Problem Description

In the manufacturing industry, data analysis plays a crucial role in optimizing production processes, improving efficiency, and making informed decisions. Your task is to develop a data analysis and visualization tool for a manufacturing company. This tool will read data from CSV files containing production data, perform various analyses, and generate visualizations to aid management in decisionmaking processes.

The challenge faced by manufacturing companies is effectively managing and analyzing the vast amount of data generated during the production process. The dataset encompasses various fields including timestamps, machine IDs, product types, product names, production counts, defect counts, environmental impacts, maintenance statuses, and maintenance dates. Managing and making sense of this data manually can be time$-$consuming and error$-$prone. The objective of the project is to develop a tool that automates data management tasks and provides advanced analytics capabilities to help manufacturing companies make informed decisions and optimize their production processes. Therefore, the objective of this project is to develop a Production Analytics Tool that addresses this data management challenge more effectively. This toll will be Python$-$based.

The production data is stored in CSV file, with each row representing a record: a specific production entry at a particular timestamp. The columns $($i$.$e$.$ the fields$)$ provide detailed information regarding various aspects of the production process, including timestamps, machine IDs, product types, product names, production counts, defect counts, environmental impacts, maintenance statuses, and maintenance dates.

Detailed description of each field:

Timestamp: Records the date and time when the production data was collected $($string$).$

Machine ID: A unique identifier for each machine involved in the production process.

Product Name: Specifies the specific name or model of the product being manufactured, typically related to automotive parts such as Air Filter, Fuel Pump, Water Pump, Pistons, etc.

Product Type: Specifies the type or category of product being manufactured.

Production Count: Represents the number of units produced during each production cycle.

Defect Count: Indicates the number of defective units identified during the production process.

Environmental Impact: Metrics representing the environmental impact of production activities: $CO2$ emissions.

Maintenance Status: Indicates whether maintenance tasks have been performed on the machines, typically denoted as "Done" or "Not Done".

Maintenance Date: Records the date when maintenance tasks were performed on the machines.

This data format in CSV files, the fields in each record are delimited with a $\text{'},\text{'}$ making it easy to read and parse. This is shown in the following sample: