Productivity Prediction of Garment Employees
Donated on 8/2/2020
This dataset includes important attributes of the garment manufacturing process and the productivity of the employees which had been collected manually and also been validated by the industry experts.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Business
Associated Tasks
Classification, Regression
Feature Type
Integer, Real
# Instances
1197
# Features
14
Dataset Information
Additional Information
The Garment Industry is one of the key examples of the industrial globalization of this modern era. It is a highly labour-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories. This dataset can be used for regression purpose by predicting the productivity range (0-1) or for classification purpose by transforming the productivity range (0-1) into different classes.
Has Missing Values?
Yes
Introductory Paper
By Abdullah Al Imran, Md Shamsur Rahim, Tanvir Ahmed. 2021
Published in International Journal of Business Intelligence and Data Mining
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| date | Feature | Date | no | ||
| quarter | Feature | Categorical | no | ||
| department | Feature | Categorical | no | ||
| day | Feature | Categorical | no | ||
| team | Feature | Integer | no | ||
| targeted_productivity | Feature | Continuous | no | ||
| smv | Feature | Continuous | no | ||
| wip | Feature | Integer | yes | ||
| over_time | Feature | Integer | no | ||
| incentive | Feature | Integer | BDT | no |
0 to 10 of 15
Additional Variable Information
01 date : Date in MM-DD-YYYY 02 day : Day of the Week 03 quarter : A portion of the month. A month was divided into four quarters 04 department : Associated department with the instance 05 team_no : Associated team number with the instance 06 no_of_workers : Number of workers in each team 07 no_of_style_change : Number of changes in the style of a particular product 08 targeted_productivity : Targeted productivity set by the Authority for each team for each day. 09 smv : Standard Minute Value, it is the allocated time for a task 10 wip : Work in progress. Includes the number of unfinished items for products 11 over_time : Represents the amount of overtime by each team in minutes 12 incentive : Represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action. 13 idle_time : The amount of time when the production was interrupted due to several reasons 14 idle_men : The number of workers who were idle due to production interruption 15 actual_productivity : The actual % of productivity that was delivered by the workers. It ranges from 0-1.
Dataset Files
| File | Size |
|---|---|
| garments_worker_productivity.csv | 92.7 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset productivity_prediction_of_garment_employees = fetch_ucirepo(id=597) # data (as pandas dataframes) X = productivity_prediction_of_garment_employees.data.features y = productivity_prediction_of_garment_employees.data.targets # metadata print(productivity_prediction_of_garment_employees.metadata) # variable information print(productivity_prediction_of_garment_employees.variables)
Productivity Prediction of Garment Employees [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C51S6D.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.