Georgios Tsaramirsis

Measuring Change Risk for Organisational Decision Making through a Hierarchical Model Process Approach

Project Management has long established the need for risk management techniques to be utilised in the succinct identification and mitigation of associated risks in projects. Such techniques aim at the reconciliation of countervailing project activities to reduce scope creep, increase the probability of on-time and within-budget delivery. Uncontrolled changes, regardless of size and complexity, can develop risks to projects and affect project success or even an organisation’s project delivery coherence. Ideally, a change or consequence based upon a decision should have a fairly high level of predictability and thus a low level of a potential risk materializing, which would significantly undo the decision taken. This paper proposes a novel modeling process approach; CRAM (Change Risk Assessment Model), which could significantly contribute to the missing formality of business models especially in the change risk assessment area.

Facilitating organisational decision making: a change risk assessment model case study

Purpose This paper aims to take the challenge to propose a novel modelling approach named Change Risk Assessment Model (CRAM), which will contribute significantly to the missing formality of business models especially in the change risk assessment area and decision-making. Organisational change risks are assessed with the aid of analytic hierarchy process (AHP) in an attempt to define the internal dynamics of organisational change management within project management eliciting also risk cause-and-effect relationships. Design/methodology/approach The study discusses interviews/survey/AHP. Findings The study presents the following findings. Change risk factors assessment (identification and prioritisation) recommendations (see Case Study) integration of change management; project management; risk management top four risk factors, namely, leadership, communication, project management team and culture. Research limitations/implications As projects can be different in a variety of factors (quality, scope), an exhaustive list of risk factors cannot be identified. There is a continuous risk identification process throughout the projects’ life cycle. For example, many risks can be classified initially as unknown and can be refined after the initiation phase of the project. AHP factors limitation (eight per level) possible bias (survey analysis). Practical implications With the aid of modelling and especially CRAM, business change risks can be assessed numerically and prioritised. Several risk factors and related attributes were identified and categorised. This empowers project managers or other stakeholders to make proper decisions about whether to take on or abandon respective organisational or project changes. Social implications One of the values of CRAM is that it can be regarded as a global change risk assessment method that can be applied regardless of project type, size or organisation. Moreover, it has the advantage that it can be used by any kind of project, as the method is designed to be tailored to specific needs, taking significant environmental change risk factors into account. AHP has numerous uses in operational research, in project management and in general in areas where decisions (evaluation and selection) have to be made. The analysis of the case study presented, indicated that it is vital to assess the degree (impact) that each risk attribute poses to address complex organisational decisions. Originality/value CRAM aims to bridge the gap between theoretical and applied work in the integrated research field of change management, project management and risk management. Furthermore, the approach attempts to develop a novel systematic methodology (model) for assigning probabilities in attributes (criteria) pair-wise comparison and more specifically, modelling and assessing change management risks, adding a different perspective and technique to the research area.

Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study

The presence of faulty valves has been studied in the literature with various machine learning approaches. The impact of using fault data only to train the system could solve the class imbalance problem in the machine learning approach. The data sets used for fault detection contain many independent variables, where the salient ones were selected using stepwise regression and applied to various machine learning techniques. A significant test for the given regression technique was used to validate the outcome. Machine learning techniques, such as decision trees and deep learning, are applied to the given data and the results reveal that the decision tree was able to obtain more than 95% accuracy and performed better than other algorithms when considering the tradeoff between the processing time and accuracy.