Browsing by Author "Momoh, Aisha"

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    A framework for complexity cost modelling of ERP implementation
    (Cranfield University, 2015-04) Momoh, Aisha; Shehab, Essam; Roy, Rajkumar
    The aim of this research is to develop a framework to predict the cost of the resource complexities inherent in an ERP implementation. The framework comprises two phases namely complexity assessment, and dynamic cost estimation for resource complexity using agent-based modelling. The complexity assessment phase involves complexity identification, uncertainty evaluation for the complexity estimates, complexity assessment, complexity classification, and complexity correlation reporting. The framework is automated in a tool known as Complexity of Resource and Assessment Costing Tool (C-REACT). A number of activities have been undertaken in order to develop the ERP resource complexity framework. Firstly, a detailed literature review was performed in order to gain a contextual understanding of ERP implementation challenges and complexities. Secondly, a case study analysis was conducted to establish the current industrial practices concerning ERP implementation challenges. Thirdly, a framework was developed and validated to identify, assess and cost ERP complexity for each resource. The key contribution of the framework is to introduce a new cost estimation process to support ERP project cost estimation by predicting the cost of ERP resource complexities, and a new process to identify, assess and control ERP complexities inherent in the implementation stage. This framework should be used in the needs identification stage of an ERP project lifecycle. The estimate will inform an organisation of the potential costs of ERP resources from a complexity perspective, thereby enabling them to make informed decisions on ERP implementation complexity and cost reduction. Knowledge of potential complexities will also aid the elimination of substantial errors during implementation. Hence the organisation will yield benefits which they would not otherwise reap in the face of complexity.