Search results

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.

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.

The purpose of this study is to present the theoretical framework of the “data envelopment analysis (DEA) Energy Management System (DEMS)” proposed to assist individual departments occupying the same buildings on university campus in assessing the energy efficiencies of their facilities, as well as to demonstrate the implementation results of the DEMS applied in the case of the Department of Architecture of NTUST in Taiwan.

The proposed DEMS considers each “space” within a department in a given “time” (such as a month) as a decision-making unit (DMU). Then, regression analysis is performed on data of “existing environment”, “occupancy” factors and “actual energy consumption EUI (energy usage intensity)” related variables. The regression equation derived is then used to calculate the “predicted EUI” for all DMUs. The “actual EUI” is further considered as the input data and the “predicted EUI” as the output data of the DEMS, on which data envelopment analysis is conducted to produce three types of energy-efficiency scores (overall efficiency, scale efficiency, pure technical efficiency) to indicate the energy efficiencies of all DMUs.

The DEMS was developed and further implemented in the Department of Architecture of NTUST in Taiwan to illustrate how it can be used to assist individual departments within universities in assessing the energy management effectiveness of their spaces.

The accuracy of the energy-efficiency scores depends greatly on the accuracy of the predicted EUIs of spaces, and, therefore, it is critical to identify a better regression model with higher predictability (R2). The relatively low actual EUIs of certain student spaces during winter and summer breaks may greatly affect the resulting energy-efficiency scores.

The DEMS allows facility managers to assess and compare the energy-efficiency scores “among different spaces”, to further review the energy efficiency of a space “over time” and to recognize the benchmark cases and pursue actions for energy improvement.

This study explores the research concepts of “space type” and “internal benchmark” with an analytical method “data envelopment analysis” to assess the energy efficiency of an individual department which may only occupy certain floors of a building.

The purpose of this paper is to analyse the use of a product‐oriented layout and a work‐cell strategy in order to maximise efficiency. These two categories of layout strategies are discussed separately, and are then used collectively in an analysis of the company. The aim is to understand how improvements on layout design could positively impact the future efficiency of the case study company.

A model was developed and measured using 26 weeks of data between the fourth quarter of 2009 and the first quarter of 2010 during layout transformations at the case study company based in upstate New York. The model compared variables such as the distance traveled to retrieve parts, average daily output of engines, labour cost per unit produced, and the amount of time the engine remains in each cell; the aim of which is to increase the efficiency of the facility.

The findings indicate that there is a strong correlation between the variables improved at both the cell‐structures and the product‐structures of the facility and the overall efficiency of the manufacturing facility itself. The results also show that an overall higher efficiency allows for the facility to handle much larger workloads and also drives down both short‐run and long‐run costs. The outcomes also allow for a suggestive redesign of the facility in order to further maximise efficiency. However, it was found that the amount of time a product remains in each cell on the assembly line does not have an effect on the overall output of diesel engines.

Various studies have been conducted focusing on the “facility layout problem,” yet thorough analyses of the redesigning of layout in regards to efficiency are not as available. Instead, an understanding of the topic was derived through sources focusing on the specificities of manufacturing layout.

This paper describes layout efficiency through redesigns and layout using work‐cells in a product‐oriented environment. This study would be useful to manufacturers having low variability in their product and having the ability to use work‐cell layout within their facility.

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.

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.

The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences.

The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences.

The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set.

The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

Examines the applicability of an optional rule for determining ideal product volumes and mixes under various technical and market conditions. Explains an alternative, similar method of estimation for use where data are inadequate. Considers difficulties and areas where the method is of dubious value.

The Nature of Business Policy Business policy — or general management — is concerned with the following six major functions: