Food service and food logistics operations face persistent challenges related to fragmented data, volatile demand, and limited decision-support tooling. Menurithm is a modular decision-support platform designed to address these challenges by integrating data ingestion, validation, planning logic, and human-centered interfaces into a cohesive system architecture. This paper presents the design, structure, and implementation considerations of Menurithm, emphasizing scalability, extensibility, and real-world deployability across diverse food operation contexts. The architecture is intended to serve as a reusable reference model for applied decision intelligence systems in food operations and logistics. This work is positioned as a design-oriented and architectural contribution rather than an empirical performance evaluation. The objective is to articulate a reusable system architecture grounded in decision-support and software design principles, informed by operational realities in food service environments. As such, the contribution lies in architectural synthesis and design rationale rather than quantitative optimization or controlled experimentation.
Brian Mbeere (Wed,) studied this question.