Yes / We present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed. / Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project.

This study examines factors that influence the intent to use and actual use of decision support software (DSS) technology by emergency management officials to facilitate disaster response management. The unified theory of acceptance and use of technology popularized by scholars from the field of information sciences (IS) for the private sector is adapted and extended to examine technology use in the public sector, specifically by emergency managers. An e-survey was sent to 1, 452 city and county emergency management officials from FEMA region VI and complete responses obtained from 194 were analyzed. Findings suggest that social influence is the strongest predictor of intent to use DSS technology by emergency managers, unlike private sector studies where performance expectancy was the strongest predictor. Additionally, effort expectancy, collaboration, social vulnerability, professionalism, performance expectancy, and gender explained 40 percent of their intent to use DSS technology. Factors explaining actual use of technology were intent to use technology, having an in house GIS specialist, and age of the emergency manager. This research successfully closes the gap in IS and disaster literature by being the first to focus on factors influencing technology use by emergency managers for decision making in disaster response. It underscores the importance of collaboration not only for post-disaster activities but also as a precursor to better disaster preparedness planning that calls for information sharing and technology acceptance and adoption across partnering jurisdictions.

More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness.

A goal-oriented, inverse decision-based design method to find satisficing solutions for multiple football helmet components that all work together to achieve a set of conflicting goals is presented. The efficacy of the method is illustrated with the design of the top region of an American football helmet. The prototype helmet was first constructed and tested with a twin-wire drop tower to study the different components effect on the system response. The inverse design method is used to design the foam liner to dissipate the maximum impact energy, and then the composite shell is designed to reduce the weight. The Concept Exploration Framework and the compromise Decision Support Problem are used to find satisficing solutions to the system-level performance goals under uncertainty. The proposed goal-oriented, inverse decision-based design method is generic and will be used to design additional components, the complete helmet, and ultimately helmets for other sports.

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