Dynamic Self-Assessment of Supply Chains Performance: an Emerging Market Approach
A dynamic self-assessment of performance on supply chains operating in emerging markets is proposed. Based on wellestablishedkey performance indicators (KPI), this paper provides a decision support aid. Although it has been validatedin the automotive industry, the standardized model’s approach makes...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/296 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/296 |
| Access Level: | acceso abierto |
| Palabra clave: | supply chain performance measurement system dynamics automotive industry emerging markets. |
| Sumario: | A dynamic self-assessment of performance on supply chains operating in emerging markets is proposed. Based on wellestablishedkey performance indicators (KPI), this paper provides a decision support aid. Although it has been validatedin the automotive industry, the standardized model’s approach makes it applicable to other industries. It is the result of alarge literature review and identification of best practices from the automotive industry in which the lack of dynamic toolsto evaluate logistics performance of suitable supply chains to the current competitive exchange rate was detected.Developed under a system dynamics approach (DS), the model analyzes different scenarios taking into account KPI andits dynamic relationships. The results obtained were validated through the statistical technique of design of experiments(DOE). This model also considers the specific features of the automotive operations in emerging countries as well astheir importance in the future development of the manufacturing industry. In this context, the tool exposed is a keybackup to decision making and to dynamically evaluate the variables with major influence on manufacturing supplychains. As a conclusion, findings are discussed and future researches are presented. |
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