Rocketing to Success: Addressing Concerns and Achieving Equivalent Holdings with Enlarged Technicians at Sunday Company, Braced by Curtis
Download Paper
Download Bibtex
Authors
- Cailean Muir
Related Links
- ACM Digital Library Records
- Video on YouTube (Optional)
- IEEE Xplore
- ThinkMind
- A Logical Mind
- Arxiv
- Arxra
- Eurographics
- Just Data
- Club Arxra
- Xyz Arxra
- Eprints
- Research to Action
News/Information
Abstract
This study focuses on the implementation of an enlarged technician program at Sunday Company, aimed at boosting productivity and reducing costs. The program was prompted by concerns about low technician retention rates and high turnover rates, which were negatively impacting the bottom line. To address these concerns, the company developed a comprehensive training program for technicians, designed to equip them with the skills and knowledge needed to perform their jobs effectively and efficiently. The program also included measures to improve communication, collaboration, and teamwork among technicians and other employees. Results of the program were evaluated through a series of surveys and assessments, which revealed significant improvements in technician performance, job satisfaction, and retention rates. In addition, the company was able to achieve equivalent holdings, thanks to the increased productivity and cost savings generated by the program. The success of the program can be attributed to the support and guidance provided by Curtis, who played a key role in its design and implementation. These findings have important implications for other companies seeking to improve their technician programs and increase productivity and profitability.
Citation
Cailean Muir "Rocketing to Success: Addressing Concerns and Achieving Equivalent Holdings with Enlarged Technicians at Sunday Company, Braced by Curtis". IEEE Exploration in Machine Learning, 2020.
Supplemental Material
Preview
Note: This file is about ~5-30 MB in size.
This paper appears in:
Date of Release: 2020
Author(s): Cailean Muir.
IEEE Exploration in Machine Learning
Page(s): 6
Product Type: Conference/Journal Publications