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InnoCrowd, a Product Classification System for Design Decision in a Crowdsourced Product Innovation
(2021)
System engineering focuses on how to design and manage complex systems. Meanwhile, in the era of Industry 4.0 and Internet of Things (IoT), systems are getting more complex. Contributors to higher complexity include the usage of modern components (e.g. mechatronics), new manufacturing technologies (e.g. 3D Print) and new engineering product development processes, e.g. open innovation. Open innovation is enabled by IoT, where people and devices are easily connected, and it supports development of more innovative products through ideas gained from predecessors and collaborators world wide. Some researchers suggest this approach is up to three times faster and five times cheaper than conventional approaches [Gassmann, 2012], [Howe, 2008], [Kusumah, 2018]. Because open innovation is relatively new, many managers do not know how to employ it effectively in some phases of product development [Schenk, 2009], [Afuah, 2017], including requirements definition, design and engineering processes (task assignment) through quality assurance. Also, they have trouble estimating and controlling development time and cost [Nevo, 2020], [Thanh, 2015]. As a consequence, the acceptance of this new approach in the industry is limited. Research activities addressing this new approach mainly address high-level and qualitive issues. Few effective methods are available to estimate project risk and to decide whether to initiate a project.
We propose InnoCrowd, a decision support system that uses an improved method to support these tasks and make decisions about crowdsourced engineering product development.
InnoCrowd uses natural language processing and machine learning to build a knowledgebase of crowdsourced product developments. InnoCrowd presents a manager with results of similar projects to show which practices led to good results. A manager of a new project can use this guidance to employ best practices for product requirements definition, project schedule, and other aspects, thereby reducing risk and increasing chances for success.
Research Report
(2024)
We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.