- Travel & Hospitality
Optimizing restaurant inventory using demand forecasting for Relish Works
Overview
Maximizing restaurant profitability by optimizing ingredient ordering
A leading food distributor sought OneSix’s expertise to create a data-driven platform that could support restaurants in managing ingredient inventory more efficiently. While many restaurants relied on manual processes and intuition to determine restocking needs, the client wanted to introduce a solution that would allow for optimal ordering strategies to balance the risks of missed demand and food wastage.
Our Solution
Building an ML platform for dynamic ingredient forecasting and optimization
OneSix developed a machine learning-powered tool to generate ingredient-level forecasts based on diners’ orders, point-of-sale data, and menu items. The platform used these forecasts to predict future inventory needs, providing optimal restocking recommendations.
By factoring in each ingredient’s shelf life, seasonality, demand patterns, and role in various menu items, the platform automated order generation, allowing restaurant operators to submit optimized orders with just two clicks. Additionally, the tool aggregated ingredient and dish-level trends, offering insights into seasonal shifts and evolving consumer preferences to further guide menu planning.
Results
Enhanced profitability through data-driven inventory management
The machine learning solution enabled restaurants to make informed restocking decisions, minimizing waste while ensuring ingredient availability for high-margin items. By automating inventory management, the platform significantly reduced manual efforts and improved restaurant profitability. The aggregated trend insights also empowered operators to adapt to seasonal preferences and make data-backed decisions in menu engineering, providing ongoing value to restaurant businesses.
“OneSix partnered with us to design a new, machine learning-powered tool for restaurant operators to manage their business. They guided us through the process of framing the problem and determining what was possible with state-of-the-art ML/AI. The solution they developed transformed a key operational challenge into an automated solution that drives new value for restaurants.”
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