Packaging plays a necessary supporting role in offshore logistics, but it also contributes to material use, waste generation, and greenhouse-gas (GHG) emissions. Within the logistics operations of Heerema Marine Contractors (HMC), the environmental impact of packaging had not previously been quantified in a structured way, and the benefit of replacing the current single-use system with reusable alternatives was therefore unclear. This thesis develops and applies a comparative decision-support framework to assess packaging-related GHG emissions in offshore contractor logistics and to evaluate whether reusable packaging can reduce that impact. The study focuses specifically on packaging production, transportation, and end-of-life treatment, and is limited to GHG emissions expressed as CO₂eq.

The framework was applied to historical package-flow data extracted from the HMC ERP system for the period 2012–2025. Because packaging characteristics are not recorded systematically in the ERP, packaging configurations were reconstructed using package-assignment rules based on unit of measure, package weight, density assumptions, and transport-support logic. The resulting model combines package interpretation, transport reconstruction, end-of-life modelling, and yard-based validation to estimate the emissions of both the current single-use packaging system and a proposed reusable alternative.

Validation showed that ERP package labels do not always reflect physical packaging practice and that some informal reuse already occurs in the current system, meaning that the baseline should be interpreted as a conservative approximation of a predominantly single-use system.

The results show that the current packaging system is dominated by wooden transport items, particularly pallets and dunnage. Across the full study period, the estimated total mass of single-use packaging was 1546 mt, with corresponding emissions of 1141 mt CO₂eq under cut-off accounting and 783 mt CO₂eq under system-expansion accounting. For the in-house subset used in the reusable comparison, the baseline reusable scenario yielded only a marginal net benefit of 4.3 mt CO₂eq under cut-off accounting, while under system expansion it performed worse than the single-use baseline by 101.5 mt CO₂eq. Sensitivity analysis further showed that the comparative outcome is influenced more strongly by reverse-logistics performance than by reusable transport-item lifetime.

The thesis therefore concludes that packaging-related GHG emissions can be assessed systematically in a data-constrained offshore environment, but that reusable packaging should not be regarded as an inherently superior solution for HMC. Any potential benefit is limited, highly context-dependent, and sensitive to both accounting assumptions and operational conditions.

The objective of this study is to develop a predictive model that can accurately predict the marshaller task demand at an airport. Amsterdam Airport Schiphol is used as a case study for this research. Marshallers form a critical link between ground operations and airside operations, ensuring safe aircraft movements and a continuous operation. Findings indicate a misalignment between static staffing practices and dynamic operational demand. The aim is to build a data-driven model using historical data and influencing factors. ADS-B vehicle data, geofence polygons, aircraft arrival times, and engine testing records were used to determine the task demand. A process of spatial matching, task labelling, and segmentation was used to combine these datasets, after which the labelled segments were then combined into hourly counts. Resulting in an aggregated dataset with hourly task count that can be used in a machine learning model. LightGBM was implemented as a multi-output model that forecasts all task types jointly. The models were trained on nine months of data and performance was evaluated using the Mean Absolute Error, Root Mean Squared Error, Mean Error and the Coefficient of Determination. The models were validated each on their own forecasting horizons: 24 hours, 168 hours, and 2160 hours, and compared to two baselines: Seasonal naïve, and weekly hourly average. The results show that the predictive capability strongly differs between the task type. Docking is the only task that can be forecasted reliably, it follows daily patterns and has a clear link with aircraft arrivals. Docking shows stable performances over all horizons with RMSE values between 1.66 and 1.75 and MAE values between 1.26 and 1.35. The model outperformed the baselines on all prediction horizons and on all evaluation metrics. The other tasks did not show any valuable forecasting, with R2 values close to zero or negative. This indicates that these tasks are irregular or occur in low volumes. Making them not suitable to provide reliable hourly forecasts with the current data. Future work could assess if additional operational factors improve the predictive structure of the other three tasks. A longer dataset may also reveal patterns that are not visible in the current nine month data. Other resolutions such as 15-minute or 30-minute time intervals, or shift intervals may be relevant.

Global growth in offshore wind power is creating a shortage of purpose-built cable-laying vessels (CLVs). Retrofitting existing offshore support vessels offers a cost-effective alternative, but these conversions bring together tensioners, carousels and ancillary equipment from multiple vendors that rely on proprietary controls, leaving co-ordination to human operators and voice radio. Manual operation limits precision, scalability and safety at a time when cable-lay tasks are becoming more complex. This thesis responds by designing and evaluating a vendor-neutral integration framework that enables multi-machine automation on retrofitted CLVs.

The research follows a stepwise approach. A literature review first identifies three principal barriers to integration: incompatible communication protocols, intrusive or unreliable sensing and lengthy re-mobilisation after each deck re-layout. From these findings, requirements are derived for an Information and Communication Technology framework that combines robust on-deck tension measurement, modular control logic and real-time data exchange.

A representative case study, consisting of a vertical two-track tensioner and a 375-tonne carousel, is modelled in OrcaFlex and equipped with Proportional-Integral-Derivative controllers. Historical field data are used to tune and verify the model, confirming that simulated tensions, velocities and cable kinematics remain within operational limits.

An OPC Unified Architecture (OPC UA) layer is then integrated as a high-level supervisor to synchronise set points, measurements and alarms. Performance is benchmarked against the verified model using key indicators such as mean-squared error, relative tension error, response time and communication overhead. Results show that OPC UA introduces less than three per cent additional latency and negligible computational load while maintaining co-ordination accuracy under nominal and extreme scenarios. The framework also reduces operator workload from two dedicated operators to one supervisory role and shortens mobilisation time by standardising plug-and-play machine interfaces.

The study demonstrates that retrofitted CLVs can achieve safe, scalable automation through a structured, simulation-verified framework rooted in open standards. The proposed architecture offers a practical pathway towards higher levels of autonomy in offshore cable installation and can be extended to additional machinery or vessel classes with minimal modification.

This thesis presents a system-level reliability modelling framework for analyzing how failures, maintenance activities, and operational disruptions spread through interconnected subsystems in container terminal operations. The research combines reliability analysis with probabilistic reasoning to provide a clear understanding of how technical, human, and environmental factors affect delays and system performance. This connection is established by translating reliability states—such as equipment health, availability, and maintenance effectiveness—into probabilistic delay outcomes, allowing the model to quantify how reliability losses lead to operational delays.
The framework integrates Fault Tree Analysis (FTA) and Bayesian Networks (BNs) to capture both causal and probabilistic relationships within the terminal system. FTA decomposes the top event— total operational delay—into its contributing factors, providing the structural foundation for the BN. The initial BN structure captures subsystem interactions through nodes representing equipment failure, availability, efficiency, and environmental conditions. Each node describes a key aspect of terminal performance. Equipment failure indicates the operational state of critical assets and is modelled with a Weibull distribution. Availability represents the percentage of time each subsystem could work. Efficiency reflects performance under different external or internal constraints. Environmental factors, yard storage fullness, and terminal busyness acted as external drivers that influenced subsystem efficiency and delay propagation.
The BN model was further expanded to include maintenance and operator availability as new influencing factors. Both preventive and corrective maintenance were considered within the model. The maintenance effectiveness was determined using Weibull-based reliability parameters. The scale and shape parameters were adjusted through maintenance effect multipliers to account for imperfect repair conditions. This method enabled the BN to show how preventive maintenance improves equipment availability. Operator availability was also considered to account for human-related variability, showing how workforce presence impacts subsystem operability and overall delays.
The analysis of the BN used forward inference and sensitivity testing. The results indicate that disruptions in one subsystem can spread through the terminal, causing cumulative delays and underscoring the strong interconnections between quay cranes, yard cranes, and horizontal transport. The maintenance analysis revealed that by implementing preventive maintenance strategies, equipment availability could be increased. Operator availability affected total delay mainly during severe disruptions like strikes, while daily variations had a limited impact.
This developed framework combines reliability modelling and operational performance analysis in one structure. It offers a clear way to study how maintenance, operator availability, and environmental conditions influence reliability and delay in terminal operations. While the framework was created for container terminals, the same approach could be adapted to other connected systems where equipment, people, and external conditions interact to affect overall performance.

Globalization, rapid technological progress, and growing product variety expose manufacturing systems to increasingly volatile and uncertain production requirements. To remain competitive, manufacturing systems must be able to adapt to these changes. A critical step towards achieving manufacturing adaptability is the identification of the required levels and types of adaptability. However, the literature lacks structured methodologies to support this process, limiting effective implementation in practice. This paper addresses this methodological gap by developing an integrated framework that unifies flexibility- and reconfigurability-oriented research streams. First, a new theoretical perspective on manufacturing adaptability is proposed, encompassing newly defined concepts and terminology. Building on this foundation, the study develops a methodological framework to support manufacturers in systematically identifying adaptability requirements. The proposed method employs a scenario-based approach to account for both expected volatility and uncertainty of predictions. The framework is applied in a case study in the food processing industry and subsequently evaluated through a series of expert interviews. Results indicate that the framework has the potential to facilitate structured requirement formulation and provides practical guidance for decision-making at both strategic and operational levels. This work contributes both theoretically, by advancing a unified perspective on manufacturing adaptability, and practically, by offering a tool to operationalize the identification of adaptability requirements.

Frugal innovation, an innovation philosophy encapsulating a significant reduction of costs and focus on core functionality of products is used by companies across the globe to reach expanded markets. Nevertheless, manufacturing processes seem to have largely been excluded from frugal innovation applications. Reconfigurable manufacturing systems offer large opportunities for increasing both the efficiency and flexibility of production systems yet have not considered manufacturing sustainability or resource-constraints. The combination of frugal and reconfigurable manufacturing into frugal, reconfigurable manufacturing systems (F-RMSs) therefore inherently offers opportunities to make frugal manufacturing viable and RMSs less wasteful. F-RMSs are explored in this paper. The opportunities inherent in their combination are explored, the exact meaning of F-RMSs delineated and criteria for its success defined. A list of core characteristics and enablers is also defined to further distinguish the F-RMS as a manufacturing system. As such the F-RMS is fully conceptualized. Subsequently, a design framework for F-RMSs is defined based on this definition and criteria. In this way F-RMS design is facilitated and its eventual implementation enabled. The framework is focused on a requirements gathering and basic design step, where advanced design and implementation guidelines are defined on a case-by-case basis. The thesis is finalized with a set of case studies where the framework is carried out at different companies to (re)design an F-RMS. The functioning of the F-RMS is thereby validated, with the opportunities inherent in their combination recognized in the practical designs created.

Semiconductor product development becomes increasingly challenging due to diminishing product life cycles, miniaturization, introduction of new physical principles, and new manufacturing processes. These problems are compounded in the absence of standardized development processes for the most complex semiconductor products like MEMS technologies, because the manufacturing of these products is often outsourced. Suppliers play a pivoting role in the realization of the product, from product design until process design and ramp-up. The supplier selection problem in this industry denotes the challenges in finding the right supplier while meeting all the technical, process and business requirements. The contribution of this research is in presenting how to develop a generic methodology for data-driven co-development. Thereby, this work presents a novel product development framework that leverages co-development and supply chain integration through data-driven decision-making. Co-development is reached through standardized methods for generating the required engineering output for supplier selection. Supply chain integration is introduced in the early stages of product development. This synthesizes with the outsourced manufacturing processes. Clustering algorithms are used to effectively shortlist suppliers based on their competences, and provide insights into supplier profiles and gaps. The latter is used to draw strategies for developing unattainable technologies. Using the framework, the required engineering output for supplier selection was generated in 77% less time while reducing information asymmetries between actors in the product development process. Furthermore, the framework made it possible to quantify decisions, allowed for supplier profile recognition and gap identification through its hybrid automated approach, and supplier shortlisting in 95% less time. This efficiency does not only showcase the immediate benefits of the proposed methodology, but also lays the foundation for future research towards a fully automated approach in semiconductor product development. The developed framework includes information flows between actors and steps in the product development process, their interfaces and demonstrates its added value and potential for a fully automated yet efficient future approach in semiconductor product development.

This research investigates the application of a digital twin in managing challenges of variability and complexity within systems by using a case study at KLM Engineering & Maintenance. Using the DMADV (define, measure, analyse, design and verify) methodology, the research evaluates the problem using a literature review, measures and analyses the current state of the Logistic Handling Area (LHA), designs a digital twin concept and verifies its value. The literature review highlights the variability in the Maintenance, Repair and Overhaul (MRO) industry, which enables the investigation of digital twin applications for dynamic coordination. The analysis phase reveals significant operational challenges arising from variability in inflows and processing times, enhanced by system complexity and integral coordination issues between departments. To address these challenges, a digital twin is designed that enables real-time monitoring of KPIs, testing of dynamic resource allocation and integral operational target setting. The value assessment shows that the digital twin can support process operators by managing variability through continuous monitoring and evaluation of resource allocation, ultimately achieving predictable and stable system performance in a complex and variable environment.

Amsterdam Airport Schiphol (AAS) faces operational challenges from fluctuating baggage volumes and limited infrastructure, necessitating innovative approaches to optimize baggage handling. This study focuses on strategic buffering of super cold transfer baggage to reduce peak occupancy at transfer infeed points. A simulation model, integrated with TBATS forecasting, evaluates three buffering strategies (fixed target, polynomial, hybrid) and two reintroduction strategies (early, optimized release) under normal, disrupted, and highdemand scenarios. The results demonstrate peak occupancy reductions of up to 26.8%, with the polynomial buffering strategy and optimized release proving the most effective, though requiring additional AGV resources. Fixed target strategies offer a more resource-efficient alternative while maintaining substantial improvements in peak shaving. Early release strategies further reduce buffer congestion, optimizing operations for space-constrained environments. This research provides actionable insights for AAS and similar airports, highlighting trade-offs between efficiency, resource requirements, and operational constraints. The findings contribute to scalable solutions for managing growing baggage volumes within existing infrastructure, enhancing system resilience and performance.

Quality control is considered an important process in manufacturing to minimise waste related to the manufacturing process. A way of performing quality control is with help of machine vision. Understanding all decisions that are to be made when designing a machine vision system for quality control is essential, but challenging. This research presents a method that aids decision-makers in the process of designing these machine vision systems for quality control so the efficiency of the decision-making process can be increased. The method results in a knowledge base and a tool that can both be considered as useful to decision-makers. The method was designed by following a Design Science Research approach and the method was applied on a use case in the automotive industry to prove that it addresses its goal.

The present study was designed to gain a deeper understanding of RTI control and Supply Chain Coordination, with a specific focus on the practical challenges experienced in the reverse logistics by asset-owner and asset-users. A case study at EPS - an RTI owner - is performed and a practical problem has been addressed with a design science and TIL systems engineering approach. It was observed that a huge amount of deposit is allocated to a retailer's generic account. The current state analysis found that this problem is mainly due to inefficiency in the current identification process with physical SSCC labels. The design goal of minimising the "Delta" -the number of unidentifiable return packages- was defined. Ways to redesign the reverse logistics were determined with the aim that all return packages can be allocated to the retailer at shop level. Based on the future state requirements and the design goal, the concept of matchmaking is developed as a way to achieve the design goal. This thesis shows that a matchmaking system can improve the traceability of return packages and that it has the potential in bringing the Delta of return packages with missing shoplink to zero.
The matchmaking system is defined as the framework that ensures matchmaking, which uses a "key" generated by the sender to represent the return package. If the receiver can find the key upon arrival of the return package at the depot, the sender can be identified. Seven matchmaking systems were considered. Five alternatives use unique tray identities as key. One alternative uses the load carrier as key and the last alternative uses the return package identity as key. The validation results show that an "all read" or "reading of all individual trays" is not a requisite for a working matchmaking system. By contrast, as long as a certain ratio of reading at two locations is reached, an all-read scenario can be mimicked. The assessment investigated the instances in which zero mismatch take place. Results show that the higher the data capture capability of both the sender and receiver, the higher the chance a match can take place and the smaller the chance of a mismatch. This thesis creates insights on requirements for enhancing traceability of RTI's in the return chain and developed a matchmaking concept that can address the practical problem of returns without traceability of shop origin. The developed matchmaking concept is the outcome of an analysis of the current state and makes use of data elements that are already being collected in the database, in the case of EPS. The study addresses how collected data can be leveraged for enhanced RTI management in the reverse logistics and may inspire practitioners to face challenges with a similar lean approach.

This research aim was to design a centralised model that redefines definitions, standardises maintenance by categorisation, and reduces waste by introducing new slot lengths, leading to an equilibrium between flights and maintenance. This equilibrium enhances the airline’s capacity to manage maintenance efficiently and ensures greater uptime. The model addresses the important aspects of airline planning, including maintenance, flight and operation planning. Lean Six Sigma theories serve as a framework to combine the different plannings. The key importance is that the model considers all the stakeholders instead of only one so that the equilibrium between excessive flying and excessive maintenance can be established. In striking such a balance, uptime is assumed to increase.