The mission policy approach to the sustainable blue economy has identified as critical the ability to anticipate the emergence of a wide range of feasible innovations as they enter the transactional environment of organizations in the marine and maritime sector. This article contributes to that growing effort by harnessing the wisdom of the crowd and presents more than 60 crowdsourced, time-specific innovation forecasts expected to impact maritime, shipbuilding, ports, offshore wind, and ocean infrastructure. Data were collected in 2020 by the EU-funded Interreg VB PERISCOPE Project, a North Sea Region initiative to catalyze transregional innovation. The results can be used strategically to develop collaborative, transregional planning and policy for innovation based on data reflecting public expectations for the future. Years from now, this article can also act as a snapshot of public expectations at the onset of the decade.
This report provides an assessment on the prospects for offshore energy hubs. Four use cases have been developed and evaluated by respondents in a survey instrument for their forecasted time horizon to implementation and their business potential as opportunities for the maritime and offshore
industries. The report is produced by the PERISCOPE Group at Aarhus University for the PERISCOPE network.
This paper considers the problem of determining the optimal vessel fleet to support maintenance operations at an offshore wind farm. We propose a two-stage stochastic programming (SP) model of the problem where the first stage decisions are what vessels to charter. The second stage decisions are how to support maintenance tasks using the chartered vessels from the first stage, given uncertainty in weather conditions and the occurrence of failures. To solve the resulting SP model we perform an ad-hoc Dantzig–Wolfe decomposition where, unlike standard decomposition approaches for SP models, parts of the second stage problem remain in the master problem. The decomposed model is then solved as a matheuristic by apriori generating a subset of the possible extreme points from the Dantzig–Wolfe subproblems. A computational study in three parts is presented. First, we verify the underlying mathematical model by comparing results to leading work from the literature. Then, results from in-sample and out-of-sample stability tests are presented to verify that the matheuristic gives stable results. Finally, we exemplify how the model can help offshore wind farm operators and vessel developers improve their decision making processes.
The value chains for offshore oil and gas and offshore wind are both basically driven by the demand for energy. This is heavily dependent on a number of factors including the price of various energy sources and the policy making of the states which influence legislation, indirect subsidies and direct investments. At the center of both value chains are the energy companies. The energy companies have a number of suppliers and sub suppliers which provide a range of equipment and services to the offshore operations. The supply industry is characterized by horizontal cooperation (between suppliers at the same level) and vertical cooperation (between suppliers in different layers). Finally the suppliers and the energy companies are supported by a number of companies which are usually not considered as part of the offshore sector but are important none the less. These companies provide a number of services including includes legal advice, financing, insurance etc. The two value chains have a number of activities in common. Both include (1) a tender and concession phase where the energy company obtains the right to explore and produce energy from the authorities. (2) An exploration phase where the physical location is examined and the installation is planned. (3) An installation phase where the equipment is produced and transported to the site where it is installed. (4) An operation phase where the energy is produced or the energy source is extracted and (5) a decommissioning phase where the field is abandoned. Most suppliers are positioned in several links of one or both value chains, at various levels (direct supplier, sub supplier, 3rd tier supplier etc.) and providing a variety of services. A supplier can move to new positions within the value chain. The increased servitization is a good example. Traditional manufacturers are often 2nd or 3rd tier suppliers in the installation phase. But by providing after sales services these companies also become direct suppliers to the energy company in the operations phase. Finally a supplier can have different positions in different geographical markets. A supplier can thus be a direct (1st tier) supplier in one market but needs to go through a local contractor (as a 2nd tier supplier) in another market – even if the provided service is exactly the same in both cases.