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To develop and test a model and software‐based support system for better understanding of the interactions between man‐machine‐maintenance‐economy (MMME), and enable cost‐effective decisions.

The study is based on published knowledge and experience within maintenance, maintenance organization and production, and a case study.

Development of a model describing interactions between man‐machine‐maintenance‐economy interactions and MMME software module. MMME test shows its ability to identify, quantify, assess and follow up losses in production time which is necessary when planning effective maintenance actions.

In the paper the focus is to quantify production time losses in order to identify the root causes of the problem. The case study is performed at a manufacturing plant for truck engines.

A systematic approach of how to quantify and evaluate losses in production time in order to identify problems and problem areas within the production. This approach is discussed and motivated with the aim of achieving more cost‐effective decisions in maintenance.

The model and software application developed enables a structured way of analyzing production time losses in order to find cost‐effective solutions to the problems. The model is very flexible enabling it to be customized for a wide spectrum of branches.

The accurate estimation of equipment utilization is very important in capital‐intensive industry since the identification and analysis of hidden time losses are initiated from these estimates. In this paper, a new loss classification scheme for computing the overall equipment effectiveness (OEE) is presented for capital‐intensive industry. Based on the presented loss classification scheme, a new interpretation for OEE including state analysis, relative loss analysis, lost unit analysis and product unit analysis is attempted. Presents a methodology for constructing a data collection system and developing the total productivity improvement visibility system to implement the proposed OEE and related analyses.

This study aims to improve the market competitiveness of iron and steel manufacturers in developing countries by reducing their production costs.

The research methodology relies on a case study-based approach. The study relies on six steps. The first is the preparation, then the five steps of the six-sigma – define, measure, analyze, improve, control. The qualitative and quantitative data were considered. The qualitative analysis relies on the experts’ judgment of internal status. The quantitative analysis uses the job floor data from three iron and steel manufacturers. After collecting, screening and analyzing the data, the root causes of the different wastes were identified that increase production costs. Consequently, lean manufacturing principles and tools are identified and prioritized using the decision-making trial and evaluation laboratory method, and then implemented to reduce the different types of waste.

The main wastes are related to inventory, time, quality and workforce. The lean tools were proposed with the implementation plan for the discovered root causes. The performance was monitored during and after the implementation of the lean initiatives in one of the three companies. The obtained results showed an increase in some performance indicators such as throughput (70.6%), revenue from by-products (459%), inventory turnover (54%), operation availability (45%), and plant availability (41%). On the other hand, results showed a decrease of time delay (78%), man-hour/ton (52.4%) and downgraded products (63.3%).

The current case study findings can be utilized by Iron and Steel factories at the developing countries. In addition, the proposed lean implementation methodology can be adopted for any other industries.

The current work introduces an original and practical road map to implement the lean six-sigma body of knowledge in the iron and steel manufacturers.

This work introduces an effective and practical case study-based approach to implementing the lean six-sigma body of knowledge in the iron and steel manufacturers in one of the underdevelopment countries. The consideration of the opinion of the different engineers from different sectors shows significant identification of the major problems in the manufacturing and utility sectors that lead to significant performance improvement after solving them.

The purpose of this study is to introduce an enterprise's productivity management named “Production efficiency improvement - PEFF”. This study shows the way of developing the management system to keep their shop floor “flexible to change” and “continuously controlling and improving” from the different levels and in various factories.

This study refers to Toyota's PEFF management system in the context of productivity enhancement through detailed management processes including yearly management, monthly management, daily management and its application in a case study from another sector as a model case of PEFF expansion. The methodology of this study is to introduce a method for production efficiency analysis, measure and select standard time through PEFF calculation.

Toyota's PEFF management can be extremely effective at developing management's ability to conduct day-to-day shop-floor management, know-how sharing and how Toyota applies PEFF to develop the world-standard on manpower efficiency for their factories. Besides, this study shows the applicable of PEFF improvement has successfully conducted in other manufacturers in a flexible way to achieve the improvement targets.

The results of this study will aid the managers in production lines to find the method of calculating and evaluating production efficiency through Toyota's management techniques such as PEFF, YIP, WVACT and standard time. However, the approach for this paper was from a synchronized system as Toyota is limited to generalized to small and medium-sized enterprises.

This paper is introducing the original Toyota's management technique to sustainable enhance their manpower performance and efficiency and answer the question of why TPS still exists in the age of digital management. PEFF management serves as an example of a value management process to help manufacturers to set guidelines to improve their productivity.

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

The main purpose of this paper is to develop an approach to analyzing the factors influencing the performance of railway infrastructure, to propose an approach to measuring the overall railway infrastructure effectiveness (ORIE), and to test these approaches in a case study to verify their effectiveness.

The methodology adopted here was to develop a concept for measurement of the overall effectiveness of a railway infrastructure similar to that for measurement of the OEE. The concept thus developed was applied on Swedish railway track sections for collecting data and for their ORIE validation, as a case study.

The findings of the ORIE case study show that the model can be used for other sections of the Swedish railways. It can also be applied to other railways with some modifications.

ORIE can measure the extent to which the railway infrastructure system manages to deliver its agreed performance to the traffic operators. Infrastructure managers can also use the ORIE as a key performance indicator, which can provide important input for effective decision making.

The paper presents a structured way of developing a conceptual ORIE model applied to the railway‐sector. This model can be used by other railways with suitable modifications.

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.

IT is important to open this editorial with an affirmation of faith. It is this:

THIS month there will be assembling at Margate the Conference on Automation organised by the Institution of Production Engineers.

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.