BI SIG: Lifecycle Management Analytics
Providing Decision Support to Product Lifecycle Management
Krish Mantripragada, Director Product Management, Agile Software
How much will it cost to develop a new product? Am I working with the optimal parts and suppliers to minimize risk and cost? Will that product fit in with our overall portfolio strategy? Will this product meet the appropriate quality standards? Am I using the best available parts and assemblies to meet compliance standards such as WEEE and RoHS? These are all questions that companies across many discrete manufacturing industries — including high-tech, automotive, aerospace, industrial products, and medical devices — are facing today when it comes to bringing successful products to the market: reduce product costs, reduce time to market, minimize supply risk, ensure compliance with worldwide environmental regulations, etc. These challenges are made all the more complex because product development processes often span multiple organizations (and information systems) within the enterprise and also extend to outsourced design and manufacturing partners. The core infrastructure required to provide business managers with the information they need to make key product decisions, simply does not exist in many companies, leaving them with a high risk of failure in achieving product revenue and profitability goals.
Many companies still rely on ordinary spreadsheet programs to help them analyze their product portfolios, manage product costs, calculate project-related rates of return, and so on. But this approach is fraught with difficulties. More often than not, one has to draw the data from disparate, sometimes globally distributed sources. So how do you make sure it is accurate and up-to date? And how do you analyze this data to make meaningful decisions? Traditional PLM solutions do a good job managing product information across the product life-cycle but fall short of providing a strong decision support environment to analyze the managed data. Product success is often governed by the ability to make the right tradeoffs among cost, time to market, supply risk, quality and other constraints throughout the product lifecycle. A product designed right today may not be suited for production a year or two into its lifecycle due to unforeseen parts obsolescence, changes in supply market, new regulations, etc.
PLM Analytics is a set of solutions that enables users to comprehensively plan and analyze product- and supply chain specific issues throughout the product life cycle. It requires technologies to enable companies to navigate across large volumes of product data and use sophisticated drill-down and charting capabilities to detect key trends and monitor metrics. It also requires the ability to quickly generate multiple designs, supply chain scenarios and understand the effect of alternative designs, sourcing strategies, supplier selections, etc. to truly optimize the product definition for the supply chain. The speaker will provide an overview on the latest advances in PLM Analytics and highlight some of the technologies developed by Agile Software in the area. Specific examples from variety of industries will be provided to explain the need and importance of these technologies.
Download the presentation here.
About the Presenters
Dr. Krish Mantripragada, Agile Software
Dr. Krish Mantripragada is Director of Product Strategy and Management at Agile Software. Krish manages the strategy, definition and development of analytics products across all Agile PLM solutions. Krish has over 8 years of industry and academia experience and is widely known in the areas of supply chain management, assembly planning and product design. Before Agile, Krish worked at i2 Technologies and United Technologies Research Center. Krish has a Ph.D. in Mechanical Engineering from MIT.
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