COGNIZANT FORAGE AI JOB SIMULATION

Problem Statement

Gala Groceries approached Cognizant to help them with a supply chain issue. Groceries are highly perishable items. If you overstock, you are wasting money on excessive storage and waste, but if you understock, then you risk losing customers. They want to know how to better stock the items that they sell.

Can we accurately predict the stock levels of products, based on sales data and sensor data, 
on an hourly basis in order to more intelligently procure products from our suppliers.

Set Up

First create a virtual environment to avoid conflicts between existing libraries.

python3 -m venv myenv

For MacOS:

source myenv/bin/activate

For Windows:

myenv\Scripts\activate

Upgrade pip & install required packages

python3 -m pip install --upgrade pip
pip install -r requirements.txt

Exploratory Data Analysis

In eda.ipynb, Data Cleaning, Visualization, Statistical Analysis can be seen.

Data Modelling

• This data model diagram shows 3 tables:
  • sales = sales data
  • sensor_storage_temperature = IoT data from the temperature sensors in the storage facility for the products
  • sensor_stock_levels = estimated stock levels of products based on IoT sensors

These tables are merged by the linked columns indicated by the arrows.

Predictive Modelling

In modeling.ipynb, Keras's RandomForestRegressor is used for predictive modeling. In model.py, TensorFlow library is used to create small neural network which reduces the MAE from 0.24 to 0.22.

Reference