We present an adaptation of the tri-level Operator-Attacker-Defender (OAD) model for designing resilient supply chains, addressing disruptions such as supplier failures, production shortfalls, and disabled transportation links. These disruptions impact the supply chain's performance regarding its ability to meet customer demand. Originally proposed by Alderson et al. in 2011, the OAD model integrates three levels of decision-making: the operator optimizing system performance, the attacker seeking to disrupt it, and strategic system design decisions to mitigate disruptions. Our conceptual model is based on a multi-commodity flow network with a defined performance metric, formulating the tri-level optimization problem accordingly. Due to its complexity, we employ a decomposition-based solution strategy. Extensive computational experiments demonstrate the model's tractability across various supply chain scenarios. A real-world case study on a global pharmaceutical supply chain illustrates the OAD model's effectiveness against climate-related disruption. With its ability to help explore trade-offs between operational efficiency, costs, and resiliency, this model contributes to Resilient Supply Chain Design (RSCD) by providing a deterministic framework for designing supply chains capable of withstanding disruptions.

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.

As global climate change concerns intensify, the search for renewable-based alternatives to products and processes that rely on fossil fuels becomes ever more important. A process that has been championed is the co-electrolysis of carbon dioxide and water, under the supply of renewable energy, via high-temperature solid oxide electrolyser systems. The technological combination can produce syngas directly. This syngas is a renewable-based alternative to fossil-based syngas, which has a widely established market and can directly be used for the generation of energy in power plants, or as a precursor for the production of carbon-based chemicals and fuels. Although many technology-specific improvements have been made in recent years that contributed to the creation of a promising outlook and an increasing number of demonstration projects, the feasibility and implications of implementing the process on an industrial scale remain largely unaddressed. Since many technologies fail in the transition from benchtop to industrial scale, a deeper understanding of the requirements that are opposed at an industrial scale should be obtained, to this end, this work applies an exploratory research approach to investigate the feasibility and opportunities of the supply chain that supports the production of syngas via co-electrolysis. To delineate the feasibility of the supply chain, this work considers two perspectives of feasibility; (i) the feasibility of the horizontal supply chain which encompasses the requirements and availability of feedstocks, technology and process scales, and market potential, and (ii) the feasibility of the vertical supply chain which encompasses the availability of materials required for the operation of solid oxide electrolyser systems. To investigate the opportunities of the supply chain, this work considers decentralised and centralised supply chain configurations. To initiate the research the individual supply chain units that are required for the process to be operatable at industrial scales were assessed. The requirements and scales of the individual units are matched with the current and expected future scales of technologies, feedstocks and markets. From this scale match, it can be concluded that based on feedstock availability and syngas market size, it is technically feasible to completely replace fossil-based syngas with renewable-based syngas produced by solid oxide electrolyser technology. However, several aspects such as the renewable energy requirements and the number of high-temperature solid oxide electrolyser systems that are required, impose practical limitations on the large-scale rollout. The influence of these practical limitations is aggravated after an assessment of the opportunities of decentralised and centralised supply chains, as both possibilities require capital investments on the scale of 1 Billion Euros and no positive return. A smaller implementation scale was also considered, and the limitations opposed to such a scale, seem much easier to overcome, highlighting potential future research directions. To delineate the feasibility from the vertical supply chain perspective, the material requirements of solid oxide electrolyser systems and the corresponding material availability were investigated. From this investigation, it can be concluded that from a global point of view, only Gadolinium opposes feasibility limitations. However, from a European point of view, the limitations seem much more severe. Based on the findings of this work, future research should focus on detailed analyses of the potential of solid oxide electrolyser supply chains at smaller scales, as there exist many opportunities that could improve overall supply chain performance, and smaller scales oppose less severe problems.

SITA is the world’s leading specialist in air transport communications and information technology which works with around 400 air transport members and has 2800 customers in 190 countries. TS6 kiosk is the newest generation of its kiosk family and is facing a complicated situation now. Usually, the production of a kiosk is using make-to-order methods. However, if this method is adopted, the customers of SITA can not receive their productions within the expected periods of time. Besides this, SITA also can not get the procurement discount from the suppliers if they only purchase a low volume and do not make changes. In order to solve this problem, demand forecasting is conducted using the historical sales data of the TS6 kiosk. Through the literature review, suitable qualitative forecasting methods and quantitative forecasting methods which were mainly used in the FMCG industry are got together and combined to increase the forecast accuracy in this research to come out with the final forecast result of the TS6 kiosk. This research also explores the possibility of demand forecasting for the slow-moving consuming industry. By successfully conducting the final forecast, the result can help SITA to shorten procurement lead time so as to meet customers’ expectations as well as save total costs.
KeyWords: Demand Forecasting, Quantitative Forecasting Methods, Qualitative Forecasting Methods, ARIMA, SARIMA, Residual Analysis.

For the shared mobility service providers, it is essential to have an economic operation and therefore, it is necessary to maximise their fleet utilisation. Of influence on the latter is the availability of the spare parts. The topics for which spare part forecasting research is already performed are different in several ways compared to the field of free-floating shared vehicles. The many different users with each different driving skill, wear and tear by operation, events as vandalism and traffic accidents and constantly standing out on the street result in sudden failures of parts. Three different forecast models are presented in this paper. The effect of including demand generating factors in the forecast approach is analysed. The methods show that a better forecast performance can be achieved while including demand generating factors into the analysis. The forecast accuracy can be further improved and some suggestions for improvement are given in the paper.

In busy passenger railway networks, a large amount of trains have to be parked in shunting yards off the mainline every night, where they will be cleaned, maintained, sorted and parked. This problem is known as the Train Unit Shunting Problem (TUSP), which is a hard combinatorial optimization problem faced by railway operators. The TUSP is currently solved using human heuristics, which is difficult and time-consuming. Reinforcement learning approaches have been developed in the last few years to efficiently approach this problem. In this research we develop, in a multi-agent deep reinforcement learning framework, an heuristic for random exploration to efficiently search the state-action space and two heuristics for train routing strategies, which aim at improving the performance and quality of the produced route plans. On one hand, we develop the Type-Based Routing Strategy, based on the idea of parking trains of the same rolling stock type on the same tracks. On the other hand, we develop the In-Residence Time Routing Strategy, based on parking trains ordered according to their departure time. Both routing strategies consists of four components: (1) standard parking rules, (2) combination and split rules, (3) conflict resolution rules and (4) unnecessary movements rules. The goal is to incorporate information from the logistics side of the problem into the framework. We demonstrated the performance of the heuristics developed in two real-life cases in the Dutch railway network. The results of the experiments carried out demonstrate the potential of the resulting framework to produce more efficient route plans and to adapt to different problems designs such as different matching problems types and different shunting yards.

This study addresses the truck rescheduling problem as the consequence of uncertain arrival time. It proposes an integrated system of predictive model powered by machine learning algorithm and exact optimization model such that it is distinct from most existing literatures in this domain. The uncertainty of truck arrival time is captured as presence probability by the developed predictive model. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) is built to solve the Probabilistic Slot Rescheduling Problem (P-SRP) in which the rescheduling is subject to expected value constraint that incorporates the presence probability of incoming trucks. The objective is to minimize the expected cost of rescheduling that would lead to more efficient and robust operation. In regard to the predictive model, evaluation according to the standardized KPI shows the ANN is the best algorithm to fit the input historical data with overall F1 score of 73%. Moreover, adding real-time elements enhances the prediction result by 20%. In regard to optimization model, the P-SRP model results on expected cost that is 42% lower than the P-SRP model. Numerical experiment based on multiple scenarios indicates that the proposed solution yields optimal added values in situation characterized by high number of reschedule being required, large scale of operation in terms of quantity of loading bay and utilization rate, the priority is to maintain initial schedule, and there is no stand-in loading bay available. Lastly, it is found that increasing operational efficiency comes at the expense of the schedule robustness, although the value is negligible. This study is limited to the conceptual setting and the use of synthetic data; therefore it should be extended to include empirical result.

To ensure safety and security of imported goods, customs authorities perform risk assessment on incoming goods. European customs use declaration data provided by traders to execute risk analysis. Lacking availability of this data to customs authorities currently hinders proper risk management and poses threats for the European socio-economy. To solve this issue, this research analysed how Blockchain, an emerging and promising technology, could solve the issue at hand. Using a design-oriented approach, the process to be supported was analysed, the requirements were elicited, and the core blockchain components were identified. This was instrumental to develop a blockchain-based platform to support the availability of declarations data to customs authorities. The feasibility of the designed platform was evaluated through a comparison with TradeLens, an existing supply chain platform underpinned by blockchain technology, and possible implementation issues were identified by using a blockchain governance framework. The platform design contributes to the import control system implementation and research on blockchain technology. The next step is the development of a Proof-of-concept to analyse whether the designed platform supports the process at stake.

With the current growth of e-commerce, couriers need to serve a bigger demand and it is often the case that receivers are not at the location when their parcel is delivered. The use of parcel lockers is seen as a good option for improving the problem of failed deliveries. However, implementing a parcel locker network has an impact on both couriers and receivers, which still prevents couriers from directly adopting this technology. The aim of this research is to develop a new tool that helps couriers analyse the impact of using a parcel locker network taking also into account receiver preferences to pick up parcels from lockers. An optimization model is combined with a choice behaviour model to determine the trade-offs between delivering to parcel lockers or to end-users, in terms of service level and total transportation costs. In order to solve this model for large instances, an Adaptive Large Neighbourhood Search algorithm has been adapted with operators and criteria specific for the problem presented. Computational experiments were carried in a set of the Solomon instances and show that the problem can be solved efficiently with the proposed algorithm. Moreover, the algorithm was applied in a small delivery area in Rotterdam, showing the sensitivity of the model to the preferences of receivers in the area.

In order for public transportation to remain an attractive travel option, its reliability must be improved. To enable this, extensive knowledge on the passenger delay phenomenon is necessary. This study explores the possibility of using empirical data of passenger delay to determine the relationships between stations through a Bayesian Network approach. This approach is able to uncover any dependencies, regardless of known or unknown causes. The results from the Bayesian Network can be used to calculate a set of newly defined informativity indicators. These indicators give information on a station’s capacity of providing information on the delay state of the rest of the network. Among the possible applications of these indicators are: providing accurate information to passengers and the operator on delays, and aiding in determining the most effective areas for delay mitigation measures. To increase the usefulness of these indicators, they are compared to centrality indicators, so that even for networks with little available data, the informativity indicators can still be approximated. It was found that the centrality and informativity indicators correlate to some extent, meaning the informativity indicators could be very meaningful and relevant in further gaining knowledge on and improving public transport reliability.

Due to its archipelagic nature, Indonesia enforces to make substantial investments in its maritime sector, especially port infrastructure, in order to promote trade, economic growth, and ease disparity among Indonesia’s island regions. The challenge, of course, is that Indonesia has many ports to invest in and a limited budget; it must prioritize among various port development projects. The government, therefore, needs to choose wisely in order to avoid wasting money by constructing shipping network plans. One of the most important aspects in shipping network planning in Indonesia is designating which ports are to become international container gateways as these ports will play an important role in connecting Indonesia with foreign markets and therefore have a strong impact on its economy. To improve the current state of policy decision-making for the Indonesian shipping network, we hereby develop adaptive policy decision pathways focused on Indonesian containerized exports. The objective of this study is to build adaptive policy decision pathways that can cope with dynamic future uncertainties and results to help the government develop a robust network investment plan that helps the Indonesian government achieve its objective of improving connectivity and value-added exports through lower logistics costs. The main general finding is that flow pattern is very sensitive to demand volumes. Moreover, Minimum Cost-Flow (MCF) model used in this study is able to identify the flow pattern so that is very useful to analyze the potential of gateways. By applying this approach, we can analyze how the flow pattern changes from period to period and therefore may lead to different policy decisions. Having this approach in shipping network planning will help policymaker to have several predictive pathways and thus can support policy decision-making.

Weather forecast agencies periodically provide information on potential typhoon behavior from its formation to dissipation. This information can be used to pre-position relief goods in areas that are potentially affected by a strong incoming typhoon. The uncertainty of the typhoon behavior with respect to a point location decreases over time, thus showing the trade-off between forecast accuracy and lead time available for pre-positioning. This thesis paper presents a iterative methodology that uses a forecast-driven facility location model for pre-positioning over large networks where lead time available for pre-positioning at each location can differ. By periodically generating damage scenarios based on new forecast data and eliciting the strategic preference of the decision maker, the model determines where, when, and how much relief goods to pre-position. The model is implemented in a case study to design the pre-positioning actions in anticipation of typhoon Haiyan in the Philippines.

The retail market is changing rapidly. Many new offline, as well as online competitors, arrived. As a result, customers became more demanding. One of the critical elements is that customers expect a high variety of products that are always available. Out of Stocks (OoSs) in apparel stores result in lost sales in the short term and a declining brand reputation in the long run. For many retailers, it is therefore critical to minimise their OoSs. A high inventory accuracy is necessary to reduce the number of OoSs. This is practically unable to achieve with traditional techniques. Radio Frequency IDentification (RFID) has the potential to increase the inventory accuracy, thereby reducing the number of OoSs. RFIDs in the apparel retail, especially more sophisticated deployments are relatively new and specific benefits for various store types are unknown. This research compares different RFID-deployments for apparel stores regarding the reduction of OoSs.

An apparel store exists out of a backroom and a sales floor. Both can be seen as inventory systems, which have to be replenished to prevent OoSs. To achieve this, retail stores use replenishment systems, based on the recorded inventory. The problem in retail stores is that there are inventory losses, mainly due to internal and external theft, which are not registered. The result is an increasing discrepancy between the actual and recorded inventory. As a result, items that appear to be available in the inventory system are not. An effect is an increasing number of OoSs. RFID can increase the number of inventory counts to align the recorded inventory with the actual inventory. The following RFID-deployments are compared in this research: handheld readers, a robot reader, a combination of overhead and handheld readers and overhead readers. The deployments are linked to inventory counts that are performed once every seven days, once a day, partly continuous and continuous inventory counts. All deployments for various store types are compared to the original situation. To be able to analyse the different situations, a base model is constructed. The base model is implemented in a discrete event simulation model in which several parameters can be adjusted.
The Key Performance Indicator (KPI) to measure the performance is the OoS percentage. The following parameters are adjusted in the experiments to mimic various store types: 1) the ratio between the sales floor and backroom, 2) the average number of items per Stock
Keeping Unit (SKU), 3) the replenishment frequency. RFID significantly reduces the OoSs compared to the original store for each store configuration.

It can be concluded that for each store configuration, RFID significantly reduces the number of OoSs. The implementations hardly differ from each other for a particular store configuration. Regarding the results of this research, it does not matter which RFID-deployment is implemented.