Increasing operational efficiency through automation and analytics :

In manufacturing units, different areas are producing the products and each process area will be under different stages of product development. Using computing interface such as Raspberry Pi we will be able to monitor and change the state of each process area. The analytics and Visualization engine will receive stored data from database and it will generate dashboards and reports for the administration.

Controller switches will control the power supply of each machine. These will be controlled by computing interface such as Raspberry Pi which is intern connected to the database. The Analytics and Visualization engine will receive stored data from database and generate dashboards and reports for the administration. MCS helps in
1. Improve Efficiency
2. Tap in to hidden Capacity
3. Reduce downtime
4. Speedy changeovers

Predictive model: Predicting the milage of a vehicle:

This case study uses a dataset containing the milage and horsepower of different vehecles to predict what will be the milage of a given vehicle if the horsepower is known. Here we are training a model to predict continuous numbers, this task is sometimes referred to as a regression task. We will train the model by showing it many examples of inputs along with the correct output. This is referred to as supervised learning

TensorFlow is used to train the model.The training error value below 0.02 is achieved. Model prediction line is plotted against the original data in the picture shown. User can also input horsepoer and check what would be the milage of the vehicle. The case study can be considered as a classification and regression problem.