What is Community Operational Research?

Community Operational Research (Community OR) has been an explicit sub-domain of OR for more than 30 years. In this paper, we tackle the controversial issue of how it can be differentiated from other forms of OR. While it has been persuasively argued that Community OR cannot be defined by its clients, practitioners or methods, we argue that the common concern of all Community OR practice is the meaningful engagement of communities, whatever form that may take – and the legitimacy of different forms of engagement may be open to debate. We then move on to discuss four other controversies that have implications for the future development of Community OR and its relationship with its parent discipline: the desire for Community OR to be more explicitly political; claims that it should be grounded in the theory, methodology and practice of systems thinking; the similarities and differences between the UK and US traditions; and the extent to which Community OR offers an enhanced understanding of practice that could be useful to OR more generally. Our positions on these controversies all follow from our identification of ‘meaningful engagement’ as a central feature of Community OR

Managing food security through food waste and loss: Small data to big data

This paper provides a management perspective of organisational factors that contributes to the reduction of food waste through the application of design science principles to explore causal relationships between food distribution (organisational) and consumption (societal) factors. Qualitative data were collected with an organisational perspective from commercial food consumers along with large-scale food importers, distributors, and retailers. Cause-effect models are built and “what-if” simulations are conducted through the development and application of a Fuzzy Cognitive Map (FCM) approaches to elucidate dynamic interrelationships. The simulation models developed provide a practical insight into existing and emergent food losses scenarios, suggesting the need for big data sets to allow for generalizable findings to be extrapolated from a more detailed quantitative exercise. This research offers itself as evidence to support policy makers in the development of policies that facilitate interventions to reduce food losses. It also contributes to the literature through sustaining, impacting and potentially improving levels of food security, underpinned by empirically constructed policy models that identify potential behavioural changes. It is the extension of these simulation models set against a backdrop of a proposed big data framework for food security, where this study sets avenues for future research for others to design and construct big data research in food supply chains. This research has therefore sought to provide policymakers with a means to evaluate new and existing policies, whilst also offering a practical basis through which food chains can be made more resilient through the consideration of management practices and policy decisions