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Abstract. While research supports the use of graphic data representations in interfaces and control systems, work in this area has focused on relatively small systems with a limited number of variables. This paper describes an approach to designing a visual application for a semiconductor manufacturing plant. This is a complex, large-scale system requiring a structured design methodology. First, using cognitive work analysis techniques an Abstraction Decomposition Space (ADS) of the system is generated. Second, as with ecological interface design, we demonstrate how this ADS can inform the display design. The complexity and scale of the system has required us to make adjustments to both of these frameworks. The resulting display requires multiple views of the system, information hiding and user interaction. Taking a wider set of analyses onboard, we present a design rationale supporting the explicit representation of hierarchies, the compatibility of views and the use of contextual navigation.

Abstract. The introduction of energy efficiency as a new goal into already complex production plans is a difficult challenge. Decision support systems can help with this problem but these systems are often resisted by end users who ultimately bear the responsibility for production outputs. This paper describes the design of a decision support tool that aims to increase the interpretability of decision support outputs. The concept of 'grey box' optimisation is introduced, where aspects of the optimisation engine are communicated to, and configurable by, the end user. A multi-objective optimisation algorithm is combined with an interactive visualisation to improve system observability and increase trust.Keywords: visualisation, optimisation, energy efficiency, manufacturing. IntroductionEnergy efficient manufacturing is a key research challenge for both industry and academia. Systemic energy waste is closely tied to strategic production decisions and therefore poses a complex operations-research problem. An example of this involves switching idle machine into a low-power mode. While this strategy is an effective way to save energy, it is not a straightforward task in many industrial environments. Energy savings are often subservient to production targets and decisions about changing machine states involve weighing up a complex set of goals and constraints. These include hard metrics such as production capacity, predicted inventory and product priorities as well as soft constraints such as technician skill level, engineering requests and machine recovery risks. Operations managers currently apply human expertise to cope with this complexity. In high product mix factories this problem can become very challenging and even before energy-saving is considered. Optimisation algorithms can be applied to reduce the problem space associated with this decision and to highlight energy saving opportunities; however an algorithmic approach is challenged by soft constraints and unpredictable changes in goals. In addition operations managers tend to be wary of decision support tools due to their perceived brittleness and lack of transparency [1].