Digital transformation in procurement is important for Purchasing and Supply Management (PSM) organisations to develop global organisational competitiveness. Generally, procurement performance can be determined and improved through the use of a maturity model. Existing models do not cover the latest (e-)procurement processes (E-Sourcing, Procure-to-Pay, and Supplier Relationship Management) resulting from digitalisation. Therefore, an extension to existing maturity models was required to assess company performance. A literature study was conducted to describe and explain meaningful concepts and several maturity models were selected to review and compare the frameworks most suitable for an extension. Experiences of a major PSM organisation called the Corporate Group strengthened the research study by determining key topics and industry best practices, providing a foundation for the extension. A company analysis resulted in an understanding of (digital) procurement and the selection of Schiele (2007) due to its comprehensiveness. Semi-structured interviews were conducted with procurement experts to review Schiele's model and develop an extension. The extended model contains an additional spend management domain and a new dimension covering e-procurement. Multiple purchasing functions and stage descriptions were developed, whereby the model consists of 56 original and 22 new assessment questions. The new dimension includes Spend & Data Management, Contract Management, Market Intelligence, E-Sourcing, Transactional Procurement, and Supplier Relationship Management. Entity A is part of The Corporate Group and provided the ideal opportunity to test the extended model through a multiple-case study. Maturity assessments were conducted of several operating companies (OpCos) to determine maturity levels and whether the extended model actually measured purchasing maturity. The extended model was able to quickly provide a performance overview and guide the OpCos towards improvement. Self-assessments as an alternative for third-party audits were provided, including a digitalised approach to process data easily. However, comparing results among OpCos outside of their sub-unit delivered an unexpected outcome. It was questionable whether the model measured purchasing maturity or rather the perception of it. Although the results were unexpected, the extension appeared to be more accurate. This was most likely due to the elaborate stage descriptions, enabling assessors to identify stage levels more easily. This research study provided a foundation for assessing e-procurement using an extended maturity model but limited itself to the Corporate Group. Thereby, it confirmed the criticism regarding the four stages being too rigid. Potential future research is recommended to improve both the extended model and the assessment approach. First, it must be shown to what extent e-procurement is covered in the new dimension and whether the extended model can be sufficiently generalised. Interviews should be conducted with procurement experts from different companies or industries to validate the extension through statistical analysis. Secondly, the assessment approach could be improved by including a control person and more experienced people, ensuring critical thinking and more realistic maturity levels. Self-assessments are not necessarily less reliable compared to external audits, but they rather need support to guide the process through a standard protocol. The subsequent step should be to formalise a proper action plan to initiate improvement.

At the UN conference of 2015, the promotion of inland waterway transport (IWT) was named as a Sustainable Development Goal by the United Nations. While IWT has been identified to make goods transport more CO2- efficient, the long-term viability of the Dutch IWT sector is at risk. Currently, Dutch IWT businesses benefit from increased demand for transport capacity during climate change induced periods of low river depth by being able to charge higher prices to shippers, thereby increasing short term profits. This thesis research project concludes that this behaviour is unsustainable due to the disregard for providing price competitive and reliable services to shippers, which will ultimately damage the reputation of the Dutch IWT sector. This can lead to shippers choosing other modes of transport, which will damage the long-term viability of IWT demand. A lack of evident optimal climate adaptation measures that would better guarantee the long-term viability of an IWT business under any climate scenario is one of the reasons why IWT businesses have thus far been hesitant towards implementing climate adaptation measures. This thesis research project studies the potential of using a robust decision analysis in supporting IWT businesses’ decision-making on climate adaptation measures that could safeguard the long- term viability of the Dutch IWT sector. It conducts a robust decision analysis and then evaluates its potential using feedback from stakeholders of the Dutch IWT sector. This thesis research project present the results from the robust decision analysis to IWT stakeholders to evaluate how these could impact their decision-making. Stakeholders noted that they believe that the produced influence diagram could be used as a standard communication object to produce a better consensus of how measures influence the long-term viability of IWT demand. Furthermore, stakeholders expressed that the robust decision analysis provided them a better understanding of which measures were able to better guarantee the long-term viability of IWT demand. Results from evaluation interviews with stakeholders therefore provide grounds to believe that robust decision analyses have potential to support IWT businesses’ decision- making on climate adaptation measures that could safeguard the long-term viability of the Dutch IWT sector.

This research focuses on optimizing the performance of a hybrid warehouse system that combines automation with manual picking processes. The study utilizes a case study of an online grocer, Picnic, and investigates the allocation of orders within a buffer and the configuration of the manual picking process. The research identifies the key performance metrics for the system, including the number of late totes, average sojourn time, average stack throughput time, and picker productivity. Several factors impacting the system's performance are examined, such as buffer lane selection and group formation strategies, minimum and maximum group sizes, number of workers, pick times, and input data. Through comprehensive experimentation and simulation modelling, the study reveals that prioritizing the deadline of totes for buffer lane selection and the IPB time for group formation results in the best system performance. The research also suggests that the configuration of the manual process should consider the group size should be adjusted based on the expected workload. The study identifies the consolidation stations as a significant bottleneck in the system, and repairing them promptly on busy days is essential to maintain performance. The research provides practical implications for Picnic, emphasizing efficient buffer allocation and group formation strategies. In conclusion, this research offers valuable insights into enhancing the performance of hybrid warehouse systems. Companies like Picnic can improve operational efficiency and customer satisfaction by optimizing buffer allocation, buffer lane selection, and group formation. The findings from this research can serve as a basis for further optimization and decision-making in similar warehouse setups.

In practice manufacturing companies have insufficient coordination and synchronisation between production planning and warehousing teams. The lack of coordination regarding production and warehousing decisions in the ordering strategy may result in unforeseen capacity issues at the warehouse due to misalignment between production plans and warehouse availability. These unforeseen issues can result in additional costs due to production line closure or last-minute stock reallocations. For this reason, it is relevant to study the effects of increasing interdepartmental coordination between production and warehousing departments on product flows between factories and warehouses in the supply chain of a manufacturing company. This thesis’ aim is to analyse the effects of coordinating the production scheduling department with the warehouse management department, against the most common practical case where the two entities hardly communicate. To this end, two optimization models are developed representing the warehouse and factory operations within a supply chain. The first model consists of a multi-period multiproduct lot size and warehouse optimization model for the decision on warehouse capacities while minimizing warehousing costs. The second model is a parallel non-identical machine scheduling model for the optimization of production schedules and the simultaneous minimization of the production costs. We compare the outcomes of the two models against a model where both decisions are integrated. The modelling decisions are taken in a setting with deterministic demand over time. An extensive sensitivity analysis aims to provide managerial guidelines for practitioners, to weigh which department can have greater impact on the total cost. Also, the integrated model can quantify the benefits of coordinating the production and warehouse departments. A case study at Kraft Heinz, a popular brand in the food industry, is used to provide a practical setting.

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.

Climate change is triggering among others a larger demand for offshore wind energy. This leads to new developments of which larger next-generation Wind Turbine Generators is the most relevant. These next-generation WTGs create problems for the carrying capacity of current-day installation jack-up vessels that work according to the conventional installation method (shuttling). These installation vessels can carry less or even nil of these WTGs per trip compared to the current-day WTGs. Fewer turbines per trip would allow the larger current-day installation vessels to maintain their work. However, they have more sailing time since more trips are required. This could also be more inefficient since the Offshore Wind Farms are moving further offshore. The other option within the conventional method is to build larger installation vessels that would carry more WTGs per trip. Another installation method in the literature called feedering is a potential substitute for the conventional method. Very limited research has been done for feedering as well the practical implementation whereas the conventional method has extensive research and is mostly used in practice. The models in the literature compared two different feeder methods, called, the base port feeder and feeder-ship method, to the conventional method. The models are generic and lack different strategies to find the best feeder solution. The feeder-ship method has the most potential to solve the aforementioned problems and is therefore further investigated to find the best installation strategy, being either the conventional or feeder method. First of all, the feeder-ship method is evaluated based on practical knowledge and adapted so a base port is used to temporarily store all components that come from the production port. The feeder sails back and forth between the base port and the installation site to supply the installation vessel with WTG components. The installation vessel will stay at the installation site to be as efficient as possible (the least amount of sailing time). This research only looks at the feeder and installation side and leaves the port elements out of the problem. Feeder strategies (within this method) differ in the number of feeder vessels/types and transfer options using either barges or Platform Support Vessels and being indirect (transfer components onto the installation vessel) or direct (install components from the feeder). The installation vessel for feedering is also looked into as being either a current-day or a special feeder purpose installation vessel. Different carrying capacities of the installation vessels are used to create different strategies for the conventional method. A stochastic Discrete Event Simulation model is created and used in this research to evaluate the strategies. The output of the DES simulation is the project duration per sailing distance and strategy. The costs for this duration is calculated in a costs model from the perspective of the contractor as well as the developer. Besides duration and costs (based on the European market), the strategies are also evaluated on emissions (fuel consumption) since this is an increasingly important element in the industry. A sensitivity analysis has been performed to get a better understanding of which critical parameters the feeder strategies are most sensitive to. The analysis is also used to find the best next-generation WTG installation strategy in Europe which is the main focus of this thesis.

A rapid increase in international trade volumes during recent years has led to truck congestion at container terminals’ gates during peak hours. This has consequences on the costs of trucking companies waiting long times behind the gates. Furthermore, it leads to containers arriving too late at the hinterland, a lower port attractiveness and high emissions of idling trucks. One possible policy to deal with this issue is to steer trucking companies towards night-time transport. However, this requires hubs with extended opening hours. The objective of this research is to investigate the implication of such policy further by evaluating and simulating the actors and their (inter)actions in the container transport system. This research uses a combination of qualitative (semi-structured interviews with actors) and quantitative (agent-based modelling, discrete event simulation, queueing theory and discrete choice modelling) methods to assess the impact of hubs with extended opening hours on freight transport under various scenarios. These scenarios compare possibilities for a chassis-exchange or a container hub and investigate the choice of trucking companies to use such hubs. Results determine that if a container hub would be introduced, other actors need to partly pay for trucking companies using the hub so that the hub capacity is fully used. This study reveals concrete recommendations on the type and location of the hubs to mitigate waiting times at terminals' gates and transport more containers per day. For example, a chassis-exchange hub can reduce the total waiting time at four container terminals by 420 hours per day. If trucking companies would invest in a chassis-exchange scenario, the profit is estimated to be around two to four thousand euros per day.

This research paper focuses on improving the performance of cross-docking operations under uncertainty in the context of e-commerce logistics. The growth of e-commerce sales has increased product returns and complexity to supply chains. To address this issue, this study investigates how cross-docking operations can be improved under external and internal uncertainty factors. The research begins with a literature review to understand cross-docking facilities (CDFs) and measures to mitigate the effects of uncertainty. The current state of a CDF in a case study for a Fourth Party Logistics (4PL) provider is examined, and by reflecting on the literature overview, two potential means for decreasing the effects of uncertainty are identified: staging-level design and load carrier-type design. A Discrete Event Simulation (DES) model is developed to test the effects of staging-level design and load carrier types on the performance of the CDF. The simulation model captures input factors such as truck arrivals, freight levels, and the purity level of cross-docking. The simulation model’s performance is tested for different scenarios, and the effects of different design alternatives are analyzed. The results demonstrate that two-stage cross-docking with pallets can significantly reduce the total makespan and improve operational efficiency compared to single-stage cross-docking with pallets. The results also show that using roll containers significantly decreases the chance of intra-terminal congestion but also results in longer unloading and reloading times. The research contributes to the understanding of cross-docking operations under uncertainty, stresses the importance of staginglevel and load carrier type design on CDF performance, and provides insights for logistics companies seeking to optimize their e-commerce supply chains.

In this thesis, the optimization of inventory management within RTI pooler networks is considered. The inventory management by RTI poolers consists of the network configurations on which the network relies, as well as the relocation schedule of RTI poolers that determines the functionality of the system.

As connections between customers and suppliers become increasingly complex, the design of transport networks is under growing pressure to achieve efficiency. In response to this challenge, this paper proposes a service network design that takes into account both the forward flows from a production facility to a customer and the returning flows from customers to the production facility. The design allows for products to be shipped directly or to be consolidated and sorted at a depot. To assess the benefits of optimising both the forward and returning flows simultaneously in terms of network costs, the paper formulates an arc-based model and applies it to a real-life case. The results indicate that the network costs can be reduced by optimising simultaneously, as depots are built in the optimal locations and with the right capacity. This contrasts with the sequential method, which does not account for the return flows and can lead to suboptimal depot placement.

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.

Maritime logistics is important for the international volume of trade. Ports are critical in cargo transhipment and provide services to vessels like traffic management, piloting, towage and mooring, which together are called the Nautical Chain since these services are dependent on each other. Due to the importance of maritime logistics, it is important that the supply chain functions resiliently and thus ports function resiliently. For ports to function resiliently, the Nautical Chain should function resiliently. Currently there is no knowledge on the resilience of the Nautical Chain, which is the gap this research addresses. The aim of this research is to gain insight in the current state of resilience of the Nautical Chain, and gain insight in the effect different mitigation strategies have on improving this resilience. A literature review is performed and a discrete event simulation model is built to gain insight in the resilience of the Nautical Chain. The results show that the largest improvement to the resilience of the Nautical Chain can be achieved by a simultaneous increase in pilots and tugs.

A relatively new concept within demand management for time window assignment is green labeling; time windows which contribute to improving the routing performance in terms of sustainability. Certain time slots are given a so-called green label. From literature, limited information is known about the choice preference with regard to green labeling and its impact on routing performance. Via choice modeling, certain attributes are estimated based on a data set from an e-grocer. Together with a beta estimate on green labeling from literature, the effect of green labeling on choice behavior is analyzed. The results are used for route optimization where the effect of various static and dynamic approaches are tested. Results show that dynamic green labeling has the most promising effect on routing performance in terms of costs and sustainability. Especially when customers are more nudged toward the largest time windows in less popular day parts. The CO2 emissions decrease by 127.4 CO2 per order and the costs decrease by 1.03 euro per order on average. These results are based on the specific situation of the e-grocer in the case study. With regard to choice modeling, this research is limited to only time window characteristics. The improvements in terms of costs and sustainability per green labeling approach, based on the attributes resulting from real-data choice modeling, contribute to more knowledge on the effect of green labeling on routing performance.

Inland Waterway Transport (IWT) is an untapped resource that can be mobilised to achieve a more sustainable transport system without compromising competitiveness. It outperforms rail and road alternatives in terms of low emissions, costs, high capacity, energy efficiency, freight safety, and security. Waterway locks are ageing assets in IWT systems and are infamous for creating bottlenecks. The effectiveness and performance of these locks can be measured and integrated into decision making to establish well-informed operational, maintenance, and renewal policies. This study addresses the following research question:How can the effectiveness of waterway locks be assessed to support lock maintenance and operation? Simulation modelling, which offers an efficient and low-risk evaluation of policy options while incorporating the intrinsic variability of the system, is selected as the core methodology. The simulation model incorporates operational aspects of the system, malfunctions, corrective maintenance activities, and the calculation of various performance indicators. An extensive list of performance indicators is complied through literature research. These indicators include infrastructure occupancy, vessel waiting times, costs, and emissions. In addition to existing indicators, three formulations for Overall Equipment Effectiveness (OEE) are proposed in lock complexes. The applicability of the selected methodology is demonstrated by employing a case study of the Volkerak complex, one of the largest and busiest lock complexes in Europe. Quantitative and qualitative data, collected through operational logs, maintenance reports, and interviews with experts, support that as the lock complex gets older, malfunctions become more frequent. SIVAK, a software package utilised by the Ministry of Infrastructure and Water Management of the Netherlands ("Rijkswaterstaat", RWS), is used as the basis of the simulation model. Extensions are made to calculate additional performance indicators and to simulate fluttering doors and slowdowns, two types of malfunctions that are diagnosed to be frequent and impactful based on maintenance reports and interviews. Experiments are designed to explore the performance of different maintenance policies, such as mean time to repair (MTTR) and inspection frequency, and different operational policies such as locking regimes under various fleet mix and lock condition scenarios. Stress tests and univariate analyses are also conducted. The study findings highlight the following: - With rising demand, the significance of lock condition in maintaining acceptable service levels and minimising CO2 emissions becomes more evident. The findings indicate a trade-off between preventive and corrective maintenance efforts. In challenging lock conditions, faster repairs and more frequent inspections are needed to prevent capacity problems, leading to longer waiting times. Notable differences in handling capacity are observed in the three lock conditions studied. - The concept of baseline OEE proves to be valuable as a maintenance-oriented metric. It emphasises that proficient maintenance strategies can counteract deficiencies in the lock system, resulting in improved capacity, reduced transit times, and reduced CO2 emissions. A general rule of thumb suggests that improving baseline OEE by one point corresponds to about a 1.2%-1.5% improvement in waiting times and emissions. - Changing MTTR and inspection policies influences baseline OEE scores, but these adjustments must be aligned with the lock condition. Frequent inspections might yield unnecessary availability losses when the lock is well maintained. Similarly, the extent of benefits of shorter MTTRs depends on the frequency of breakdowns. - A prominent dilemma in lock systems involves balancing transit times and the number of levellings. Locking regimes capture this trade-off, where reducing waiting time thresholds increases levellings and operational costs. However, some strategies can achieve an improvement in both aspects. These include expanding traffic range and considering the current state of the system when assigning lock chambers to incoming vessels. - Service-based OEE, integrating operational and maintenance policies, aligns better with waiting times and CO2 emissions compared to the service level alone. This composite index can serve the purpose of monitoring waterway network lock systems, helping to identify losses due to unavailability and reduced speed.

This research explores the integration of electric vehicles (EVs) into the logistics of bulk-liquid transportation, specifically within Heineken Netherlands’ operations, using a two-echelon network. This study is pivotal as it aligns with global moves towards zero-emission regulations and sustainability in logistics. The primary question it addresses is optimizing a logistic network and truck operations for bulk liquid delivery by transitioning to EVs within a two-echelon framework, ensuring efficiency while adhering to sustainability and regulatory demands. The investigation employs a sequential exploratory strategy, beginning with qualitative analysis to identify core challenges and opportunities, followed by quantitative methods to refine network design and decision-making. Advanced clustering techniques such as the center of gravity, p-median, and k-means are utilized to determine optimal depot locations, essential for overcoming the operational range and charging limits of EVs. This approach aids in developing a logistics network that is both operationally efficient and environmentally sustainable. A significant portion of the study focuses on vehicle routing within the two-echelon location-routing model. It considers critical factors like the limited range of EVs, multi-compartment transport requirements for bulk liquids, and specific customer delivery windows. The model integrates these elements to optimize vehicle routes for efficiency and regulatory compliance, illustrating its practical use through the two-echelon multi-compartment electric vehicle routing problem with time windows (2E-MCEVRPTW). The practical application of this research is demonstrated in a case study of Heineken Netherlands, highlighting the logistical complexities of transitioning to an EV fleet for beer distribution. The study examines operational challenges such as vehicle range and product diversity management, proving the viability and effectiveness of the proposed models. Results discussion reveals that strategic network design using the center of gravity method significantly enhances kilometer savings and operational efficiencies. However, the benefits diminish with additional hubs, indicating an optimal hub number exists. While transshipment costs pose a significant challenge, outweighing the kilometer savings, potential cost reductions through increased reefer capacity and reduced transshipment times are identified, pointing to possible areas for improvement. The study concludes that the two-step optimization process, integrating network design and vehicle routing, effectively addresses the research question. It not only shows the potential of EVs in transforming logistics but also underscores the economic and operational challenges of adopting a two-echelon network. The findings lay a groundwork for future innovations in sustainable logistics, though they caution the need for tailored solutions across different operational contexts and suggest further research into computational strategies and customer clustering for enhanced route optimization.

The installation rate of offshore wind energy has to be quadrupled by 2030 to meet the green climate ambitions of European countries, but this growth is hindered by logistical challenges. Therefore, this study explores how two transportation and installation strategies, shuttling and feedering, affect the installation rate with the inclusion of manufacturing ports. Shuttling is where the installation vessel collects components itself at a port, and feedering is where the installation vessel remains offshore and gets supplied directly via feeder vessels. This is a novel approach as feedering and manufacturing ports are often not considered and production rates at manufacturing ports have not been considered at all. A rolling horizon simulation model is developed, which uses a Markov simulation model for weather forecasting, with a 72.92% forecast accuracy over two weeks, and a greedy algorithm for transportation and installation optimization. Results indicate that accurate initial buffer calculations, depending on the production rate at the manufacturing ports and project-dependent characteristics, can increase the installation rate significantly for either strategy. Shuttling becomes more efficient than feedering if the distance between the manufacturing ports and the offshore wind farm is too large or the size of the feeder vessels is too small. Feedering is more efficient in all other circumstances and, on average, results in a 9.2% higher installation rate and reduces project duration by 29 days compared to shuttling.