interace, Other Upgrades Notes Five Ideas for Demand Planning Building on the SOA Blueprint Innovation Now A Team Effort Lean %2B Technology = LEAN^2 Finding Flaws Before They Spread Product Scan Advertiser Index Next

Managing Automation - December 2008

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