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| The aim of the project is to provide a method and a set of corresponding models for the analysis and management of Soft Reliability Problems that can be used as an integral part of the product creation process of highly innovative products, such as professional office equipment or multimedia consumer products. The project addresses an important bottleneck that prevents technically feasible products from being accepted and appreciated by customers in the market. | The aim of the project is to provide a method and a set of corresponding models for the analysis and management of Soft Reliability Problems that can be used as an integral part of the product creation process of highly innovative products, such as professional office equipment or multimedia consumer products. The project addresses an important bottleneck that prevents technically feasible products from being accepted and appreciated by customers in the market. | ||
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| | //[[http://www.srl.gatech.edu/Members/akoca|Aylin Koca]] (Business Process Design)//|| | | //[[http://www.srl.gatech.edu/Members/akoca|Aylin Koca]] (Business Process Design)//|| | ||
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| - | | {{funk.jpg?100}} | Especially in strongly innovative product creation processes, developers are often uncertain about the way their products will be eventually used in the field. If a problem occurs, answers to questions like //"Was this function broken from the beginning, or did it stop working after it was used for a longer period already?" (first use vs. extended use)// can help to effectively track existing problems. Therefore, we develop a new system-architecture that helps to define observation (i.e., logging) requirements on an abstract level. Part of this architecture is a graphical specification language, which helps to define platform- and system-independent observation applications. See [[DPUIS]] for further information about this part of the project. | | + | | {{funk.jpg?100}} | Especially in strongly innovative product creation processes, developers are often uncertain about the way their products will be eventually used in the field. If a problem occurs, answers to questions like //"Was this function broken from the beginning, or did it stop working after it was used for a longer period already?" (first use vs. extended use)// can help to effectively track existing problems. Therefore, we develop a new system-architecture that helps to define observation (i.e., logging) requirements on an abstract level. Part of this architecture is a graphical specification language, which helps to define platform- and system-independent observation applications. See [[Model-driven Design of Self-observing Products]] for further information about this part of the project. | |
| | //[[http://www.mathias-funk.com/|Mathias Funk]] (Electronic Systems)//|| | | //[[http://www.mathias-funk.com/|Mathias Funk]] (Electronic Systems)//|| | ||
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| | {{rozinat.jpg?100}} | [[http://www.processmining.org|Process mining]] is a field of research, which attempts to extract process-related information from log data. A number of real-life applications have shown that, this way, process analysis can be based on objective information (that is, how the process //is actually// executed as opposed to what //people think// how it is executed). In this part of the project, we focus on how process mining could be applied to gain insight into both the field //feedback process// (e.g., based on help desk and repair shop data) and the //usage process// of a product (based on log data recorded by the product itself). Knowledge about the feedback process helps to improve the //efficiency of information flows// in the product creation process. Knowledge about //user profiles// helps to effectively ensure the reliability of (future) products as, e.g., test sequences can be defined based on realistic usage scenarios. | | | {{rozinat.jpg?100}} | [[http://www.processmining.org|Process mining]] is a field of research, which attempts to extract process-related information from log data. A number of real-life applications have shown that, this way, process analysis can be based on objective information (that is, how the process //is actually// executed as opposed to what //people think// how it is executed). In this part of the project, we focus on how process mining could be applied to gain insight into both the field //feedback process// (e.g., based on help desk and repair shop data) and the //usage process// of a product (based on log data recorded by the product itself). Knowledge about the feedback process helps to improve the //efficiency of information flows// in the product creation process. Knowledge about //user profiles// helps to effectively ensure the reliability of (future) products as, e.g., test sequences can be defined based on realistic usage scenarios. | | ||
| | //[[http://www.tue.nl/staff/a.rozinat|Anne Rozinat]] (Information Systems)//|| | | //[[http://www.tue.nl/staff/a.rozinat|Anne Rozinat]] (Information Systems)//|| | ||