Benjamin Chukudi Oji and Sunday Ayoola Oke
There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these…
Abstract
Purpose
There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.
Design/methodology/approach
Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.
Findings
The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.
Originality/value
This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.
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Victor Chidiebere Maduekwe and Sunday Ayoola Oke
Key performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the…
Abstract
Purpose
Key performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.
Design/methodology/approach
The causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.
Findings
The results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.
Originality/value
Hardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.
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Gerald Kenechukwu Inyiama and Sunday Ayoola Oke
Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process…
Abstract
Purpose
Downtime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.
Design/methodology/approach
Using the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.
Findings
The analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.
Originality/value
The major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.
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Desmond Eseoghene Ighravwe and Sunday Ayoola Oke
Maintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to…
Abstract
Purpose
Maintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to carry out maintenance jobs. In contemporary period, curiosity has proliferated about how sustainability affects manufacturing plans. The purpose of this paper is to offer a comprehensive notion of maintenance sustainability in maintenance planning. The literature has downplayed maintenance sustainability but may support in understanding how to crack the present company-community conflicts about the negative influence of manufacturing on the environment.
Design/methodology/approach
This study develops the idea of selecting the proper maintenance strategy based on integrated fuzzy axiomatic design (FAD) principle and fuzzy-TOPSIS. This work suggests that the maintenance function is an uncertain, activity-oriented system. To fully appreciate the proposed framework, the work employs data from a cement manufacturing plant to test the structure. This study offers 20 influential factors on which it build the fundamental structure of maintenance system sustainability for manufacturing concerns. A novel literature contribution that departs from existing conceptions is the classical determination of weights of each sustainability factor, employing fuzzy entropy weighting approach. Furthermore, work innovatively determines the ranking of some important tenets of sustainability in maintenance and optimises the maintenance consumables employing the FAD principle.
Findings
Interestingly, the output of the investigation revealed differences as the work adopts fuzzy-TOPSIS in comparison with FAD principle.
Originality/value
Case examination of a real-life manufacturing venture validated the claims, showing maintenance workforce training as a top-echelon strategy for maintenance system sustainability.
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Sunday Segbenu Nunayon, Emmanuel Abiodun Olanipekun and Ibukun Oluwadara Famakin
University campuses are one of the major consumers of electricity. Therefore, it is important to investigate factors related to electricity saving. This study aims to examine the…
Abstract
Purpose
University campuses are one of the major consumers of electricity. Therefore, it is important to investigate factors related to electricity saving. This study aims to examine the key drivers in achieving efficient electricity management (EEM) practices in public universities.
Design/methodology/approach
To achieve the objective, 23 drivers of EEM practices were identified through a comprehensive literature review and an empirical questionnaire survey was performed with 1,386 electricity end-users of three public universities having staff and students’ halls of residences in Nigeria. The collected data were analyzed using the statistical package for social sciences (SPSS version 21) to identify the number of components that could represent the 23 identified drivers.
Findings
The relative importance index ranking results indicated that 18 drivers were critical. The top five most critical drivers were understanding of the issues, understanding the vision and goal of an energy management programme, knowledge and skill, risk identification and good and effective communication among relevant stakeholders. An exploratory factor analysis revealed that the underlying grouped drivers were raising awareness, top management support and robust energy management team, risk management and stakeholders’ participation. This study also indicates that the most dominant of the four underlying groups was raising awareness, which highlights the role of increasing awareness and public consciousness as a significant catalyst in promoting EEM practices in public universities.
Research limitations/implications
Geographically, this study is limited to the opinion of respondents in public university campuses in Nigeria. Although this study could form the basis for future studies, its limitation must be considered carefully when interpreting and generalizing the results.
Practical implications
This paper has highlighted a few drivers of EEM practices in public universities. The results of this study present scientific evidence that can be used as a basis for formulating public policies that could be incorporated into the energy management regulations of university buildings. It is most important for policymakers to pay adequate attention to the most critical drivers especially those that are related to the “raising awareness” factor to promote sustainable campuses.
Originality/value
This study provides practical knowledge for university management to develop effective methods to implement the identified drivers of efficient and sustainable electricity management on the campus. This study also contributes to the body of knowledge in the field of energy management.