Big Problem Perspectives Working Group
Smart Systems Thrust
Smart systems (including here manufacturing, grid and smart supply chain along with cloud computing) require going beyond traditional disciplinary and sectoral boundaries. Their emergence is driven by intensifying global competition, economic uncertainty and constraints, along with exponential growth in information technology. The following is useful background on the example domains.
Cloud computing services, loosely defined, provide ubiquitous, virtual on-demand internet/network-based access to storage, servers, software and applications. As such, they significantly reduce capital expenditure and the need for in-house expertise and increasing flexibility in terms of media, work location and collaboration. But, although cloud computing is growing rapidly, there are lingering concerns over migration paths, participation and choice, security, implied interoperability and portability across varying global infrastructure and regulatory frameworks, as well as related investment planning with changing technologies.
Smart manufacturing (SM)
SM entails plant and enterprise-wide data sharing and standardized processes that bridges current silos with the intent to support rapid/agile decision-making and communication. Integration is enabled across not only machines and manufacturing cells but also across the supply/value chain. With increasing self-aware, self-diagnosing and optimizing machines, SM is expected to reduce costly downtime and maintenance, improved energy efficiency (particularly when coupled with smart grid) and to enable more accurate planning as well as innovation. But SM is highly complex and dynamic and must be continually reconfigured as new technology is developed and incorporated. Clear reference architectures are essential that detail key technology and functional elements in the systems and how they interact, remain interdependent, and will be impacted by change and system expansion. Full implementation of SM is currently limited by the inability of manufacturers to adapt legacy production and make optimal use of real-time actionable data. There are also potential severe consequences of disruptions to operations, damage to expensive equipment and personnel injuries. Systems must also enable use of competing vendors and globally distributed activity while ensuring consistent knowledge, data gathering and understanding across value chains. Response to local reporting requirements while recognizing varying levels of understanding, contexts and proprietary concerns is also critical. Differences in culture between manufacturing and IT developers, and the multitude of stakeholders with varying agendas are further complicating factors.
Smart Grid (SG)
SG responds to the pressing need for enhanced grid reliability, improved precision of monitoring and control, greater flexibility in energy sources and allocation and overall reduction in energy cost. But – at least in the short term – optimization across industries and companies may force behavior changes and reduce efficiency, performance and competitiveness of individual firms, particularly manufacturers. As with SM, but even more intense, are the numerous stakeholders with varying perspectives and demands.
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