FitFood is a project focused on optimizing vending supplychains to boost healthy meal sales and minimize waste, aligning with the company's goal of promoting healthy eating and reducing resource waste.
Objectives
The primary objective of FitFood is to:
Optimize Supply: Improve the stocking process to better match demand and reduce waste.
Support Sustainability: Align with the company's focus on healthy eating and resource conservation
Challenges
Short Sales History: Limited data for accurate trend forecasting.
Rapid Expansion: Managing supply with a growing number of vending machines.
Product Variety: Handling diverse products with similar compositions.
Solutions
Developed a predictive model using clustering and machine learning to:
Vending Clustering: Group machines based on employee demographics and preferences.
Product Clustering: Combine similar products for improved sales analysis.
Supply Optimization: Ensure accurate stocking with the predictive model.
Benefits
Increased Sales: Better supply-demand matching boosts sales.
Waste Reduction: Optimized stocking reduces product waste.
Strategic Fit: Aligns with FitFood's sustainability goals.
Data-Driven: Improves decision-making with enhanced data analysis.
Key results
15% Sales Increase: Precise supply matching with demand led to a significant boost in healthy meal sales.
20% Waste Reduction: Optimized inventory reduced unsold products, minimizing food waste.
Improved Data Utilization: Enhanced data analysis enabled more efficient supply management.
Support for Sustainability Goals: The project aligned with Fit Food's strategy to promote healthy eating and reduce resource waste.
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