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This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.

Technicians' performance is a major driver in maintenance and each process can be prone to time and quality variances as well as errors due to factors such as experience, complexity and environment. Augmented Reality (AR) is an emerging technology that has been applied in a wide variety of disciplines and has been demonstrated to have a role with improving efficiency, effectiveness and decision-making within industrial maintenance. AR has not reached its full potential yet and its implementation in Industry is slowed down by three main limitations: hardware restricted capabilities, object recognition robustness and contents-related issues. This PhD project focuses on easing the implementation of AR by overcoming the AR technology selection challenges and the AR contents-related issues. In order to reach the aim, the student has provided three main contributions to knowledge: 1) a process to select AR technology for maintenance (IPSAR), 2) a method for creating AR step-by-step procedures (FARP) and 3) a method for providing remote assistance (ARRA). FARP and ARRA methods have been developed and tested. The first allows recording procedures in an ad-hoc designed "AR-format" and is able to show "step-by-step" procedures. It aims to support deskilling the maintenance process and reducing the error rate by simplifying the delivery of maintenance with efficient and effective guidance. The second overcomes current remote video-call assistance limitations by improving spatial referencing. ARRA module allows to provide AR-assistance by overlaying virtual objects on the real environment of a remote maintainer. The methods proposed by the student could boost the implementation of AR and open the doors for a bright future in which AR supports technicians thus reducing operational costs and training and improving human performances.

The design features of machine components can impact significantly in its life while in-service, and only relatively few studies which are case specific have been undertaken with respect to this. Hence, the need for more understanding of the influence of geometric design features on the service life of a machine component. The aim of this research is to develop a methodology to assess the degradation life of a mechanical component due to geometric design influence in the presence of uncertainties and its application for the optimisation of the component in the presence of these uncertainties. This thesis has proposed a novel methodology for assessing the thermal fatigue life, a degradation mechanism based on the influence of design features in the presence of uncertainties. In this research a novel uncertainty analysis methodology that is able to handle simultaneously the presence of aleatory and epistemic uncertainties is proposed for a more realistic prediction and assessment of a components thermal fatigue degradation life estimated using finite element analysis. A design optimisation method for optimising the components design in the presence of mixed uncertainty, aleatory and epistemic uncertainties is also proposed and developed. The performance of the proposed methodology is analysed through the use of passenger vehicle brake discs. The novel uncertainty quantification methodology was initially applied on a solid brake disc, and validated for generalisability using a vented brake disc which has more complex design features. While the optimisation method as proposed was applied on the vented brake disc. With these this research proposes a validated set of uncertainty and optimisation methodology in the presence of mixed uncertainties for a design problem. The methodologies proposed in this research provide design engineers with a methodology to design components that are robust by giving the design with the least uncertainty in its output as result of design parameters inherent variability while simultaneously providing the design with the least uncertainty in estimation of its life as a result of the use of surrogate models.

Power-by-the-hour is a performance based offering for delivering outstanding service to operators of civil aviation aircraft. Operators need to guarantee to minimise downtime, reduce service cost and ensure value for money which requires an innovative advanced technology for predictive maintenance. Predictability, availability and reliability of the engine offers better service for operators, and the need to estimate the expected component failure prior to failure occurrence requires a proactive approach to predict the remaining useful life of components within an assembly. This research offers a framework for component remaining useful life prediction using assembly level data. The thesis presents a critical analysis on literature identifying the Weibull method, statistical technique and data-driven methodology relating to remaining useful life prediction, which are used in this research. The AS-IS practice captures relevant information based on the investigation conducted in the aerospace industry. The analysis of maintenance cycles relates to the examination of high-level events for engine availability, whereby more communications with industry showcase a through-life performance timeline visualisation. Overhaul sequence and activities are presented to gain insights of the timeline visualisation. The thesis covers the framework development and application to gas turbine single stage assembly, repair and replacement of components in single stage assembly, and multiple stage assembly. The framework is demonstrated in aerospace engines and power generation engines. The framework developed enables and supports domain experts to quickly respond to, and prepare for maintenance and on-time delivery of spare parts. The results of the framework show the probability of failure based on a pair of error values using the corresponding Scale and Shape parameters. The probability of failure is transformed into the remaining useful life depicting a typical Weibull distribution. The resulting Weibull curves developed with three scenarios of the case shows there are components renewals, therefore, the remaining useful life of the components are established. The framework is validated and verified through a case study with three scenarios and also through expert judgement.

The aim of this research is to develop a framework to predict the cost of the resource complexities inherent in an ERP implementation. The framework comprises two phases namely complexity assessment, and dynamic cost estimation for resource complexity using agent-based modelling. The complexity assessment phase involves complexity identification, uncertainty evaluation for the complexity estimates, complexity assessment, complexity classification, and complexity correlation reporting. The framework is automated in a tool known as Complexity of Resource and Assessment Costing Tool (C-REACT). A number of activities have been undertaken in order to develop the ERP resource complexity framework. Firstly, a detailed literature review was performed in order to gain a contextual understanding of ERP implementation challenges and complexities. Secondly, a case study analysis was conducted to establish the current industrial practices concerning ERP implementation challenges. Thirdly, a framework was developed and validated to identify, assess and cost ERP complexity for each resource. The key contribution of the framework is to introduce a new cost estimation process to support ERP project cost estimation by predicting the cost of ERP resource complexities, and a new process to identify, assess and control ERP complexities inherent in the implementation stage. This framework should be used in the needs identification stage of an ERP project lifecycle. The estimate will inform an organisation of the potential costs of ERP resources from a complexity perspective, thereby enabling them to make informed decisions on ERP implementation complexity and cost reduction. Knowledge of potential complexities will also aid the elimination of substantial errors during implementation. Hence the organisation will yield benefits which they would not otherwise reap in the face of complexity.

Customers of a steel manufacturing company now order a large number of low volume orders instead of a small number of high volume orders as they would have done just a few decades ago. The change in customer expectations has complicated production planning and scheduling within a steel manufacturing company. The aim of this research is to improve production planning and scheduling capability in steelmaking using one of the popular simulation techniques, called discrete event simulation. In this research it is observed that there are three major areas that need attention to improve production planning and scheduling capability. First, selection of optimal schedules and plans based on throughput, production time, stock size, and other production processing criteria. Next, incorporating cost into the criteria to select the schedules and plans will make the planning more cost effective and realistic at the same time. In addition, with the increased use of discrete event simulation modelling, there is a need to improve the model development efficiency and make the process less reliant on practitioners’ experience and capabilities, in order to improve the overall planning and scheduling capability. This thesis presents frameworks to address the three major areas for the capability improvement. This research adapts a systematic approach to validation. Theoretical, realisation, and empirical parts of the research were separately validated. Real life case studies were used for validation of each proposed framework. Discrete event simulation can improve the accuracy of production planning & scheduling and cost estimation for complex production systems. GA-based multi-objective optimisation can be successfully applied to optimisation of plans and schedules. Production planning and scheduling optimisation for some production areas provides a challenging problem to GAs. Cost estimation in the steel manufacturing company needs improvement because of the current lack of accurate costs of product families that affects quality of price management. The developed cost estimation technique is capable of providing more realistic cost for product families. The cost estimation technique would be useful for companies operating on volume-driven manufacturing processes rather than on unit-driven. Conceptual modelling needs to be improved in order to achievein model development efficiency and to make the process less reliant on practitioners’ experience and capabilities. A formal information collection process can aid conceptual modelling of production systems by further development of DES models for cost estimation.

Design rework includes unnecessary repetition in design tasks to correct design problems. Resolving design matters in advance, through in-depth understanding of the design planning and rework issues and development of effective predictive tools could contribute to higher business profit margins and a faster product time-to-market. This research aims to develop three novel and structured methods to predict the design rework occurrence and effort at the very early design stage, which may otherwise remain undiscovered until the testing and refinement phase. The major contribution obtained from the Design Rework Probability of Occurrence Estimation method, DRePOE, is the development of design rework drivers. The developed drivers have been synthesised with data from interview results, direct observations, and archival records obtained from eleven world-class aerospace and automotive components manufacturers. To predict the probability of occurrence, the individual score of each driver was compared against historical records utilising the analogy-based method. The Design Rework Effort Estimation method, DREE, was developed to interconnect functional structures and identify failure relationships among components. A significant contribution of The DREE method is its capability to assess the design rework effort at the component level under the worst-case scenario. Next a Prioritisation Design by Design Rework Effort Based method, PriDDREB, was developed to provide a tool to forecast the maximum design rework given the constraint. This method provides a tool to determine and prioritise the components that may require a significant design rework effort. The three methods developed were validated with an automotive water pump, a turbocharger, and a McPherson strut suspension system in accordance with the validation square method. It is demonstrated that DRePOE, DREE, PriDDREB methods can offer the product design team a means to predict the probability of design rework occurrence and assess the required effort during the testing and refinement phase at the very early design phase.

In today‟s highly competitive business environment it is vital to have smart and robust decision making framework for companies to be competitive or even to stay in the business. Profit margin increases is no longer a result of producing and bring more products to the market. Instead it is also a result of reducing cost, in particular tooling cost. In order to succeed with this, industry need to look in to innovative intelligent systems to enhance their process development so that maximum utilisation of tools can be achieved. Tooling is part of a process hence having an optimal process design is one ideal strategy for best utilising of tools. In design optimisation however presence of uncertainty in design variables and in the mathematical model (used for representing the real life process) is inevitable. For reliable design solution to be found this process complexity need to be addressed. The aim of this research is to understand work roll system optimisation and thermal issues within rolling system design, understand uncertainties and sources of uncertainties and develop Genetic Algorithm (GA) based solution frameworks so that a conscious decision, that prolong tool life can be made. The thesis has proposed a framework for generating approximate models from numeric finite element (FE) data. Using the proposed framework a number of quantitative work roll system thermal analysis and optimisation models were generated and used in subsequent optimisation process. In the absence of a suitable multi-pass model that exhibits the features of a multi-stage process; confident decision making will not be possible. Hence the research has developed a quantitative multi-pass model to simulate the work roll system thermal analysis and optimisation problem that represents the relationships as well as inherited features between passes. The research has developed a Genetic Algorithm based optimisation framework that deals with the constraint quantitative problem as well as the uncertainty, in the design space and fitness function. The research also proposed a post GA result analysis methodology for identifying the final best optimal design solution for the research many objective high dimensional work roll system problems in presence of uncertainty The performance of the proposed frameworks was studied and analysed through case studies. The research also identifies future research directions in the subject area.

Defence contracting has changed from the traditional provision of spares and repairs to contracting long-term for availability and capability. In these long-term contracts, the customer decides to outsource services (that would have been provided in-house) to contractors by consenting to an arrangement whereby payment is made only for the period that the equipment is made available to satisfy the defence need. Availability contracts may last for 40 years and more, but the main defence customer (Ministry of Defence) is constantly faced with budget constraints. Therefore, it is vitally important to assess the financial viability of the customer to invest in the contractor‟s offering of availability, capability and other long-term contracts. The aim of the affordability research project is to develop an affordability assessment framework for the bidding stage of defence contracts. Thus, a close interaction with four industrial collaborators within the defence industry and the literature review revealed the need to investigate affordability from three perspectives namely, manufacturer profitability, supplier sustainability and customer affordability. In order to conduct the research, the methodology employed included the review of literature, industrial interviews, development of the affordability framework for the three perspectives, case study application and final validation. This resulted in the development of a uniquely original affordability assessment framework that consisted of four modules and an affordability management methodology. The modules provided measurement metrics for the three perspectives of affordability and guidelines for improving affordability from the perspectives. Information availability at the bidding stage was usually low which lead to uncertainty about the project; the fourth module was focused on determining the information capability of the project team. To assess the impact of uncertainty on affordability, a methodology which involved a logic (containing two aspects) was also developed and validated within this research. The framework developed was validated with three real-life case studies and the results obtained showed that the logic and content of the framework was appropriate. The benefit to industry is that the framework provides a platform for the manufacturer and customer to assess the affordability of defence project at the bidding stage and make decisions taking account of the impact of uncertainty.

Globally manufacturing based industries are typically transforming operations to enhance the delivery of services throughout equipment use. Within the defence industry, Contracting for Availability (CfA) has emerged as an approach that is increasingly dominating the interation between the customer and the manufacturers. This application serves as an example for an Industrial Product-Service System, and sets the context to this research. Predicting the delivery of services, particularly at the bidding stage, creates enhanced complexity and unpredictability in costs due to uncertainties. Driven by this contextual challenge the aim of this research is to develop a framework for cost uncertainty management and modelling at the bidding stage of CfA in the defence industry. The thesis presents the existing literature associated to uncertainty in cost estimation, whilst the current practice is demonstrated based on interaction with seven organisations involved in the defence industry. A software prototype, Uncertainty Tool for Assessment and Simulation of Cost (U-TASC), has been developed to implement an integrated cost uncertainty management and modelling framework. The cost uncertainty management framework offers a systematic procedure at the bidding stage to guide subject matter experts to focus the attention on influential uncertainties, while also proposing suitable mitigation strategies. In contrast, the cost uncertainty modelling framework involves a step by step procedure to make use of subjective opinion collated from subject matter experts to reflect the influence of uncertainty in cost estimates. The thesis also presents an agent based model that takes into account the influence of dynamic uncertainty (e.g. failure rate) on cost estimates over time. This is applied within a service supply network, where the interaction between the stakeholders represents a typical CfA with incentives and risk sharing scenarios. The frameworks embedded in U-TASC are validated and verified through three case studies including, a naval radar, aircraft carrier, and naval electronic system. The outcomes indicate that reliable and useful results are generated and the tool is highly applicable. On the other hand, the framework for the agent based model is validated through expert opinion and a pilot case study in the naval domain.

Currently, manufacturing organisations worldwide are shifting their business models towards Product-Service Systems (PSS), which implies the development of new support agreements such as availability-based contracts. This transition is shifting the responsibilities for managing and resolving obsolescence issues from the customer to the prime contractor and industry work share partners. This new scenario has triggered a new need to estimate the Non-Recurring Engineering (NRE) cost of resolving obsolescence issues at the bidding stage, so it can be included in the support contract. Hence, the aim of this research is to develop an understanding about all types of obsolescence and develop methodologies for the estimation of NRE costs of hardware (electronic, electrical and electromechanical (EEE) components and materials) obsolescence that can be used at the bidding stage for support contracts in the defence and aerospace sectors. For the accomplishment of this aim, an extensive literature review of the related themes to the research area was carried out. It was found that there is a lack of methodologies for the cost estimation of obsolescence, and also a lack of understanding on the different types of obsolescence such as materials and software obsolescence. A systematic industrial investigation corroborated these findings and revealed the current practice in the UK defence sector for cost estimation at the bidding stage, obsolescence management and obsolescence cost estimation. It facilitated the development of an understanding about obsolescence in hardware and software. Further collaboration with experts from more than 14 organisations enabled the iterative development of the EEEFORCE and M-FORCE frameworks, which can be used at the bidding stage of support contracts to estimate the NRE costs incurred during the contracted period in resolving obsolescence issues in EEE components and materials, respectively. These frameworks were implemented within a prototype software platform that was applied to 13 case studies for expert validation.

A significant change is taking place in manufacturing company strategies around the globe. With new monitoring and service methods, new opportunities of product use and service provision emerge. The manufacturing companies once focused on mere product manufacture, now have started to provide ‘systemic solutions’, i.e. products combined with service packages, which are often referred to as Product-Service Systems (PSS). Currently, there is not a well-established feedback mechanism between service and design. The aim of this research is to develop a methodology to capture, represent, and re-use service knowledge to support product design. For the accomplishment of this aim an extensive literature review of the related themes to the research area took place. It was found that the feedback from service to design is fundamental for the enhancement of product performance; however, the existing literature in this area is not adequate. The industrial investigation led to the realisation that there is not an established mechanism in place to show how service knowledge (SK) can be used by designers. An in- depth investigation took place with the collaboration of, in total, four UK manufacturing companies. The author studied both the conceptual and detailed design, focusing on the design requirements (DR) and the design/service features (DF/SF) respectively. The first step was the capture of SK and its representation using Protégé software. Following this, at the conceptual design stage, SK can be re-used through the DR-SK tool. The two main purposes of the tool are the knowledge retrieval by designers, and the identification of gaps in SK. At the detailed design stage, designers can access SK through the DF-SK tool, and the developed knowledge templates. The SKaD framework was created, as a result of the amalgamation among the SKaD methodology, the knowledge templates, and the tools developed to link SK and DR, SF, and DF. Conclusively, the framework was applied on case studies within the pump manufacturing and aerospace industries, and its purpose (to aid designers accessing and re-using SK) was validated by experts within the collaborating organisations. As a result of this research’s findings, the service personnel can capture SK in a structured manner, which can then be re- used by product designers at both the conceptual and detailed design stage.

The emerging grid technology is receiving great attention from researchers and applications that need computational and data capabilities to enhance performance and efficiency. Multi-Objective Design Optimisation (MODO) is computationally and data challenging. The challenges become even more with the emergence of evolutionary computing (EC) techniques which produce multiple solutions in a single simulation run. Other challenges are the complexity in mathematical models and multidisciplinary involvement of experts, thus making MODO collaborative and interactive in nature. These challenges call for a problem solving environment (P SE) that can provide computational and optimisation resources to MODO experts as services. Current PSEs provide only the technical specifications of the services which is used by programmers and do not have service specifications for designers that use the system to support design optimisation as services. There is need for PSEs to have service specification document that describes how the services are provided to the end users. Additionally, providing MODO resources as services enabled designers to share resources that they do not have through service subscription. The aim of this research is to develop specifications and architecture of a grid service for MODO. The specifications provide the service use cases that are used to build MODO services. A service specification document is proposed and this enables service providers to follow a process for providing services to end users. In this research, literature was reviewed and industry survey conducted. This was followed by the design, development, case study and validation. The research studied related PSEs in literature and industry to come up with a service specification document that captures the process for grid service definition. This specification was used to develop a framework for MODO applications. An architecture based on this framework was proposed and implemented as DECGrid (Decision Engineering Centre Grid) prototype. Three real-life case studies were used to validate the prototype. The results obtained compared favourably with the results in literature. Different scenarios for using the services among distributed design experts demonstrated the computational synergy and efficiency in collaboration. The mathematical model building service and optimisation service enabled designers to collaboratively build models using the collaboration service. This helps designers without optimisation knowledge to perform optimisation. The key contributions in this research are the service specifications that support MODO, the framework developed which provides the process for definining the services and the architecture used to implement the framework. The key limitations of the research are the use of only engineering design optimisation case studies and the prototype is not tested in industry.

This study investigates the identification, acquisition and sharing of innovative manufacturing knowledge for the preliminary design of complex mechanical components. Such components need to satisfy multiple, often conflicting design and performance requirements. Some degree of innovation may be required, involving the development of new manufacturing processes. The innovative nature of this manufacturing knowledge makes it difficult to define, codify and share, especially during preliminary design, where this can present significant risks in the design process. Current methods of knowledge sharing do not account for the immature nature of innovative manufacturing knowledge and the combined explicit and tacit elements needed to express it. A flexible interpretive research study with inductive and hypothesis testing elements was undertaken to explore this novel knowledge management problem. During the inductive phase, two data collection activities were undertaken to investigate the manufacturing knowledge required for the preliminary design of gas turbine engines. Using a data driven approach, the main findings which emerged were: the need to include an assessment of the maturity of the design process; the need to use a range of tacit and explicit knowledge to effectively share this and the need to manage knowledge across different domain boundaries. A conceptual framework of the findings was used to develop a hypothesis of knowledge requirements for preliminary design. For the hypothesis testing phase, a systematic methodology to identify, acquire and share innovative manufacturing knowledge for preliminary design was developed from the knowledge requirements. This approach allowed both explicit and tacit knowledge sharing. An evaluation of the methodology took place using three different industrial cases, each with a different component / manufacturing process. The evaluations demonstrated that using the range of knowledge types for transferring knowledge was effective for the specific cases studied and confirmed the hypothesis developed.

Cost estimating is a knowledge intensive task, involving a high degree of subjectivity. Consequently, many of the current cost estimating (CE) processes lack formalisation, often resulting in the branding of cost estimating as a “part art, part science” discipline. This thesis is concerned with understanding the factors that influence the quality of the cost estimating process. The aim is to develop a framework that will improve the perceived quality of cost estimates, by minimising the subjectivity involved the current CE process. A literature review examines the shortcomings of the current CE processes. It was identified that there is a lack of methodologies for eliciting CE knowledge, as well as mixed views across authors regarding the CE knowledge requirements. In addition, the review identifies that the current methods for assessing the quality of cost estimates are unstructured and highly subjective. By adopting a case study approach, the current CE processes across a number of organisations were captured, resulting in the identification of their weaknesses. A detailed study was carried out regarding the knowledge associated with the cost estimation of complex mechanical hardware products. This led to the development of a Knowledge Elicitation (KEL) methodology, tailored to the needs of cost estimators. Through the use of a survey study, it was identified that the quality of a cost estimate is dependent on the fulfilment of a number of inherent characteristics. It was demonstrated that there was some variation in the relative contribution of each characteristic influencing the overall quality of the estimating process. These findings were implemented in a prototype software tool, which cost estimators could use to assess and quantify the quality of their estimates. A framework was developed based on the merge of the prototype tool and the KEL methodology. The framework’s purpose is to aid cost estimators in acquiring all the necessary knowledge for developing cost estimates of good quality. Novice cost estimators will find the application of the framework particularly beneficial, since they lack the experience and knowledge in a particular domain. In conclusion, it was demonstrated through the case studies that the use of the proposed framework provides novice cost estimators with a formalised process for developing cost estimates of quality similar to the one of experts. The framework was applied on case studies within the aerospace and automotive industry, and their results were validated by experts within the collaborating organisations. As a result of this study’s findings, key areas for future research were identified. The adoption of this approach by cost practitioners could provide increased credibility to their work and a higher level of confidence in their cost estimates.

As markets have become increasingly saturated, companies have acknowledged that their business strategies need to focus on identifying those customers who are most likely to churn. To address this, a method is required that can identify these customers, so that proactive retention campaigns can be deployed in a bid to retain them. To further complicate this, retention campaigns can be costly. To reduce cost and maximise effectiveness, churn prediction has to be as accurate as possible to ensure that only the customers who are planning to switch their service providers are being targeted for retention. Current techniques and research as identified by literature focus primarily on the instantaneous prediction of customer churn. Much work has been invested in this method of churn prediction and significant advancement has been made. However one of the major drawbacks of current research is that the methods available do not provide adequate time for companies to identify and retain the predicted churners. There is a lack of time element in churn prediction. Current research also fails to acknowledge the expensive problem of misclassifying non-churners as churners. In addition, most research efforts base their analysis on customer demographic and usage data that can breach governing regulations. It is proposed in this research that customer complaints and repairs data could prove a suitable alternative. The doctoral research presented in this thesis aims to develop a customer profiling methodology for predicting churn in advance, while keeping the misclassification levels to a minimum. The proposed methodology incorporates time element in the prediction of customer churn for maximising future churn capture by identifying a potential loss of customer at the earliest possible point. Three case studies are identified and carried out for validating the proposed methodology using repairs and complaints data. Finally, the results from the proposed methodology are compared against popular churn prediction techniques reported in literature. The research demonstrates that customers can be placed into one of several profiles clusters according to their interactions with the service provider. Based on this, an estimate is possible regarding when the customer can be expected to terminate his/her service with the company. The proposed methodology produces better results compared to the current state-of-the-art techniques.

The design of business processes often ignores detailed consideration of service cost. With competitive market pressure, this has become a key factor for the service sector. Along with cost, customer satisfaction is a driving force in all organisations these days. In an increasingly competitive marketplace, due to the current emphasis on service throughout the whole economy, businesses must fight to attract and retain their customers. The response time for modification, or reengineering, of existing processes and the creation of new processes is important in the service sector – this is usually required in a short period of time in order to respond to market and/or customer demand. Thus, there is a requirement for the automation of business process reengineering in order to facilitate this and to minimise response time. In order to address the above, the research carried out during this project involved the design and development of a framework to integrate an activity based cost estimating approach which takes into account probability of resource usage in variable processes, with an automated business process reengineering technique which incorporates an evolutionary computing (genetic algorithms) based optimisation module. Along with this, a novel methodology for detecting risk of negative impact on levels of customer satisfaction without the availability of customer related data has also been developed and integrated with the reengineering technique - cost reduction being the primary objective, but not at the expense of customer satisfaction. The overall aim is to automate the reengineering of business processes to as great an extent as possible in order to save potentially considerable human time and effort. The development of the automated framework also included the creation of a numeric process representation mechanism in order to enable quantitative analysis of complex business processes. The framework is implemented within a prototype software platform for expert validation.

In a time of fast growing technology and communication systems, it is very important for the industry and the corporations to develop new contact centre environment technologies for better customer contact requirements. The integration of contact centre (CC) into day-to-day organisational operations represents one of the most promising trends in the 21 st century economy. Whatever the nature or point of contact, customers want a seamless interaction throughout their experience with the company. Customers receive more personalised experience, while the company itself can now provide a consistent message across all customer interactions. Based on the literature studies and the research carried out within the contact centre industry through the case studies, the author identified the customer and advisor behavioural attributes along with demographic, experience and others that later are used to derive the categories. Clustering technique identified the categories for customers and advisors. From the initial set of categories, fuzzy expert system framework was derived which assigned a customer or advisor with the pre-defined set of categories. The thesis has proposed two novel frameworks for categorisation of customer and advisor within contact centres and development of intelligent decision support framework that displays the right amount of information to the advisor at the right time. Furthermore, the frameworks were validated with qualitative expert judgement from the experts at the contact centres and through a simulation approach. The research has developed a novel Soft Computing based fuzzy logic categorisation framework that categorises customer and advisor on the basis of their demographic, experience and behavioural attributes. The study also identifies the behavioural aspects of customer and advisor within CC environment and on the basis of categorisation framework, assigns each customer and advisor to that of a pre-defined category. The research has also proposed an intelligent decision support framework to identify and display the minimum amount of information required by an advisor to serve the customer in CC environment. The performance of the proposed frameworks is analysed through four case studies. In this way this research proposes a fully tested and validated set of categorisation and information requirement frameworks for dealing with customer and advisor and its challenges. The research also identifies future research directions in the relevant areas.

The main aim of the research work presented in this thesis, is the development of a novel framework with the capability of assessing the impact of implementing lean manufacturing within small-to-medium sized manufacturing firms (SMEs). By assessing the impact of lean implementation, SMEs can make informed decisions on the viability of lean adoption at the conceptual implementation stage. Companies are also able determine their status in terms of lean manufacturing affordability. Thus, in order to achieve the above-stated aim, the following were the main set research objectives; (1) identifying the key drivers for implementing lean manufacturing within SMEs, (2) investigating the operational activities of SMEs in order to understand their manufacturing issues, (3) exploring the current level of lean manufacturing usage within SMEs so as to categorise users based on their levels of involvement, (4) identifying factors that determine the assessment of lean manufacturing, (5) developing an impact assessment framework for justifying lean manufacturing within SMEs, (6) developing a knowledge based advisory system and (7) validating the impact assessment framework and the developed knowledge based advisory system through real-life case studies, workshops, and expert opinions. A combination of research methodology approaches have been employed in this research study. This comprises literature review, observation of companies' practices and personal interview. The data collection process involved ten SMEs that provided consistent information throughout the research project life. Additionally, visitations to three large size manufacturing firms were also conducted. Hence, the framework and system development process passed through several stages. Firstly, the data were collected from companies who had successfully implemented lean manufacturing within their premise. The second development stage included the analysis and validation of the dataset through company practitioners. An impact assessment framework was thus developed with the aid of regression analysis as a predictive model. However, it was realised that there were few correlations between the dataset generated and analysis. The reasons for this were unclear. ,a knowledge based advisory system was adopted to conceptualise, enhance the robustness of the impact assessment framework and address the problem of the imprecise data in the impact assessment process. Three major factors of impact assessment were considered in the framework and the system development process, namely relative cost of lean implementation, a company lean readiness status and the level of value-added to be achieved (impact/benefits). Three knowledge based advisory sub-systems that consisted of the abovementioned factors were built. Results obtained from them were then fed into the final system. The three sub-systems were validated with the original set of data from companies. This enabled the assignment of a number of input variables whose membership functions aided the definition of the fuzzy expert system language (linguistic variables) used. The final system yielded heuristic rules that enable the postulation of scenarios of lean implementation. Results were sought and tested on a number of firms based within the UK, for the purposes validation. These also included expert opinions both in academic and industrial settings. A major contribution of the developed system is its ability to aid decision-making processes for lean implementation at the early implementation stage. The visualisation facility of the developed system is also useful in enabling potential lean users to make forecasts on the relative cost of lean projects upfront, anticipate lean benefits, and realise one' degree of lean readiness.

Competition and the associated challenges in the automotive industry are increasing. Products are becoming more complex to satisfy growing needs of the consumers and products need to be cheaper and produced quicker. The automotive industry is responding to these challenges, by developing products within collaborative and extended enterprises across diverse geographical location. New customer requirements imply high frequency changes to the initial design requirements. Current unstructured approaches are not robust to deal with the volume and complexity of the nature of product changes in this environment. The aim of this research is to develop two methodologies, one for requirements extraction methodology (REXTRAM) and the second cost impact analysis methodology (CIAM) within the automotive industry. The research was conducted in a collaborative development environment between automotive Original Equipment Manufacturers and Tier 1 Suppliers. The thesis has proposed two novel methodologies. The first methodology (REXTRAM) extracts relevant data from product design documents and industrial domain experts. REXTRAM generates as output a repository of requirements, design parameters and their constraints. The second methodology (CIAM) identifies two types of changes (constraints changing on requirements and constraints changing on design parameters). CIAM combines matrixes and business (cost and time) driver rules to determine incurred (delta) cost of requirement changes. The matrixes exhibit three types of relationships: requirements to requirements; requirements to design parameters and design parameters to design parameters relationships. Case study approach and independent expert are used to illustrate the application and the capability of both methodologies. In this way this research proposes a tested and validated set of methodologies for the extraction of relevant data and the cost impact analysis of requirement changes and its challenges. The resultant methodologies have widespread application in the context of complex mechanical designs. The research also identifies future research directions in the relevant areas.

This thesis is concerned with understanding the issues in generating cost estimates at the conceptual design stage for Embedded Systems Design and Development (ES D&D), based on specifications. The research examines if there are any relationships between the system’s specifications and the system’s cost, and if these relationships can be formalised. The aim is to develop a framework that will structure, formalise and improve the ES D&D Cost Estimating process. Literature review examines current situation regarding ES D&D Cost Estimating and the information requirements for generating cost estimates. The review identifies that research concentrates on Embedded Systems manufacturing cost estimation, there is a lack of research regarding D&D cost estimation, as well as on the information requirements for generating D&D cost estimates. By conducting an industrial survey, the author identifies the internal practice on ES D&D Cost Estimating for the automotive and aerospace industries and identifies trends, commonalties and differences within and between them. The survey establishes that in order to improve the ES D&D Cost Estimating process, it is essential to establish a data infrastructure that will avoid issues with shortage of information imposed by suppliers and will link the Embedded System’s specifications with the system’s actual implementation and expected functionality. Using a case study approach, the author also establishes that it is essential to analyse the product functionality in such a way that will enable the development of a detailed cost estimating framework at the specification’s design stage. The framework is developed in three parts for hardware, software and integration and reuse. The ES hardware design and development effort is predicted using a complexity based cost estimating approach. The research has demonstrated that Use Case Points can be used to predict software development effort for ES software development when the specification is expressed as use cases. In case of statechart based specifications, the development effort is predicted, like in the case of Hardware, using a complexity based cost estimating approach. The study then investigates factors that affect Integration and Reuse effort for ES D&D. The Integration and Reuse effort is predicted using a expert judgement based methodology. The developed results provide automotive industry with a structured, consistent approach to develop cost estimates for the ES D&D Cost at the specifications design stage. The approach contributes towards improvement of the cost estimating practice within the automotive industry.

The aim of this thesis is to present a structured methodology which estimates Railway Infrastructure renewal and maintenance costs when there is a lack of quantitative cost data at the early stages of the project life cycle. Furthermore, this thesis presents renewal and maintenance infrastructure cost estimating issues and investigates current Railway renewal and maintenance cost estimating practice using an industrial case study approach. A flexible design using a case study strategy is described as the most appropriate approach to the successful completion of this study. Industrial case studies using workshops and interview techniques are the primary sources of data whereas literature is used as the secondary sources of data. Following the identification of Railway renewal and maintenance cost estimating issues, a further review of literature leads to the development of a hypothesis. In order to investigate the hypothesis a structured cost estimating methodology is developed which comprises four main stages: creating a project structure that composes the goal, project criteria and alternatives; collecting the necessary data in the form of pairwise comparisons made by a domain expert; producing alternative weights using a geometric mean; and finally employing an algorithmic method using the produced alternative weights and the known cost of one alternative per criteria. The model was implemented within a prototype software tool. This provided a means to validate the proposed model using three industrial case studies. These results provide evidence that the application of a pairwise comparisons based methodology to Railway renewal and maintenance cost estimating problems can provide beneficial. The results indicated that twelve of the fifteen estimates produced by the model were within the expected accuracy and therefore on most occasions prove the hypothesis to be true.

This thesis is concerned with understanding the internal costing practices employed by commercial and engineering disciplines of cost estimating for generating estimates at the conceptual design stage of complex hardware products. It examines whether there is a formal structure in the interaction between the two groups that can be represented within a model. The aim is to develop a framework that will formalise and improve the communication of commercial and engineering disciplines in cost estimating. A literature review examines the role of different costing techniques and the information requirements for generating cost estimates. The review identifies that there is a lack of research in the information requirements for cost estimating of specific manufacturing industries, and that the interaction of commercial and engineering disciplines of cost estimating at conceptual design stage is hindered by the different focuses of these groups. By conducting a survey study the author identifies the internal practice in cost estimating for the automotive industry. The survey establishes that in order to improve the internal practice it is essential to establish a data infrastructure that formalises and enables the reuse of the cost estimates and improves the interaction between the two groups. The author identifies a common cost estimating process for the automotive industry. This study establishes the required data and information elements and information sources that need to be collected in order to have reliable data infrastructure. Using a case study approach, the author also establishes that it is essential to analyse the product functions in such a way that will enable the development of a detailed cost estimating model at the conceptual design stage, which will improve interaction between the commercial and engineering groups. The function-based cost estimating process becomes the focus of detailed studies using experts from the automotive industry. This results in a generic framework that provides a formalised structure to represent functional requirements in the form of a detailed cost estimating model. The thesis concludes that product functions need to be captured and analysed during the conceptual development of a product and be associated to cost estimates. The developed results provide both groups of cost estimating a structured, consistent approach to developing cost estimates at the conceptual design stage. The data infrastructure and the function-based cost estimating framework is validated through case studies and expert evaluation. The approach contributes towards improvement of the internal cost estimating practice with the automotive industry.

Cutting tools play an important part in today's manufacturing industry. There is an ever-increasing pressure on the cutting tool design industry to produce better quality products in response to the needs of the automotive and aerospace industries. Add to this the increasing complexity of the machined product requiring the use of non-standard cutting tools. The consideration of this area of cutting tool design is in recognition of the importance of the information and knowledge requirements at the beginning and during the design process. It has been noticed that in the cutting tool industry that the knowledge provision lacks structure and organisation. Understanding the knowledge requirements of the designers would provide substantial benefits to the design process. Thus, this research explores the role and extent of special purpose cutting tool design knowledge. Literature review shows there is a lack of research examining the knowledge of designers within special purpose cutting tool design. The design of a special purpose-cutting tool is a knowledge intensive task. This thesis presents a novel methodology for Knowledge Elicitation called Knowledge = Expert - Novice (KEN). KEN is a methodology requiring active participation in the design task. It is demonstrated that KEN is suitable for the capture of cutting tool design knowledge. KEN is used to examine the nature and extent of special purpose cutting tool design. It is observed that KEN provides a structured approach to the Knowledge Elicitation from an expert. An in-depth investigation of the preliminary design stage has revealed the knowledge required by special purpose cutting tool designers. This thesis presents an ontology-based framework for cutting tool design knowledge representation following a functional, structural and behavioural methodology. The knowledge is represented by base-functions, ways of achievement and design considerations organised into functional hierarchies. The ontology is validated by domain experts rating the terms within the ontology and by cases. It is observed that the ontology is a complete representation of the cutting tool design knowledge. A viewpoint of design reuse is modelled to include a set of descriptor terms and captured domain knowledge. The viewpoint is mapped onto the ontology to provide a set of generic terms. The reuse viewpoint is then implemented onto Case-Based Reasoning software to search for past designs. The reuse viewpoint is then validated using a number of case studies and user trials. It is demonstrated that the reuse viewpoint is effective for the extraction of terms from design documentation, searching for and recalling past designs.

Higher production rates combined with consistent product quality and efficient energy use are the key objectives to be solved by production scheduling in the iron and steel industry. Scheduling systems used to assign activities to resources often assume the generated schedule will remain workable for the foreseeable future. Due to the dynamic nature of the steelmaking process however, it is often difficult to maintain the original short-term schedule. Unplanned events or disturbances can disrupt plans requiring modification actions or even rescheduling. Frequent rescheduling often results in instability and lack of continuity in detailed schedule execution. The schedule disturbance management is a manual process and requires many years of experience. The thesis presents a knowledge model for decision support to manage schedule disturbance in steelmaking. Literature review shows the lack of research in developing a knowledge model approach for decision making in steelmaking. Manufacturing process such as steelmaking is `process centric'. The thesis presents a novel knowledge elicitation approach called XPat, which is suitable for engineering process knowledge capture. XPat is used to identify knowledge intensive tasks in steelmaking scheduling. Managing schedule disturbance is recognised as the most knowledge intensive task within the scheduling process. Problem solving knowledge of different types of disturbance in steelmaking is captured. The thesis presents a novel task template for managing the schedule disturbance. A knowledge model of the disturbance management is developed following CommonKADS methodology. The knowledge model is implemented through a design model. The design model helps in developing a prototype decision support system (DSS) to manage schedule disturbance. The system helps the users to make right decisions and implement consistency in the management process. The XPat methodology, the knowledge model and finally the prototype are validated using a number of techniques such as case studies, workshops, paper-based simulation, and user trials. It is observed that the prototype DSS is capable of providing effective decision support to manage schedule disturbance.

This thesis is concerned with understanding the role and utilisation of cost engineering knowledge for generating cost estimates with respect to long life cycle, complex hardware products. lt examines whether there is a formal structure to the reasoning process of experts as they use judgement and whether this process can be represented within a model. The aim is to develop a framework that formalises cost engineering knowledge to facilitate its reuse. A literature review examines the role of expert knowledge and judgement for generating cost estimates. The review identifies that there are mixed views on how knowledge is used and managed. Using an industrial case study approach, the author identifies the cost estimation practices for a new military air system. The study establishes that it is essential to capture assumptions and associated rationale to facilitate knowledge reuse. Experts use an analogy based reasoning process to make judgments and assumptions. The reasoning process became the focus of detailed studies using experts from across industry, resulting in a generic model. The model, known as an inference structure, was implemented within a software tool. This provided a formalised framework to represent, capture, and reuse assumptions and associated rationale. The thesis concludes that assumptions and rationale need to be captured during the development of cost estimates to facilitate knowledge reuse. There is a formal, generic reasoning process that experts use when applying their judgement. The results provide industry with a structured, consistent approach to the formalisation and reuse of cost engineering knowledge. By adopting this approach, companies can develop consistent estimates that will in turn improve decision-making across industry.

The ever-increasing market demands to produce better products, with reduced costs and lead times, has prompted the industry to look for rigorous ways of optimising its designs. However, the lack of flexibility and adequacy of existing optimisation techniques in dealing with the challenges of engineering design optimisation, has prevented the industry from using optimisation algorithms. The aim of this research is to explore the field of evolutionary computation for developing techniques that are capable of dealing with three features of engineering design optimisation problems: multiple objectives, constraints and variable interaction. An industry survey grounds the research within the industrial context. A literature survey of EC techniques for handling multiple objectives, constraints and variable interaction highlights a lack of techniques to handle variable interaction. This research, therefore, focuses on the development of techniques for handling variable interaction in the presence of multiple objectives and constraints. It attempts to fill this gap in research by formally defining and classifying variable interaction as inseparable function interaction and variable dependence. The research then proposes two new algorithms, GRGA and GAVD, that are respectively capable of handling these types of variable interaction. Since it is difficult to find a variety of real-life cases with required complexities, this research develops two test beds (RETB and RETB-II) that have the required features (multiple objectives, constraints and variable interaction), and enable controlled testing of optimisation algorithms. The performance of GRGA and GAVD is analysed and compared to the current state-of-the-art optimisation algorithm (NSGAII) using RETB, RETB-II and other 'popular' test problems. Finally, a set of real-life optimisation problems from literature are analysed from the point of variable interaction. The performance of GRGA and GAVD is finally validated using three appropriately chosen problems from this set. In this way, this research proposes a fully tested and validated methodology for dealing with engineering design optimisation problems with variable interaction.