Start-up firms play an important role in the economy. Statistics show that a large percent of start-up firms fail after few years of establishment. Raising capital, which is crucial to success, is one of the difficulties start-up firms face. This Ph. D thesis aims to draw suggestions for start-up firm survival from mathematical models and numerical investigations. Instead of the commonly held profi t maximizing objective, this thesis assumes that a start-up firm aims to maximize its survival probability during the planning horizon. A firm fails if it runs out of capital at a solvency check. Inventory management in manufacturing start-up firms is discussed further with mathematical theories and numerical illustrations, to gain insight of the policies for start-up firms. These models consider specific inventory problems with total lost sales, partial backorders and joint inventory-advertising decisions. The models consider general cost functions and stochastic demand, with both lead time zero and one cases. The research in this thesis provides quantitative analysis on start-up firm survival, which is new to the literature. From the results, a threshold exists on the initial capital requirement to start-up firms, above which the increase of capital has little effect on survival probability. Start-up firms are often risk-averse and cautious about spending. Entering the right niche market increases their chance of survival, where the demand is more predictable, and start-ups can obtain higher backorder rates and product price. Sensitivity tests show that selling price, purchasing price and overhead cost have the most impact on survival probability. Lead time has a negative effect on start-up firms, which can be offset by increasing the order frequent. Advertising, as an investment in goodwill, can increase start-up firms' survival. The advertising strategies vary according to both goodwill and inventory levels, and the policy is more flexible in start-up firms. Externally, a slightly less frequency solvency check gives start-up firms more room for fund raising and/or operation adjustment, and can increase the survival probability. The problems are modelled using Markov decision processes, and numerical illustrations are implemented in Java.

This thesis stems from the future scenario that as additive manufacturing (AM) technologies become cheaper and more readily available, consumers without formal design training will begin to customise, design and manufacture their own products. Much of this activity is likely to infringe on brands' intellectual property. The research explores the feasibility of a situation in which, rather than attempting to prohibit such activity, manufacturers engage with consumers to facilitate it, thus retaining control (albeit reduced) over their brand's image and the quality of products offered. The research begins with a literature review encompassing AM technologies and their adoption by consumers; mass customisation (MC) and the management of variation in product offering; and traditional models of industrial design (ID), including user-centred design and co-design. It finds that conventional definitions of MC and ID are unable to provide for the possibility of consumer intervention in the shape and non-modular configuration of products. Further research was then conducted in the areas of Open Design (including crowdsourcing, open sourcing and 'hardware hacking') as well as bespoke customisation, which were found to be much more accommodating of the scenario proposed. A new term, 'consumer design', is introduced and defined, together with the hypothesis that in future, the role of the industrial designer may be to design 'unfinished' products. An original classification of consumer involvement in ID is presented. Empirical research, undertaken with consumers using an iterative design software package (Genoform), demonstrated a preference for designing within pre-determined boundaries. Action research was conducted to assess consumer-oriented 3D CAD software, and compare its capabilities with that of MC toolkits. A survey of senior designers and brand managers revealed strategies for implementing and managing a brand's product design language, and a guide was created to show the relative importance of designed features. Using these findings, a prototype toolkit was created to demonstrate how a brand might facilitate consumer interaction with the shape design of a complex consumer electronics product (in this case a mobile phone). The toolkit was tested with both consumers and experienced designers to assess its viability. The research finds that it is possible to create a consumer-design toolkit which enables untrained users to change the form of a product, whilst maintaining brand equity and ensuring the product's functionality and manufacturability.

The aim of the research is to evaluate the possibility of transferring the Efficient Consumer Response (ECR) model developed in the Fast Moving Consumer Goods (FMCG) sector into the pharmaceutical sector and to propose an adapted model for the German market. The German pharmaceutical market is consolidating distribution channels and demand power is shifting towards pharmacies (Hofmann, 2013a). The manufacturers` aim for differentiation requires being closer to patients and pharmacists. Therefore, they increasingly do business directly with pharmacies (Insight Health, 2013). Wholesalers are caught between the strong supply power of manufacturers and increasing demand power of pharmacies (Hofmann, 2013b). Exploratory research was undertaken using the case study method to consider how the ECR model from FMCG can be adapted for the pharmaceutical wholesale business. A single case study was considered as different wholesalers would not participate due to their competitive market and because I am an employee of the case company (Celesio AG). The study was conducted in the German subsidiary (GEHE Pharma). Semi structured interviews with key account managers from FMCG and pharmaceutical manufacturers, Celesio AG management board, GEHE Pharma management and retail pharmacists were conducted. Additional data were generated linked to participative observation during manufacturer meetings between GEHE Pharma and pharmaceutical manufacturers, as well as from secondary and internal documentary material. Findings suggest that several similarities between the FMCG market and the pharmaceutical market exist. No aspect was found which would not allow implementing ECR principles into the pharmaceutical market in Germany. The model is adapted according to the research findings. The adjusted model considers that the pharmaceutical market shows more complexity in terms of the market actors. In this market three main participants exist: pharmaceutical manufacturers, pharmaceutical wholesalers and retail pharmacists. Whereas in the FMCG market the ECR model incorporates the relationship directly between FMCG manufacturers and grocery retailers; no wholesaler is considered in that model. Therefore, the adapted model needs some adjustments for the pharmaceutical wholesale market, which are presented in the research. Furthermore, the research delivers evidence that the ECR model is not static and can be adjusted in terms of the number of participants, content and different dimensions in the relationship between different stakeholders and can, therefore, also be implemented in other industries. exist: pharmaceutical manufacturers, pharmaceutical wholesalers and retail pharmacists. Whereas in the FMCG market the ECR model incorporates the relationship directly between FMCG manufacturers and grocery retailers; no wholesaler is considered in that model. Therefore, the adapted model needs some adjustments for the pharmaceutical wholesale market, which are presented in the research. Furthermore, the research delivers evidence that the ECR model is not static and can be adjusted in terms of the number of participants, content and different dimensions in the relationship between different stakeholders and can, therefore, also be implemented in other industries.

The thesis is presented in three papers format. Paper 1 presents the first bibliometric literature survey of its kind on aggregate production planning (APP) in presence of uncertainty. It surveys a wide range of the literatures which employ operations research/management science methodologies to deal with APP in presence of uncertainty by classifying them into six main categories such as stochastic mathematical programming, fuzzy mathematical programming and simulation. After a preliminary literature analysis, e.g. with regard to number of publications by journal and publication frequency by country, the literature about each of these categories is shortly reviewed. Then, a more detailed statistical analysis of the surveyed research, with respect to the source of uncertainty, number of publications trend over time, adopted APP strategies, applied management science methodologies and their sub-categories, and so on, is presented. Finally, possible future research paths are discussed on the basis of identified research trends and research gaps. The second paper proposes a novel decision model to APP decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of present study, are discussed. The third paper is to appraise the performance of different APP strategies in presence of uncertainty. The relevant models for various APP strategies including the pure chase, the pure level, the modified chase and the modified level strategies are derived from the fundamental model developed for the mixed chase and level strategy in paper 2. The same procedure, which is used in paper 2, follows to solve the models constructed for these strategies with respect to the aforementioned objectives/criteria in order to provide business and managerial insights to operations managers about the effectiveness and practicality of these APP policies under uncertainty. Multiple criteria decision making (MCDM) methods such as additive value function (AVF), the technique for order of preference by similarity to ideal solution (TOPSIS) and VIKOR are also used besides multi-objective optimisation to assess the overall performance of each APP strategy.

The study of foresight methodology has traditionally focused on the anticipation and development of future scenarios. It is somewhat surprising that, despite the impact that the advice generated with foresight may have had on Research and Innovation(R&I) policy action, the analysis of the process whereby foresight actually creates policy recommendations has so far been ignored in the literature. This thesis explores this process, trying to identify those elements that have a greater influence in the final advice characteristics. The research draws on the study of two European cases, which are analysed with very different methods. The first case is addressed with critical discourse analysis, which constitutes a methodological innovation in the area of foresight evaluation. The second case is explored through action research, which facilitated an in-depth examination of the foresight process and an exhaustive tracking of the activities that gave rise to the final recommendations. In both cases special attention is paid to the role and utility of future anticipation. The combination of these methods helped in understanding: the effect that repositioning advisors’ mindsets in highly transformed futures has in the volume and originality of the insights generated, the importance of achieving a balanced representation of the R&I actors in the discussion groups, and the relevance that argumentation has in the formation of final advice. Understanding these factors would contribute to improve the quality and consistency of foresight advice discourses, thus augmenting their possibilities for acceptance and implementation by policy makers.

Performance measurement is an important concern that has recently attracted much attention in the logistics area from both practitioners and academics. The performance measurement of logistics companies is based upon diverse performance indicators. However, to date, limited attention has been paid to the performance measurement of logistics companies and, also, performance measurement processes have become more complex for logistics companies due to the existence of numerous performance indicators. In this regard, the way in which decision makers in logistics companies deal with some vaguenesses, such as deciding on the most important indicators holistically and determining interrelationships between performance indicators, has remained an issue that needs to be resolved. This study, therefore, aims to offer a comprehensive decision model for identifying the key logistics performance indicators and determining the interrelationships among these indicators from logisticians’ perspective. In line with this purpose, the research first presents a stakeholder-based Balanced Scorecard (BSC) model which provides a balanced view by including financial and non-financial performance indicators and a comprehensive approach as a response to the major shortcoming of the generic BSC regarding the negligence of various stakeholders. Then, a large number of performance indicators used in logistics are systematically examined under the proposed model, and the key indicators are selected through an online survey conducted in the Turkish logistics industry. Subsequently, since the performance measurement indicators are not independent of each other, it is critical to understand the causal relationships among different indicators. In such cases, group decision making techniques are capable of modelling such complexities. After a systematic comparison of these techniques, a realistic and easy-to-follow multi-criteria decision making technique, the Analytic Network Process (ANP), is revealed as a suitably powerful method to determine the interrelationships among the indicators. Additionally, a case study approach based on the data obtained from three logistics companies is used to illustrate both the applicability of the model and the practicality of the ANP application. Furthermore, the sensitivity of the results about the case companies is also analysed with several relevant ‘what-if’ scenarios. Thus, real-life practices of three case companies are investigated with the proposed approach. Consequently, this research proposes the BSC-ANP integration which provides a novel way and in-depth understanding to evaluate logistics performance indicators for the competitiveness of logistics companies. Thus, in order to address the aforementioned vaguenesses, the proposed model in this study identifies key performance indicators with the consideration of various stakeholders in the logistics industry to decide on the most important indicators, and evaluates the interrelationships among the indicators by using the ANP. The results of the study show that the educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies and four prominent indicators (educated employee, managerial skills, cost, and profitability) need to be primarily considered by logistics companies. In this way, with this integration, not only the performance indicators in logistics, but also different stakeholders of logistics companies are assessed by the ANP method. This means that the results of this research are not only useful for helping logistics companies to decide which indicators should be focused on to become more competitive, but also can be used as a reference model by different stakeholders in their decision-making processes in order to select the best logistics provider.

The Vehicle Routing Problem with all its variants and richness is still one of the most challenging Combinatorial Optimization problems in the Management Science / Operations Research arena since its introduction in the 1950s. In this research we introduce a real life Vehicle Routing Problem, inspired by the Gas Delivery industry in the UK. It has various real life attributes which have not been researched in the past, such as demand-dependant service times, light load requirements and allowable overtime coupled with unlimited vehicle fleet. A Mixed Integer formulation of the problem is presented and the problem is solved to optimality, reporting optimal solutions and lower and upper bounds. After solving the real life routing problem, both optimally and heuristically some interesting observations and practical implications are reported, relating to better fleet utilization and better working time utilization. We design three initial solution methods, namely the Adapted Sweep, the Adapted Nearest Neighbour and the Parallel Clustering method. They are motivated by the real attributes of the Vehicle Routing Problem under research and show a very good performance in terms of reaching a good initial solution quality as compared to other famous initial solution methods in the literature. Moreover, the Adapted Sweep and the Adapted Nearest Neighbour have computational times of less than one second. Two new hybrid metaheuristic methods are designed in order to address the real life Vehicle Routing Problem. A Population Variable Neighbourhood Search with Adaptive Memory Procedure is the first method, which aims to incorporate and hybridize the learning principles of Adaptive Memory into a method which does not make use of memory structures in its original form, namely the Variable Neighbourhood Search. Moreover, we use a Population version of the Variable Neighbourhood Search in order to provide diversification to the method and to aid the learning aspect of the method. The Population Variable Neighbourhood Search with Adaptive Memory Procedure was tested extensively on the real life Vehicle Routing Problem, as well as relevant literature benchmark instances and it was found to perform well in comparison with the optimal solutions we generated. Moreover, the method shows a good performance on the benchmark instances with less than 1% deviation from the Best Known Solutions in the literature. We later extend the Population Variable Neighbourhood Search with Adaptive Memory Procedure (PVNS_AMP) and hybridize it with aspects from Tabu Search in order to create the second new hybrid metaheuristic method, namely the Population Variable Neighbourhood Search with Adaptive Memory Procedure and Tabu Search principles (TS_PVNS_AMP). The TS_PVNS_AMP was found to have better performance on the RVRP without overtime, and superior performance on the RVRP with overtime as compared to the PVNS_AMP. Moreover, the TS_PVNS_AMP showed a better performance than the PVNS_AMP on the relevant literature benchmark instances reaching Best Known Solutions in the literature with less than 0.5 % deviation from the Best Known Solutions on average. We have also tested our proposed algorithms on other VRP problems, such as the Heterogeneous Fleet VRP with imposed fleet and the School Bus Routing Problem. We have done this experimentation in order to test the generalizability of the methods and their flexibility in addressing other problems from the Vehicle Routing family. Our methodology showed good performance on the literature benchmarks for both problems in terms of solution quality and computational time, as well as a good degree of flexibility in terms of finding good heuristic solutions.

There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks.

Whilst numerous studies have retrospectively reported the impact of negative user emotions, motivational problems or value clashes during software developments, few Requirements Engineering (RE) studies have considered the elicitation of users’ values, motivations or emotions (VM&Es) and there is little advice for practising analysts as to how to deal with these factors. This thesis explores the impact of users’ VM&Es within RE work. The starting point was a review of the current state of analyst practice. A literature survey considered the RE guidance available to analysts on the elicitation and understanding of ‘soft issues’ such as VM&Es. In parallel, a series of interviews with 12 industry analysts sought their views on the relevance of users’ VM&Es, the impact on requirements work, and approaches to identifying such information. This study identified behaviours adopted by experienced analysts that would be useful to promote to novice analysts, and documented the analysts’ own requirements for a method to support them in eliciting VM&Es. These findings informed the design of the Value Based Requirements Engineering (VBRE) method and website (www.vbre.org.uk), intended to support requirements analysts in identifying and considering the impact of such ‘soft factors’. Research into RE method adoption highlights the importance of industry input, so a Participatory Design (PD) approach was taken in developing VBRE, iteratively evaluating and refining the method with input from practising analysts. A series of complementary evaluations of the method are presented. An experimental study investigated the method’s utility and usability with computer science undergraduate students, whilst a set of four case studies explored adoption of the VBRE method with industry analysts. The analysts used the method during their RE work, adapting the approach according to their circumstances and levels of experience. The participants credited the method with a positive impact on their RE work and the novice analysts reported feeling more confident of their abilities to handle ‘soft issues’. The key contributions of this work are:1. An exploration of the views of practising analysts as to the relevance and impact of VM&Es within their RE work.2. Development of an analysis method and support materials to aid analysts in identifying users’ VM&Es.3. A demonstration of the utility of adopting a PD approach to the development of RE methods.4. An evaluation of the use of the method in industry, exploring the use of case studies to understand how novice and expert analysts adopt and adapt the VBRE approach. This thesis is unusual in taking a PD approach to developing a solution for a RE problem: that analysts need to understand users’ VM&Es and their impact on software projects. The VBRE method attempts to address this gap, and the positive reception given by the analysts involved in evaluation of the method indicates they see utility in the approach. Future work will focus on continuing to collaborate with industry analysts to understand their use of the VBRE method, identifying improvements to the method and website, and gathering examples of the method’s impact.

This thesis explores how organisations manage new product development (NPD) focused innovation across a portfolio of core, adjacent and breakthrough environments. The study focuses on the search and select phases of the innovation process, and how incumbents identify and validate a range of opportunities. Organisations face the paradox of how to establish search and select routines for focal markets, while also setting up routines to sense and respond to disruptive innovation signals from adjacent and more peripheral environments. The study builds on research into peripheral vision, and considers how organisations operationalise innovation search and select in disrupting environments. To analyse how organisations manage search and select in turbulent environments, the author conducted research in the disrupting higher education (HE) publishing industry using qualitative research methods. The study focused on ten case companies, and the researcher conducted 61 interviews with 63 individuals over a six month period across ten companies publishing 9,000 out of the world’s 32,000 academic journals. The interviewees ranged from CEOs and CTOs to production, operations, editorial, publishing, sales and marketing directors and managers. The analysis revealed 11 search and select capabilities that need to be in place to manage NPD effectively in HE publishing. The research identified five contextual factors that influence how search and select is operationalised in disrupting environments. A framework is proposed to enable the mapping of individual opportunities within a wider NPD portfolio. The project identified ten key market insight areas where firms in the HE publishing sector need to focus. The findings have implications for practice, especially for HE publishers, online media companies, and business to business service organisations. Further research is proposed into how the cognitive frames of boards and senior teams affect the structure and operationalisation of NPD portfolios; how visual media companies search for, develop (ideate) and select programme and film projects in the disrupting media sector; and how workflow mapping and the identification of jobs-to-be-done is deployed within the NPD process in different settings.