Term Project

Production Analytics Tool

Grade weight: $10\%$ Due: $27/04/2024$ Team Members: $3$

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. Therefore, 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; specific production

entry at a particular timestamp. The columns 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:

CO$2$ 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 $\text{'},$ making it easy to

read and parse. as shown in the following sample:

Qatar University

College of Engineering, TIEE

Computer Programming

GENG$106$

Term Project $$ Spring $2024$

$2$

When the value of a certain field of a particular record is needed, it can be obtained by splitting the record

based on the $,$ character.

The goal is:

$1.$Read the data from the CSV file and store it in : a data structure $($eg: a list $),$ in other CSV file $($store

the result of analysis in a new CSV file$).$

$2.$Calculate and display the following statistics for a particular parameter:

$-$ average value: what is the average number of defects found per product type.

$-$ Examine how production entries are distributed across different environmental impact

categories by presenting the percentage of entries in each category $($low$,$ medium, high$).$

$-$ Count the number of machines with maintenance status as "Done" and "Not done".

$-$ Use of matplotlib : Visualize the trend of production counts over time

$3.$Identify and display the value for a user$-$specified parameter.

$4.$Implement both while and for loops to navigate and analyze the data effectively.

$5.$Allow the user to select different parameters for analysis and choose when to exit the program.

$1.$ Requirements

You are required to develop the Production Analytics Application. Your application should perform the

following operations and must be menu driven.

$$ Add a new reading: the user to enter data for a new production entry, including timestamp,

machine ID$,$ product type, product name, production count, defect count, environmental impact,

maintenance status, and maintenance date. The function should validate the input and append

the data to the production data file, separating the fields with the $\text{'},\text{'}$ character. The function should

accept only machine$\_$ID defined in a predefined list $($We check if the machine ID provided in the

new reading exists in the existing dataset. If it doesn't exist, we display an error message. If it

exists, we add the new reading to the CSV$).$

$$ Update reading: accepts the Machine ID and the Timestamp for which the p