Search results

Presents the main findings of an internal and generic benchmarking exercise on the engineer availability process. This process seeks to predict customer demand in terms of the incoming call rate and match this against the available supply of engineering resources. Describes how the internal benchmarking revealed considerable differences in the way in which the different internal business units managed their engineer availability process. Explains that the main reason for the difference was in the clustering of self‐managing work groups of engineers. Also points out that there are three main elements involved in ensuring the success of an engineer availability process: an accurate forecast of customer demand; a method of operating which yields an effective revision of supply to fit customer demand; and a high support environment.

Outlines the main findings of a study of a process in a major organization (involved in the servicing of capital equipment) which seeks to predict customer demand for service calls and match these against engineering resources. The process is called the engineer availability process (EAP) and has been examined in two geographical areas of the host company. Among the findings is that, no matter what means are used to predict demand, there is always a need for a flexible revision of supply. This revision can be met between formal and informal clustering of engineers. The former delivers a consistent output of jobs per day across a clustered team whereas with informal clustering, the distribution across teams is less consistent and puts greater pressure on individual teams to match supply and demand.

The purpose of this paper is to study the evolution of a system stationary availability and determine the optimal preventive maintenance period, which maximises it in a context where preventive and corrective maintenance actions are imperfect and have non‐negligible durations.

The quasi‐renewal process approach and a (p, q) rule are respectively used to model corrective and preventive maintenance. Considering the durations of the preventive and corrective maintenance actions as well as their respective efficiency extents, a mathematical model and a numerical algorithm are developed in order to compute the system stationary availability.

It has been proven that for any given situation regarding the system, the repair and preventive maintenance efficiency extents, and the downtime durations for preventive and corrective maintenance, there is necessarily a finite optimal period T* of preventive maintenance which maximises the system stationary availability. A sufficient condition for the uniqueness of the optimal solution has also been derived. Numerical examples illustrated how preventive and corrective maintenance efficiency extents affect simultaneously the system optimal availability.

The study considers a general industrial framework where preventive and corrective maintenance actions are imperfect. In fact, neither the best‐qualified technicians nor the most suitable tools or spare parts are found to carry out maintenance actions. In such a context for a large variety of technical systems, when implementing preventive maintenance policies one should take into account the efficiency extents of maintenance actions as well as their durations in order to evaluate and optimise the system availability. The paper provides maintenance managers with a decision model allowing not only the computation and optimisation of system availability, but also the investigation of how preventive and corrective maintenance efficiency extents affect simultaneously the system optimal availability. The proposed model also allows one to find to what extent corrective actions ineffectiveness should be tolerated without having an important availability loss.

The paper proposes a modified formulation of the quasi‐renewal process taking into account the non‐negligible duration of corrective maintenance actions and periodic preventive maintenance. A new numerical algorithm is also developed in this context to compute the quasi‐renewal function that it is impossible to find in closed form. This allowed the computation and optimisation of system stationary availability.

Discusses in a light‐hearted way, the serious process of planning and implementing change in engineering design departments. Provides an outline framework.

Building information modelling (BIM) is a novel technological advancement in the built environment. Despite the potentials of BIM, its adoption and implementation are undermined in facility management (FM) operations. This might be because of limited information on the critical success factors (CSFs) that can enhance its adoption. The study aims to integrate building information modelling to improve facility management operation by adopting fuzzy synthetic approach for evaluating the critical success factors.

Data for the study were sourced from practising and registered facility managers within Lagos metropolis, Nigeria. The data collected were analysed using a combination of methods which include mean item score, factor analysis and fuzzy synthetic evaluation (FSE).

The factor analysis results showed that six underlying groups of CSFs would enhance the effective adoption of BIM in facility operations. The FSE results showed that out of the six groups, the three topmost important CSF grouping (CSFG) in the decision rule would enhance the effectiveness of BIM adoption for FM operations.

The result of this study provides a credible road map for facility managers, policymakers and other stakeholders in FM operations on the CSFs and CSFG required for the adoption of BIM.

Previous studies that aimed at integrating BIM into FM are limited. Hence, this study provides a broad perspective on the CSF required for BIM adoption and implementation in FM operations using the FSE approach.

Benchmarking is recognised as an essential tool for continuous improvement of quality. A large number of publications by various authors reflect the interest in this technique. Reviews of literature on benchmarking have been done in the past by a few authors. However, considering the contributions in the recent times, a more comprehensive review is attempted here. In this paper, the authors have reviewed benchmarking literature in a way that would help researchers, academicians and practitioners to take a closer look at the growth, development and applicability of this technique. The authors have examined various papers and have proposed a different scheme of classification. In addition, certain gaps that would provide hints for further research in benchmarking have been identified.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.

The research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.

The proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.

The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.

These models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.

A unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.

The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.

The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.

Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.

This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.

This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.