Predicting A Garment Factory's Productivity

Authors: Justin Wong, Longfei Guan, Anirudh Duggal

Adapted with permission from STAT 301 Project by: Justin Wong, Kevin Yu, Zhuoran (Serena) Feng, Fiona Chang

Summary

The trillion-dollar garment industry is largely fueled by the production and performance of employees that work in manufacturing companies as a labor-intensive, low-skilled industry. As the industry is driven by ever-changing consumer demands and fashion trends, the need for manual processes is inevitable.

Through statistical inference, we seek to dig deeper into the relationship between important attributes of the garment manufacturing process and its employees’ productivity in the following question: What factors affect the productivity of a garment factory?

To answer this question, we performed data analysis to search for the most optimized model. Using forward selection and LASSO, we compare different models and determine which factors are the best in explaining relationships between the factors and the actual productivity of the garment factory. Both of the models produced a fairly poor adjusted 𝑅² values of 0.17 and 0.169 when testing the model with the testing data. Additionally, neither of the selected models were significantly better than the full model according to the corresponding F-tests. Lastly, we discuss the implications of our results, the limitations of the project, and propose future questions that can be asked based on our project.

Running the Analysis

Using Docker

1. Install Docker
2. Clone the repository

   git clone https://github.com/DSCI-310/dsci-310-group-01.git
   

3. Use the terminal/command line to navigate to the root directory of the project

   cd dsci-310-group-01
   

4. Obtain the Docker Image from Dockerhub
   • Use the terminal/command line to pull the image

   docker pull jwong086/dsci-310-group-01:latest 
   

   • Use the terminal/command line to find the IMAGE ID

   docker images jwong086/dsci-310-group-01 
   

   • Copy the IMAGE ID in the third column and use the terminal/command the tag the image

   docker tag <IMAGE ID> dsci-310-group-01-env
   

5. Run the following to set up the environment:

   docker run --rm -p 8787:8787 -e PASSWORD=x  -v /$(pwd):/home/rstudio/project dsci-310-group-01-env
   

6. In a browser navigate to localhost:8787
7. Use the following credentials to sign in:
   • USERNAME = rstudio
   • PASSWORD = x
8. Access the analysis

• Navigate to the /project folder using:

cd project

• In the Rstudio terminal run the following to produce the html report:

make report

• To reset the repo to a clean state use:

make clean

• To obtain the files needed to make report from clean slate use:

make all

Using Makefile

1. Install the listed dependencies below
2. Clone the repository

   git clone https://github.com/DSCI-310/dsci-310-group-01.git
   

3. Use the terminal/command line to navigate to the root directory of the project

   cd dsci-310-group-01
   

4. Run the following in the terminal/command line to produce the html report:

   make report
   

5. To reset the repo to a clean state use:

   make clean
   

6. To obtain the files needed to make report from clean slate use:

   make all
   

Viewing the Analysis

The corresponding analysis files are found here.

Dependencies

Using R version 4.2.2

R libraries

• remotes:2.4.2
• tidyverse:2.0.0
• broom:1.0.3
• ggally:2.1.2
• leaps:3.1
• glmnet:4.1-6
• testthat:3.1.6
• bookdown:0.33
• docopt:0.7.1
• here:1.0.1
• ggplotify:0.1.0
• rmarkdown:2.21
• devtools:2.4.5
• grp1ProjectPackage:1.0.0

License

Licensed under the MIT License and

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Permission From Past Teammates

Permission from past teammates was obtained. Additional evidence available on request.