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Business Impact

  • 25%

    Reduction in food wastage

  • $4.5

    Million savings per month

  • 25%

    Improvement in model accuracy

Customer Key Facts

  • Location : North America
  • Industry : Restaurants

Problem Context

The customer’s legacy statistical-based modeling approach forecasts demand in terms of dollars earned, transactions made, item quantities sold and ingredients consumed at 15-minute intervals. The forecast has to be made several weeks in advance to cater to the business requirements.

Challenges

 

  • Minute grain (15-min interval) of forecasting
  • Scaling to 2,000 stores for 120 products  
  • Seasonal variations in data
  • Scarce and missing data points
Challenges

Technologies Used

AWS Lambda
Amazon S3
Amazon Athena
Amazon EMR
Amazon Aurora
Amazon Sagemaker
TensorFlow
Jenkins

Machine Learning Forecasting Model for a Quick Service Restaurant

The customer wanted a demand forecasting solution with a dashboard to receive updates on a periodic basis to have better control on demand fluctuations in order to have an efficient supply chain, which in turn would reduce costs, food wastage, and optimize available warehouse space.

Solution

Quantiphi built a forecasting engine to predict sales at a daily and 15-minute level. The solution encompasses an intuitive UI that not only visualizes inventory management, but also diagnoses and finds preventive measures to help curb wastage, set alerts, and control variables for forecasting. In turn, optimizing warehouse management and assisting in manpower scheduling. This helps to improve the estimates of the suggested quantities of ingredients to be ordered from the distribution centers. The solution was scaled to more than 1,119 stores.

Result

  • Quantiphi’s demand forecasting models were able to perform better than the customer’s legacy system by about 25 percent in terms of error reduction
  • Sensitivity analysis capability shown by the models based on the previous occurrences of the external factors affecting the sales

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