Browsing by Author "Mkandawire, Burnet O'Brien."

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Application of maintenance tools and strategies in integrated risk management of critical physical assets.
(Inderscience Publishers., 2011) Mkandawire, Burnet O'Brien.; Ijumba, Nelson Mutatina.; Whitehead, Howard.
This paper critically analyses various tools, techniques and strategies; and proposes an ‘integrated risk management model’ that utilises advantages of the best combination of tools, techniques and strategies to manage risks thereby optimising operating costs whilst maximising returns on critical assets in high voltage networks; and physical assets in general. We used a triangulation method involving a longitudinal single case study within Malawian power sector, multiple (34) industrial case studies and sample surveys of selected Malawian and South African industries. It was shown that the electric power industry (70%) lacked a clear systemic maintenance and refurbishment risk management model due to the difficulty in determining optimum combination of tools. They also lacked technical skills needed to apply proactive strategies. The core value of tools is in planning of maintenance and refurbishment; and in contextualising, exploring, assessing, treating and monitoring of risks.
Asset management optimization through integrated systems thinking and N-1 contingency capability for refurbishment.
(Institute of Electrical and Electronics Engineers., 2011-09) Ijumba, Nelson Mutatina.; Whitehead, Howard.; Mkandawire, Burnet O'Brien.
This paper presents a systems view of refurbishment systems to evaluate root causes of suboptimal refurbishment. Case studies from ten selected South African and Malawian firms from largest electric power utilities, mining, petrochemical, and processing industries were used to establish causal relationships. Sample surveys of thirty four Malawian firms were used as part of a multimethod or triangulation approach to provide generalizations, validation and reliability. Of the surveyed firms, 66.7%, and of case studied firms 100%, showed that deferred refurbishment was a result of constrained capacity which led to components operating at higher loads, to lack of maintenance windows and to increased failure rates; there was no formal refurbishment model and technical skills base was the weakest asset management link. The study advances a novel way of depicting root causes of suboptimal refurbishment in typically complex dynamic structures using integrated systems thinking approach and applies analytical optimization tools, namely: Linear Programming (LP), metrics and N-1 contingency capability for refurbishment model for drilling deeper into causal typologies portrayed by systems thinking in order to solve optimization problems. A Total Refur-bishment Process model is advanced to replicate refurbishment decision structures for long term sustainability of industries as validated by industries studied.
Modelling physical asset risk profile using systems thinking augmented by stochastic and probabilistic inferences.
(2015) Mkandawire, Burnet O'Brien.; Saha, Akshay Kumar.; Ijumba, Nelson Mutatina.
Current quantitative approaches to power asset management risk modelling have focused on financial aspects such as net present value. These approaches can neither determine nor trend the impact of technologies or renewal strategies on failure risk. As a result of this, combined with the fact that benefits of renewal strategies are hard to determine as renewal does not add additional capacity that is needed for revenue generation, the value of the strategies is not appreciated. In addition, it is currently difficult to measure the effectiveness of risk assessment activities in Reliability-centered maintenance (RCM) programs when the number of equipment is large and not adequate data is available. Thus, the main objective of this research was to develop a failure risk trend monitoring model and to improve performance measurement in the RCM activities. This could be useful for the management of power infrastructure assets such as transformers. The risk trending model was developed by integrating systems thinking and system dynamics concepts with Markov processes, Weibull distribution and bathtub curve analysis to produce a quantitative measure of risk, called the risk factor. A set of 12 MVA substation transformer failure data was applied to compute the maximum likelihood estimates (MLE) of the Weibull parameters which were fitted into the risk factor, which was in turn trended with respect to changes in the number of components renewed during the asset life cycle. The risk trending model quantitatively determined the impacts of the renewal strategies on the transformer failure risk profile which can be used to provide strategic direction to asset managers regarding the most appropriate timing of renewal strategies to maximize financial benefits. Besides, the Markov analysis was applied to trend the profile of mean-time-to-first-failure (MTTFF) and average annual repair costs which was used as a measure of the effectiveness of the RCM programs. It was shown that the MTTFF is inversely proportional to the annual repair costs. Furthermore, the systems approach revealed that: the best and sustainable metrics are those that indicate the loss margin and the run-to-failure strategy is a quick-fix, but very unsustainable in the long run. The model developed can be used in risk assessment and in planning and development of asset management strategies in power utilities and in physical asset management firms in general.