Knowledge Management in Safety-Critical Systems Analysis

Knowledge management in the design of safety-critical systems addresses the question of how designers can share, capitalize, and reuse knowledge in an effective and reliable way. Knowledge management is situated in groups, organizations, and communities, playing different roles in the design process. Design of safety-critical systems has specific properties, such as dealing with complexity, traceability, maturity of knowledge, interaction between experts, awareness of the status of information, and trust in knowledge. Documentation is of crucial importance in design processes, ensuring that these properties are taken care of in a proper and reliable way. However, writing is not an easy task for engineers, and support is needed. Several knowledge management solutions, both tools and organizational setups, are available to support design work, such as active notification of changes, personal and team workspaces, active design documents and knowledge portal solutions.

Knowledge Integration through Strategic Alliances and Virtual Networks

According to the Knowledge-Based View, knowledge integration is one of the main capabilities that organizations must possess in today’s markets. In some high-tech industries, especially sciencebased industries such as biotechnology that need to integrate different bases of specialized expertise, the sources of knowledge are spread across a great variety of organizations. Strategic alliances are an option that may solve problems of speed or cost in these cases. Hence, in this chapter we identify advantages that inter-organizational cooperative agreements may have in the creation of knowledge, with a special emphasis on the case of strategic alliances in which the main aim is the joint creation of knowledge between partners and not simply the appropriation of this knowledge by one of the members of the agreement. In a second phase, we argue that virtual networks add more advantages to this type of alliance because of their special features. We define the virtual network as a strategic, temporary agreement between organizations that collaborate and coordinate their work through information technologies. This dimension adds greater flexibility to strategic alliances. We identify a virtual network typology by analyzing their properties and their value for the integration of knowledge.

Integrated Modeling

Modeling is a key task in order to analyze, understand, and improve business processes and organizational structures, and to support the design, implementation, and management of information and communication technologies in general and knowledge management systems (KMSs) in particular. Process-oriented knowledge management (Maier, 2004; Maier & Remus, 2003) is a promising approach to provide the missing link between knowledge management (KM) and business strategy, and to bridge the gap between the human-oriented and technology-oriented views (e.g., Hansen, Nohria, & Tierney, 1999; Zack, 1999). However, existing modeling approaches for business processes, including their extensions for KM, still lack concepts to support knowledge work, which is often unstructured, creative, and learning and communication intensive. Recently, the activity theory has been proposed to provide concepts to analyze knowledge work (e.g., Blackler, 1995), but it has not yet been integrated with business process modeling for designing KM initiatives and KMSs. The following sections analyze the characteristics of knowledge work, distinguish important perspectives for modeling in KM, and discuss extensions of process modeling approaches including activity modeling. Then, the process-oriented and the activity-oriented perspectives on knowledge work are compared and connected by means of the concept of knowledge stance.

Evidence-Based Best Practices Collections

For capturing and transferring knowledge between different projects and organizations, the concept of a Best Practice is commonly used. A similar but more general concept for knowledge capturing is often referred to as a Lesson Learned. Both best practices and lessons learned are frequently organized in the form of knowledge collections. Such collections exist in many forms and flavours: From simple notes on a white board, to paper file collections on a shelf, to electronic versions filed in a common folder or shared drive, to systematically archived and standardized versions in experience and databases, or even specific knowledge management systems. In the past few decades, many organizations have invested much time and effort in such specific knowledge collections (e.g., databases, experience repositories) for best practices and/ or lessons learned. The driving force behind all these activities is to disseminate knowledge about proven solutions to their workforce. Ultimately, the goal is to avoid mistakes and improve the overall workflow and processes to possibly save money and gain a competitive advantage.

Conceptual Model for Corporate Universities

In today’s corporate surroundings, business organizations are facing increasingly complex and volatile circumstances, characterized by rapid change. More and more, knowledge is critical for individual success in our information society. Consequently, processes related to its acquisition and management tend to have great value to specialists in the field of learning and training. Learning from knowledge and experience accumulated in the past contributes to improvements in performing business practices and processes and to the creation of knowledge assets implicitly stored in its results. As ongoing learning and sharing of knowledge lead to innovation (Law & Ngai, 2008), organizational members should have a shared vision to guide them in a common direction, generating tension that leads to learning (Kulkarni & Freeze, 2006). Thus, corporate university (CU) plays an important role as a source for business (Crocetti, 2002). It is a centralized learning utility that manages resources to support a strategic approach to organizational education (Gregg, 2005). This educational model provides a management structure to develop and implement employee learning opportunities aligned with the organization’s future learning vision (Gould, 2005).

Knowledge Transfer between Academia and Industry

Many policy makers and researchers consider knowledge transfer between academia and industry as one of the most promising measures to strengthen economic development. The idea of linking academia and industry is not new. Back in 1910 research universities were established, which strongly emphasized industry-related research as part of their activities and were funded by enterprises in order to tap this knowledge (see Matkin, 1990, for the history of technology transfer at four U.S. research universities—MIT, U.C. Berkeley, Penn State, and Stanford). Knowledge transfer has increased considerably during the last few decades. Many universities have established offices aimed at improving relations with industry. The performance of these offices varies considerably. One example for a quantitative performance indicator is license revenues of U.S. universities (Artley, Dobrauz, Plasonig, & Strasser, 2003). Only a handful of examined universities actually draw profit from it. The majority pay more for legal advice and fees than they earn from license income. It is obvious that the performance variances depend on many factors like staff resources at the transfer offices, type of university research (basic vs. applied, technical vs. non-technical domains), the brand of the university as well as prior industrial relationships, to name just a few. Not all of these factors can be changed in the short run, but knowing them and streamlining actions towards their improvement can lead to sustainable changes, in the end positively influencing economic performance. Despite the long history and recent efforts to improve university-industry collaborations, the full potential does not yet seem to be exploited (Starbuck, 2001). Jankowski (1999) and Clough (2003) confirm the decrease of federal funding for universities and point to increasing collaborations between academia and industry, which in their view comprises the danger of leaving fundamental frontier research, vital for breakthrough innovations, behind. At the same time, industry increasingly relies on external knowledge sources to keep up with the pace of their competitors (Business-Higher Education Forum, 2001; Tornatzky, 2000). In many cases, these external sources are customers and suppliers (Adametz & Ploder, 2003; Dachs, Ebersberger, & Pyka, 2004). This may be due to similar rationales, profit, and already-existing customer-client relationships. However, industry more and more turns to universities when looking for support. According to Godin and Gingras (2000), universities are still one of the major producers of knowledge, despite an increase of other R&D institutions. Collaborations between academia and industry bring partners with different competencies together and cover the whole range of the R&D chain, from basic research to application. By fulfilling the needs of both partners, universities as well as enterprises, and building up trust, knowledge transfer leads to knowledge flows and production of new knowledge, and thus creates a fertile environment for innovation. The article at hand examines motives as well as barriers related to knowledge transfer out of a systemic as well as a process-related view and provides some general suggestions for further improvements.

Knowledge Sharing in Legal Practice

Given the reliance on knowledge-based resources over traditional assets, the professional context serves as a heightened environment in which to investigate knowledge sharing. Within legal practice, the success of a law firm is connected to the firm’s ability to leverage knowledge (Sherer, 1995), and this has led to a call for knowledge management to be a business imperative within legal practice (Parsons, 2004; Rusanow, 2003).

Knowledge Management and the Non-Profit Sector

We begin our discussion with the predictable qualification about definition and scope. The non-profit sector (also called, variously, the citizen sector, social sector, et al) is an exploding phenomenon in its own right, with 1.5 million now accounting for more than $1 trillion in revenues in the U.S.; during the past 15 years nonprofits grew faster than the overall economy, with thirty thousand new organizations created each year. Worldwide growth is also increasing, with global expenditures on this sector accounting for nearly 5 percent of combined global gross domestic product (Crutchfield & McLeod Grant, 2008, pp. 2-3). The range of kinds of organizations is dizzying, from rich foundations to just-scraping-by neighborhood associations to educational institutions, to environmental boutiques to global health and human services providers to professional associations to job training to youth leadership to…the list goes on and on. Needless to say, generalization about millions of different organizations in this sector have to be accepted for what they can only be—broad thematic indicators of representative concepts and practices.

Knowledge Dissemination in Portals

While there are many aspects to managing corporate knowledge, one key issue is how to disseminate corporate documents with appropriate context. Upon finding an article on a certain subject, for example the material properties of titanium, a reader is likely to be interested in related articles such as applications of titanium or manufacturing methods for titanium parts. Each related article has the potential to increase the reader’s knowledge of the subject. Therefore, organizing documents into categories of interest plays an essential role in discovering and interpreting information. Furthermore, categories can be expected to provide historical context, describing how titanium was used in early designs or initial practices used for the repair of titanium parts.

Knowledge and Intellectual Property Rights

The role and importance of knowledge in economic development is a theme that can be traced back to writers such as Adam Smith, Karl Marx and Joseph Schumpeter. These authors dealt with how knowledge was created, its various uses and how to capture knowledge for gain. Later, writers such as Friedrich Hayek developed this field into areas such as the use of knowledge in society and the role of information in markets. In the economics literature, a critical distinction is made between information and knowledge. Information is seen as data that is out there for most, if not all, to see, whereas knowledge is the use and (unique) interpretation of this data. With the development of the World Wide Web, access to information and the cost of collecting information has changed dramatically. This has implications for its value. Anything that is easy to collect is less valuable than something that is more difficult to collect. Furthermore, something that is valuable will be sought by others and therefore needs to be protected against theft.