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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.

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.

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.

Prediction of the duration of the downtime caused by maintenance, especially in the cases where the system considered consists of several repairable items, presents a challenge for maintenance managers, because of possible revenue losses during these intervals of time. Responds to this challenge through the new methodology for the fast, accurate prediction of maintainability measures related to the group replacement maintenance policy. It is applicable to group maintenance tasks in which individual replacement tasks are performed: simultaneously, sequentially, and combined. The method presented could be successfully used at the planning stage of the operations/production process when the information available is based on previous experience only, as well as at the stage when the process is performed. The applicability and usefulness of the methodology proposed is demonstrated through an illustrative numerical example.

The purpose of this paper is twofold: first, a case study on the application of lean production principles in a manufacturing facility is presented to demonstrate the impact of frequent and systematic use of a Kaizen event on quality and delivery performance. Second, the detailed description and analysis of the Kaizen event and its impact are provided, including a comprehensive analysis of the role of Kaizen events on employee participation and motivation.

The study utilizes a Kaizen event’s case study data with the help of various waste detection and elimination tools and techniques. Changes in overall productivity along with potential long-term improvements in the delivery process are also analyzed and documented.

Pre- and post-quality measures are provided to demonstrate the results of the event on the production quality and on the performance of the overall manufacturing processes. Qualitative findings regarding performance measurements and the impact on the employees are reported.

The Kaizen team applied analytical techniques to one manufacturing site in North America of a company that has a manufacturing presence in 20 different countries.

Kaizen studies involving packaging operations are quite limited. This study fills this gap by detailing the Kaizen event implementation in a packaging delivery and dispensing systems manufacturer for the cosmetic industry. The implementation of this Kaizen event is detailed along with the data and techniques utilized for process improvement. The study also reports findings regarding the impact of the Kaizen event on employee participation.

This research is focused on the application of reliability‐centred maintenance (RCM) in a chipping and sawmill company. The aim of the study was to illustrate the application of RCM in a chipping and sawing mill.

RCM is a structured process, which develops or optimises maintenance requirements of a physical resource in its operating context in order to realise its inherent reliability by logically incorporating an optimal combination of reactive, preventive, condition‐based and proactive maintenance practices. A detailed analysis of the RCM approach is presented as a step towards improving preventive maintenance (PM) within a sawmill.

The study shows that the way that PM tasks are specified is a good indicator of the effectiveness of the PM program and could be a major source of maintenance‐related downtime. It is also revealed that most maintenance programs, which purport to be proactive, are in fact reactive. The paper also shows that RCM can be successfully applied to industries anywhere; even in less industrialized countries.

The paper focuses on a pilot study of a section of a chipping and sawmill. The development and implementation of the RCM approach is elaborated based on a pilot program in the edging unit of a sawmill company. Further application to the entire plant, albeit time‐consuming, is recommended.

Application of RCM in sawmill industries, within developing countries, has had limited application. The paper demonstrates that regardless of technological challenges in less developed economies, maintenance approaches such as RCM can still be fruitfully applied in order to achieve maintenance excellence. The paper should be useful for maintenance practitioners and researchers, particularly in less industrialized countries.

The purpose of this paper is to present a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine.

In total, four years of cost data are collected. Data are analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.

The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT.

The proposed approach can be used by the decision maker in determining the ERT of production machines which used in mining industry.

The research proposed in this paper provides and develops an optimisation model for ERT of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.

The purpose of this paper is to assess TPM pillars for manufacturing performance improvement in the manufacturing organizations of Northern India and to identify critical and non-critical components based on failure history, to minimize machine downtime, maximize component/machine availability and to identify failure modes, their causes and effects of these failures on machines or components in the case company under study.

In this paper, TPM pillars in the paint manufacturing plant have been elaborated to ascertain the tangible and intangible benefits accrued as a result of successful TPM implementation. The approach has been directed toward justification of TPM implementation for its support to competitive manufacturing in the context of Indian manufacturing industries.

Findings suggest that maintenance planning is more effective than small improvements for achieving benefits from TPM pillars. Moreover, results indicated that critical components show average reliability and failure probability of about 50 percent.

The present study encompasses systematic identification of maintenance-related losses, setting up of targets regarding maintenance performance improvements and developing guidelines for achieving enhanced manufacturing system performance through strategic TPM implementation in the manufacturing plant, which can also be important to all concerned with maintenance in various manufacturing enterprises.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here.

The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a large number of papers in this field and presents the overview of various TPM implementation practices demonstrated by manufacturing organizations globally. It also highlights the approaches suggested by various researchers and practitioners and critically evaluates the reasons behind failure of TPM programmes in the organizations. Further, the enablers and success factors for TPM implementation have also been highlighted for ensuring smooth and effective TPM implementation in the organizations.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. It will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the significance of TPM.