The past three decades have seen phenomenal growth in investments in the area of product lifecycle management (PLM) as companies exploit opportunities in streamlining product lifecycle processes, and fully harnessing their data assets. These processes span all product lifecycle phases from requirements definition, systems design/ analysis, and simulation, detailed design, manufacturing planning, production planning, quality management, customer support, in-service management, and end-of-life recycling. Initiatives ranging from process re-engineering, enterprise-level change management, standardization, globalization and the like have moved PLM processes to mission-critical enterprise systems. Product data representations that encapsulate semantics to support product data exchange and PLM collaboration processes have driven several standards organizations, vendor product development efforts, real-world PLM implementations, and research initiatives. However, the process and deployment dimensions have attracted little attention: The need to optimize organization processes rather than individual benefits poses challenging “culture change management” issues and have derailed many enterprise-scale PLM efforts. Drawn from the authors’ field experiences as PLM system integrators, business process consultants, corporate executives, vendors, and academicians, this paper explores the broad scope of PLM, with an added focus on the implementation and deployment of PLM beyond the development of technology. We review the historical evolution of engineering information management/PLM systems and processes, characterize PLM implementations and solution contexts, and discuss case studies from multiple industries. We conclude with a discussion of research issues motivated by improving PLM adoption in industry.

The development of products in large industrial organizations involves numerous engineers from different disciplines working on interdependent components Objectives are sometimes in conflict The need for overall coordination, consistency, control, and integrity of data, design ideas, and design rationale is critical The information generated by each designer must be viewed in the context of information generated by other designers, the enterpnse historical data, and the organization as a whole. The paper outlines major requirements facing concurrent engineering (CE) It focuses on the ability of collaborating designers to proceed independently, correlate interdependency, use existing information (data, knowledge, and processes), and negotiate conflicts arising from design inconsistencies To provide information-based support for such environments a concept of design schemata is introduced to support the concurrent, collaborative, and historical aspects of CE environments from an enterprise perspective. The need for a data dictionary that supports these schemata and its different dimensions is also recognized The dictionary provides conceptual centralization of design information relative to the enterprise. This includes data, as well as its definition (meta-data), and must allow the design process to evolve in a global enterprise perspective These discussions lead to a series of research issues that must be addressed by the CE research community.

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Product definition information drives many business processes, yet its management at a coarse level (documents and files) precludes efficient automation and decision support. Information management at finer levels of granularity realizes the full potential of computable representations. This paper presents an industry oriented perspective of engineering information management (EIM) technologies and implementations and offers classifications of information systems as it relates to EIM systems and business processes. The concept of structured business objects that encapsulate the information and business-process definition at appropriate levels of granularity to support enterprise process dynamics is introduced. This provides a key construct to model the unique, and sometimes opposing, process perspectives within the enterprise. The paper then discusses key EIM and integration issues and future directions.