PLM is rapidly evolving into an enterprise platform that supports the new product development and launch process from ideation to service vs. a workgroup application that supports only engineering activities. Because of this, PLM has an opportunity to be the decision support dashboard to enable smarter product planning, development, launch and post-launch support. But to make this a reality, rich analytics -one of the final frontiers of PLM-must be incorporated into decision support platforms so organizations have the ability to understand and optimize the performance of their people, value chain partners, processes and products across the new-product development and launch (NPDL) process.
Applying decision support to enable a performance driven approach to PLM will allow companies to bring higher quality products to market more quickly, resulting in happier customers and greater revenue. More specifically, applying analytics to the NPDL process will help organizations accelerate the achievement of performance goals such as improved return on R&D investment, faster time-to-market, higher product launch success rates, faster engineering changes, lower direct materials costs, and improved product margins. The only problem is that analytics solutions that look across the entire product lifecycle have not been prevalent.
Analytics has been lacking in PLM
Analytics has been sorely lacking in the world of product lifecycle management because to date PLM systems have been used mostly by R&D and engineering where design and data management is the focus and analytics has been less of a perceived need. It is primarily the extended mix of product development team members such as product management, procurement, marketing and manufacturing who will initially adopt product analytics and a performance-driven approach to PLM. However, the "traditional" PLM consumers, like R&D and engineering, certainly could benefit from applying analytics to improve engineering cycle time, design performance (through simulation software), and the performance of engineering team members.
Other performance-driven initiatives in PLM that could be optimized by analytics include outsourcing design and manufacturing, rationalizing/extending product lines, parts and materials reuse, make-to-demand, and product safety compliance. It is critical for companies to remember that establishing a broad set of analytics across their organizations will require leveraging information from multiple areas, including pricing, forecasting, and supply chain cost to serve.
The benefits extend across the value chain
PLM typically is deployed to support a linear development/ launch/end-of-life process without feedback loops (or performance stage gates, to put it in product development terms) to improve people, product and process performance along the way. Applying analytics to take a performance-driven approach to PLM enables continuous improvement across the value chain (design, product management, marketing, supply chain, manufacturing) throughout the cyclical process of developing products, resulting in higher quality, more profitable products, brought to market on time and at the right time.
The opportunity for PLM is expanding and our outlook for the PLM market is optimistic, with a projected 9 percent compounded annual growth rate through 2012, although some slowdown is bound to occur in the short term with the current economic state.
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