Closing the loop of products and materials in Product Service Systems (PSS) can be approached by designers in several ways. One promising strategy is to invoke a greater sense of ownership of the products and materials that are used within a PSS. To develop and evaluate a design tool in the context of PSS, our case study focused on a bicycle sharing service. The central question was whether and how designers can be supported with a design tool, based on psychological ownership, to involve users in closing the loop activities. We developed a PSS design tool based on psychological ownership literature and implemented it in a range of design iterations. This resulted in ten design proposals and two implemented design interventions. To evaluate the design tool, 42 project members were interviewed about their design process. The design interventions were evaluated through site visits, an interview with the bicycle repairer responsible, and nine users of the bicycle service. We conclude that a psychological ownership-based design tool shows potential to contribute to closing the resource loop by allowing end users and service provider of PSS to collaborate on repair and maintenance activities. Our evaluation resulted in suggestions for revising the psychological ownership design tool, including adding ‘Giving Feedback’ to the list of affordances, prioritizing ‘Enabling’ and ‘Simplification’ over others and recognize a reciprocal relationship between service provider and service user when closing the loop activities.
Closing the loop of products and materials in Product Service Systems (PSS) can be approached by designers in several ways. One promising strategy is to invoke a greater sense of ownership of the products and materials that are used within a PSS. To develop and evaluate a design tool in the context of PSS, our case study focused on a bicycle sharing service. The central question was whether and how designers can be supported with a design tool, based on psychological ownership, to involve users in closing the loop activities. We developed a PSS design tool based on psychological ownership literature and implemented it in a range of design iterations. This resulted in ten design proposals and two implemented design interventions. To evaluate the design tool, 42 project members were interviewed about their design process. The design interventions were evaluated through site visits, an interview with the bicycle repairer responsible, and nine users of the bicycle service. We conclude that a psychological ownership-based design tool shows potential to contribute to closing the resource loop by allowing end users and service provider of PSS to collaborate on repair and maintenance activities. Our evaluation resulted in suggestions for revising the psychological ownership design tool, including adding ‘Giving Feedback’ to the list of affordances, prioritizing ‘Enabling’ and ‘Simplification’ over others and recognize a reciprocal relationship between service provider and service user when closing the loop activities.
The present study aims at understanding and addressing certain challenges of automation of 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.The approach followed in the current study, consists of three steps. First, the identification of the feasibility and most promising procedures for automated composite repair by analysis of current state-of-the-art methods as prescribed by OEMs and standards. Processes which are tedious or even contain health risks may qualify for automation. Second, a comparison of curing alternatives for composite repairs is made, by means of the creation and testing of specimen using different curing strategies. Lastly, a benchmark test of human made composite repairs is used in order to set a reference baseline for automation quality. This benchmark can be then applied to define a lower limit and prevent over-optimization. The employed methodology includes data collection, analysis, modelling and experiments.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
‘Dieren in de dijk’ aims to address the issue of animal burrows in earthen levees, which compromise the integrity of flood protection systems in low-lying areas. Earthen levees attract animals that dig tunnels and cause damages, yet there is limited scientific knowledge on the extent of the problem and effective approaches to mitigate the risk. Recent experimental research has demonstrated the severe impact of animal burrows on levee safety, raising concerns among levee management authorities. The consortium's ambition is to provide levee managers with validated action perspectives for managing animal burrows, transitioning from a reactive to a proactive risk-based management approach. The objectives of the project include improving failure probability estimation in levee sections with animal burrows and enhancing risk mitigation capacity. This involves understanding animal behavior and failure processes, reviewing existing and testing new deterrence, detection, and monitoring approaches, and offering action perspectives for levee managers. Results will be integrated into an open-access wiki-platform for guidance of professionals and in education of the next generation. The project's methodology involves focus groups to review the state-of-the-art and set the scene for subsequent steps, fact-finding fieldwork to develop and evaluate risk reduction measures, modeling failure processes, and processing diverse quantitative and qualitative data. Progress workshops and collaboration with stakeholders will ensure relevant and supported solutions. By addressing the knowledge gaps and providing practical guidance, the project aims to enable levee managers to effectively manage animal burrows in levees, both during routine maintenance and high-water emergencies. With the increasing frequency of high river discharges and storm surges due to climate change, early detection and repair of animal burrows become even more crucial. The project's outcomes will contribute to a long-term vision of proactive risk-based management for levees, safeguarding the Netherlands and Belgium against flood risks.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
