High-tech systems are produced in a joint effort of some hundred teams of highly specialized engineers employed by the system integrator and dozens of suppliers. Because of the sheer size and complexity of the supply chain, it is impossible to oversee the entire operation. Instead, the process is somehow orchestrated by sharing information and coordinating the production planning between upstream teams that produce a subcomponent of the system and downstream teams that need it. Each team decides on its own operations according to this bilateral coordination and information sharing. From all bilateral coordination and decisions together thus emerges the responsiveness, resilience, and cost effectiveness of the overall supply chain. Three complementary work packages together aim to improve this global supply chain performance via concrete improvements to the local planning and coordination process: 1. Coordinated production planning in high-tech supply chains aims to improve the production planning and forecast sharing capabilities of individual actors. Planning models in WP1 are local. To ensure improvement of the global supply chain we complement it with 2. An agent-based model for high-tech supply chains, which develops an accurate and detailed descriptive model of the entire supply chain for understanding and explaining the connection between local decisions and global performance. 3. Emergent behavior and resilience in stochastic processing networks: Practitioners prefer easy-to-understand analytical rules for production planning and capacity allocation that perform well on supply chain level. Using probabilistic scaling techniques, this WP develops such rules based on an abstraction of the detailed supply chain models.

We study the transition to sustainable ecology-based agriculture by using mixed-cropping systems that combine multiple crop species on a single field. Our team elucidates the ecological processes that make mixed cropping systems sustainably productive and we identify which socio-economic and societal or institutional factor need to be resolved to overcome the lock-in in current conventional farming systems. To allow a broad spectrum of farmers, consumers and stakeholders to reach transition goals, we embrace variation in transition paths. We explicitly compare how existing international value chains require adjustments as well as how new short and local value chains can emerge.

Research summary ProRail acts in a multi-stakeholder environment, with various downstream stakeholders, e.g., governmental bodies and train operators, and various upstream stakeholders, e.g., currently four contractors and their suppliers. The various stakeholders have conflicting interests: both maintenance activities and trains compete for limited time on the tracks. Our research addresses this challenge for ProRail as capacity and asset manager by investigating in three work packages (WPs): 1. The conflicting and dynamic interplay of the performance requirements of rail system operation and maintenance and the interactions of ProRail with its clients and contractors in determining key performance indicators (KPIs). Using semi-structured interviews, multiple case studies and action research we intend to provide relational value strategies (e.g., role sharing, cross-project alliances) for the interaction of ProRail with its this chain into recent (cooperative and noncooperative) game-theoretical models and analyses to improve our understanding of the impact of acknowledging the individual decision makers and their incentives. Finally, the impact on design decisions and contract conditions is studied and described. Contracting insights from WP2, focusing on an asset life cycle plan, and WP3, focusing on parts supply, will be combined and condensed. Utilisation summary Our results are relevant for a number of ProRail?s departments as well as stakeholders. WP1 will be relevant for the procurement, contract management, and asset management of ProRail. The relational value strategies and the KPIs will serve as input for setting up contractual arrangements with the maintenance contractors, optimizing the maintenance strategies, and supporting ProRail in accountability issues. A close cooperation between researchers and practitioners and the co-development of relational value strategies and KPIs will ensure the utilisation and implementation of the results. The results of WP2 are relevant in the context of SAM, ProRail?s System Asset Management. The PhD student in WP2 should therefore be trained by the SAM-academy that is being developed at ProRail and we expect the student to be part of a project team that implements SAM for one system. Being part of such a team guarantees information exchange between ProRail and the student, leading to useful results that can be implemented as soon as they are there (showing the proof of concept). WP3 will identify the improvement potential in the spare parts supply chain of ProRail, taking the current multitude of arrangements between ProRail and upstream stakeholders into account. Improvements, proposals for fair divisions of improvement gains, and incentive schemes to attain these gains will be provided. Additionally, insights will be given regarding new designs and setting up contracts. Besides the abovementioned efforts, the students will work at ProRail?s premises for one or two days a week and they will all attend the master class that ProRail plans to give. In most of our research activities, we will cooperate closely with people at ProRail to understand the setting and to get data that we need. This also enables us to directly feedback the results of our research. We further plan regular meetings, both with people involved in one WP and with all people involved in the project. In both cases, participants come from universities, ProRail, and supporting organisations. Near the end of our project, we expect the PhD students to pair with a ProRail representative in order to get the results written down in ProRail-style documents that can be used in everyday practice. We envision one such document on KPIs and their interface with the asset life cycle plan, with input from the PhD students in WPs 1 and 2, and one such document on defining contracts with upstream stakeholders, with input from the PhD students in WPs 2 and 3. We further intend to involve Bachelor and Master students throughout the project, who can also be located at one of ProRail?s stakeholders (supporting organisations). The activities that are specifically aimed at utilisation are bundled as WP4, so that progress in this respect can be monitored clearly. This research will also impact the curriculum of civil, mechanical and industrial engineers, e.g., in the Master?s course Infrastructure Management of UT-CME and the courses that UT-OPM is developing.stakeholders and, based on that, the development of KPIs of the railway system itself. 2. The development of an asset life cycle plan that specifies for all components on a track what type of maintenance (e.g., corrective and condition based) to perform, and when to do that, such that the defined KPIs (WP1) are achieved. Using historical data and physical models of degradation, it can be determined which (critical) failure modes to expect how often. Using quantitative models that combine this (technical) knowledge with life cycle cost data we can determine the optimal asset life cycle plan for a complete track, balancing time for operations and maintenance. We will further investigate how changes in the KPIs that ProRail agrees with its downstream stakeholders influence the asset life cycle plan and thus the life cycle costs. This links WP2 to WP1. 3. Efficiency, competition, and cooperation in ProRail?s upstream supply chain. We thoroughly analyze the current supply chain to quantify coordination potential. Subsequently, in order to obtain an overall optimal supply chain performance, we will incorporate the specific structure of

English: This living lab aims to support the creation, development and implementation of next generation concepts for sustainable healthcare logistics, with special attention for last mile solutions. Dutch healthcare providers are on the verge of a transition towards (more) sustainable business models, spurred by e.g., increasing healthcare costs, ongoing budget cuts, tight labor market conditions and increasing ecological awareness. Consequently, healthcare providers need to improve and innovate their business model and underlying logistics concept(s). Simultaneously, many cities are struggling with congestion in traffic, air quality and liveability in general. This calls for Last Mile Logistics (LML) concepts that can address challenges like effective and efficient resource planning, scheduling and utilization and, particularly, sustainability goals. LML can reduce environmental and social impact by decreasing emissions, congestion and pollution through effectively consolidating in-flows of goods and providing innovative solutions for care, wellbeing and related services. The research and initiatives in the living lab will address the following challenges: reducing the ecological footprint, reducing (healthcare-related) costs, improving service quality, decreasing loneliness of frail citizens and improving the livability of urban areas (reducing congestion and emissions). Given the scarcity and fragmentation of knowledge on healthcare logistics in organizations the living lab will also act as a learning community for (future) healthcare- and logistics professionals, thereby supporting the development of human capital. By working closely with related stakeholders and using a transdisciplinary research approach it is ensured that the developed knowledge and solutions deliver a contribution to societal challenges and have sound business potential.

Efficiency and reliability in (city) logistics and supply chain planning is key to remain competitive and improve sustainability. The objective of this project is to research, build and test (in practice) advanced decision support systems for both multi-channel (retail, detail and e-tail) and multi-company collaboration. The starting point of our multi-channel and multi-company decision support systems involves connectivity, allowing data to be exchanged, shared and connected. Once connectivity is in place, intelligence needs to be built in order to make use of these comprehensive data sources. An information sharing platform will be developed which encapsulates information about the different processes, external factors (e.g. weather, vacation, etc.) and uses that information to provide effective decisions support services to its users. Specifically, adequate, timely and accurate information, based on various data sources is required. This could be (real‐time) information aggregated from multiple sources, including (cooperative) devices, such as transportation infrastructure sensors, but of course also the various information systems from the logistics service providers, shippers etc. The decision support systems to be developed will focus on collaboration. In the different distribution channels companies can benefit a lot from cooperation. Think of using the Retail network to position trailers close to cities, from which Detail and E-tail distribution could be handled. As such, the Retail network brings value for Detail and E-tail distribution. In Detail and E-tail networks, large consolidated volumes need to be transported, for which the Retail network could be used.