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The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

The paper identifies six requirements: four critical dimensions (what to measure) and two characteristics (how to measure) of an overall manufacturing performance measurement system. The overall equipment effectiveness (OEE) measure in such a system is assessed against these ideal requirements. The current measurement systems, and the potential of OEE, of three manufacturing organisations are evaluated with the dimensions and characteristics as comparative data. A common weakness of the systems was that they did not measure flow orientation or external effectiveness to any great extent. Another weakness was a high degree of complexity and lack of continuous improvement. Field experiments in the studied organisations showed that use of OEE in combination with an open and decentralised organisation design could improve several of those weaknesses.

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 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.

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.

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.

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.

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.

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.

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.

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) 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.

The current paradigm for assessing overall equipment effectiveness (OEE) is challenged as being anachronistic to the needs of businesses that now require a more holistic indicator of plant and process effectiveness. The purpose of this paper is to introduce a new framework that expands the original OEE measure to inform business performance at multiple levels focusing on adding benchmarkable indicators of asset management effectiveness and process capability. The ability to compare internal performance against external competition and vice verse is argued as being a critical attribute of any performance measurement system.

The research methodology taken incorporated an action research approach using a pilot study combining case study research with an action research process of planning, observing and reflecting summarized as taking an action case research design.

The OEE and related literature is replete with many different enhancements to the original OEE framework. Many of the revised OEE frameworks move away from a standard OEE format taking away the opportunity to benchmark against plant and process performance at multiple levels.

The enhanced OEE framework is developed and tested in situ at a single factory manufacturing large batches of similar products. Future research should look to further develop the OEE framework in both continuous process environments and asset intensive service industry environments.

The enhanced OEE framework introduces a measure of Six Sigma process capability using extant data from the OEE framework. Similarly, indicators of plant reliability, maintainability and asset management effectiveness are calculated taking extant data from the OEE framework. This enhanced OEE framework combines measures of process effectiveness, asset management effectiveness, gross process performance, net process performance and Six Sigma process capability into a single lean Six Sigma key performance indicator of process/plant performance.

The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry.

The paper assesses empirical OEE data gathered from 98 Swedish companies between 2006 and 2012. Further analysis using Monte-Carlo simulations were performed in order to study how each OEE component impacts the overall OEE.

The paper quantifies the various equipment losses in OEE, as well as the factors availability, utilization, speed, quality, and planned stop time. From the empirical findings, operational efficiency losses are found to have the largest impact on OEE followed by availability losses. Based on the results, improvement potentials and future trends for maintenance are identified, including a systems view and an extended scope of maintenance.

The paper provides detailed insights about the state of equipment effectiveness in terms of OEE in the manufacturing industry. Further, the results show how individual OEE components impact overall productivity and efficiency of the production system. This paper contributes with the identification of improvement potentials that are necessary for both practitioners and academics to understand the new direction in which maintenance needs to move. The authors argue for a service-oriented organization.