Abstract:The modern industrial food supply system faces many major environmental and social sustainability challenges. Regional food systems, in which consumers prefer geographically proximate food producers, offer a response to these challenges. However, the costs associated with distributing food from many small-scale producers to consumers have been a major barrier to long-term regional food system success. Logistics best practices from conventional supply chains have the potential to improve the efficiency and effectiveness of regional food supply chains (RFSCs). This paper provides a structured and in-depth review of the existing literature on RFSC logistics, including recommended and implemented best practices. The purpose of the review is to provide RFSC researchers and practitioners with convenient access to valuable information and knowledge derived from years of experimentation and research. This information will help to inform practitioners' implementation decisions and to increase researchers' awareness of the existing work on RFSC logistics, the unmet needs of practitioners, and topics that have not been fully explored, yielding insights into potential future directions for RFSC research. The overarching aim of the paper is to facilitate improvements in RFSC logistics, thereby improving regional food system viability.
PurposeThe purpose of this research is to gain a better understanding of how a crowd-shipping platform can achieve a critical mass of senders and carrier crowd members to yield network effects that are necessary for the platform to grow and thrive. Specifically, this research studies the participation decisions of both senders and carriers over time and the impacts of the resulting feedback loop on platform growth and performance.Design/methodology/approachAn agent-based model is developed and used to study dynamic behavior and network effects within a simulated crowd-shipping platform. The model allows both carriers and senders to be represented as autonomous, heterogeneous and adaptive agents, whose decisions to participate in the platform impact the participation of other agents over time. Survey data inform the logic governing agent decisions and behaviors.FindingsThe feedback loop created by individual sender and carrier agents' participation decisions generates complex and dynamic network effects that are observable at the platform level. Experimental results demonstrate the importance of having sufficient crowd carriers available when the platform is initially launched, as well as ensuring that sender and carrier participation remains balanced as the platform grows over time.Research limitations/implicationsThe model successfully demonstrates the power of agent-based modeling (ABM) in analyzing network effects in crowd-shipping systems. However, the model has not yet been fully validated with data from a real-world crowd-shipping platform. Furthermore, the model's geographic scope is limited to a single census tract. Platform behavior will likely differ across geographic regions, with varying demographics and sender/carrier density.Practical implicationsThe modeling approach can be used to provide the manager of a volunteer-based crowd-shipping program for food rescue with insights on how to achieve a critical mass of participants, with an appropriate balance between the number of restaurant food donation delivery requests and the number of crowd-shippers available and willing to make those deliveries.Social implicationsThis research can help a crowd-shipping platform for urban food rescue to grow and become self-sustainable, thereby serving more food-insecure people.Originality/valueThe model represents both senders and the carrier crowd as autonomous, heterogeneous and adaptive agents, such that network effects resulting from their interactions can emerge and be observed over time. The model was designed to study a volunteer crowd-shipping platform for food rescue, with participant motivations driven by personal values and social factors, rather than monetary incentives.
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