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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.

Overall equipment effectiveness (OEE) is being used increasingly in industry. This paper defines OEE and explores the purpose of this concept in modern operations. The paper discusses OEE as a total measure of performance that relates the availability of the process to the productivity and quality of the product. Therefore, the concept of OEE is appropriate to all operations containing plant and machinery. Research has shown that the most successful method of employing OEE is to use cross‐functional teams aimed at improving the competitiveness of business.

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.

Presents a practical analysis of operational performance measurement at Airbags International Ltd (AIL), a supplier of airbag safety devices to the automotive industry. First, the primary measure of overall equipment effectiveness (OEE) is described. Its implementation and use within the operational environment of AIL is then described and analysed. Finally, presents the potential benefits of developing OEE as an operational measure and contrasts AIL’s performance with other applications of OEE found with the research literature.

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.

Continuous manufacturing systems used within the steel industry involve different machines and processes that are arranged in a sequence of operations in order to manufacture the products. The steel industry is generally a capital‐intensive industry and, because of high capital investment, the utilisation of equipment as effectively as possible is of high priority. This paper seeks to illustrate a new method, overall equipment effectiveness market‐based (OEE‐MB) for the precise calculation of equipment effectiveness for full process cycle in order to respond to the steel market.

A refinement of the existing concept of OEE is developed based on a new scheme for loss analysis within market time. The paper illustrates the concept with a case study based on compact strip manufacturing processes within the steel industry.

While the results for OEE by ignoring a considerable amount of possible hidden losses might be satisfying, the OEE‐MB report shows potential room for improvement. It reflects changes in both the internal and external market for the steel industry, and therefore provides a tool not only for monitoring but also for managing improvement.

OEE‐MB is an applicable method for the precise calculation of equipment effectiveness that provides a sound perspective on improvement of steel plants by taking into consideration all losses within market time for meeting both internal and external demands.

OEE‐MB monitors production and measures the equipment effectiveness for full process cycle in order to meet the market. It makes communication more efficient and easier within the steel industry and may be used as a benchmark to achieve world‐class standard.

The main purpose of the research is to develop a comprehensive model for measuring overall equipment effectiveness in the capital‐intensive industry such as steel, oil and chemical companies so as to meet their essential requirements.

Market time is used as a representation of all the losses, which affect incurred equipment effectiveness. Based on a comprehensive scheme for loss analysis within market time, the concept of Integrated Equipment Effectiveness (IEE) is developed. Multiple case studies including three different cases within one large Asian steel making company were developed to assess the proposed model.

The case study reveals the importance of the new scheme for loss analysis in the capital‐intensive industry. IEE provides a whole perspective on effectiveness based on loading, capital and market features.

IEE monitors manufacturing process to utilise equipment effectively as much as possible and also measures the equipment effectiveness for full process cycle in order to respond to the market. It provides a sound perspective on improvement to the capital‐intensive industry.

The paper provides information on a new model to more accurate estimation of equipment effectiveness in the capital‐intensive industry. It helps to optimise resource allocation and make better strategic decisions. The model may be applied as a benchmark to achieve world‐class standard.

Overall equipment effectiveness (OEE) and process capability (PC) are commonly used and well‐accepted measures of performance in industry. These measures, however, are traditionally applied separately and with different purposes. The purpose of this paper is to investigate the relationship between OEE and PC, how they interact and impact each other, and the possible effect that this relationship may have on decision making.

The paper reviews the OEE and PC background. Then, a discrete‐event simulation model of a bottling line is developed. Using the model, a set of experiments are run and the results interpreted using graphical trend and impact analyses.

The paper demonstrates the relationship between OEE and PC and suggests the existence of a “cut‐off point” beyond which improvements in PC have little impact on OEE.

PC uses the capability indices (CI) to help in determining the suitability of a process to meet the required quality standards. Although statistically a Cp/Cpk equal to 1.0 indicates a capable process, the generally accepted minimum value in manufacturing industry is 1.33. The results of this investigation challenge the traditional and prevailing knowledge of considering this value as the best PC target in terms of OEE.

This paper presents a study where the relationship between two highly used measures of manufacturing performance is established. This provides a useful perspective and guide to understand the interaction of different elements of performance and help managers to take better decisions about how to run and improve their processes more efficiently and effectively.

The purpose of this paper is to reduce or eliminate the overall equipment effectiveness (OEE’s) speed loss in a lean manufacturing environment.

This action research study uses the lean manufacturing 5-whys analysis technique to reduce or eliminate the speed loss.

The application of the 5-whys analysis technique in a manufacturing industry (XYZ Corporation) completely eliminated its top speed loss and resulted in a valuable savings of USD 32,811.5 per annum.

The 5-whys analysis technique which has been primarily known to improve the OEE’s quality loss and changeover loss has been proven to be an effective approach to also tackle speed loss; a loss which has been regarded as the most dominating loss among all the types of OEE’s losses and a difficult one to eliminate.

Little or no attempt has been made to date to expand the use of the 5-whys analysis technique beyond its originally intended purpose. The lessons learnt in this study could be applied to other organizations. The outcome of the study has also opened the possibility of widening the horizon of the use of the 5-whys analysis technique beyond its original intended objective and could be applicable to solve other losses of OEE and non-value added activities of lean philosophy in general.

The importance of physical assets has been increasingly recognized in recent decades. The significant returns on small improvements in overall equipment effectiveness (OEE) justify investment in the management of physical assets, but the wide variation of OEE across firms raises a question: “Why do these differences persist despite a high return on investments to maximize OEE?”. To address this question the dynamic processes that control the evolution of OEE through time need to be better understood. This paper aims to answer this question.

Building on insights from system dynamics and strategy literature, the paper maps the reinforcing feedback loops governing the maintenance function and its interactions with various elements in a firm. Building on strategy literature it hypothesizes that these loops can explain wide variations in observed persistent variations in OEE among otherwise similar firms. The paper draws on previous literature, extensive case studies and consulting projects to provide such mapping using the qualitative mapping tools from system dynamics.

The research outlines several reinforcing loops; once active, any of them could lead a firm towards a problematic mode of operation where reactive maintenance, poor morale, and a culture of fire‐fighting dominate. Actions taken to fix problems in the short‐run often activate vicious cycles, erode the capability of the organization over the long run, and lead to a lower OEE.

Knowing the factors affecting the asset management function of a plant increases the plant's safety and limits its environmental hazards.

Some of the common dynamics of organizations' asset management practices are illustrated and modeled. The strategic importance of OEE and its effect on companies' market capitalization is demonstrated.