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| Content Provider | Springer Nature Link |
|---|---|
| Author | Cardoso, Francisco Rafael Achcar, Jorge Alberto Piratelli, Claudio Luis Garcia Hermosilla, José Luis Barbosa, José Camilo |
| Copyright Year | 2013 |
| Abstract | The search for competitive advantages for organizations has been made very complex by technological advancement and the ease of reproduction of resources, such as projects, products, and processes. Even new ideas are likely to be copies. However, the process of generation and conversion of improvements in productive activity is highly complex and little is known about it by firms. Understanding this process may add competitive benefits to any organization. In this paper, we present statistical modeling for data related to the counting of suggestions made by employees at a food company and its viability for improving productivity at the company. This counting, carried out in the years from 2008 through 2011, was performed and encouraged by the company’s managers in order to achieve a general improvement in productivity. Poisson and binomial regression models were considered in analyzing the data under the statistical Bayesian framework. It was shown that the generation of ideas has a significant relationship to the hierarchical position of the employee, his schooling, how long he has been at the company being studied, his age, and training, but these factors do not show significant effects related to the proportions of viable ideas (potential competitive advantages for the organization). |
| Starting Page | 2059 |
| Ending Page | 2070 |
| Page Count | 12 |
| File Format | |
| ISSN | 02683768 |
| Journal | The International Journal of Advanced Manufacturing Technology |
| Volume Number | 70 |
| Issue Number | 9-12 |
| e-ISSN | 14333015 |
| Language | English |
| Publisher | Springer London |
| Publisher Date | 2013-11-12 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Poisson regression Binomial regression Bayesian analysis Markov Chain Monte Carlo methods Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software |
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