Garment productivit 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 includes important attributes of the productivity of the employees which had been collected manually and also been validated by the industry experts.
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.
- Regression: Predication of the productivity performance of the working teams in their factories.
- What are the major challenges the company is facing?
- What are the key performance indicators (KPIs) or metrics that the company aims to improve?
- What does success look like for the business in this area?
predicting if the workers can achieve the set target Identifying the top factors influencing the productivity level of employees. How to increase employee productivity and predicting if we have a low or medium or hight productivity Reduce interruptions and minimize downtime. Optimize resource utilization to reduce costs. Improve team and resourceplanning. Reduce production time to deliver orders faster.
Achieve a model precision of 80% or higher. Reduce false positives Forecast team productivity using available data (headcount, SMV, incentives, etc.). Identify inefficiencies to reduce downtime. Optimize schedules to maximize productivity while minimizing costs. Segment teams by performance to adjust resources and incentives. Anticipate production interruptions and propose corrective actions. Forecast order volumes to improve planning.