System-Dynamics Modeling of an Expertise-Based Talent Management Strategy for Engineering Changes Analysts in a Car Manufacturing Plant

This paper develops a System Dynamics (SD) model to delineate the theoretical framework and operational mechanics of an Expertise-based Talent Management Strategy (TMS) for Engineering Changes Analysts (ECANs) within the automotive industry. The research emphasizes the conceptualization and design aspects of the model, which aims to facilitate the management and development of ECANs critical to the engineering changes process in car manufacturing. The study elaborates on the dynamic interactions and dependencies within the Engineering Changes Administration (ECA) department, focusing on how various levels of ECAN expertise-Novice, Competent, and Expert-affect the adaptation and effectiveness of engineering processes. By structuring the TMS to categorize ECANs according to their skills and responsibilities, the model provides a strategic approach to talent development, aiming to enhance the alignment of workforce capabilities with operational demands. The paper outlines the participatory methodology used in model construction, engaging key stakeholders from a leading OEM to ensure the model's practical relevance and accuracy. Theoretical insights derived from the model offer a comprehensive understanding of talent management dynamics, serving as a valuable tool for both academic research and practical application in complex manufacturing settings, where strategic talent management is pivotal for sustaining innovation and operational excellence.

Authors

Institutions

Publication Details

Published
2024-08-04
DOI
https://doi.org/10.23919/picmet64035.2024.10653004
Citations
2
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
1.29
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

System-Dynamics Modeling of an Expertise-Based Talent Management Strategy for Engineering Changes Analysts in a Car Manufacturing Plant

Andreas Topp, G.L. Emmett, Armando Elizondo-Noriega, José L. Ceciliano-Meza et al.
2 citations
Human-Automation Interaction and Safety
1.29
article

System-Dynamics Modeling of an Expertise-Based Talent Management Strategy for Engineering Changes Analysts in a Car Manufacturing Plant

Andreas Topp, G.L. Emmett, Armando Elizondo-Noriega, José L. Ceciliano-Meza, A.A. Castillo-Paz, J. Rouen, R. Linan, L. Duarte-Pacheco, J.F. Piñal-Moctezuma
article en
2 citations

Abstract

This paper develops a System Dynamics (SD) model to delineate the theoretical framework and operational mechanics of an Expertise-based Talent Management Strategy (TMS) for Engineering Changes Analysts (ECANs) within the automotive industry. The research emphasizes the conceptualization and design aspects of the model, which aims to facilitate the management and development of ECANs critical to the engineering changes process in car manufacturing. The study elaborates on the dynamic interactions and dependencies within the Engineering Changes Administration (ECA) department, focusing on how various levels of ECAN expertise-Novice, Competent, and Expert-affect the adaptation and effectiveness of engineering processes. By structuring the TMS to categorize ECANs according to their skills and responsibilities, the model provides a strategic approach to talent development, aiming to enhance the alignment of workforce capabilities with operational demands. The paper outlines the participatory methodology used in model construction, engaging key stakeholders from a leading OEM to ensure the model's practical relevance and accuracy. Theoretical insights derived from the model offer a comprehensive understanding of talent management dynamics, serving as a valuable tool for both academic research and practical application in complex manufacturing settings, where strategic talent management is pivotal for sustaining innovation and operational excellence.

Tecnológico de Monterrey (MX)
Openalex Percentile: Top 19%
Human-Automation Interaction and Safety
1.29
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.