Abstract
A study commissioned by the US National Institute of Standards and Technology (NIST) found that ISO 10303, the Standard for the Exchange of Product Model Data (STEP), has the potential to reduce mitigation and avoidance interoperability costs in the aerospace, automotive and shipbuilding industries by approximately $928 million (2001$) a year (Gallaher, O’connor, & Phelps, 2002). Studies like these show the benefits and importance of using data-exchange standards to enable technical and business information to be shared electronically throughout an extended manufacturing enterprise (Ray & Jones, 2006). The literature surrounding these data-exchange standards indicates that a fairly large corpus of information is available with regards to the history, practical implementation and benefits ofdata-exchange standards like STEP (Kemmerer, 1999). However, a further review of the literature shows that there is very limited empirical research into the factors that impact the adoption of data-exchange standards. This means that practitioners devoted to the ongoing development and use of standards like STEP, and academics, still lack a significant body of evidence regarding the factors and barriers critical to the adoption of these standards. The research reported in this book seeks to address this gap by developing conceptual models for data-exchange standards adoption, which are tested through a series of qualitative case studies and action research. This chapter begins by giving an overview of the emergence and development of product data-exchange standards like STEP and the rationale behind the research presented. Following on from that is an overview of current work and research relating to the adoption of STEP. The aim, objectives and scope of this research are then stated.
TopProduct Data Exchange
Increased competitiveness among manufacturing organizations means companies are no longer able to compete solely on the cost or functionality of their products. More emphasis is being placed on the quality, and reliability of their products and the ability to respond quickly to customer needs (Fowler, 1995). This increased competitiveness has contributed to the rise of the concept of the “extended enterprise”, where companies have to work more closely with their suppliers, customers and partners in order to shorten product development life cycles and to highlight potential problems as early as possible (Al-Timimi & McKrall, 1996). Subsequently, Product Life cycle Management (PLM) has emerged as a business strategy for creating, sharing, validating and managing a company’s product related intellectual capital within and across the extended enterprise over the entire life cycle spectrum from conception to retirement (Rachuri et al., 2008).
Data is intellectual capital that is created, revised, updated and used throughout the life cycle of a product, and within these collaborative and extended working environments, companies are increasingly dependent on the effective and accurate exchange of product data with their different partners (Ray & Jones, 2006). Consequently, product data is essential to the enterprise, or as King (2002b) asserts it is the ‘strategic through-life asset’ of the enterprise. This is largely due to the use of product data in the decision-making process. Poor data management and the exchange of product data that is inaccurate, incomplete or ambiguous compromises the quality of a product resulting in either an increase in the costs associated with maintaining the quality of a manufactured product over its life cycle, or expensive accidents. This is highlighted in the case of the Mars Climate Orbiter Crash of 1999, the loss of the $125 million spacecraft was attributed to the fact that its spacecraft and navigational teams were using different units of measure, as was noted on CNN.com, “one engineering team used metric units while another used English units for a key spacecraft operation” (CNN, 1999).