The Sustainable Development Goals are a high level development plan for a world free of poverty, with decent work for all and less environmentally damaging patterns of production and consumption. This thesis explores whether paying living wages to Brazilian, Russian, Indian and Chinese (BRIC) workers in the Western European clothing supply chain could contribute towards the realisation of the Sustainable Development Goals. This thesis principally uses two modelling frameworks. A global multi-regional input-output framework, extended to enable assessment of fairness in global supply chains, and a system dynamics model of the Western European clothing supply chain. This allows us to explore both the different ways in which clothing retailers might be able to pay for a living wage in their supply chains and associated sustainability impacts. Our analysis makes three key contributions. (1) Empirical evidence suggesting that in the Western European clothing supply chain, consumption drives environmental impact, and BRIC wages are ‘unfair’ and unable to support a ‘decent’ quality of life. (2) Extension of the limited evidence base on the employment effects of living wages in developing countries. We point to a potentially powerful employment multiplier effect (which may mean that living wages increase employment). However, we also suggest that productivity gains following wage increases could exacerbate job losses. (3) Mixed evidence on the environmental impacts of paying a supply chain living wage. While this is likely to marginally reduce the environmental impacts of affluent country consumption our findings also suggest that global environmental impacts could rise due to increased developing country consumption. Based on these findings, we argue that paying a living wage to those developing country workers employed in affluent country supply chains could contribute to a more sustainable world by reducing poverty and improving working conditions. We further argue that the risk of increased total environmental damage could be minimised through investment in more sustainable infrastructure in developing countries themselves, and we also highlight the need for additional reductions in the environmental impacts of affluent country consumption, beyond supply chain living wage initiatives. Finally, we suggest that efforts to move to craft based production methods could be used to resist labour productivity growth, minimising the risk of job losses.

New technologies have been developing rapidly in the last decades. Enterprises require incorporating these technologies in the development of new products. That creates a high pace of new product flow with an increasingly small life cycle. In order to support the fast pace, manufacturing lines have to adapt to the new product requirements as fast as possible. Production ramp-up is a phase in the manufacturing line that has a significant role on the required time to market but currently constitutes a bottleneck in the manufacturing process. Studies have focused on analysing ramp-up and defining its requirements to make the process more efficient. Literature shows that the key for improvement is to enhance the awareness and understanding of human operators and carry out the process more efficiently. Research studies are limited on the analysis without providing solutions on how to improve awareness. This thesis proposes an integrated approach to support decision making during production ramp-up. The work is composed of three main parts. First, a formal model is defined in order to capture the process followed on the shop floor. The model is designed as a Markov Decision Process reflecting the sequence of actions and their effect during the process. The model is composed of and defined through three main elements, namely a state space, a list of actions, and a reward formed as measure of performance during ramp-up. The second contribution of this work delves into the requirements and development of a decision support system for a more efficient ramp-up process. A decision support system is designed to operate complementary to the ramp-up process and support human operators. It captures ramp-up experience in a structured manner through a ramp-up model, processes it through a learning mechanism and communicates the extracted knowledge to human operators. The proposed system operates in two modes and supports the two identified ramp-up cases. Finally, a reinforcement learning algorithm is proposed, to extract the most effective policy for ramp-up and with a limited number of episodes. The algorithm is an outcome of a comparison study between model-based and model free algorithms. The proposed algorithm shows efficiency under the limitations of ramp-up and lack of data.

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The discipline of Operations Management has made significant impacts on both Academia and Industry by individuating unique operational practices that lead to superior organisational performance. Predominantly research has assessed the impact of operational techniques at the organisational level. Micro-economic theories have then been utilised to explain how operational practices result in unique capabilities, and so derive organisational outputs. However, scholarship has identified an issue with this method of explanation. By assessing operational techniques at the organisational level, research has assumed how operational techniques operate at the micro-routine level. Furthermore, under this logic operational practices are often conceived as a 'black box', which are actioned unproblematic on a day-to-day basis within organisational routines to achieve these outputs. New research in other management disciplines have shown that organisational features present at a macro- level often have vastly different properties when viewed from a micro-routine dynamic perspective. As such, it is unclear how operational techniques work at the micro-level within routines, and the performance benefits that they engender. To address the issue this research takes a novel approach within OM by utilising a practice- based routine perspective. This perspective analyses individual's actions in naturalistic settings, as such, it details how operations management techniques affect peoples' behaviours as they enact their work within routines. An operations management technique called lean new product development is studied within a single case setting to understand the effects it is having on individuals' learning behaviours within organisational routines. The impact lean new product development has on learning was chosen given its prominence in explaining superior performance at the organisational level within the lean literature. Critically the practice-based perspective allows learning to be operationalised via specific behaviours within the routine, and so assess the impact of lean new product development at the micro-level. This research details a mechanism of how techniques such as lean new product development result in stimulating learning. It was shown that wider social dynamics affect how these techniques are utilised within routines. Contrary to prevailing research indicates that the performance benefits of operational techniques are subject to dynamic, complex, and often fragile social processes occurring at the micro routine level, not apparent at the macro, organisational level. As such, the research contributes to the literature as to how operational techniques can improve routine performance and result in capability formulation.

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This thesis deals with the firm-, macro-and institutional-level determinants of research and development (R&D) investment, assessing the impact of R&D spending on firm performance and the financing of R&D investment in emerging markets. The recent financial crisis has had adverse effects worldwide. This study finds that the financial crisis had a significant negative impact on firms’ R&D investment in emerging markets. It also finds that the R&D investments of both local firms and multinational enterprises (MNEs) were affected, and that the latter was affected 1.63 times more than the former. However, when the firms were split between innovative and non-innovative, it was observed that innovative firms continued to invest in R&D during the recession, while non-innovative firms cut down their R&D investment. In addition, it is found that, during a financial crisis, the firm-level determinants of R&D are firm age, firm size, export orientation, debt ratio and foreign ownership. This implies that the assumptions of the resource based view (RBV) hold true, even during a financial crisis. The results suggest that affected and less-/unaffected countries’ R&D determinants behave differently during a financial crisis. They also show that the probability of a decrease in R&D investment in affected countries is 60 percent higher than in less-/unaffected countries. Similarly to firm-level factors, macroeconomic factors also influence R&D expenditure. GDP growth, exports, trade openness, patents and financial crisis are the main macroeconomic determinants of a country’s R&D expenditure. Moreover, analysis suggests that macroeconomic determinants of R&D investment behave differently in advanced and emerging countries, owing to their different nature and purpose, and the countries’ levels of economic development. In addition to firm and macroeconomic factors, the institutional environment plays an important role in R&D investment in emerging countries. The results show that government effectiveness and rule of law have significant positive impacts, while corruption and political instability have significant negative impacts on R&D investment in emerging countries. However, opponents of country-level factors have claimed that these factors influence the innovative activities and firm performance of emerging countries indirectly. This study finds that investor protection (safeguards) tends to have a greater moderating effect on the relationship between R&D and firm performance than country-level governance (systems). The results indicate that safeguards promote firm-level innovation in emerging markets, while systems are substituted by firm-level corporate governance in emerging countries. Moreover, in the case of risky and uncertain investments such as R&D, investors seek protection from possible losses. It is also observed that R&D financing behaves differently according to different levels of multi-nationality and financial systems. Local firms do not use external funding, while MNEs use both internal and external funding for R&D investments due to the availability of organisational slack. A country’s financial systems may restrict firms from choosing particular sources of finance. Firms within bank-based systems tend to rely on external funding and firms within market-based systems depend more on internal funding for R&D investment. The results indicate that market-based firms follow pecking order theory. Secondary data for the analysis were collected from various sources, including DataStream, annual financial reports, LexisNexis, the World Bank’s Development Indicators, Worldwide Governance Indicators and Protecting Minority Shareholder data, and the International Country Risk Guide database. Both static and dynamic panel data techniques, including generalised methods of moment (GMM) estimation, were used for the analysis. Dynamic GMM panel estimation was used to control for endogeneity and unobserved heterogeneity, and to provide efficient and consistent estimation even in the presence of heteroscedasticity. The study also adopted an instrumental variable (IV) approach with OLS and Granger causality tests for the analysis. This study will be helpful to various stakeholders, including investors and managers, lenders and policy makers in emerging markets.

Systems utilised in complex business contexts commonly need to be well integrated within the business in which they operate. There is a risk of poor acceptance, adoption, and sustainment in the business if the system does not take into account key business related considerations. Utilising DePuy Orthopaedics resource management business context as a case study, an assessment of potential System Development Methodologies (SDM) that could be utilised in the development of an IT system was carried out. It was established that no single SDM could provide the level of support that was identified as required. Further investigation of system development within complex businesses ascertained that methodology tailoring is an approach utilised to ensure that methodologies incorporate business specific complexities with a subsequent aim to ensure that developed systems are fit for purpose. However, there is little proven procedural guidance that illustrates an approach towards SDM tailoring in the complex business environment of resource management for New Product Development at DePuy. There was, therefore a gap in research and an identified need for an approach that provides the appropriate level of support for SDM tailoring for the development of systems in this complex business context. Utilising the knowledge gathered within DePuy Orthopaedics, as well as from literature in the field, methods for managing business related complexities in system development were established, developed, and formalised into an approach that addressed the tailoring of SDMs. The approach was influenced by the critical evaluation of DePuy’s complex business context which aided in the creating and application of the approach. The approach was validated through its application in a business context, where it was used to develop a tailored methodology for use in developing and implementing a resource management IT system at a portfolio level. The validation proved the approach was legitimate and produced a methodology and subsequent system which were readily adopted and accepted by the company. Furthermore, the tailored methodology was evaluated by industrially based engineers with experience of systems development in a complex business context, resource management and systems engineering. From this evaluation, it was established that the tailored methodology was sound and can be said to be valid in the context that it was developed.

Due to the need to access external knowledge for new product development (NPD), open innovation has been widely used in the biochip industry. Since current resources owned by single firm do not have all the capabilities, NPD in biochip requires strong interdisciplinarity, wide diversity of technological knowledge, and integration capabilities. In the present study, management strategy for open innovation is investigated for NPD in the Korean biochip industry. Open innovation is classified in three steps: (1) switching phase about starting open innovation in the NPD initial stage; (2) implementation phase about open innovation management in the NPD middle stage; and (3) transition phase about change to close innovation in the NPD final stage. Three models for three phases are developed and then tested by carrying out surveys in the Korean biochip firms. In addition, the transition phase model is evaluated in the Korean bio-pharmaceutical firms. The switching phase model suggests that research capability and external trust are the main variables that affect switching cost, which relates to the perception of advantage of open innovation. The implementation phase model suggests that technological novelty affects degree of openness, which, in turn, relates to technological capability and firm performance. Furthermore, institutional-, environmental-, and firm characteristics affect the depth- and breadth of open innovation activity. The transition phase model suggests that knowledge connected with product innovation has an impact on the open-close transition tendency, which relates to perception of advantage of close innovation. Based on the results for three phases of open innovation, we propose the management strategy for open innovation during NPD. Therefore, based on the results of analyses of the proposed model, we can evaluate the factors that affect open innovation activity and develop an appropriate management strategy of open innovation for NPD of biochip.