This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.
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This paper aims to develop a tool for measuring the clients’ maturity in smart maintenance supply networks. The assessment tool is developed and validated for corporate facilities management organizations using case studies and expert consultation. Based on application of the assessment tool in five cases, conclusions are presented about the levels of maturity found and the strengths and limitations of the assessment tool itself. Also, implications for further research are proposed.
DOCUMENT
This paper aims to develop a tool for measuring the clients’ maturity in smart maintenance supply networks. The assessment tool is developed and validated for corporate facilities management organizations using case studies and expert consultation. Based on application of the assessment tool in five cases, conclusions are presented about the levels of maturity found and the strengths and limitations of the assessment tool itself. Also, implications for further research are proposed.
MULTIFILE
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.
Het is een tijds- en kostenintensief proces om de conditie van assets in de publieke ruimte te monitoren. Nieuwe technologie in de vorm van 3D LiDAR scanning biedt nieuwe mogelijkheden voor conditiemonitoring. Het doel van deze KIEM-aanvraag is (i) om de hardware geschikt te maken voor frequente en goedkope opnames in de stedelijke omgeving, (ii) de analysetechnieken van de geproduceerde datasets verder te ontwikkelen en (iii) een geannoteerde dataset gefocust op asset management te produceren. Dit zorgt ervoor dat publieke en MKB-partijen slimmere, snellere en volledigere onderhoudsbeslissingen kunnen nemen. Het consortium van Fietskoerier.nl, Sonarski, Gemeente Amsterdam en de Hogeschool van Amsterdam heeft elkaar gevonden in de vraag: “Hoe kan (publieke) LiDAR data bijdragen aan SMART Asset Management?” Dit project bevat een unieke combinatie van twee technologieën die op dit moment in ontwikkeling zijn (i) sensor data gedreven conditiemonitoring en (ii) point cloud algoritmes op LiDAR data. Fietskoerier.nl heeft de resources om op een duurzame manier de stad in kaart te brengen. Sonarski heeft een oplossing voor het uitvoeren van de 3D scans en Gemeente Amsterdam is een belangrijke kennispartner en heeft groot scala aan assets in de publieke ruimte. De deelnemers van dit project zien deze aanvraag als een eerste stap en hebben de intentie om te groeien tot een groter consortium welke de gehele keten van onderhoud omvat.
With the help of sensors that made data collection and processing possible, many products around us have become “smarter”. The situation that our car, refrigerator, or umbrella communicating with us and each other is no longer a future scenario; it is increasingly a shared reality. There are good examples of such connectedness such as lifestyle monitoring of elderly persons or waste management in a smart city. Yet, many other smart products are designed just for the sake of embedding a chip in something without thinking through what kind of value they add everyday life. In other words, the design of these systems have mainly been driven by technology until now and little studies have been carried out on how the design of such systems helps citizens to improve or maintain the quality of their individual and collective lives. The CREATE-IT research center creates new solutions and methodologies in “digital design” that contribute to the quality of life of citizens. Correspondingly, this proposal focuses on one type of digital design—smart products—and investigate the concept of empowerment in relation to the design of smart products. In particular, the proposal aims to develop a model with its supplementary tools and methods for designing such products better. By following a research-through-design methodology, the proposal intends to offer a critical understanding on designing smart products. Along with its theoretical contribution, the proposal will also aid the students of ICT and design, and professionals such as designers and engineers to create smart products that will empower people and the industry to develop products grounded in a clear user experience and business model.
