Substantial amounts of food products are wasted in multiple stages of the supply chain. Produce is lost at the field, during delivery as well as in stores and customers’ homes. Consequently, optimizing logistics processes in the food industry does not only enable one to decrease costs, it potentially results in reduced food waste.
Jedermann et al. (2014) provide an interesting overview on how intelligent food logistics can help to decrease food waste. The focus is set on inventory strategies and the integration of sensing systems to continuously monitor food quality. Data can be used to improve the management of inventory to reduce spoilage. Among others, the authors list the number of hubs and decisions points in the supply chain as a major strategic question, which requires further investigation.
While consolidation can help to decrease shipping costs, additional wait times and detours to reach the consolidation point potentially increase delivery times. Furthermore, loading and unloading processes often results in sub-optimal temperatures impacting food quality. In contrast, consolidation can even improve overall food quality by allowing decision makers to re-consider inventory decisions.
As shown, multiple highly interesting and complex research questions in this field need closer investigation. As food quality is stochastic and impacted by a wide range of influencing factors within food logistics operations, simulation and optimization methods can provide a strong tool to investigate different problem settings to decrease food waste. For instance, within e-grocery operations, this enables one to jointly investigate ordering picking and routing decisions to facilitate sustainable operations.
Reference:
– Fikar, C (2018) A decision support system to investigate food losses in e-grocery deliveries. Computers & Industrial Engineering 117, 282-290. doi.org/10.1016/j.cie.2018.02.014
– Jedermann, R., Nicometo, M., Uysal, I., Lang, W. (2014) Reducing food losses by intelligent food logistics. Philos. Trans. A. Math. Phys. Eng. Sci. 372, 20130302. doi:10.1098/rsta.2013.0302
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Decision Support for Time-Critical Logistics Operations 
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