KITCHEN & FLEET INTELLIGENCE

A predictive operations AI built to forecast demand, control production waste, and route a live delivery fleet in real time.

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Case detail
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Project challenges

Running a central kitchen at scale demands accurate demand forecasting and tight delivery control. Volatile daily orders posed major planning and costing demands.

  • Forecasting demand without overstocking.
  • Planning manpower against unpredictable volume.
  • Tracking production, consumption and wastage daily.
  • Allocating riders across live, shifting routes.
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The solution

We built one system that connects to the kitchen's existing tools. It predicts how much raw materials and staff are needed, keeps track of costs and wastage, and assigns each delivery to the right rider as orders come in.

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The result

The chain gained tighter cost control, leaner staffing, and consistently faster delivery times.

35

%

Reduction in food wastage

25

Riders tracked in real time

20

%

Faster average delivery time

Project information

Date:
Apr 2024
Client:
Moshi Kitchen Chain
Industry:
Food & Delivery
Services:
Machine Learning, Dashboard Engineering, Security Audits
Technology stack:
Python, Scikit-learn, AWS Lambda, MySQL

"We stopped guessing the next day's prep. Wastage dropped and nobody is standing around idle."

James Mathew
Operations Director
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