Enhancing Textile Cone Counting Efficiency Using Figurative Numbers and Desktop-Based Solutions: A Systems Approach

Abstract

Manual cone counting in textile factories is time-consuming and error-prone, which negatively affects inventory accuracy, shift transitions, and overall operational efficiency. This study addresses that problem by developing a desktop-based cone counting application using figurative numbers, specifically triangular numbers, to model stacking patterns and automate the counting process. The aim is to reduce human error, improve consistency, and enhance digital inventory tracking in resource-constrained manufacturing environments. The research question guiding this work was: How can figurative numbers and a desktop-based application optimize the cone counting and tracking process in textile production, minimizing human error and enhancing operational efficiency?

The study followed a design science research methodology to design, build, and evaluate the solution. The application was developed using Python’s Tkinter library, incorporating Lean Six Sigma principles, Urdu language support, and Excel-based data logging to meet the needs of non-technical users. Field tests conducted in a textile plant in Pakistan demonstrated that the application reduced cone count errors by over 95% with standard deviation dropping from 68.59 to 2.83 and saved an average of 17.25 minutes per production shift. Statistical analysis, including paired t-tests and confidence intervals, confirmed the significance of these improvements.

This study illustrates how mathematical modelling and human-centric interface design can modernize traditional inventory practices in textile manufacturing. It bridges the gap between number theory and industrial systems design by showing how abstract mathematical principles can solve practical challenges.