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To highlight the differences and common features of taboo search (TS) and genetic algorithms (GA) in solving the problem of board‐type sequencing on the assembly line simultaneously with the combined problem of feeder assignment and component placement sequencing in the printed circuit board (PCB) industry.

Two metaheuristics (search techniques) are used to solve three problems associated with the PCB assembly line: TS and GA. The implemented approach is used to solve the three problems on a single pick‐and‐place sequential machine with a stationary board table and stationary feeders, and with the use of the Euclidean metric.

The achieved results show a satisfactory reduction in assembly time, when TS and GA are compared with a random solution, with a slight superiority of TS over GA. However, the program running time is longer for TS.

The hypothetical case study used shows that in real life the savings could reach an average of 6 per cent when TS is used. Slightly lower savings are possible when GA is used.

This paper provides a clear insight into how some of the problems associated with the production of PCBs can be solved simultaneously using metaheuristics such as TS and GA.

Presents the results of a survey into the nature and scale of tool management problems within the UK manufacturing industry and outlines strategies to improve the management and control of tooling. Discusses the qualitative and quantitative data collection techniques used to collate data. Describes the characteristics of the sample to provide a better understanding of their operations. Identifies most common tool management problems and reports their impact on the efficiency of manufacturing systems. Claims that more than 65 per cent of the sample acknowledge that the management of control of tooling is a significant operational problem. Provides an insight into the major causes of lack of interest in tool management. Identifies computerized tool management systems as a potential solution. Stresses that systematic analysis of tooling problems and identification of root causes are essential prior to implementation of computerized systems.

In recent years, computer simulation has become a mainstream decision support tool in manufacturing industry. In order to maximise the benefits of using simulation within businesses simulation models should be designed, developed and deployed in a shorter time span. A number of factors, such as excessive model details, inefficient data collection, lengthy model documentation and poorly planned experiments, increase the overall lead time of simulation projects. Among these factors, input data modelling is seen as a major obstacle. Input data identification, collection, validation, and analysis, typically take more than one‐third of project time. This paper presents a IDEF (Integrated computer‐aided manufacturing DEFinition) based approach to accelerate identification and collection of input data. The use of the methodology is presented through its application in batch manufacturing environments. A functional module library and a reference data model, both developed using the IDEF family of constructs, are the core elements of the methodology. The paper also identifies the major causes behind the inefficient collection of data.

To show necessity of risk evaluation during modelling and simulation of production systems. To show an approach to risk evaluation of manufacturing system which has serial reliability structure.

Modelling and simulation of a manufacturing system allows one to conduct verification of different solutions in the area of production planning, before they are started. This is impossible with traditional methods.

Reliable results of simulation research can be obtained only if they are based on an integrated model of the company, that covers all components of the manufacturing process including also its organization and risk in production processes.

The paper describes the stages and results of a project carried out in 2002 in an international company. The proposed method of risk evaluation may be helpful to determine the risk level in the chosen production line and eventually for the whole enterprise manufacturing systems.

The risk concept was treated as a synonym of unreliability. This kind of approach enabled decomposition of the production system into several areas and determination of the reliability structure.

Effective product life cycle management will save costs and resources and improve customer service. Seeks to present a simulation model of the complete life cycle of a batch of products undergoing breakdown and repair and to show the ability of the model to predict future waste arisings and cost savings.

ARENA system simulation software is used for a novel application – a full life cycle of manufacture, use, repair and ultimate disposal. Two batches of products are compared: products with and without features which improve reliability. The number of replications for the Monte Carlo process can be calculated from the statistics of the model data.

The model demonstrates the predicted flows of products through their life cycle. The software has in‐built probability distributions that are not fully suitable for the problem modelled, requiring some artificial treatment, especially when using the delay function. The number of replications should be increased, requiring additional computer time.

The model is potentially valuable for producers wishing to predict the effect on future costs or the risk of modifying designs. The method can also be used to assist waste management using the output graphs of disposed components or products; hence the economics of component remanufacture or reuse can be modelled.

The paper presents the first known application of manufacturing system simulation software for modelling product life cycles.

Time to market is the essential aim of any new product introduction process. Performance measures are simple quantities that indicate the state of manufacturing organisations and are used as the basis of decision‐making at this crucial early stage of the process. Fuzzy set theory is a method for using qualitative data and subjective opinion. Fuzzy sets have been used extensively in manufacturing for applications including control, decision‐making, and estimation. Type‐2 fuzzy sets are a novel extension of type‐1 fuzzy sets. Aims to examine this subject.

This research explores the increased use of type‐2 fuzzy sets in manufacturing. In particular, type‐2 fuzzy sets are used to model “the words that mean different things to different people”.

A model that can leverage design process knowledge and predict time to market from performance measures is a potentially valuable tool for decision making and continuous improvement. A number of data sources, such as process maps, from previous research into time to market in a high technology products company, are used to structure and build a type‐2 fuzzy logic model for the prediction of time to market.

This paper presents a demonstration of how the type‐2 fuzzy logic model works and provides directions for further research into the design process for time to market.

This paper sets out to describe transform methods to control vendor‐managed inventory (VMI). It aims to examine the limits of modelling approaches within control‐theoretic models.

Modelling was achieved with the Simulink package using the equations developed by Disney and Towill for a two‐tier VMI system. Discrete and continuous models were considered together with two forms of production delays: a finite delay and the Forrester exponential delay. Standard control engineering analyses of these delay representations were compared to illustrate how the system response and stability depend on their formulation and to determine the permissible gains.

Response by a discrete or continuous model to step inputs in sales rates depends on the type of delay representation but the responses do not differ by more than 5 per cent if the same delay form is used in the models. The prime effect of using a finite delay is to deepen the stock‐out and increase the required order rate compared with the same response observed with the use of exponential forms of delay. Total time for recovery is similar with all models. It is shown that the continuous model with an exponential delay is always stable and when using a fixed delay the continuous model can be made stable.

The models presented here illustrate that the various forms of control‐theoretic models present similar results irrespective of technique used, provided that they have the same delay type. The range of gains for the required order rate can be computed for any VMI system, knowing that they can be designed to guarantee stable operation.

This work extends that of Disney and Towill to include different modelling representations. It allows operational gains to be safely chosen for stable operation.

The effective implementation of the six sigma strategy within UK manufacturing industries, in particular SMEs, can be considered to be poor. SMEs cite high costs and complexity of implementation as being the major limiting factors as to its widespread use. This paper aims to describe the application of six sigma in an SME and to show how the company applied a cost‐effective six sigma methodology to eradicate a critical to quality (CTQ) issue. The paper seeks to conclude by developing a strategic framework for the widespread use of six sigma in SMEs.

This paper applies the six sigma strategy in an SME in order to eradicate a major CTQ issue. It identifies the approach employed, the tools and techniques used and shows the savings that were made through the structured application of the DMAIC procedure, which is at the heart of the six sigma approach.

Through the correct application of experimental design approaches, this paper identifies the optimum parameter settings that enabled the company to eradicate the CTQ issue and achieve significant improvements in quality and cost from a modest financial outlay.

The design and implementation of an SME‐specific six sigma strategy and its application to a real engineering problem will enable companies to apply the techniques and to attain improvements in terms of cost and quality.

The application of the six sigma strategy and the resulting conclusions as to its effectiveness for industry are the real value of this paper. This paper will be valuable for quality professionals, design engineers and manufacturing specialists in a wide range of industries.

The aim of this research paper is to present the critical factors that constitute a successful implementation of lean manufacturing within manufacturing SMEs.

A combination of comprehensive literature review and visits to ten SMEs based in the East of the UK were employed in the study. The companies' practices were observed to highlight the degree of lean manufacturing utilisation within these companies. This was followed by interviewing of the relevant and key personnel involved in lean implementation. Results were analysed and validated through workshops, case studies and Delphi techniques.

Several critical factors that determine the success of implementing the concept of lean manufacturing within SMEs are identified. Leadership, management, finance organisational culture and skills and expertise, amongst other factors; are classified as the most pertinent issues critical for the successful adoption of lean manufacturing within SMEs environment.

Continued scepticism within SMEs about the benefits of lean to their business is one of the fundamental limitations this research faces. SMEs are, therefore, not very willing to provide useful information and data, timely for further investigation.

The novelty of this research project stems from the realisation of critical factors determining a successful implementation of lean manufacturing within SMEs environment. The results would provide SMEs with indicators and guidelines for a successful implementation of lean principles.