Knowledge Management for Production

Knowledge Management for Production

Marko Anzelak (Alpen-Adria-Universität Klagenfurt, Austria), Gabriele Frankl (Alpen-Adria-Universität Klagenfurt, Austria) and Heinrich C. Mayr (Alpen-Adria-Universität Klagenfurt, Austria)
DOI: 10.4018/978-1-60566-026-4.ch373
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Knowledge is one of the key drivers of innovation and success in the modern, information-based society. Consequently, knowledge has to be “operated” and “managed,” which causes particular challenges due to the intangible nature of knowledge: “… it is fluid as well as formally structured; it is intuitive and therefore hard to capture in words or understand completely in logical terms. Knowledge exists within people, part and parcel of human complexity and unpredictability. ” (Davenport & Prusak, 1998, p. 5) Being held in minds, knowledge is not easily accessible and hence, not manageable in the usual sense. Nevertheless, knowledge management (KM) tries to establish appropriate processes of externalizing, internalizing, and applying the knowledge of people involved in a given environment. Within that context, the notion of knowledge has undergone various definition attempts and interpretations. From an economic and corporate perspective, knowledge was viewed as a commodity, like other products, to be packaged, archived, retrieved as needed, and sent across networks. An example of this approach is the “Wissenstreppe” (knowledge staircase), proposed by Klaus North (2002). This model proposes eight steps, each of which is linked to an instruction on how to reach the next step. The lowest level [1] consists of symbols. Combining these with rule-based syntax creates data [2], and the addition of semantics produces information [3]; information enriched by connectivity leads to knowledge [4]. Knowledge combined with applicability results in ability [5], which in combination with willing can be converted to behaviour [6]. Effective behaviour leads to competence [7]. Competences leading to a unique selling proposition (USP) create competitive advantage [8]. Knowledge became increasingly a decisive factor in competitive gain (e.g., Bryant, 2006), leading to an expanding demand for KM. However, manifold problems caused the failure of several KM initiatives, and led to the rediscovery of earlier approaches, such as that of Michael Polanyi (1973, 1985). Casselman and Samson (2005) extended the two types of knowledge, explicit knowledge and tacit knowing. Explicit knowledge can be represented by signs (symbols, text, and images), and thus stored electronically. As such, it is quite similar, or even might be seen as synonymous, to “information.” Tacit knowing is always tied to a subject, that is, to a mind, and therefore, cannot be stored in a technical system. Nonetheless, it is possible to initiate processes that lead to the generation, externalisation, internalisation, and thus, to the sharing of tacit knowing. Information technology (IT) is the natural enabler of managing explicit knowledge since it supports to store and handle signs: electronic content of any kind is easy to extend, rework, comment, structure, and complemented by metadata. These basic features of any document-based information management are strengthened in combination with standard or tailor-made KM Systems (KMS), like the one described in this chapter to support knowledge processes.
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Tacit knowing can be understood as knowledge that is required to perform a behaviour, such as riding a bicycle, for which explicit knowledge is not mandatory (Dreyfus, Dreyfus, & Athanasiou, 1988): Even a child can learn to ride a bike without explicitly knowing specific rules or being able to articulate rules or formulas for balance calculations underlying bicycle riding. Knowledge that can be transformed into a skill is strongly embedded in experience, for example, gained from practising or sensing. Something can be understood comprehensively; reasons and connections can be recognised. Nevertheless, tacit knowing comprises aspects that are difficult to codify, such as personal convictions, perspectives, and values (Nonaka & Takeuchi, 1995).

Key Terms in this Chapter

Information: Refers to quantitative, arranged, perceivable, and distinguishable (mental) units that have representations (signs). Long ago, Shannon defined information as the “reduction of uncertainty,” Wiener made a disctinction from matter and energy.

Knowledge: Refers to quality and potential. It is related to understanding, experience, and expertise, and is gained from practising or sensing. Knowledge “can be seen as a culturally recognized set of performances called “knowing” that suggest that a person “has” the potential for further performances […] and, thus, is said to have “knowledge” of a certain form ( Day, 2005 , p. 631). For Siemens (2006) all knowledge is information, but NOT all information is knowledge.

Tacit Knowing: The process of creating knowing how , of creating general understandings, insights, experience, and/or expertise from particular entities. Knowing is one psychological category of intentionality. ( Day, 2005 ; Polanyi 1973 , 1985 )

Explicit Knowledge: Knowledge that can be represented in signs (symbols, tests, and images). Thus, explicit knowledge can be stored in technical systems, and can be managed. As such, some see it as synonymous to information .

Knowledge Management Systems (KMS): Software systems that provide features to collect, store, organize, distribute, and retrieve explicit knowledge in the form of information. KMSs are intended to support knowledge processing.

Knowledge Management: Means the organization of explicit knowledge in technical systems and the enhancing of tacit knowing by supporting organizational knowledge processing.

Lazy: Knowledge: Simply based on facts. It lacks understanding, insight, and experience and therefore, lacks capability.

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