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1 – 10 of over 18000Elena Stefana, Paola Cocca, Federico Fantori, Filippo Marciano and Alessandro Marini
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.
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
The OECL and OEE methodologies rank problematic machines differently.
Research limitations/implications
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.
Practical implications
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.
Originality/value
This OECL method is able to overcome some of OEE’s weaknesses. It can properly prioritise problematic machines by considering both cost and losses.
Details
Keywords
Luis Alejandro Gólcher-Barguil, Simon Peter Nadeem, Jose Arturo Garza-Reyes, Ashutosh Samadhiya and Anil Kumar
Equipment performance helps the manufacturing sector achieve operational and financial improvements despite process variations. However, the literature lacks a clear index or…
Abstract
Purpose
Equipment performance helps the manufacturing sector achieve operational and financial improvements despite process variations. However, the literature lacks a clear index or metric to quantify the monetary advantages of enhanced equipment performance. Thus, the paper presents two innovative monetary performance measures to estimate the financial advantages of enhancing equipment performance by isolating the effect of manufacturing fluctuations such as product mix price, direct and indirect characteristics, and cost changes.
Design/methodology/approach
The research provides two measures, ISB (Improvement Saving Benefits) and IEB (Improvement Earning Benefits), to assess equipment performance improvements. The effectiveness of the metrics is validated through a three stages approach, namely (1) experts' binary opinion, (2) sample, and (3) actual cases. The relevant data may be collected through accounting systems, purpose-built software, or electronic spreadsheets.
Findings
The findings suggest that both measures provide an effective cost–benefit analysis of equipment performance enhancement. The measure ISB indicates savings from performance increases when equipment capacity is greater than product demand. IEB is utilised when equipment capacity is less than product demand. Both measurements may replace the unitary cost variation, which is subject to manufacturing changes.
Practical implications
Manufacturing businesses may utilise the ISB and IEB metrics to conduct a systematic analysis of equipment performance and to appreciate the financial savings perspective in order to emphasise profitability in the short and long term.
Originality/value
The study introduces two novel financial equipment performance improvement indicators that distinguish the effects of manufacturing variations. Manufacturing variations cause cost advantages from operational improvements to be misrepresented. There is currently no approach for manufacturing organisations to calculate the financial advantages of enhancing equipment performance while isolating production irregularities.
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Keywords
Overall equipment effectiveness (OEE) is a metric for estimating equipment effectiveness of the industrial systems. The purpose of this paper is to identify maintenance…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Mahsa Fekri Sari and Soroush Avakh Darestani
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details
Keywords
The purpose of this paper is to develop a novel training material for the performance indicator overall equipment effectiveness (OEE) in the form of a game-based learning (GBL…
Abstract
Purpose
The purpose of this paper is to develop a novel training material for the performance indicator overall equipment effectiveness (OEE) in the form of a game-based learning (GBL) approach to be used in Industry and University context. The paper will present the development of the game as well as results from tests of the game with Industry employees and University students.
Design/methodology/approach
The data that are used in the game have been acquired from real OEE data logs of a reference company. The game has been refined iteratively using feedback from the participants of the tests.
Findings
The paper presents the game with its components and learning objectives. A comparison of various theoretical factors on GBL and the novel training material is performed and future improvements are suggested.
Research limitations/implications
The game has been developed based on OEE data logs from only one reference company.
Practical implications
The training material and specifically the game can be used to train Industrial workers and University students to better envision OEE as a performance indicator.
Originality/value
Serious games on lean manufacturing have been developed and played for a long time. While some of these games include OEE as an important result parameter, none really demonstrates how it is measured and analyzed.
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Keywords
Jorge Aníbal Restrepo, Emerson Andres Giraldo and Juan Gabriel Vanegas
This study proposes a novel method to improve the accuracy of overall equipment effectiveness (OEE) estimation in the metallurgical industry. This is achieved by modeling the…
Abstract
Purpose
This study proposes a novel method to improve the accuracy of overall equipment effectiveness (OEE) estimation in the metallurgical industry. This is achieved by modeling the frequency and severity of stoppage events as random variables.
Design/methodology/approach
An analysis of 80,000 datasets from a metal-mechanical firm (2020–2022) was performed using the loss distribution approach (LDA) and Monte Carlo simulation (MCS). The data were further adjusted with a product price index to account for inflation.
Findings
The variance analysis revealed supporting colleagues (59.8% of variance contribution), food breaks (29.8%) and refreshments (9.0%) as the events with the strongest influence on operating losses.
Research limitations/implications
This study provides a more rigorous approach to operational risk management and OEE measurement in the metal-mechanical sector. The developed algorithm supports the establishment of risk management guidelines and facilitates targeted OEE improvement efforts.
Originality/value
This research introduces a novel OEE estimation method specifically for the metallurgical industry, utilizing LDA and MCS to improve accuracy compared to existing techniques.
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Keywords
Marcello Braglia, Mosè Gallo, Leonardo Marrazzini and Liberatina Carmela Santillo
This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in…
Abstract
Purpose
This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.
Design/methodology/approach
This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.
Findings
An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.
Originality/value
The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.
Details
Keywords
Chew Keat Cheah, Joshua Prakash and Kok Seng Ong
The purpose of this paper is to introduce a practical integrated overall equipment effectiveness (OEE) framework that encompasses the core characteristics of OEE.
Abstract
Purpose
The purpose of this paper is to introduce a practical integrated overall equipment effectiveness (OEE) framework that encompasses the core characteristics of OEE.
Design/methodology/approach
The paper reviewed the backgrounds of OEE and improvement frameworks and explored the limitations. An integrated OEE framework was developed by synergizing the strengths of OEE and improvement frameworks to complement the shortcomings. This new framework underlies the OEE concepts and provides structural improvement steps. It was applied to systematically assist and guide OEE practitioners in a case study.
Findings
The review of OEE literature found that there is a lack of improvement frameworks with systematic steps specifically developed for OEE implementation. Conversely, a review on improvement frameworks of different methodologies revealed that they do not fully capitalize on the use of performance measures as benchmarks and improvement drivers. An integrated framework that incorporated the advantages of both OEE and conventional improvement frameworks was developed and validated through a case study over a period of 38 weeks. The OEE performance before the improvements was low (73.4 percent) due to high availability losses (76.5 percent). Both OEE and availability achieved the target of 76.5 percent and 80 percent, respectively, after using the framework for improvements.
Research limitations/implications
The reviewed papers represent a sample of papers present in the literature and were selected based on relevancy. A greater number of papers incorporated into the literature review would certainly bring out a more comprehensive study.
Practical implications
The proposed integrated OEE framework provides OEE practitioners with systematic directions and steps combined with benchmarking and loss prioritization features for effective improvement efforts. In addition, it provides overview for the practitioners to make better decisions in project management. This helps address the common issues of practitioners not sure of what the next improvement step is. A case study using the proposed framework at a semiconductor company had successfully achieved the OEE benchmarks and set target for conversion time.
Originality/value
This paper provides a new integrated OEE framework offering a systematic approach toward implementing OEE improvements.
Details
Keywords
Imane Mjimer, ES-Saadia Aoula and EL Hassan Achouyab
This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the…
Abstract
Purpose
This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.
Design/methodology/approach
To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.
Findings
For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.
Research limitations/implications
The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.
Practical implications
The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.
Originality/value
The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.
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