The logistics industry is increasingly prioritising sustainability, requiring a comprehensive approach to carrier selection for transporting tank containers overseas. Current studies often focus only on economic factors, but social and environmental aspects are also crucial for sustainable logistics practices. This research introduces a Multi-Criteria Decision-Making (MCDM) framework combining the Best-Worst Method (BWM) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Through a literature review, expert interviews, and secondary data analysis, relevant criteria are identified. The framework is applied to a case study to evaluate carriers for transporting tank containers overseas on
three major shipping lanes, incorporating sustainability objectives alongside traditional criteria. The results show a balanced and structured carrier selection process contributing to current sustainability goals. The findings highlight the possibility of including sustainability criteria without significant costs, providing practical recommendations for logistics companies to improve their carrier selection practices and support long-term environmental and social benefits.

In today's fast-paced global environment, the primary challenge for high-tech companies is maintaining a consistent supply of raw materials in the form of specialized parts from their suppliers. This is essential to sustain manufacturing operations and meet performance targets, ultimately ensuring customer satisfaction. The specialized parts used as raw materials are often technically intricate and costly, which contributes to lengthy lead times for manufacturing and sourcing. Consequently, these parts are generally maintained at low inventory levels. If received incorrectly or found defective during use and if the nature of the defect is such that they are unserviceable, they require immediate intervention such as repair, reconditioning, remanufacturing, or quality analysis to ensure operational continuity. Direct replacement of these parts with new, fault-free parts is often not feasible within the required timelines due to their lengthy sourcing durations. In this context, the emerging field of Reverse Logistics becomes critical, facilitating the efficient handling of these defective parts by shipping them to their respective suppliers to take the appropriate action and ensure that they are swiftly returned to operational status, thereby minimizing downtime and maintaining the supply chain's integrity. These are termed as high-impact issues in Reverse Logistics.
Any delay in shipping defective parts or products to their suppliers extends the time required to perform necessary actions such as repairs or quality checks. These delays result in downtime and can halt the manufacturing operations of high-tech companies until the corrected parts are received back and reintegrated into the production process.
To achieve these objectives, it is essential to have a robust standardized information-sharing and communication process flow throughout the organization. This will ensure timely and accurate management of these defective parts as requested by various stakeholders within high-tech companies. The unavailability of such a process flow leads to significant delays and operational inefficiencies, undermining the company's ability to respond quickly to critical situations and maintain consistent manufacturing outputs.
Stakeholders throughout the organization, responsible for the supply and demand planning of critical and low-inventory parts, initiate requests to the Reverse Logistics Team for shipping defective parts to the supplier when they are deemed as unserviceable or incorrectly received and there is no new inventory to replace them. For the Reverse Logistics Team to achieve timely shipments, they require access to relevant information that is timely, accurate, and centralized through a platform or interface that offers transparency, traceability, and accountability. This system must also be scalable to accommodate an increasing volume of requests in the foreseeable future.
Due to the lack of a standardized process flow, stakeholders currently employ fragmented and inconsistent methods for communicating and sharing information about defective parts with the Reverse Logistics team. These methods are highly unreliable and risky, lacking traceability and visibility in a centralized interface where all incoming and processed requests can be monitored. The existing methods need continuous monitoring by the RL team to ensure they do not miss any requests. Furthermore,
when these existing communication practices are deployed, necessary preliminary steps required before sending requests to the Reverse Logistics team get bypassed, creating bottlenecks and hindering their ability to efficiently manage and expedite the shipment of defective parts. Therefore, the necessity of developing and integrating a standardized process with an advanced Information System like ERP, and adopting it organization-wide, will significantly streamline the defect-handling process. This strategic integration enhances transparency, traceability, and accountability, ensuring that requests from various stakeholders to ship defective parts from high-tech companies to suppliers are managed efficiently and effectively. The Reverse Logistics Team is pivotal in ensuring that these defective parts are promptly shipped to suppliers upon notification.
The literature underscores the necessity for a process flow that expedites the shipment of unserviceable and incorrectly received parts to suppliers for necessary corrective actions. It highlights a notable deficiency in the standardization and formalization of process flows within Reverse Logistics.
This leads to the research question of the thesis which is How can high-tech companies standardize their information-sharing and communication processes to effectively manage high-impact issues in reverse logistics? To address this research question, the research methodology used in this research draws on the Information Systems Research Framework, integrating aspects of people, organization, and technology, both current and prospective. A comprehensive qualitative analysis was conducted, involving 22 semi-structured interviews for thematic analysis complemented by document analysis to delve into the challenges and impacts arising from the absence of standardized information-sharing and communication processes. Utilizing the Technology-Organization-Environment (TOE) framework, this research explores the technological, organizational, and external environmental contexts that influence technology adoption, specifically Information Systems in an organization. Additionally, it incorporates business process management and standardization theories to elucidate the standardization of business processes and the role of information systems therein. This provides foundational insights for developing an Information Systems based process flow, while also emphasizing the importance of change management theories and models for ensuring its successful adoption. This comprehensive approach facilitates a deep understanding of the factors necessary for successfully developing and adopting information systems as a standard process flow within high-tech companies to manage high-impact issues.

This rigorous qualitative analysis based on the literature framework and theories reveals that high-tech companies seeking to develop and standardize their information-sharing and communication processes to manage issues related to the shipment of unserviceable and incorrect parts in Reverse Logistics require a comprehensive approach. This approach incorporates technological, organizational, and environmental factors that significantly influence the development and successful adoption of such standardized process flows.

This research, therefore, by studying the challenges and impacts resulting from the lack of a standardized information sharing and communication process flow identifies key technological, organizational, and external environmental factors, along with their inter-dependencies, that influence the development and successful adoption of such an Information System-based standardized process flow. A framework called IS-PSF is developed which consists of these factors and inter-dependencies identified which are further spread across the phase of change management (transition from an existing state which in this case is the unavailability to the future state which is post-adoption of the to be Information System based process flow). The research methodology employed to develop the IS-PSF framework is distinctive, integrating the TOE framework with theories of business process standardization and change management models.

There are many relevant academic and practical insights derived from this research. The research demonstrated that while documentation of agreed procedures, rules, and guidelines can contribute to standardization, true standardization is unachievable without the integration of information systems alongside these formalization efforts based on the nature of the business processes used. The research highlighted that high-tech companies should deploy a proactive strategy for understanding the need for standardization by monitoring the performance of the business processes using performance indicators or metrics. The research showcased that having a robust and matured IT infrastructure alone is not sufficient but the role of human factors and how the stakeholders make sense of a technology (information systems in this case) are crucial for selecting an appropriate IS on which the standardized process can be developed and successfully adopted.

The research highlights the role of effective change management communication and strategy is vital to ensure a successful transition from the existing non-standardized fragmented information-sharing and communication practices to the standardized Information System process flow. An effective change management strategy decides whether the change management will be successful or a failure. The research also points out the importance of periodic reviews of the process flow by the process owners for continuous improvements. The research highlights that at times, owing to customer requirements, market volatility, environmental regulations, and the aim to set industry standards, high-tech companies standardize their processes in Reverse logistics.

From a practical perspective, the framework developed in the research helps managers make informed decisions on technology investments and change management strategies, allowing for seamless adoption of new systems across organizational boundaries. The study's practical application of the conceptual framework bridges the gap between theory and practice, providing actionable insights for improving process performance and stakeholder satisfaction in high-tech industries.

Overall, this study contributes to the development and successful adoption of an Information Systems Standard Process Flow. This framework is designed to efficiently and effectively manage issues related to unserviceable and incorrectly received parts, aiming to expedite their shipment to suppliers for necessary actions. The goal is to minimize delays in the process flow through the development and successful implementation of a standardized information system-based process flow.

In the contemporary global environment, supply chains are increasingly vulnerable to disruptions, with environmental and supplier disturbances posing frequent challenges. This research aimed to understand the influence of a systematic supplier selection approach, combined with an effective risk mitigation strategy, on supply chain performance amidst supply-side disruption risks. We identified a significant gap in current supply chain management: the lack of a structured supplier selection methodology, which can lead to inefficiencies and performance declines. To address this, our study employed the best worst method (BWM) for systematic supplier assessment and ranking. Simultaneously, a discrete-event simulation model on the Simio platform was utilized to emulate supply chain dynamics, factoring in various suppliers and potential disruptions.
Our findings, contextualized within the polymer industry, highlighted Material Quality, Supply Reliability, Price, and Lead Time as the most critical criteria for supplier selection. The BWM rankings correlated strongly with actual supply chain outcomes, emphasizing its effectiveness. In terms of risk mitigation, flexibility-oriented sourcing strategies were superior to redundancy-oriented ones, demonstrating better management of supply-side disruptions and reduced safety stock levels.
These results have both societal and scientific implications. Societally, a methodical supplier selection process ensures resilient supply chains, essential for sectors like healthcare and food, and promotes global collaboration and innovation through diversified supplier sourcing. Scientifically, our research validates the BWM's utility for supplier selection, challenging conventional beliefs linking higher costs to superior risk mitigation. A key insight was the growing importance of flexibility in modern supply chain risk management.
For the polymer sector, we recommend a BWM-based structured supplier selection process. By partnering with top-ranked suppliers and diversifying geographically, businesses can reduce safety stock levels. Our simulation model provides a practical tool for decision-makers, facilitating scenario testing to identify optimal strategies.
However, our study's limitations must be acknowledged. Tailored primarily for the polymer industry, the specific assumptions underpinning our simulation may not be universally relevant. Thus, while our insights are valuable, their applicability might be limited by data constraints and the polymer sector's distinct attributes.
In conclusion, a methodical supplier selection, paired with a flexibility-oriented sourcing approach, optimizes supply chain performance in the face of supply-side disruptions. Enhancing these elements can guide businesses in developing agile supply chains, ensuring cost-effectiveness and improved performance.

The pivotal sector of transportation has shown signs of a surge in demand. The European Union projects a 42% increase in passenger transport from 2019 until 2050. Policymakers and stakeholders must collaborate to address the increasing transportation demand while considering environmental, societal, and economic benefits Despite efforts to mitigate emissions, the transportation sector has not achieved substantial reductions. The emergence of Hyperloop technology presents a disruptive solution that could address this transportation challenge in Europe. It has been pursued as a viable alternative to air travel, rail, and traditional forms of transportation due to its affordability, sustainability, and rapid speeds of up to 1200 km/h. Even though Hyperloop is a promising alternative in the transportation sector, the technology is still largely in development. There are multi-dimensional considerations in understanding whether the Hyperloop will become a mainstream transport option for passengers and whether the conflicting objectives will result in an efficient Hyperloop network. A knowledge gap was identified with a lack of studies to explore the relationship between the network design objectives and the network design itself.
In order to identify the impacts of the Hyperloop network design in the global transportation sector, a literature review was conducted on the transformative potential of the Hyperloop. Key strengths were identified as a reduction in travel times and low operational emissions. On the other hand, the high capital resources required and the uncertainty around the safety of technology were the main points of criticism. In order to analyze the potential demand for Hyperloop and model the modal shift, a Multi- Nominal Logit was employed where a utility function was formulated for the total benefit passengers receive upon completing a trip. The key attributes for the utility function were selected as travel time, travel costs, number of transfers, and safety perception, in alignment with previous studies on the subjects. A utility-based probabilistic mode choice was determined for the available demand. A multi-objective optimization problem was formulated for the facility-location network design of Hyperloop.
The decision variables of the model were formulated as the decision to open a Hyperloop hub at a location and the decision to build infrastructure between the selected Hyperloop hubs. The model output is an alternate network optimized for four different objective functions. These objectives are determined to be (1) Utility Maximization, (2) Probability of Purchase Maximization, (3) Emission Minimization, and (4) Revenue Maximization as these factors were determined to be key performance indicators in a prospective Hyperloop network. The model aims to provide the decision-makers with an overview of the trade-offs involved with varying objective criteria considered in the network generation.
A case study was created to test the model within Europe. The main aim of the case study is to assess the economic and environmental impacts of the Hyperloop system and provide recommendations to policymakers regarding the conception of the Hyperloop network within the European Union. The case study employs the NUTS classification and excludes European countries where the demand data is incomplete and focuses on countries within the TEN-T network. Furthermore, three categories of experimental scenarios were set up to assess the sensitivity of the model to parameter values. The categories are (1) pricing strategy scenarios, (2) safety perception scenarios and (3) policy intervention scenarios. The findings reveal significant disparities in network characteristics based on different objective criteria. The Utility Maximization objective focuses on maximizing trip utility, leading to a network design with direct links between hubs, resulting in compact networks and lower infrastructure costs. However, Spain and Italy have lower priority in this design. On the other hand, the other three objectives (probability of purchase maximization, emission minimization, and revenue maximization) yield networks with a minimum-spanning tree pattern. These networks outperform the utility maximization network in terms of attracting passengers, reducing emissions, and economic performance. To maximize societal benefits, it is recommended to prioritize the remaining three objectives. The study finds that Hyperloop becomes more competitive for longer-distance trips. Experimentation with ticket prices, safety perception, and policy interventions demonstrates their influence on modal share, revenue stability, and carbon emissions. Higher ticket prices discourage Hyperloop usage, safety perception plays a crucial role, and policies discouraging short-haul flights result in higher Hyperloop modal share and lower emissions. These findings highlight the importance of considering ticket prices, safety perception, and strategic policies to promote sustainable transportation and reduce carbon emissions through a modal shift to Hyperloop.
Future research opportunities include expanding the utility function to incorporate additional attributes affecting mode choices, exploring modal shifts from other modes to Hyperloop, relaxing assumptions about geographical obstacles and hub locations, integrating strategic and tactical planning, and validating the model with a broader range of origin-destination pairs. Computational performance can be enhanced using meta-heuristics to compare different heuristics for network outputs and efficiency.

The financial industry has been significantly transformed by innovations, but traditional banks have managed to maintain their dominant position without implementing many innovations. However, new technologies have caused a shift and should no longer be ignored. Financial institutions are now facing competition from high-tech newcomers, also known as Fin- or BigTechs, that threaten their dominance. A recent study shows that financial institutions have a growing interest in (cloud) platform business models (Fintechs) without being handled in a responsible way. Firms are under pressure to transform, but guidance on how to design and control this transformation is hard to find. EU regulators are concerned about the speed and scale at which financial institutions are moving critical functions and market operations to a few cloud platforms and will soon have to demonstrate their ability to recover from a cyber-attack. Choosing a secure cloud platform is essential, but the complexity, risks and regulation make this decision-making process challenging. This study aims to identify the key factors influencing the adoption of cloud platforms within the Dutch financial sector and determine the most suitable Cloud platform for this sector.

Based on the knowledge gap and the aim of this research, the following main research question was formulated: What are the key influencing factors that determine the adoption of a cloud platform within the cloud platform battle within the Dutch financial sector? The study uses a qualitative approach and the Best-Worst Method to determine the key factors and the most suitable cloud platform. As the focus is on standard selection, a modified version of van der Kaa's 2011 framework was eventually chosen to identify the most important factors in this battle. From the 29 factors in the framework, the 24 most relevant were selected and five additional specific Cloud platform factors were added. This resulted in a new set of 29 factors relevant to Cloud platform adoption. Data is collected through literature review and expert interviews. Interviews were conducted with 13 IT/Cloud experts from various Dutch financial institutions, varying from the consulting, banking, insurance, government and academic sectors.

Ultimately, this research showed that the factors: 1) Security & privacy, 2) Brand reputation and credibility, 3) Pricing strategy, 4) Commitment and 5) Compatibility, are the key influencing factors determining the adoption of a cloud platform within the cloud platform battle within the Dutch financial sector. Based on these key factors and the platform comparison, it was found that among the three most dominant cloud platforms (Amazon Web Services (AWS), Microsoft Azure, and Google Cloud), Microsoft Azure is currently the most suitable cloud platform for the Dutch financial sector.

E-commerce is rising in demand and sales are forecasted to reach 7.4 trillion dollars by 2025. Many products are shipped to consumers from warehouses directly rather than being displayed in a physical retail store. Smooth and effective warehousing operations are more crucial with the increased dependency on warehouses nowadays. More than 130 key performance indicators (KPIs) exist for warehouses. Often a summarized and meaningful metric is desired to give an accurate evaluation of the overall warehouse performance (OWP). The most common method of measuring OWP used in the literature is done using data envelopment analysis (DEA). However, it is difficult to find empirical evidence that DEA has significantly improved performance evaluation and benchmarking in actual non-production conditions. A new method for measuring OWP is explored using a widely accepted overall metric in the manufacturing industry, namely overall equipment effectiveness (OEE). The use of OEE as a performance measure for warehouses or in logistics generally has not been researched extensively yet. The research problem lies in evaluating the overall performance of warehouses using a modified OEE framework. Therefore, a challenge to identify the components of a modified OEE for warehousing was present. Additionally, a modified OEE formula for the automated warehouse was made based on the major losses that occur in such warehouse. The nature of the items processed at a warehouse and the warehouse utilization stage impacts the KPIs to be considered by a modified OEE model. A framework for a modified OEE for warehouses was developed using these inputs. It was agreed that one of the main uses of an OWP measure lies in reporting performance to different stakeholders, mainly the warehouse contractor. Additionally, using OEE can give insight to areas of improvement which gives a science-based ground for continuous improvement. The research was done using a single case study in the inbound part of an automated warehouse for online clothing retail with an industry leader. Expanding this research into other warehouse areas is suggested, and a final OWP using a modified OEE model may be achieved after numerous iterations and case studies. Further quantitative analysis of the performance of OEE as an OWP can validate the results more. It is argued that a universal indicator of some sort using OEE can be achieved in the future, this can then be used for benchmarking alike warehouses.

Previous academic work on Portfolio Decision Analysis (PDA) have pointed at the advantage of using PDA in multi-actor situations and environmental sectors, but have also pointed at the lack of research done in such case studies. Currently there exist situations where PDA would be beneficial but current methods are not able to analyse the systems. This is because most of the PDA and MCDA methods require certain data that is often absent in real life case studies, such as reliable information on alternatives performances including synergies and the availability of time and willingness for detailed preference elicitation. Multi-actor situations make preference elicitation an even more time consuming aspect. Therefore, this thesis develops a novel methodology making PDA analysis accessible, robust and fast in decision support to multi-actor systems lacking detailed synergy and preference information. First the portfolios are created in a linear-additivity framework covering the solution space (creating thus all feasible portfolios). The methodology uses different strains of thought and concepts within the MCDA community, such as a combination of stochastic and heuristic approaches to analyse the portfolios. The stochastic techniques include Monte Carlo sampling for uncertainty propagation of the alternative performances and stochastic multicriteria acceptability analysis (SMAA) to handle attribute weight uncertainties. The heuristics applied stand within the Fast and Frugal Heuristics tradition (FFH). The studied heuristics are the Take-The-Best Heuristic and a Lexicographic heuristic to incorporate synergy information. Application results in portfolio shortlists for the involved actors. In order to analyze consensus among the actors a Core Indices (CI) approach is applied. Subsequently a Sobol sensitivity analysis is conducted on the most promising portfolios. The integration of these methods result in an overarching unconventional approach capable of informing decision makers on most promising portfolios and most important attributes. An application of this methodology is shown through application to both simulated data and a real life case study in a multi-actor case-study in India, on wastewater treatment, reuse and resource recovery. The case study encompasses multiple stakeholders in which, due to the political nature of the situation, it is difficult to obtain explicit preference. The alternatives also likely have interactions and interdependencies, affecting the portfolio set performance. The application shows that the method is able to deal with the complexities of real life case studies. For both the simulated data set as for the case study consensus on portfolios, alternatives and corresponding attributes have identified for further decision-making. Regarding the analysis of the attribute weights uncertainty the SMAA-O approach is deemed better than the Take-The-Best approach if a complete ordinal ranking of the attributes is available. Furthermore, the approach of lexicographic heuristics to deal with the lack of detailed synergy information seems promising for situations where synergy information is lacking. For the general application of the method there are, however, some limitations due to assumed linear properties, these encompass: The assumed linear additivity of the alternatives in the portfolio creation, the monotonic behaviour of the marginal value functions and the hierarchical addition aggregation value function. These form also the points where the method can be expanded upon to integrate these assumptions and corresponding uncertainties. In the future the method can be expanded upon in the open source software to make further use of the computing power able to perform stochastic analysis and to build upon the novel application of heuristics, quickly allowing PDA analysis in ever more environmental case studies.

The Netherlands has committed to reducing greenhouse gas emissions by 95% by 2050. In this respect, hydrogen is a promising part of the pathway to meeting climate targets. However, there is a knowledge gap regarding the potential domestic and global supply chains in the Netherlands and their techno-economic and socio-political performance under current technology, market, and energy policy conditions.

Therefore, this thesis aims to identify the key trade-offs of future hydrogen supply chain portfolios that meet stakeholders' objectives in the Netherlands: The drivers, barriers, and facilitators for hydrogen supply chains in the Netherlands are identified. Possible technology combinations that can form domestic and global hydrogen supply chains, including resulting portfolios, are created. The techno-economic and socio-political performance is assessed, and trade-offs are identified.

The results were derived from literature studies on hydrogen supply chain components and technologies, performance criteria, and hydrogen supply chains in the Netherlands. Additional expert interviews provided country-specific insights on the strategies, utilization, and evaluation of hydrogen supply chains in the near future (2030).

The system analysis on hydrogen supply chains showed that the socio-technical landscape, regime, and niche levels contain drivers, barriers, and facilitators to their adoption. The main drivers for hydrogen supply chains in the Netherlands are affordability, sustainability, and acceptability. When considering the performance of the supply chains according to the performance criteria, it becomes apparent that there is no universal supply chain portfolio that can fulfill all stakeholders' objectives in the Netherlands. The key trade-off identified is that higher sustainability comes with higher acceptability but leads to lower affordability.

Comparable results in literature confirm that each supply chain has trade-offs on a techno-economic and socio-political dimension that need to be weighed by stakeholders depending on the use case and objective. To accelerate the implementation of hydrogen to reach national climate targets and improve energy security, additional policies are needed in the Netherlands to overcome the existing barriers and uncertainties. In particular, policies must define conversion, transport, and reconversion strategies and support schemes, consider impacts and risks for society, and emissions from the entire supply chain when importing hydrogen for emission reduction purposes.

The fourth industrial revolution is upon us, and entire industries are seeking to reap the benefits resulting from the use of the technologies that the revolution brings. Waste management is one of those industries. The rapid growth of the human population causes more consumer goods to be produced every day. This underlines the importance of effective and efficient waste management.

The Internet of Things (IoT) has proven to be a useful tool to increase the efficiency of waste collection and while many waste management organizations are beginning to adopt these solutions, many are struggling to fully adopt and implement the technology. It is still unclear, due to lack of research, what factors are hindering the adoption and implementation of IoT technology in these organizations and how the process can be improved. The research scope of this study was three-sided: Industry specific, organizational, and technological. The focus was set on the waste management industry, and the intra-organizational barriers that hindered the adoption and implementation process of IoT-powered fullness sensors. The core problems at hand were identifying what the intra-organizational IoT adoption barriers are, what are the most influential barriers, what mitigation strategies can be employed to mitigate these barriers and how it can all be illustrated within an IoT adoption and implementation process framework.

The main research question formulated to answer these problems is: How can the adoption and implementation process of IoT-powered fullness sensors in waste management be improved? Four sub-research questions were formulated that held partial information which were needed to answer the main question.

The overarching structure of this research project follows the Design Science Research Methodology (DSRM). DSRM provides a commonly accepted approach which involves a rigorous six step activity process for creating and evaluating an IT artifact intended to solve organizational problems. This methodology was altered to fit this particular study, and thus followed five of the six steps. An analysis of literature was performed to identify the general intra-organizational innovation adoption barriers. An exploratory case study was conducted within a large waste management company in Iceland which recently decided to install IoT-powered container fullness sensors to increase the efficiency of their processes. The case study revealed, through interviews, which of the identified barriers had the most significant effect on their adoption and implementation process. Expert interviews and desk research were used to formulate strategies that organizations could employ to mitigate the most prominent barriers identified. After all the interviews had been transcribed, coded, and categorized, data triangulation was used where data from multiple different interviewees was compared and analyzed. From the results of these research efforts, a framework explaining the IoT adoption and implementation process for waste management was then designed and developed. Expert interviews were again conducted to evaluate the framework and confirm the framework’s theoretical validity, application and expected performance in terms of its set goals and objectives.

The results of this research are the identified general intra-organizational innovation adoption factors, the most influential factors affecting IoT adoption and implementation within waste management along with their proposed mitigation strategies as well as a designed IoT adoption and implementation process framework in which these strategies are incorporated. The general intra-organizational innovation adoption factors identified are: Leaders’ attitude towards change, Centralization, Complexity, Formalization, Interconnectedness, Organizational slack, Size, Culture, Degree of risk-taking, End user behavior, Strategic objectives and Uncertainty of business benefits. The following are the three most influential barriers to IoT adoption and implementation within waste management and their proposed mitigation strategies: The first barrier is Uncertainty of business benefits and its proposed mitigation strategies are: Gaining a Proof of Value (PoV) and Incremental scale-up. The second barrier is Strategic objectives and its proposed mitigation strategies is: Using information to gain a competitive advantage. The proposed mitigation strategy for the third barrier, Degree of risk-taking, is Renting with an option to buy.

The applicability of the designed framework in a real-life setting is yet to be tested. Future research could involve using the framework and applying it in an actual implementation of IoT fullness sensors in a waste management organization.

The European aviation sector agrees that a mix of several smaller operational innovations collectively have great potential to help shortening the pathway towards net zero aviation. Nevertheless, from the operator's point of view, hardly any insights have been provided on how the sustainability perspective aligns with industry specific corporate objectives. In order to improve the effectiveness of sustainability-driven decision-making in airlines, this study aimed to address this gap. Specifically, the goal was to view the evaluation of operational innovations as a multi-criteria decision-making problem. To that end, a case study was conducted at the low-cost arm of Europe's third largest airline group.

The definitions of the main criteria to support the evaluation of operational improvements in flight operations were synthesized from literature and expert opinion. Using the Best-Worst Method, inter-criteria preference data were collected from seventeen senior and executive airline managers. The data were analyzed through the aggregation of individual preferences as well as probabilistic modeling.

Results from both approaches showed that "Safety" plays by far the most important role in the evaluation of operational innovations. It is followed by the four criteria, "Employee Experience", "Passenger Experience", "Finance", and “Sustainability", between which there seems to be no evident relative preference based on the aggregation of individual data. From the probabilistic approach could be concluded that Finance outranks Sustainability, which in turn outranks Employee Experience and Passenger Experience. Still, neither the aggregation of individual data, nor the probabilistic model revealed practically meaningful differences between the magnitudes of the weights assigned to the four intermediate criteria. Both approaches established however, that the criterion “On-Time Performance” is clearly the least important.

The results presented in this study can be interpreted three ways: i) as a high-level framework to assess the priority of sustainability relative to the five other dimensions of the firm, which can be used by airline managers to develop an effective corporate strategy for the appraisal of operational improvements in flight operations; ii) as the conceptual foundation for the quantitative comparison of a set of projects with respect to the six perspectives presented in this report; and iii) knowing the relative preference from key decision-makers can help other practitioners to improve the performance of their innovative efforts.

Offshore wind plays a major role in the transition of conventional energy towards sustainable energy sources. To keep up with the increasing installation demand in this sector new offshore wind installation vessels are needed. There is a need for more certainty and predictability regarding the requirements of the future offshore wind installation vessels. To translate the market developments towards input variables for future vessel concept designs, the methods ’needs analysis’ and ’concept exploration’ from the systems engineering framework are applied. Epoch-era analysis is selected as scenario modelling method to capture the uncertain and the fast changing market by generating various future possibilities each posing different vessel requirements. A parametric model is created to determine the performance of a set of vessels defined by their length, beam, depth, crane capacity, speed, and transport strategy.
The case study shows that the application of Epoch-era analysis and parametric modelling is capable of generating insights in important aspects for designing future offshore wind installation vessels. The strength of this method is that it has the flexibility to select different key performance indicators but also provides the opportunity to incorporate the importance of short term against long term goals. It can therefore be tailor-made to a stakeholders strategy. Applying their wishes while analysing many different options results in robust input variables for the concept vessel design.

This thesis proposes a truck arrival shift (TAS) policy to control truck arrivals at seaport terminals. The aim is to reduce congestion at terminal gates which is caused by a lack of port-hinterland alignment. We proposed, developed, and applied a modeling framework to assesses the impact of the TAS policy for the use case of the Port of Rotterdam. This policy is designed for the implementation of a Time Slot Management System (TSMS) and takes the behavioural aspect of Truck Operating Companies (TOC) into account. The time of day preferences of TOC for container pick-ups are inferred from the exchange of information between port and hinterland stakeholders using discrete choice modelling (DCM). These preferences are used to shift truck arrivals and consequently reduce the high waiting time of trucks at terminals gates. To evaluate the effectiveness of the designed TAS policy, we developed a simulation platform that resembles terminal operations using discrete-event simulation (DES). For the allocation of trucks to a certain time period, a choice-based heuristic is designed to approximate the optimum configuration of the TAS policy. The optimum TAS policy design shows that significant gain can be obtained at a low shift rate. Moreover, a measurable amount of waiting time gain can be achieved by the application of the designed TAS policy.

Over the last few years, regulations, changes in governance, and societal pressure have led to a push to rethink a firms’ approach to sustainability. This push created a need to place sustainability and numerous relevant technologies and approaches at the centre of the firms’ decision-making process. Within the financial industry, the combination of novel data technologies such as Big Data and Artificial Intelligence (AI) with the inclusion of sustainability, or so-called “ESG”, Environment, Social, and governance data spearpoint this new ‘sustainable’ frontier.
Literature shows that the implementation of the combination of ESG data, Big Data and AI, within a financial institution and as a corporate resource is a rather novel subject, with no directly related literature available. Thus, this thesis aims to address the topic of implementing ESG data, Big Data and AI within a financial institution. The main research question answered within this thesis is set out to explore this combination of sustainability data, also known as ESG data, Big Data and AI. This study aims to provide a starting point to fill this knowledge gap by creating novel theoretical propositions to be tested in future research. The following research question has been devised to address this research problem.

What observations can be extracted from assessing the introduction of a Big Data and AI toolset applying ESG data within a procedure?

The main research question is answered through the use of theoretical propositions. These novel theoretical propositions illustrate key observations made during the case study. For each proposition, future research directions are given. These propositions, thus, the answer to the main research question are:
- The perception within a firm of using Big Data and AI within a process could affect the learning rate and the learning approach taken by the user. This affects the acceptance of the technology. Thus, the perception could affect the adoption rate of Big Data and AI within a firm.
- If Big Data and AI are used within a process, people tend to be convinced by Big Data and AI used within the process, thus Big Data and AI can be used to convince people of the validity of the results of the process.
- If conferred management information is substantiated by an information process using Big Data and AI, then people do not have the tendency to acknowledge the inherent biases in such processes.
- If Big Data and AI are used within a process, data quality and source are perceived as of less importance.
- There could be causation between one's knowledge of Big Data and AI, and the perception of bias when assessing a process that uses Big Data and AI.
- ESG data is context-dependent, illustrating that a structured or unstructured approach to ESG data depends on the application of ESG data.

For centuries, Homo sapiens have been trying to predict the future through supernatural or scientific methods. Since prehistory, this quality has been essential to humans (e.g., anticipate prey, then ambush it). Even in today’s society, predicting the future remains essential in many industries and research domains. Although, we are still far from producing flawless forecasts (e.g., weather) because future events are uncertain. When decisions contain uncertainty, governments, organizations, and individuals alike tend to be interested in the advice of others.

One such case that was interesting is when predicting the outcome of a standard battle. Within such a battle, high-tech firms compete to obtain most customers in a given market through their technological inventions. To predict which technology will be the standard on the market, experts are independently interviewed to determine the importance of factors (e.g., weights) that can influence this battle. Were the second round of expert interviews need to assign a value for each of these factors for every competing technology—resulting in a performance grade used to make the prediction. This prediction indicates which firm/technology will likely have the upper hand in the market. The factors originate from the list of factors in combination with the Best-Worst Method (BWM), which allows evaluating the multiple-criteria decision-making (MCDM) problem (Rezaei, 2015a; v.d. Kaa, G., v.d. Ende, J., de Vries, H.J., van Heck, 2011).

Multiple studies expressed concerns that it is challenging to persuade and find experts willing to participate in the interview. Instead of finding a better way to approach the experts, this study focused on another solution not applied before in predicting standard battles. Hence, the objective was to understand, test, and examine how the Collective Intelligence (CI) of the crowd (i.e., group of random individuals) performs compared to experts. The idea of CI is that it does not reside in any individual but emerges from the group. When people's opinions are combined, their advice should be more truthful or similar to a typical expert's.

In other words, this quantitative exploratory study investigated if CI in comparison to experts differs when predicting standard battles. Hence, a literature review was required to provide deeper insights and factors that influence CI. This study explored the underlying mechanism of CI and established a conceptual model based on the theoretical background, which indicates the (moderating) relationship between ‘Diversity’ (DIV), ‘Group Size’ (GS), ‘Performance’ (PERF) of the crowd.

The variable DIV was measured based on differences in gender, age, degree, job, and nationality and expressed by the Simpson’s index, reflecting the number of different species and distributions (SIMPSON, 1949). As for GS, the only attribute measured was the number of people in a contrived group. Further, the definition of the PERF of a collective is the quantifiable difference in their solution relative to the prediction proposed by the experts (Wagner et al., 2010). Hence, this was dubbed ‘Relative Performance’ (RP) for the rest of this paper.

Prior research on standard battles was selected to test and validate the assertions in this study. This selection was based on several factors, such as the outcome of the battle was predicted by experts but where the ground truth is unknown. Doing so allowed validating if the crowd performs differently than experts in prediction tasks. The selected study involved the battle of two wind turbine technologies (WTT) dubbed ‘Gearbox’ (GB) and ‘Direct Drive’ (DD) (van de Kaa et al., 2020). Further, to interview the crowd, the traditional BWM questionnaire was converted into a cross-sectionally distributed online survey (i.e., MTurk) to obtain the data. Two hundred respondents completed the survey, but only 137 remained after pre-qualifications.

In this research, ‘groups’ were contrived from the sample that completed the prediction task. Here, the group members employ their expertise to carry out the given task. In other words, all group members performed the same activity and then were randomly pooled based on varying group sizes. Next, simplified random sampling was carried out twice. The first time this resulted in six groups with a respective size of 5, 10, 20, 30, and 40. A second time was required because the former did not provide a good range of DIV scores. Because of this limitation, the sample was sampled multiple times, which resulted in multiple subgroups with each a DIV score—resulting in five groups of size 5, 10, 20, 30, 40, and 137 with respectively 26, 13, 6, 4, 3, and 1 subgroup for comparison.

After sampling the data, the variables were tested for normality and homoscedasticity. The results indicate that the independent variables (i.e., DIV, GS) do not satisfy the normality assumption. Hence, the variables are not suited for parametrical testing. However, both the non- and parametrical tests were applied. Because ANOVA seems not to be very susceptible to modest divergence from normality. Namely, various studies used a variation of non-normal distributions and concluded that the false positive rate is not affected much if the notion of normality is not satisfied (Glass et al., 1972; Harwell et al., 1992; Lix et al., 1996). In addition, it was required to compare more than two groups. Hence, the One-Way ANOVA test (OWAT) and the Kruskal-Wallis Test (KWT) were selected to investigate the relationship between the variables (DIV, GS, RP). More specifically, the effects that GS or DIV can have on the RP of the crowd. In addition, due to systematic limitations with simplified random sampling, Bootstrapping (BOOT) and Monte Carlo (MC) were also performed respectively for the OWAT and KWT to investigate the relationship between DIV and RP. Lastly, the moderation effect was tested based on the Linear Regression (LR) method.

The results did not show any significant differences between DIV and RP, and any significant moderation effect. Hence, the initial hypothesis that a more diverse group of individuals would perform better was refuted. In addition, this research rejects the proposition that the relationship between GS and RP should be positively moderated by how diverse the crowd is. Consequently, this research was not able to conclude how these variables affected the PERF of the crowd. Nevertheless, the former results weaken the theory of (S. Krause et al., 2011; Nguyen et al., 2018; Surowiecki, 2005), who underlines the importance of DIV. In contrast, the results gave ample support to (Reynolds et al., 2017) and their claim that there is no correlation between DIV and PERF. However, their second assertion about an existing relationship between cognitive DIV was not tested. Hence, it is recommended that future studies investigate this relationship.

The findings did show a significant difference between groups for the variable GS in the case of GB WTT. Namely, when the size of the group increases, the PERF also proportionately increases with an upper limit, dubbed ‘Optimal Group Size’ (OGS). A U-shape relationship defines this relation between the variables. However, both the OWAT and KWT provide contradicting findings. Namely, findings from the OWAT suggest that the OGS consists of 15 people and that there is indeed a U-shape relationship between GS and RP, which proves that the claim of (Hashmi, 2005) and our hypothesis is correct. In contrast, the KWT indicates a relatively linear relationship, where the group of 10 and 20 performed significantly better than 30. However, OWAT showed that the group of 30 performed significantly better than the group of 10 and 20 people. Whereas the OGS was 10 and not 15 individuals. The results from the (non) parametrical for the location of OGS indicate that a group of 10 or 15 outperforms the other groups of smaller and bigger sizes. Hence, supporting the claim of (Carvalho et al., 2016) and weakening (S. Krause et al., 2011).

To conclude, how CI operated in this research depended on survey completion time, consistency ratio, selection of best and worst criteria, PERF grade, and the final prediction. Hence, this research concluded that the CI of the crowd did show differences in predicting the outcome of a standard battle compared to the expert pool. This was primarily based on the fact that the crowd had a PERF score that was two-thirds lower than that of the experts. Although, the crowd performed in some aspects in similar or better ways. This research only tested one case, limiting our insights if this happened due to chance or not. In addition, the main goal of this study was to investigate if the crowd could come up with the exact prediction. Meaning that the conclusion and the process (e.g., selection criteria and grading WTT) towards this conclusion should be similar to experts. However, this was not the case. In addition, the consistency ratio remains a matter of doubt since most of the results were initially unreliable, which required (logical) corrections to obtain reliable results. Making the comparison based on the consistency ratio rickety. To reiterate, the crowd performed differently than experts when predicting the outcome of the WTT battle. Despite various differences, the individual and groups would conclude a similar prediction like in the case of experts.

This thesis focuses on the transition from multichannel to omnichannel retailing. To provide insight into this, the current challenges and related indicators are identified by means of a multiple case study. In addition, a theoretical framework for the omnichannel retailing concept is designed. On the one hand, this will help to increase the understanding of omnichannel retailing and, on the other hand, it will provide insight into where retailers currently find themselves in this transition and what challenges this involves.

Blockchain Technology has gained popularity in recent years and one of its most promising applications is in Supply Chain Management. The visibility enabled by distributed ledger technology can empower Real-Time Tracking of Products (also referred to as the ‘Holy Grail’ of logistics), a faster and leaner Global Trade, and automatic controls with Smart Contracts. However, to realize this potential, all of the members of a supply chain have to be on board, including small-to-medium-sized enterprises (SMEs), which often have neither the resources nor the expertise to join a blockchain-based network. Hence, as part of the Spark! Living Lab, a consortium that comprises TNO and TU Delft among other partners, my research was aimed at providing recommendations on how to support SMEs in the adoption of blockchain. First, a set of twenty-three factors relevant to blockchain adoption intention have been identified through a systematic literature review. Then, an online questionnaire has been employed to collect the factors' preferences of twenty respondents from the target population. The collected data has subsequently been processed with the Bayesian BWM, a novel MCDM method, to establish a weight-wise ranking of the identified factors. Finally, the results obtained have been used to provide suggestions to the Spark! Living Lab on the factors they should put more emphasis on when supporting SMEs in their blockchain journeys.

To decrease Europe’s harmful emissions, the European Union aims to substantially increase its offshore wind energy capacity. To further develop offshore wind energy, investment in ever-larger construction vessels is necessary. Designing these vessels is difficult however, as this market is characterised by a large but seemingly unpredictable growth of market demand, turbine size and distance from shore. Currently no way of dealing with such market uncertainty within the ship design process was found to exist. This research aims to develop a method that is able to deal with market uncertainty in early ship design by increasing knowledge when design freedom is still high. The method uses uncertainty modelling prior to the requirement definition stage by performing global market research, and during the concept design stage by iteratively co-evolving the vessel design and business case in parallel. The method consists of three parts; simulating an expected market from data, modelling multiple vessel designs, and an uncertainty model that evaluates the performance of the vessels in the market. The case study into offshore wind foundation installation vessels showed that the method can provide valuable insight in the effect of ship design parameters like main dimensions, crane size and ship speed on the performance in an uncertain market. These results were used to create an initial design that is expected to perform well in the uncertain market. The developed method thus provides a way to deal with market uncertainty in the early ship design process.

Increased competitiveness with other transport systems and declining operation margins have motivated freight forwarders in the air cargo transport industry to look into horizontal collaboration. This thesis focuses on developing a fully integrated five phase auction based coopetition model, a form of horizontal collaboration where competition is preserved. A combinatorial auction is used to exchange transportation requests without having to reveal critical company information. Freight forwarders submit requests into an auction pool, where they are grouped into attractive bundles by a central planner and offered for auction. The request selection and bundling procedures, developed in this thesis, are based on the time windows of the request deliveries. A freight forwarder’s bid on each bundle is equal to the marginal profit of that bundle, which is obtained by solving two NP-hard routing problems with a simulated annealing and large neighbourhood search metaheuristic. A unique aspect of the auction, is that the dock capacity of the ground handlers is taken into account, which helps to alleviate truck congestion at the ground handlers. The potential of the auction based coopetition model is shown for an air cargo supply chain scenario. There is a clear increase in profitability for the collaborating freight forwarders, because the auction model decreases the transportation costs for the entire coalition. This cost reduction is achieved by an increase in transport efficiency, while the collaboration disadvantages as seen in literature are limited.