Industrial Symbiosis Networks (ISNs) consist of firms that exchange residual materials and energy locally, in order to gain economic, environmental and/or social advantages. In practice, ISNs regularly fail when partners leave and the recovery of residual streams ends. Regarding the current societal need for a shift towards sustainability, it is undesirable that ISNs should fail. Failures of ISNs may be caused by actor behaviour that leads to unanticipated economic losses. In this paper, we explore the effect of these behaviours on ISN robustness by using an agent-based model (ABM). The constructed model is based on insights from both literature and participatory modelling in three real-world cases. It simulates the implementation of synergies for local waste exchange and compost production. The Theory of Planned Behaviour (TPB) was used to model agent behaviour in time-dependent bilateral negotiations and synergy evaluation processes. We explored model behaviour with and without TPB logic across a range of possible TPB input variables. The simulation results show how the modelled planned behaviour affects the cash flow outcomes of the social agents and the robustness of the network. The study contributes to the theoretical development of industrial symbiosis research by providing a quantitative model of all ISN implementation stages, in which various behavioural patterns of entrepreneurs are included. It also contributes to practice by offering insights on how network dynamics and robustness outcomes are not only related to context and ISN design, but also to actor behaviour.
In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.
As a logical consequence of the advancements in automation of production of composite aircraft structures, more attention is paid to the automation of maintenance. Current repair procedures involve manual labour and exposure to harmful particles (such as dust, vapours) while final quality and evidencing depends largely on the skills of repair technicians. The current study aims to automate composite repair procedures for the aviation sector with the objective to counter these disadvantages. Main research question: ‘What is required for a robot system to assist in composite repairs’This research is part of a larger, SIA-RAAK funded project FIXAR, running in three Universities of Applied Sciences in the Netherlands and a cluster of knowledge institutions and industry partners.In the repair process of aircraft structures, repair by means of scarf or lap joints is common practice. First paint layers must be removed to inspect the area and prepare for further repair. Then damaged material is removed. Material is replaced and the repair is finished and painted. Tasks within the repair process that are considered dull or harmful are sanding and material removal. Current investigation focussed on automation of these tasks.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.
Our country contains a very dense and challenging transport and mobility system. National research agendas and roadmaps of multiple sectors such as HTSM, Logistics and Agri&food, promote vehicle automation as a means to increase transport safety and efficiency. SMEs applying vehicle automation require compliance to application/sector specific standards and legislation. A key aspect is the safety of the automated vehicle within its design domain, to be proven by manufacturers and assessed by authorities. The various standards and procedures show many similarities but also lead to significant differences in application experience and available safety related solutions. For example: Industrial AGVs (Automated Guided Vehicles) have been around for many years, while autonomous road vehicles are only found in limited testing environments and pilots. Companies are confronted with an increasing need to cover multiple application environments, such restricted areas and public roads, leading to complex technical choices and parallel certification/homologation procedures. SafeCLAI addresses this challenge by developing a framework for a generic safety layer in the control of autonomous vehicles that can be re-used in different applications across sectors. This is done by extensive consolidation and application of cross-sectoral knowledge and experience – including analysis of related standards and procedures. The framework promises shorter development times and enables more efficient assessment procedures. SafeCLAI will focus on low-speed applications since they are most wanted and technically best feasible. Nevertheless, higher speed aspects will be considered to allow for future extension. SafeCLAI will practically validate (parts) of the foreseen safety layer and publish the foreseen framework as a baseline for future R&D, allowing coverage of broader design domains. SafeCLAI will disseminate the results in the Dutch arena of autonomous vehicle development and application, and also integrate the project learnings into educational modules.
Logistics companies struggle to keep their supply chain cost-effective, reliable and sustainable, due to changing demand, increasing competition and growing service requirements. To remain competitive, processes must be efficient with low costs. Of the entire supply chain, the first and last mile logistics may be the most difficult aspect due to low volumes, high waiting and shipping times and complex schedules. These inefficiencies account for up to 40% of total transport costs. Connected Automated Transport (CAT) is a technological development that allows for safer, more efficient and cleaner transport, especially for the first- and last-mile. The Connected Automated Driving Roadmap (ERTRAC) states that CAT can revolutionize the way fleets operate. The CATALYST Project (NWO) already shows the advantages of CAT. SAVED builds on several projects and transforms the challenges and solutions that were identified on a strategic level to a tactical and operational (company) level. Despite the high-tech readiness of CAT, commercial acceptance is lacking due to issues regarding profitable integration into existing logistics processes and infrastructures. In-depth research on automated hub-to-hub freight transport is needed, focusing on ideal vehicle characteristics, logistic control of the vehicles (planning, routing, positioning, battery management), control modes (central, decentralized, hybrid), communication modes (vehicle-to-vehicle, vehicle-to-infrastructure) and automation of loading and unloading, followed by the translation of this knowledge into valid business models. Therefore, SAVED focuses on the following question: “How can automated and collaborative hub-to-hub transport be designed, and what is the impact in terms of People, Planet and Profit (PPP) on the logistics value chain of industrial estates of different sizes, layouts and different traffic situations (mixed/unmixed infrastructure)?“ SAVED results in knowledge of the applicability of CAT and the impact on the logistics value chain of various industrial estates, illustrated by two case studies.
