Managing food security through food waste and loss: small data to big data
| dc.contributor.author | Irani, Zahir | |
| dc.contributor.author | Sharif, Amir M. | |
| dc.contributor.author | Lee, Habin | |
| dc.contributor.author | Aktas, Emel | |
| dc.contributor.author | Topaloğlu, Zeynep | |
| dc.contributor.author | van't Wout, Tamara | |
| dc.contributor.author | Huda, Samsul | |
| dc.date.accessioned | 2018-01-03T14:53:24Z | |
| dc.date.available | 2018-01-03T14:53:24Z | |
| dc.date.issued | 2017-11-03 | |
| dc.description.abstract | 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. | en_UK |
| dc.identifier.citation | Irani Z, Sharif A, Lee H, et. al., (2018) Managing food security through food waste and loss: Small data to big data. Computers & Operations Research, Volume 98, October 2018, pp. 367-383 | en_UK |
| dc.identifier.issn | 0305-0548 | |
| dc.identifier.uri | https://doi.org/10.1016/j.cor.2017.10.007 | |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/12841 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Elsevier | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Food security | en_UK |
| dc.subject | Qatar | en_UK |
| dc.subject | Big data framework | en_UK |
| dc.subject | Food waste | en_UK |
| dc.subject | Food loss | en_UK |
| dc.subject | Fuzzy cognitive map (FCM) | en_UK |
| dc.subject | Interrelationships | en_UK |
| dc.subject | Design science | en_UK |
| dc.title | Managing food security through food waste and loss: small data to big data | en_UK |
| dc.type | Article | en_UK |