The Applied Knowledge Engineering methodology guides the development of Knowledge Networking Solutions using a systemic view of the organisation. Much work in this area has emphasised the need to address both the social and technical interactions in the organisational system through learning processes. Learning processes are needed to help decision makers develop abilities to view new situations systemically and dynamically (i.e. seeing interrelationships rather than linear cause-effect chains, and processes of change rather than snapshots) [Senge93]. A crucial precondition for performance optimisation and control is enterprise integration. Process engineering provides both support for integration and facilitates measurement and feedback loop design. The key, however, to a responsive organisation is innovation in designing and implementing control mechanisms. Control decisions are based on comparing the state of a system at two different levels. One level describes the present instantaneous condition of the system and the other the presumed knowledge about the system [Forrester94]. The relationships involved are complex. One must assess the effects of system characteristics (e.g. the specific performance measures employed, the frequency at which performance is measured, the basis for performance evaluation and the time period over which variances are corrected) singly and in the various combinations used by performance indicators [Weil78]. Emphasis should be given to the following two distinct but also interrelated aspects of control engineering: a) understanding / rationalisation of the system elements to be controlled; b) control models defining the main control variables including information and knowledge and associated measurements together with feedback flows and control rules. The Applied Knowledge Engineering methodology uses as cornerstones the following models: · The Knowledge model · The Knowledge Network model · The Unified Process and Knowledge Management model · The KNS Architecture << >>
The Applied Knowledge Engineering methodology guides the development of Knowledge Networking Solutions using a systemic view of the organisation. Much work in this area has emphasised the need to address both the social and technical interactions in the organisational system through learning processes. Learning processes are needed to help decision makers develop abilities to view new situations systemically and dynamically (i.e. seeing interrelationships rather than linear cause-effect chains, and processes of change rather than snapshots) [Senge93].
A crucial precondition for performance optimisation and control is enterprise integration. Process engineering provides both support for integration and facilitates measurement and feedback loop design. The key, however, to a responsive organisation is innovation in designing and implementing control mechanisms.
Control decisions are based on comparing the state of a system at two different levels. One level describes the present instantaneous condition of the system and the other the presumed knowledge about the system [Forrester94]. The relationships involved are complex. One must assess the effects of system characteristics (e.g. the specific performance measures employed, the frequency at which performance is measured, the basis for performance evaluation and the time period over which variances are corrected) singly and in the various combinations used by performance indicators [Weil78].
Emphasis should be given to the following two distinct but also interrelated aspects of control engineering:
a) understanding / rationalisation of the system elements to be controlled;
b) control models defining the main control variables including information and knowledge and associated measurements together with feedback flows and control rules.
The Applied Knowledge Engineering methodology uses as cornerstones the following models:
· The Knowledge model
· The Knowledge Network model
· The Unified Process and Knowledge Management model
· The KNS Architecture