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.

The sector Ground, Road, and Water Construction is faced with a very dated infrastructure and a vast replacement task in the coming 20 years. This implies that there is now a great opportunity to make smart investments so that we get a better functioning and more sustainable infrastructure. Ideally, infrastructure is almost always available and can be adapted when the needs of the users change. A high availability can be realized by acting quickly when a piece of infrastructure breaks down. This will be studied from a service logistics perspective. Adaptiveness can be obtained by including multiple scenarios in the design of new infrastructure and building the initial infrastructure such that future extensions and changes can be made relatively easily and against reasonable costs. This will be studied from a robust design angle. Adaptiveness is about being able to use infrastructure for more years by being able to adapt the infrastructure to the new needs. This avoids that you have to build new infrastructure at that time. Hence, adaptiveness already contributes to the sustainability of infrastructure. In this project, we will also study other forms of sustainability: taking energy usage into account when designing new infrastructure, re-use of materials at the end of the lifetime, re-use of infrastructure elements (e.g., bridges) at other places when replaced by elements with other features (e.g., a larger capacity), standardization of infrastructure elements so that the same spares can be used at many places, and so on.

The focus of the proposed research is on advanced capital goods such as lithography systems, trains, and baggage handling systems. Users of such systems (manufacturers of integrated circuits, railways, airports) are highly dependent on these systems and thus require high system availabilities, i.e., high percentages of time that the systems are up and running. Simultaneously, they want low total costs for the initial buy of a system, the maintenance and service logistics during the long usage period and the disposal costs at the end of the life cycle. These costs are denoted as the Total Cost of Ownership (TCO). The proposed research is aimed at logistics control innovations that simultaneously increase system availability and decrease TCO. The focus will be on industries where the maintenance of the installed systems in the field is executed by the Original Equipment Manufacturer (e.g., ASML) or a large maintenance organization (e.g., Nedtrain). In the first two subprojects, we develop new logistics control concepts for the service supply chain, which supplies spare parts within tight time constraints: (i) a system-focussed inventory control concept for the whole chain consisting of one or more central depots and many local warehouses; (ii) a concept for the interaction between the service supply chain and the repair loops for repairable parts. In the third subproject, we develop quantitative models that show the effect of component reliabilities and redundancy on downtime, service logistics costs and TCO. In that way, we get quantitative support for (re)design decisions for capital goods.

Proper maintenance is crucial for the reliability, availability, safety, and cost-effectiveness of high tech systems. At the same time, maintenance is very expensive, requiring specialized personnel and equipment. In the Netherlands alone, maintenance of capital goods costs €300 million euros annually, whereas malfunctions due to poor maintenance cost another €80 million, not even mentioning the significant societal burden of defects: accidents, injuries, and malfunctioning of public infrastructure. The holy grail in maintenance is predictive maintenance (PM): by exploiting recent advances in the Industrial Internet-of-Things, sensor technology, data analytics, and optimization, we can predict failures better and perform just-in-time-maintenance. By repairing or renewing the system just before it fails, maintenance cost are lowered, while the up-time increases. Despite significant effort in industry and academia, realizing just-in-time maintenance remains challenging. It requires very accurate predictions of the system health and failure times —mispredictions may lead to more, rather than fewer failures— as well as operational ways to turn these predictions into effective and usable maintenance decisions. These challenges encompass multiple phases of the PM work flow, and therefore demand a holistic multidisciplinary approach. With a truly multidisciplinary consortium, we bring together the required expertise to enforce scientific breakthroughs: we will develop novel combinations of model-driven and data-driven failure prediction techniques, equipping (black box) data analyses with pivotal domain knowledge; multi-scale optimization techniques enabling optimization across different levels of the PM workflow; and integral approach to health predictions and maintenance optimization, which also consider human and organizational factors. In this way, the PrimaVera project will not only lead to better asset performance and lower cost. We will also lay the foundations for autonomous maintenance, where assets continuously monitor themselves and initiate maintenance decisions themselves.

Emergencies such as the breakdown of an MRI-scanner or a domestic fire demand a timely response. This means that the resources required for addressing such incidents (spare parts and fire trucks, respectively) need to be stored in relative proximity of potential incidents and dispatched on short notice. This requires a network of resources in several storage locations. Owners of such Emergency Resource Networks (ERNs) face three issues: (i) Where should resources be stored, and how many resources need to be available at each location? (ii) How should resources be dispatched in response to an emergency? (iii) Can the performance of the system be improved by proactive relocation of resources? Answering (i)-(iii) is essential to achieve the required timely response against low costs. We construct the first general model for ERNs, unifying and extending results from disjoint application areas. In addition, we make the following contributions: 1. The rate at which emergencies occur varies over time, as a result of daily and seasonal effects. We investigate how to deal with this variability. 2. Questions (i)-(iii) have only been addressed sequentially: determine the storage locations, then the resource levels, finally the dispatching rules. This decoupling leads to relatively poor solutions. In this project, we provide a better integration of the decision-making process. 3. We implement a proof of concept planning tool, and test its efficiency at Philips Healthcare Service Parts Supply Chain, and Amsterdam/Amstelland Fire Brigade, using historical data and in close collaboration with these external partners.