The paper reports on the application of a combination of Operational Research (OR) approaches to a real-world case of re-designing the strategy of a department management team, in an aircraft manufacturing organization in Brazil. We combine approaches from 'Soft' OR Problem Structuring Methods in a multi-methodological framework arguing that this helps to discuss, develop and implement a new departmental strategy. The proposed framework uses elements of Soft Systems Methodology; Strategic Options Development and Analysis to understand and structure the situation; Value-Focused Thinking to define the means and objectives; and ValueFocused Brainstorming to highlight the potential solutions. We conceptualize a four-phased systemic framework linking elements of the above four 'Soft' OR approaches and apply it to a real-world case in a department where a new team leader was appointed with the specific brief to improve the departmental strategy and its performance in general. Findings suggest that combining the approaches into a framework encourages trust and participation from stakeholders which, in practice, is translated into an improved strategy for the organization. A reflection on the multi-methodological approach is proposed. Conclusions and points for further research are suggested.
Designed experiments are powerful methodologies for gaining insights into the behaviour of complex simulation models. In recent years, many new designs have been created to address the large number of factors and complex response surfaces that often arise in simulation studies, but handling discrete-valued or qualitative factors remains problematic. We propose a framework for generating a design, of specified size, that is nearly orthogonal and nearly balanced for any mix of factor types (categorical, numerical discrete, and numerical continuous) and mix of factor levels. These new designs allow decision makers structured methods for trade-off analyses in situations that are not necessarily amenable to other methods for choosing alternatives, such as simulation optimization or ranking and selection approaches. These new designs also compare well to existing approaches for constructing custom designs for smaller experiments, and may also be of interest for exploring computer models in domains where fewer factors are involved.
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