Knowledge intensive processes characteristics and improvement: a banking best practice example

This paper identifies characteristics of knowledge intensive processes and a method to improve their performance based on analysis of investment banking front office processes. The inability to improve these processes using standard process improvement techniques confirmed that much of the process was not codified and depended on tacit knowledge and skills. This led to the use of a semi-structured analysis of the characteristics of the processes via a questionnaire to identify knowledge intensive processes characteristics that adds to existing theory. Further work identified innovative process analysis and change techniques that could generate improvements based on an analysis of their properties and the issue drivers. An improvement methodology was developed to harness a number of techniques that were found to effective in resolving the issue drivers and improving these knowledge intensive processes.

Modelling the resistance of Enterprise Architecture adoption: linking strategic level of Enterprise Architecture to organisational changes and change resistance

During the last few years Enterprise Architecture (EA) has received increasing attention among industry and academia. By adopting EA, organisations may gain a number of benefits such as better decision making,increased revenues and cost reduction, and alignment of business and IT. However, EA adoption has been found to be difficult. In this paper a model to explain resistance during EA adoption process (REAP) is introduced and validated. The model reveals relationships between strategic level of EA, resulting organisational changes, and sources of resistance. By utilising REAP model, organisations may anticipate and prepare for the organisational change resistance during EA adoption.

Identification of fractional-order transfer functions using a step excitation

This brief proposes a new method for the identification of fractional order transfer functions based on the time response resulting from a single step excitation. The proposed method is applied to the identification of a three-dimensional RC network, which can be tailored in terms of topology and composition to emulate real time systems governed by fractional order dynamics. The results are in excellent agreement with the actual network response, yet the identification procedure only requires a small number of coefficients to be determined, demonstrating that the fractional order modelling approach leads to very parsimonious model formulations.