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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.

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.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.