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The purpose of this paper is to describe the overall equipment cost loss (OECL) methodology and an implementation of this methodology, to compare the outcomes of OECL with those of overall equipment effectiveness (OEE), and finally to identify the benefits offered by this new methodology.

The proposed methodology, OECL, combines six large loss models and a financial model in the performance evaluation of equipment. The six large losses are converted into monetary units. OECL is a new way of evaluating equipment performance that differs from the original OEE methodology and overcomes some of the limitations of OEE. This new methodology can be used to rank problematic machines by accounting for production elements together with finance elements.

The OECL and OEE methodologies rank problematic machines differently.

Efforts were made in this research to identify factors affecting OECL outcomes, but it was found that it was not possible to apply OECL to all scenarios.

The OECL model can be implemented in a real manufacturing company to help decision-makers better determine the magnitudes of equipment problems and rank problematic pieces of equipment appropriately.

This OECL method is able to overcome some of OEE’s weaknesses. It can properly prioritise problematic machines by considering both cost and losses.

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.

Today's customer‐focused paradigm of business environment puts tremendous pressures of quality, delivery, dependability, flexibility and cost on the manufacturing organisation. Automatic manufacturing systems offer several advantages and are increasingly being adopted as a strategy to improve the performance of manufacturing organisations. Automatic manufacturing systems are highly sophisticated and expensive, and it is therefore important to maximise their productivity. Yet, one can improve only what one can measure. Performance measurement is the key to improving performance, and is a prerequisite to diagnosing, trouble‐shooting and improving the production system. Accordingly, performance measurement has been attracting increasing attention over the last two decades, and several frameworks have emerged for the design, review, evaluation and improvement of performance measurement systems for businesses and manufacturing organizations. The performance measurement, monitoring and continuous productivity improvement of automatic manufacturing systems has assumed special significance on account of their high investments and operating costs.

A review of the current literature is undertaken to determine the current status and the status of performance measurement in automated production systems.

Overall equipment effectiveness (OEE) has emerged as one important and universally accepted metric for measuring the overall performance of single automatic equipments. OEE has been further adapted and extended into several variations for use as a metric for automatic manufacturing systems consisting of several automatic machines.

This paper reviews the recent developments and the current status of performance measurement of automatic manufacturing systems.

Overall equipment effectiveness (OEE) provides a quantitative metric based on the elements availability, performance and quality for measuring the performance effectiveness of individual equipment or entire processes. Although these elements are important, other performance factors such as the efficient use of raw materials and the production environment (e.g. production system, logistics, labour, etc.) in which the equipment or process operates may also have a significant contribution to process performance. The purpose of this paper is to present an alternative measure derived from OEE, overall resource effectiveness (ORE), which considers these factors.

The paper reviews the OEE’s background and explores its limitations. Then, it shows the conceptual and mathematical development of the ORE measure and the formulas used for its calculation. Empirical and simulation-based investigations and applications of ORE are carried out through two cases study for its validation.

The results derived from both the empirical and simulation-based investigations demonstrate that OEE may not be an appropriate measure for some specific processes and that ORE may offer a more complete perspective on and information of key performance indicators.

ORE can provide production managers with more complete information concerning the performance of their processes. This will allow them to take better decisions regarding the management and actions needed to improve their processes.

This paper presents a novel and alterative approach to measure the performance of manufacturing equipment and processes.

As overall equipment effectiveness (OEE) is a metric to estimate equipment effectiveness of production systems, the purpose of this paper is to identify strategic management tools and techniques based on OEE assessment of the ice cream production line.

This paper presents the collection and the analysis of data for ice cream production under real working conditions. The data cover a period of eight months. A framework process to improve the OEE of an automated production system was proposed. Six major stoppage losses, i.e. equipment failure, setup and adjustment, idling and minor stoppage, reduced speed, defects in the process, and reduced yield, were examined with the help of Pareto analysis. In addition, the actual availability (A), performance efficiency (PΕ) and quality rate (QR) measures, together with the complete OEE for each working day, week and month of the production line were shown.

The main goal of the study is to identify major stoppage losses, in order to examine and improve the overall equipment efficiency (OEE) of the ice cream production line through the application of an adequate management, i.e. TPM approach. Based on the obtained results, maintenance management strategy and production planning have been suggested to improve their maintenance procedures and the productivity as well.

The proposed method can be applied to each automated production system. The main benefits of this method are the improvement of productivity, quality enhancement of products, the reduction of sudden breakdowns and the cost of maintenance. Moreover, the analysis provides a useful perspective and helps managers/engineers make better decisions on the operations management of the line, and suggestions for improvement were proposed and will be implemented accordingly.

The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.

In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.

The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

The steel industry is a capital‐intensive industry and equipment utilisation as effectively as possible is of high priority. One of the key difficulties in the steel industry is the need to synchronise several processes to create a flow through every machine and plant. This paper aims to introduce the concept of integrated equipment effectiveness (IEE), which is a new approach for overall equipment effectiveness (OEE) measurement in three elements, consisting of “OEE loading‐based”, “OEE capital‐based”, and “OEE market‐based” so as to meet these essential requirements.

Based on a comprehensive scheme for loss analysis, the concept of integrated equipment effectiveness is developed. The case study is conducted in the factory of one large Asian steel‐making company in order to examine the proposed model.

The case study reveals the importance of the new scheme for loss analysis in a steel‐making plant. IEE gives managers of steel plants a whole perspective on effectiveness. It also indicates the level of synchronisation of a specific machine for making steel within an entire organisation.

IEE monitors the manufacturing process to utilise equipment effectively as much as possible and also measures equipment effectiveness for the full process cycle in order to respond to the market. IEE makes communication easier and more efficient. It provides a sound perspective on improvement in steel making and also can be used as a benchmark.

The paper provides information on a new method for precise estimation of equipment effectiveness in a steel‐making plant. It helps in optimising resource allocation and in improving strategic decision‐making.

Overall equipment effectiveness (OEE) is a metric for estimating equipment effectiveness of the industrial systems. The purpose of this paper is to identify maintenance improvement potentials using an OEE assessment within the croissant production line.

The present work is carried out by analyzing the failure and repair data of the line. The failure data cover a period of 15 months. During this period the croissant production line usually operates over the entire day (24 h per day) in three 8-h shifts per day, and pauses at the weekends. Descriptive statistics of the failure and repair data for the line based on scheduled and unscheduled interruptions were carried. Moreover, the actual availability (A), performance efficiency (PE) and quality rate (Q) measures, together with the complete OEE for each working day for the croissant production line, were shown.

The main objectives are to understand the operation management of the croissant production line, and to measure the OEE characteristics in precise quantitative terms. OEE analysis can help the company to identify the primary problems concerning the A, PE and Q and acts immediately.

This paper presents a successful evaluation of OEE which will provide a useful guide to aspects of the production process, which identifies the critical points of the line that require further improvement through effective maintenance strategy (i.e. total productive maintenance). Moreover, the analysis provides a useful perspective and helps managers and engineers make better decisions on how to improve manufacturing productivity and quality.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Overall equipment effectiveness (OEE) is the key metric to measure the performance of individual equipment. However, when machines operate jointly in a manufacturing line, OEE alone is not sufficient to improve the performance of the system as a whole. The purpose of this paper is to show how to overcome this limitation, by presenting a new metric (overall equipment effectiveness of a manufacturing line – OEEML) and an integrated approach to assess the performance of a line.

An alternative losses classification structure is developed to divide the losses that can be directly ascribed to equipment, from the ones that are spread in the line. Starting from this losses classification structure, an approach based on OEE is developed to evaluate the criticalities and the effectiveness of the line.

This method has been applied to an automated line for engine basements production. Results show that OEEML successfully highlights the progressive degradation of the ideal cycle time, explaining it in terms of: bottleneck inefficiency, quality rate, and synchronisation‐transportation problems.

OEEML alone fails to explain to which extent effectiveness is supported by in process‐inventories and should be integrated with additional metrics to estimate the inventories‐related costs.

OEEML provides practitioners with an operative tool useful to highlight the points where the major inefficiencies take place and to foresee the potential benefits of corrective actions.

In relation to other methodologies, OEEML presents two main advantages: it detects and quantifies the line's critical points and it can be applied even in presence of buffers, without underestimating the efficiency of the system.