Analysis of trends in the performance of urban water utilities: a case study of Embu Water and Sanitation Company

Poor performance and lack of sustainability of water utilities in developing countries begs for a reform of institutional and policy environment in which they operate. Kenyan Water Act 2002 was passed, in part, to address reforms to the institutional framework and improving finance mechanisms. This case study assessed the institutional capacity of Embu Water and Sanitation Company (EWASCO) and the trends in performance since the water sector reforms. Interview guides, review of documents related to EWASCO, observations and literature review were used to collect information on several performance categories. EWASCO has been accorded substantial but regulated level of managerial and policy autonomy. A strategic management concept has been used to derive a solution-oriented planning framework for its operations. Performance efficiency has been achieved through commercial, managerial and technical best practises leading to improved financial sustainability enabling EWASCO to increase service coverage. EWASCO has some useful lessons for other utilities

Maintenance Performance Optimization for Critical Subsystems in Cement Pre-Grinding Section: A Case Study Approach

This paper aims to develop a simulation-based framework to identify critical equipment, critical maintenance and operational factors (e.g., maintenance actions, spare sourcing lead times and fill rate) affecting plant performance (availability and maintenance cost). The study develops a framework that utilizes empirical maintenance data. Pareto analysis is employed to identify critical subsystems, while expert input is incorporated to derive model variables. A full factorial Design of Experiment (DOE) is employed to establish the variables with significant main and interaction effects on the plant availability and maintenance cost. The framework is applied to a real case study of a cement-manufacturing firm, where a simula- tion model is developed based on the empirical maintenance and operational data while considering the availability and maintenance cost as the performance measures. Simulation results highlight the bucket elevator as the critical subsystem. At the same time, spare parts importation probability, among other parameters like the preventive maintenance interval and utilization of adjust maintenance action, significantly affects the performance (availability and maintenance cost) as main and interaction effects. The research was applied to only one case study, in this case, a cement grinding plant. The study provides a pragmatic reference model framework to practitioners that enhances maintenance decision-making by identifying critical equipment, maintenance and operational parameters and disclosing their effect (main and interaction) on the plant performance (availability and maintenance cost). This study is one of the first to (i) investigate the maintenance and operational factors’ main and interac- tion effects on maintenance cost and (ii) integrate the spare parts importation probability as a factor affecting plant performance. The developed framework assists in determining critical systems to be optimized, considers various maintenance strategies simultaneously, the stochasticity of spare parts availability and replenishment and ultimately discovers the interactions for decision support

Simulating the influence of maintenance actions on equipment reliability

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Maintenance objective selection framework applicable to designing and improving maintenance programs

Maintenance optimization is applied by organizations to develop robust maintenance programs while attempting to establish a trade-off between competing maintenance requirements and resources. For this reason, maintenance decisions derived from maintenance optimization models are adversely affected, if (a) the maintenance objective(s) applied as input to the optimization models is not dynamically reviewed as the organizations environment context changes, and (b) there is continued use of historical maintenance objectives, oftentimes the practice for organizations lacking a framework for selecting maintenance objectives (MO’s). To address this, an interactive maintenance objective selection framework for stakeholders that aligns with and considers changes in the organization’s business, operational, and technical context, where dynamic maintenance objectives are selected and prioritized for application in real-life maintenance optimization models is proposed. The framework uses an analytic network process (ANP) based methodology, for selecting the relevant MO’s in view of competing dynamic criteria, for instance, employing remanufactured spares to optimize availability and maintenance cost. The applicability of the framework is demonstrated in case studies of companies operating in diverse industries like aviation and manufacturing in Africa. The study highlights the effects of dependencies between competing maintenance objectives, where the dependencies invariably influence how organizations prioritize MO’s to use for maintenance optimization programs. The additional value of the proposed framework lies in assisting organizations select maintenance objectives applicable to the organization while considering competing objectives and evolving business context

A cost-based failure prioritization approach for selecting maintenance strategies for thermal power plants: a case study context of developing countries

The failure mode and effect analysis (FMEA) employing the risk priority numbers (RPN) has been used extensively for identifying and prioritizing failure modes with a view of mitigating their impact on equipment failure. However, in its traditional form, the prioritization approach through the RPN lacks the objectivity required for robust risk assessment, more so, where maintenance data is available, which could enhance such objectivity. This paper extends a quantitative approach for prioritizing failure modes and component failures in facilities, and more specifically, leverages on maintenance data often recorded in such facilities. To enhance the objectivity of the risk prioritization process, the proposed approach integrates three objective measures—the cost of failure, failure occurrence rate and percentage downtime effects of equipment failure. The integrated measures are demonstrated as more robust for prioritizing risks as opposed to ordinal indices as the case in the conventional FMEA approach. Using historical maintenance records, a three-step ranking approach is proposed for prioritizing critical failure modes in a thermal power plant where a case study is discussed. Moreover, the study compares the results derived from the prioritization approach with that derived utilizing the conventional RPN method. The comparative study demonstrates the added value of a more objective and quantitative prioritization approach for maintenance decision support. Ultimately, the critical failure modes are evaluated using a decision scheme to allocate appropriate maintenance strategies as the final step of risk assessment (i.e. risk treatment). The proposed approach is viewed as generalizable, intuitive and offering insights to the maintenance practitioners.status: Published onlin

A review on lubricant condition monitoring information analysis for maintenance decision support

Lubrication Condition monitoring (LCM) is not only utilized as an early warning system in machinery but also, for fault diagnosis and prognosis under condition-based maintenance(CBM). LCM is considered as an important condition monitoring technique, due to the ample information derived from lubricant testing, which demonstrates an introspective reflection on the condition and state of the machinery and the lubricant. Central to the entire LCM program is the application concept, where information from lubricant analysis is evaluated (for knowledge extraction) and analyzed with a view of generating an output which is interpretable and applicable for maintenance decision support (knowledge application). For robust LCM, varying techniques and approaches are used for extracting, processing and analyzing information for decision support. For this reason, a comprehensive overview of applicative approaches for LCM is necessary, which would aid practitioners to address gaps as far as LCM is concerned in the context of maintenance decision support. However, such an overview, is to the best of our knowledge, lacking in the literature, hence the objective of this review article. This paper systematically reviews recent research trends and development of LCM based approaches applied for maintenance decision sup-port, and specifically, applications in equipment diagnosis and prognosis. To contextualize this concern, an initial review of base oils, additives, sampling and testing as applied for LCM and maintenance decision support is discussed. Moreover, LCM tests and parameters are reviewed and classified under varying categories which include, physiochemical, elemental, contamination and additive analysis. Approaches applicable for analyzing data derived from LCM, here, lubricant analysis for maintenance decision support are also classified into four categories: statistical, model-based, artificial intelligence and hybrid approaches. Possible improvement to enhance the reliability of the judgement derived from the approaches towards maintenance decision support are further discussed. This paper concludes with a brief discussion of plausible future trends of LCM in the context of maintenance decision making. This present study, not only highlights gaps in existing literature, by reviewing approaches applicable for extracting knowledge from LCM data for maintenance decision support, it also reviews the functional and technical aspects of lubrication. This is expected to address gaps in both theory and practice as far as LCM and maintenance decision support are concerned.status: Published onlin

A comparative analysis of maintenance strategies and data application in asset performance management for both developed and developing countries

Purpose: The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and Kenya, respectively). Design/methodology/approach: Empirical observations and theoretical interpretations on maintenance practices under APM are delineated. A comparative cross-sectional survey study is conducted through an online questionnaire with 151 respondents (101 Kenya, 50 Belgium). Descriptive statistics and inferential statistics like independent t-test and phi coefficient were used for analyzing the data. Findings: In both countries, reduction of maintenance and operational budget, return on assets, asset ageing and compliance aspects were established as critical factors influencing the implementation of asset maintenance and performance management (AMPM). A significant difference in staff competence in managing vibration, ultrasound and others like predictive algorithms was found to exist between the firms of the two countries. The majority of firms across the divide utilize manual and computer-based tools to integrate and analyse various maintenance data sets, while standardization and maintenance knowledge loss were found to adversely affect maintenance data management. Research limitations/implications: The study findings are based on the limited number of returned responses of the survey questionnaire and focused on only two countries representing developed and developing economies. This study not only provides practitioners with the practical guidelines for benchmarking, but also induces the need to improve the asset maintenance strategies and data application practices for asset performance management. Practical implications: The paper provides insights to researchers and practitioners in the articulation of imperative effective maintenance strategies, benchmarking and challenges in their implementation, considering the different operational context. Originality/value: The paper contributes to theory and practice within the field of AMPM where no empirical research comparing developed and developing countries exist

A data mining approach for lubricant-based fault diagnosis

Purpose: The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set. Design/methodology/approach: The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models. Findings: The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs. Practical implications: The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors. Originality/value: Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS