Manufacturing systems are becoming increasingly complex with the involvement of several plants, dozens of production lines, and thousands of different SKUs. Products have to be stored between the production stages, and the export to the end-users. Within this context, storage assignment decisions are crucial in order to reduce the number of handling. This report considers an integrated approach of several streams of literature and methods to capture the characteristics of the system under study. The literature disciplines are storage assignment, ABC inventory classification, routing problems, and cross-docking. By means of a simulation with several heuristics, and multi-criteria decision-making with the application of the best-worst method, several storage policies and products-oriented rule sets are tested. The findings indicate that considering product features in the storage assignment, provides better performance on the number of handling, storage time, and meeting the due date. Particularly favouring important products results in a higher performance on these goods as well as on the overall performance of the system. The sensitivity of the multi-criteria policy is tested with disruptions in the production process, and the outcomes show that the performance even increases. Future research could integrate the products-oriented rules for the storage assignment and picking, and also with a possibility of relocation. Moreover, the equipment's (train and crane) could be included.

The ever increasing need of developing efficient solutions for the transport of cargo, able to reduce transport costs while complying to green policies has promoted the use of multimodal transport during the last decades. Multimodal
transport consists of the use of at least two modes of transportation for the transport of cargo from origin to destination. Governments and transport authorities (network planners) seek to make the optimal investment decisions when choosing where to allocate their budgets. To accomplish this they need to predict the response of the formulated policies on the cargo flow distributions over a multimodal network. Cargo flows over a multimodal network are in turn dependent on the behavior of water and land transport companies (network users), aiming in minimizing their own total costs of transportation. This research has developed a Decision Support Tool that is able to model cargo flows over a multimodal network, and predict the changes of cargo flows as a response to network planners policies. The research has used the freight transport network of Brazil as a testing ground for the model, and focused on improvements on the railway infrastructure. The results of the model suggest that the establishment of new railway connections and higher railway operating speeds can reduce the total costs and environmental footprint of transportation over a multi modal network. Finally, according to the model assumptions, it was concluded network users have a preference towards faster railway services.

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.

As the margins for improvements in the current freight transport system become limited, researchers address more and more the importance of collaboration between the actors which is crucial for the implementation of new, more efficient transport concepts as synchromodality. In addition, rail is concerned as a sustainable mode of transport that can also achieve economies of scale due to its ability to haul large quantities of goods. This study investigates cooperation policies that affect actors’ behavior to better utilize the rail use and lead to a more efficient system. We propose an innovative approach that combines gaming, simulation and optimization as a mixed method to test and evaluate these policies. The port to hinterland freight transportation system in the range of Port of Rotterdam is used as a case study. First, gaming sessions are organized in order to observe actors’ behavior and collect data. The game that is used was initiated by Port of Rotterdam, especially to identify the problems in this system. Subsequently, by assessing the observed data, a simulation model is developed and different policy scenarios are simulated to quantify their performance. In addition, the optimization model is developed, which sets the upper bound for performance and used as a solid base for comparison between the policy alternatives. Finally, the explanation of the difference between the policies’ and the optimized performance can give an insight on what are the root causes of the inefficiency, what is the best allocation of the resources and where the solutions should be focused.

The last decade saw the rise of e-commerce trade and the shift of the manufacturing industry to the emerging economies, China first of all. In this context, the European Customs Authorities experienced an explosion of small parcels coming from e-commerce websites, often from China, and faced difficulties to detect fiscal frauds and security threats using their conventional risk management systems. To address this problem, the European project PROFILE brings together the customs administrations of Netherlands, Belgium, Sweden, Norway, and Estonia, aiming to provide the EU with a shared platform for: (1) accurately assessing customs risks; (2) optimizing operation and logistics by integrating multiple sources of information; (3) developing a shared data platform to share customs risk management (CRM) practices.

As part of this project, the Dutch Customs Administration (DCA) and International Business Machines (IBM) Corporation are collaborating to deploy the cutting-edge technologies of artificial intelligence to automatically cross-check the customs declarations coming from Chinese e- commerce against online information. Through a Design Science approach, I carried out this research for the Delft University of Technology, written in collaboration with IBM Netherlands, aiming to deliver a preparatory study for the developing team before the PROFILE project begins. This includes knowledge brokering between the Dutch Customs Administration and IBM Netherlands so that a more precise problem scope can be defined, and the requirements elicited. In particular, this research focuses on the first part of the project: the development of an adaptive web-crawler for e-commerce, able to compare the declarations documents against online information.

According to the Dutch Customs Administration, the web-crawling system should gather the description of the goods from declarations, search the product on the web, find its price of sale on the e-commerce platforms, compare it with the value declared in the declaration, and return a risk indicator of green/red flag to the targeting officer. The design process of this system follows approaches coming from the systems engineering discipline, starting with the requirement analysis, addressing them with the state-of-the-art big data analytics, and finally deriving the logical components of the system, whose design is presented through a logical architecture.

First, the application domain is investigated. When goods entry the Netherlands need an entry declaration. These goods arrive at the harbor of Rotterdam or airport of Schiphol, where some of these are imported into the country and become import/export, and others stop temporarily as transit waiting to be shipped somewhere else. The Dutch Customs Administration monitors these processes through risk management systems aiming to stop non-compliant goods. This research describes these practices, with a higher focus on the e-commerce risk targeting. About the e- commerce world, a study of the e-commerce processes behind an online purchase is also carried out through a real purchase on Chinese e-commerce. This was used to observe how the Chinese sender described the item, and how the Dutch Customs assessed the risk and decided on the duties to be paid. This led to reflect on the possible frauds scenarios and how to address them. Finally, the Dutch Customs also reported that the products descriptions are often vague and ambiguous, and a more accurate formulation of the problem is described.

Secondly, an in-depth literature on the fields of web-crawling and big data analytics techniques is carried out. The possible technologies that could be useful to address the requirements and the problem formulation are investigated. Starting with an analysis of the existing literature on the field of big data analytics, this research also covers the recent trends of machine learning and artificial intelligence. To avoid reporting a too big literature, the topics reported have been accurately chosen, for instance describing only the techniques for web analytics and text analytics.

This literature on big data analytics is further broken in two sub-topics, one more theoretical, which classifies the types of analytics methods and defines the technology of machine learning and natural language processing, including the last paradigms of deep learning and reinforcement learning, and one more practical, where guidelines for the design, development, and implementation of machine learning techniques are proposed. It is here that a theoretical framework to systematically reflect on the challenges of the field of big data analytics has been identified. This framework is then used to systematically collect the main technological challenges of the use case under analysis and translate them into non-functional requirements.

Finally, the last part of the literature describes what a web-crawler is and what web- crawling/web-craping means. This later extends to the concepts of focused web-crawling and smart, intelligent, adaptive web-crawling, where machine learning techniques are deployed to improve performance. The literature concludes by providing related works of machine learning techniques implemented in smart web-crawling of the e-commerce websites and stating the knowledge gap that needs to be bridged to address the use case under analysis.

After the application domain and the literature review, the knowledge from these previous phases combines in a continuous iterative process according to the design science methodology (Hevner, 2014). Through unstructured interviews with the DCA and IBM experts, the requirements elicitation is carried out. The approach by Armstrong and Sage (2000) deriving from the field of systems engineering is used. The main objective of the system to be developed is broken down into a series of sub-activities that must be carefully structured to formulate the requirements. About the non-functional requirements, instead of reflecting on the different domains – technological, environment, law compliance, etc. – as it is proposed by the same systems engineering approach mentioned earlier, this research uses the framework identified in the literature review about the main challenges of big data project (Sivarajah, 2016).

To derive the components of the architecture from the requirements and customer needs, the methodology proposed by Suh (1998) called Axiomatic Design has been used, mapping the requirements into architectural components in a rigorous manner. In this way, the design domains proposed by this methodology – customer, functional, physical and process domains – are taken as the reference point for the design process: first, the business needs are identified, then these are translated into requirements, which are mapped into design features. The process domain is left out of this research and will be addressed by the IBM development team in Ireland.
The design cycle leads to the design of a web-crawling system represented through a service- oriented architecture (SOA). Its block diagram and black-box description of each application service are provided. Furthermore, the architecture functionality is described with an architecture walk-through and a sequence diagram in the unified modeling language (UML). The result is an innovative real-time web-crawling system to identify the value of a given product on the e-commerce websites. It deploys natural language process models to filter the non-relevant search results, and other machine learning models to best matching the remaining relevant results with a given item description.

The design and architecture description of this innovative web-crawling system is the main artifact of this research, while the mixed methodology of systems engineering methodologies and big data frameworks is another important scientific contribution.

Due to the complexity of aircraft spare part management, the aviation supply chain and ecosystem are not deemed as transparent as desired. As a result, participants face additional cost of communication and compliancy as proprietary component data is not made immediately accessible. To solve these problems, Maintenance, Repair and Overhaul (MRO) providers currently consider to use Blockchain to track and trace aircraft spare parts. Guided by Business Model Stress Test, this thesis evaluates the robustness of MRO business models when they use Blockchain as an aircraft spare part track and trace capability. Blockchain as an aircraft spare part track and capability is not only strategically relevant for cost-conscious innovating MROs, it also does not impose a risk to the robustness of their business model in most scenarios. MROs should engage in a regulatory-backed Blockchain consortium through which they share limited component data throughout the entire industry. If this is not possible, they can retreat from the consortium under the notion that their business model is threatened. Even though Blockchain could improve aviation supply chain and ecosystem transparency, the feasibility of the consortium is questionable. To remedy this concern, future research should focus on identifying strategic incentives for Original Equipment Manufacturers and aircraft operators to participate in the Blockchain consortium.

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.

Over eight years from its conception, Blockchain is considered as a ground-breaking innovation in information technology. The technology’s promises of complete disintermediation and enhanced process visibility turned supply chain and logistics into a fertile ground for a blockchain potential implementation. Despite these high expectations, both practitioners and researchers still struggle in identifying the blockchain real benefits to the industry. Moreover, the several private projects and start-ups offering blockchain solutions, which are blossoming in the logistics environment, are threatening the role of the Port of Rotterdam and Portbase as the port inter-organizational information system. Commissioned by Smartport, this research aims to identify the potential blockchain uses on port logistics as well as their relative impact. Therefore, the functionality offered by the current market applications are categorized into four business cases, which identify different uses of the blockchain. These blockchain business cases are subsequently analyzed under six different points of view in order to evaluate the expected benefit that the major stakeholder expect to gain from the technology implementation. Finally, the impact of these business cases is tested on the business model components of the current port information system, Portbase, to identify the disruptive power of the technology. To face the potential issue of disintermediation; a set of solutions have been developed for Portbase on how to adapt its business model in case of a blockchain implementation. This strong conceptualization of the blockchain technology helps the main logistic stakeholders to understand and discuss the potential application of blockchain technology on port logistics, and it provides a much-needed basis for further scientific research. Further development of this conceptualization is needed to structure the ongoing blockchain discussions in both scientific literature and practice.