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Automated flow line manufacturing systems are becoming more and more relevant in industry, especially in the food and beverage sector. Improving the efficiency of automated flow line manufacturing systems is the core objectives of all companies as measured by the overall equipment effectiveness (OEE) index. The purpose of this paper is to carry out an innovative micro downtime data collection and statistical analysis in the food and beverage sector; it introduces a numerical indicator called “Cost Performance Indicator-CPI” to estimate the performance improvement of investment activities. Moreover this analysis will be used as a basis to carry out a new simulative model to study micro downtime of automatic production lines. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities.

Descriptive and statistical analyses are carried out about OEE, time to repair (TTR) and time to failure (TTF) data. The least efficient production line is identified and principal causes of inefficiency are investigated. Micro downtime (downtime lower than 15 minutes) covers 57 percent of inefficiency. Investigations are carried out into the three principal machines affected by this inefficiency. The study then investigates the causes of micro downtime of these machines using ad hoc data collection and analysis. The probability distributions of TTF and TTR are evaluated and an analysis of micro downtime causes and a cause-effect is carried out. The most attractive investment in terms of recoverable OEE (1.44 percent) and costs is analyzed through the calculation of a CPI. One of the conclusions is to recommend the introduction of a payback period with a variable contribution margin.

This study get the basis for the construction of a new simulative model based on ad hoc micro downtime probability distributions, applied in automated flow line manufacturing systems. It gives an effort to downtime analysis in automated production lines and a guideline for future analysis. Results of this study can be generalized and extended to other similar cases, in order to study similar micro downtime inefficiency of other production lines. The statistical analysis developed could also potentially be used to further investigate the relationship between the reliability of specific machines and that of the entire line.

The case study presents a new detailed micro downtime data collection and statistical analysis in the beverage sector with the application of a numerical indicator, the CPI, in order to drive future actions. In addition, the presented micro downtime data collection and statistical analysis will be used to construct a new simulative model to support improvement activities. Moreover, results can be generalized and used as a basis for other micro downtime analyses involving the main causes of inefficiency in automated production lines.

This paper aims to propose a practical method of performing maintainability analysis of an offshore system at operation phase having some improvement trend.

The analysis follows a systematically developed method of analyzing maintenance data, identifying critical factors affecting system performance, and developing suitable downtime distribution model with some applications of statistical analysis techniques and expert opinion.

Improvement in spare part logistics is found significant in reducing downtime thus should be well feedback to design and plant engineers so that it can be incorporated in new offshore system. The downtime models developed based on the steady state analysis and expert input are found to be practical for prediction of the system maintainability performance.

The analysis focuses on the downtime which includes the repair, logistics and administrative delay time. At the operation phase, plant personnel are mostly interested in the availability and downtime performance of the system thus this study provides an excellent example of how the analysis can be done practically and effectively.

Maintainability analysis at operation phase is crucial to assess and predict the maintenance system performance and provide valuable feedback to the design phase and existing plant for further improvement. The methodology developed here is practical hence can assist plant personnel to perform maintainability analysis effectively.

This paper present a generic method of analyzing maintainability at the operation phase. The proposed distribution method via steady state and expert input approaches provides a practical method for formulating downtime distribution model when improvement trend exists.

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.

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.

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.

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.

The purpose of this paper is to analyse and compare the downtime of four drilling machines used in two underground mines in Sweden. The downtime of these machines was compared to show what problems affect downtime and which strategies should be applied to reduce it.

The study collects failure data from a two-year period for four drilling machines and performs reliability analysis. It also performs downtime analysis utilising a log-log diagram with a confidence interval.

There are notable differences in the downtime of most of the studied components for all machines. The hoses and feeder have relatively high downtime. Depending on their downtime, the significant components can be ranked in three groups. The downtime of the studied components is due to reliability problems. The study suggests the need to improve the reliability of critical components to reduce the downtime of drilling machines.

The method of analysing the downtime, identifying dominant factors and the interval estimation for the downtime, has never been studied on drilling machines. The research proposed in this paper provides a general method to link downtime analysis with potential component improvement. To increase the statistical accuracy; four case studies was performed in two different mines with completely different working environment and ore properties. Using the above method showed which components need to be improved and suggestions for improvement was proposed and will be implemented accordingly.

In this article a case study is reported which deals with identifying the sources of downtime, and a procedure which could help management to predict whether the daily production standards in auto assembly‐line operations could be met given the intensity of sources of downtime. This study was conducted for a large auto engine manufacturer. The engine assembly line under study is subject to good preventive maintenance to avert unexpected breakdowns that would shut down the line or lower the quality so as to reduce the yield in total. Two multivariate statistical procedures are used – factor analysis and multiple discriminant analysis.

Improvement maintenance’s aim is identifying and implementing improvement action on equipment in service. The purpose of this paper is to propose an approach based on downtime’s analysis to raise up equipment’s performance, such as reliability, maintainability, availability, and safety. Steps of this approach will be illustrated by industrial applications.

Furthermore, it proposes an approach based on a combination of several methods (FMECA, cause and effect diagrams, ABC analysis, 5W, RCA method, etc.). This approach is based on the downtime analysis and lead to improve maintenance’s performance and equipment’s reliability, maintainability, availability and safety. Those objectives have a strong correlation with the downtime.

This approach led to apply easily improvement maintenance by using known methods. In the industrial application, this approach helps the authors to choose the good solution with the better life durance.

After following the steps simulated by the proposed approach, actions and concrete measures will be deducted and illustrated by the industrial’s application.

At present, there is not an explicit improvement maintenance, to lead to specific maintenance actions on equipment in service, in order to improve the maintenance function goals.

Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC.

In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling.

Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively.

The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD).

The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine.

Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits.

This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods.

This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time.

Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.

The purpose of this paper is to investigate performance times from a quality engineering perspective for response and repair of pipes at a public utility. The objective is to scientifically determine the pipe that offers the most desirable downtime (DT) and time to repair (TTR).

Four types of water supply pipes – concrete cylinder (CC), cast iron (CI), ductile iron (DI), and polyvinyl chloride (PVC) – in prevalent use at the City and County of Honolulu Board of Water Supply were analyzed to determine the pipe type that is most consistently repaired to desired performance specifications. Data for mean downtime (MDT) and mean time to repair (MTTR) were used to evaluate the stability and capability of the repair processes for each pipe type. The analysis was completed through the use of control charts, operating characteristic (OC) curves, and process capability indices.

The results of the analysis indicated that CI pipes were the worst material in terms of DT and TTR. The control charts for MDT for all pipe types, and the MTTR for CI and CC pipes, were found to be out of statistical control, but the control charts for the MTTR of DI and PVC pipes were discovered to be in control. According to the OC curves, in which the hypothesis stated that the average MDT or MTTR was between the specification limits, there was a high tendency in all pipe types to accept the hypothesis when it was true. However, the probability of type I errors was high from operational standards at the USL level. Process capability analyses found that only CC pipes were able to meet performance design specifications; however, repair times are extremely large for CC pipes. Overall, it is recommended that CI pipes be replaced when the opportunity arises.

This investigation serves to address a major query in asset management at the public utility, that of which pipes should be selected during design and procurement from a maintenance perspective. In addition, the study helps to understand the trend of DT and TTR for the various pipes.

Quality water supply is of paramount social importance in modern cities.

A quality engineering approach to asset management for pipe systems at public utilities that serves to add a new dimension to asset performance analysis is adopted.

The main objective of this study was to analyse the economics of introducing IT in the maintenance department. The economics in this case was determined by conducting a quantitative analysis on the reduction of operational costs, on increase in productivity and on quality improvement. A comparison was made to analyse company performance in the maintenance before and after the introduction of IT in the maintenance department. The analysis shows that there were reductions of operational and inventory holding costs. Likewise, it was shown that there was also improvement in product quality and productivity.