Nissan Operational Costs Prediction Application Documentation

Overview

This application predicts the operational costs based on various input features. It is designed to help Nissan in forecasting and managing their operational expenses efficiently.

Streamlit App Link

Purpose

The primary purpose of this application is to provide a tool for predicting operational costs. By entering various operational parameters, users can obtain an estimate of the future costs, enabling better planning and decision-making.

Instructions for Use

1. Enter Input Values:

   • Monthly Production Volume: Enter the total units produced per month.
   • Number of Employees: Enter the total number of employees involved in production.
   • Average Equipment Downtime (hours): Enter the average hours of equipment downtime per month.
   • Raw Material Cost: Enter the total cost of raw materials per month.
   • Machine Maintenance Cost: Enter the total cost of machine maintenance per month.
   • Logistics Cost: Enter the total logistics cost per month.
   • Energy Consumption Cost: Enter the total energy consumption cost per month.

2. Predict Operational Costs:

   • After entering all the values, click the 'Predict' button.
   • The application will process the input values and display the predicted operational cost.

Features

Monthly Production Volume

• Description: Total units produced per month.
• Input Type: Numeric (integer).
• Example: 1000.

Number of Employees

• Description: Total number of employees involved in production.
• Input Type: Numeric (integer).
• Example: 50.

Average Equipment Downtime (hours)

• Description: Average hours of equipment downtime per month.
• Input Type: Numeric (float).
• Example: 4.0.

Raw Material Cost

• Description: Total cost of raw materials per month.
• Input Type: Numeric (float).
• Example: 5.0.

Machine Maintenance Cost

• Description: Total cost of machine maintenance per month.
• Input Type: Numeric (float).
• Example: 5.0.

Logistics Cost

• Description: Total logistics cost per month.
• Input Type: Numeric (float).
• Example: 6.0.

Energy Consumption Cost

• Description: Total energy consumption cost per month.
• Input Type: Numeric (float).
• Example: 8.0.

Example Usage

1. Enter the following values:

   • Monthly Production Volume: 1000
   • Number of Employees: 50
   • Average Equipment Downtime (hours): 4.0
   • Raw Material Cost: 5.0
   • Machine Maintenance Cost: 5.0
   • Logistics Cost: 6.0
   • Energy Consumption Cost: 8.0

2. Click 'Predict':

   • The application will display the predicted operational cost, e.g., 3567.63.

How Nissan Can Use This Application

Forecast Operational Costs

By inputting the current production data, Nissan can predict future operational costs and plan budgets accordingly.

Optimize Resource Allocation

Understanding the impact of various factors such as equipment downtime and raw material costs helps in optimizing resource allocation.

Improve Efficiency

By regularly monitoring the predicted costs and comparing them with actual expenses, Nissan can identify areas for improvement and take corrective actions.

Strategic Decision Making

The application provides valuable insights that assist in making strategic decisions regarding production volumes, staffing, and maintenance schedules.

Troubleshooting

Common Issues and Solutions

• Error in Prediction: Ensure that all input values are entered correctly and within realistic ranges.
• Application Not Loading: Check the internet connection and try refreshing the browser.

Deployment

Streamlit Sharing

1. Push your project to GitHub: Ensure your project is available in a GitHub repository.

   git init
   git add .
   git commit -m "Initial commit"
   git branch -M main
   git remote add origin https://github.com/yourusername/yourrepository.git
   git push -u origin main

2. Create an account on Streamlit Sharing: Go to Streamlit Sharing and sign up or log in.

3. Deploy your app: Follow the instructions on Streamlit Sharing to deploy your app from your GitHub repository.

Contact Information

For further assistance, please contact:

Name: Gopal Bagaswar
Email: gopalbagaswar19@gmail.com
LinkedIn: Gopal Bagaswar