Mass-customization challenges the one-size-fits-all assumption of mass production, allowing customers to specify the options that best fit their requirements when choosing a product or a service. In business process management, to achieve mass-customization, providers offer to their customers the opportunity to customize the way in which a process will be enacted. We focus on monitoring as a specific customization aspect. We propose a multidimensional classification of modeling patterns for customized monitoring infrastructures. Patterns enable the provider to offer a set of customizable options to customers and design a monitoring infrastructure that fits the preferences specified by customers on such options. An example in the online advertising industry demonstrates how our framework can improve the services currently offered by providers.
This survey is about recognizing patterns in the way Small and Medium Enterprises (SMEs) organize their procurement activities. The scope of the survey is limited to the key commodities of the SME.A key commodity is defined as the purchased product or service group which is essential for realizing the value proposition for the customers of the SME.Prior outcome of our research indicated the existence of four procurement oriented patterns in SMEs. 4 Procurement Oriented Patterns where part of the study: Pattern 1 Focal company: ICT turn-key designerValue proposition of the focal company: ICT Design and assembly of offices on a high quality level at a reasonable price. Operational excellence: standardization in commodities, low transaction costs internally and externallyPurchased key commodity: Standard ICT software and hardwarePattern 2 Focal company: Horse shoes manufacturerValue proposition of the focal company: Standard horse shoes assortment at reasonable prices in a competitive environmentPurchased key commodity: Standard quality iron, reliable deliveryPattern 3 Focal company: IT innovation driven companyValue proposition of the focal company: Developing innovative software made applicable for practical usage in devices at a reasonable pricePurchased key commodity: Delivering applicable solutions on the bases of regular soft- and hardware, to enable the companies’ innovative software function in practicePattern 4 Focal company: designer and manufacturer of trailersValue proposition of the focal company: Designing and manufacturing trailers tailor made for specific requirements of customersPurchased key commodity: Designing and manufacturing axles which align to the specific trailer wishes of the customer of the focal companyFINDINGS Pattern recognitionAbout 50 % of the respondents recognized the four presented patterns from own experience and/or read literature. Respondents also suggested pattern variants. It is concluded that this Delphi study strengthens the view that these patterns exist in SMEs. Further research may include further empirical testing of these patterns and their variants. Perceived strengths or weaknesses. Respondents mentioned a wide variety of strengths and weaknesses of the patterns. No clear conclusions can be drawn from this data. Adequacy of the pattern descriptions. One of the outcomes of this Delphi study is an improved conceptual framework for describing procurement activity patterns. This framework can be used for collecting SME data in future research, for example by modifying the existing survey questions which are used in the WIM research program to describe SME procurement activities. The improved model includes more variables and values than the initial model. Thus future research may lead to more detailed patterns descriptions. Missing patterns and pattern variantsApart from the suggested pattern variants, respondents do not miss patterns which are quite different from the four patterns suggested by the research team. Methodological remarksThe Delphi study method did not allow for fast feedback on panel member contributions and fast group think processes. For the future it is advised to consider other methods in similar cases, for example the World Cafe method.
Wereldwijd onderzoek: Hoe gebruiken nieuwsmedia social media? Jongeren lezen geen krant meer, ze kijken op hun smartphone die ze altijd bij de hand hebben. Binnen het lectoraat social media en reputatiemanagement van NHL hogeschool te Leeuwarden heeft een groep internationale studenten in 12 landen onderzoek gedaan. Hierbij hebben ze meer dan 150 social media sites bestudeerd van nieuws media. De resultaten maken deel uit van een internationaal onderzoek van NHL Hogeschool en Haaga Helia University. De onderzoeksvraag was: Wat speelt zich af in de nieuwsmedia? Persbureaus kunnen het overzicht gebruiken om hun social media te optimaliseren. En voor ieder die journalistiek een warm hart toedraagt is het interessante informatie over de nieuwsmedia in een overgangssituatie (2nd edition)
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
