The goal of this study was to shed light on the impact of recipe variation on logistics cost and identify key cost drivers. A comprehensive analysis of inbound logistics data for a meal-kit delivery company was conducted to dive into recipe specific logistics metrics, controllable factors that predict cost, and optimize routing of loads. Leveraging innovative technical tools such as data visualization, principal components analysis, k-means clustering, k-nearest neighbors, artificial neural networks, and simulated annealing, the study examined a meal-kit company’s data to accomplish these objectives. The results of the study revealed that recipe selection can impact logistics cost, with certain recipes leading to higher logistics spend compared to others. Moreover, the study identified other strong predictors of cost, underscoring the importance of careful routing decisions. By shedding light on these key insights, this study offers valuable guidance to stakeholders seeking to optimize logistics cost in the meal-kit delivery industry.