Abstract
Demand for and production of organic fresh food play an increasing role worldwide. As a result, a growing amount of fresh fruits and vegetables has to be transported from predominantly rural production regions to customers mostly located in urban ones. Specific handling and storage conditions need to be respected along the entire supply chain to maintain high quality and product value. To support organic food logistics operations, this work investigates benefits of facilitating real-time product data along delivery and storage processes. By the development of a simulation-based decision support system, sustainable deliveries of organic food from farms to retail stores are investigated. Generic keeping quality models are integrated to observe impacts of varying storage temperatures on food quality and losses over time. Computational experiments study a regional supply chain of organic strawberries in Lower Austria and Vienna. Results indicate that the consideration of shelf life data in supply chain decisions allow one to reduce food losses and further enables shifting surplus inventory to alternative distribution channels.
Highlights
Rising world population, ongoing urbanization and a shift to more fresh diets challenge the management of food supply chains (Parfitt et al 2010; United Nations 2014)
The FEFO stock rotation scheme in combination with the FEFO demand priority of customers performs best regarding the amount of reference strawberries sold and lost
As in such a setting the lowest quality is always prioritized, goods are stored for shorter periods, i.e., less quality is lost and more goods can be sold
Summary
Rising world population, ongoing urbanization and a shift to more fresh diets challenge the management of food supply chains (Parfitt et al 2010; United Nations 2014). Increased consumers’ demand for fresh, safe and high quality foods and technological developments raise the importance of shelf life prediction (Giménez et al 2012) In this context, the term ‘keeping quality’ describes the period, e.g., in days, during which a perishable product maintains its required quality attributes, i.e., the time until a product becomes unacceptable (Tijskens and Polderdijk 1996). As a result, making supply chain decisions based on real-time data sets enables competitive advantages for food logistics providers (Zhong et al 2016) By incorporating such quality data and coordinating various decisions along the entire supply chain, sustainable operations can be supported. The contribution of this work is twofold: (i) it presents a DSS to investigate a regional supply chain of perishables under the consideration of shelf life data, and (ii) provides managerial implications on how to incorporate such data to improve both food losses and operations.
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