With over a century of experience, Michigan-based Dow is a global leader in the innovation, creation, and distribution of specialty chemicals, advanced materials, and plastics.

As a large company with 54,000 employees worldwide, Dow has a massive collection of computers and devices which employees use. The computers are of varying operating systems, models and generations. Dow collects computer usage and performance data on the computers in order to efficiently use their resources.

Our Improving the Performance of the Corporate Computer system analyzes the data so that events such as application crashes, blue screens and application hang times can be minimized. Data analysis reveals the optimal parameters for computers so that Dow employees can determine which are best to use. Data analysis also reveals applications most susceptible to crashes so that potential workflow improvements can be made.

Using the collected data, our system uses a machine learning model that predicts the performance of a computer given a set of properties.

We developed a web application where employees from Dow can visually input computer specifications and other parameters. Then, the application runs this input through our machine learning algorithm and visually displays the result for the employee to see.

This helps Dow easily determine whether it is worth investing money to upgrade or purchase different computers or components. Employees can use the web application to determine whether they should make changes to their system and what the expected outcome would be. In the long run, this helps Dow be more efficient with costs and its employees be most productive.

Data analysis is conducted using Python in Jupyter notebooks. The machine learning algorithm is developed using Python and Microsoft Azure. The web application is developed in Python Flask.